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Coté A, O'Farrell A, Dardani I, Dunagin M, Coté C, Wan Y, Bayatpour S, Drexler HL, Alexander KA, Chen F, Wassie AT, Patel R, Pham K, Boyden ES, Berger S, Phillips-Cremins J, Churchman LS, Raj A. Post-transcriptional splicing can occur in a slow-moving zone around the gene. eLife 2024; 12:RP91357. [PMID: 38577979 PMCID: PMC10997330 DOI: 10.7554/elife.91357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024] Open
Abstract
Splicing is the stepwise molecular process by which introns are removed from pre-mRNA and exons are joined together to form mature mRNA sequences. The ordering and spatial distribution of these steps remain controversial, with opposing models suggesting splicing occurs either during or after transcription. We used single-molecule RNA FISH, expansion microscopy, and live-cell imaging to reveal the spatiotemporal distribution of nascent transcripts in mammalian cells. At super-resolution levels, we found that pre-mRNA formed clouds around the transcription site. These clouds indicate the existence of a transcription-site-proximal zone through which RNA move more slowly than in the nucleoplasm. Full-length pre-mRNA undergo continuous splicing as they move through this zone following transcription, suggesting a model in which splicing can occur post-transcriptionally but still within the proximity of the transcription site, thus seeming co-transcriptional by most assays. These results may unify conflicting reports of co-transcriptional versus post-transcriptional splicing.
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Affiliation(s)
- Allison Coté
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Aoife O'Farrell
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Ian Dardani
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Margaret Dunagin
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Chris Coté
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Yihan Wan
- School of Life Sciences, Westlake UniversityHangzhouChina
| | - Sareh Bayatpour
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Heather L Drexler
- Department of Genetics, Blavatnik Institute, Harvard Medical SchoolBostonUnited States
| | - Katherine A Alexander
- Department of Cell and Developmental Biology, Penn Institute of Epigenetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Fei Chen
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Asmamaw T Wassie
- Department of Cell and Molecular Biology, University of PennsylvaniaPhiladelphiaUnited States
| | - Rohan Patel
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
| | - Kenneth Pham
- Department of Cell and Molecular Biology, University of PennsylvaniaPhiladelphiaUnited States
| | - Edward S Boyden
- Departments of Biological Engineering and Brain and Cognitive Sciences, Media Lab and McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shelly Berger
- Department of Cell and Developmental Biology, Penn Institute of Epigenetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | | | - L Stirling Churchman
- Department of Genetics, Blavatnik Institute, Harvard Medical SchoolBostonUnited States
| | - Arjun Raj
- Department of Bioengineering, University of PennsylvaniaPhiladelphiaUnited States
- Department of Genetics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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Kariyawasam H, Hettiarachchi R, Yang Q, Matlock A, Nambara T, Kusaka H, Kunai Y, So PTC, Boyden ES, Wadduwage D. QuATON: Quantization Aware Training of Optical Neurons. Res Sq 2024:rs.3.rs-4076842. [PMID: 38585742 PMCID: PMC10996779 DOI: 10.21203/rs.3.rs-4076842/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Optical processors, built with "optical neurons", can efficiently perform high-dimensional linear operations at the speed of light. Thus they are a promising avenue to accelerate large-scale linear computations. With the current advances in micro-fabrication, such optical processors can now be 3D fabricated, but with a limited precision. This limitation translates to quantization of learnable parameters in optical neurons, and should be handled during the design of the optical processor in order to avoid a model mismatch. Specifically, optical neurons should be trained or designed within the physical-constraints at a predefined quantized precision level. To address this critical issues we propose a physics-informed quantization-aware training framework. Our approach accounts for physical constraints during the training process, leading to robust designs. We demonstrate that our approach can design state of the art optical processors using diffractive networks for multiple physics based tasks despite quantized learnable parameters. We thus lay the foundation upon which improved optical processors may be 3D fabricated in the future.
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Affiliation(s)
- Hasindu Kariyawasam
- Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Ramith Hettiarachchi
- Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Quansan Yang
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
| | - Alex Matlock
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
| | - Takahiro Nambara
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Advanced Research Core, Fujikura Ltd., Kiba, Tokyo, Japan
| | - Hiroyuki Kusaka
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Advanced Research Core, Fujikura Ltd., Kiba, Tokyo, Japan
| | - Yuichiro Kunai
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Advanced Research Core, Fujikura Ltd., Kiba, Tokyo, Japan
| | - Peter T C So
- Department of Mechanical Engineering, MIT, Cambridge, MA 02139, USA
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
- K. Lisa Yang Center for Bionics, MIT, Cambridge, MA 02139, USA
- Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA 02139, USA
| | - Dushan Wadduwage
- Center for Advanced Imaging, Faculty of Arts and Sciences, Harvard University, Cambridge, MA, USA
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3
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Shin TW, Wang H, Zhang C, An B, Lu Y, Zhang E, Lu X, Karagiannis ED, Kang JS, Emenari A, Symvoulidis P, Asano S, Lin L, Costa EK, Marblestone AH, Kasthuri N, Tsai LH, Boyden ES. Dense, Continuous Membrane Labeling and Expansion Microscopy Visualization of Ultrastructure in Tissues. bioRxiv 2024:2024.03.07.583776. [PMID: 38496681 PMCID: PMC10942445 DOI: 10.1101/2024.03.07.583776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines a novel membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validated the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supported the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrated the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yielded ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.
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Affiliation(s)
- Tay Won Shin
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Hao Wang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Picower Inst. for Learning and Memory, Cambridge
| | - Chi Zhang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Bobae An
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Yangning Lu
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Elizabeth Zhang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Xiaotang Lu
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | | | - Jeong Seuk Kang
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Amauche Emenari
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Panagiotis Symvoulidis
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Shoh Asano
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Leanne Lin
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
| | - Emma K. Costa
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
| | | | - Adam H. Marblestone
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- present address: Convergent Research
| | - Narayanan Kasthuri
- Center for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, USA
- Department of Neurobiology, University of Chicago, Chicago, IL, USA
| | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Picower Inst. for Learning and Memory, Cambridge
| | - Edward S. Boyden
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
- Howard Hughes Medical Institute, Cambridge, MA 02139
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139
- Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139
- K. Lisa Yang Center for Bionics, Massachusetts Institute of Technology, Cambridge, MA 02139
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139
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4
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Murdock MH, Yang CY, Sun N, Pao PC, Blanco-Duque C, Kahn MC, Kim T, Lavoie NS, Victor MB, Islam MR, Galiana F, Leary N, Wang S, Bubnys A, Ma E, Akay LA, Sneve M, Qian Y, Lai C, McCarthy MM, Kopell N, Kellis M, Piatkevich KD, Boyden ES, Tsai LH. Multisensory gamma stimulation promotes glymphatic clearance of amyloid. Nature 2024; 627:149-156. [PMID: 38418876 PMCID: PMC10917684 DOI: 10.1038/s41586-024-07132-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 01/25/2024] [Indexed: 03/02/2024]
Abstract
The glymphatic movement of fluid through the brain removes metabolic waste1-4. Noninvasive 40 Hz stimulation promotes 40 Hz neural activity in multiple brain regions and attenuates pathology in mouse models of Alzheimer's disease5-8. Here we show that multisensory gamma stimulation promotes the influx of cerebrospinal fluid and the efflux of interstitial fluid in the cortex of the 5XFAD mouse model of Alzheimer's disease. Influx of cerebrospinal fluid was associated with increased aquaporin-4 polarization along astrocytic endfeet and dilated meningeal lymphatic vessels. Inhibiting glymphatic clearance abolished the removal of amyloid by multisensory 40 Hz stimulation. Using chemogenetic manipulation and a genetically encoded sensor for neuropeptide signalling, we found that vasoactive intestinal peptide interneurons facilitate glymphatic clearance by regulating arterial pulsatility. Our findings establish novel mechanisms that recruit the glymphatic system to remove brain amyloid.
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Affiliation(s)
- Mitchell H Murdock
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cheng-Yi Yang
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Na Sun
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ping-Chieh Pao
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cristina Blanco-Duque
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin C Kahn
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - TaeHyun Kim
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nicolas S Lavoie
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matheus B Victor
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Md Rezaul Islam
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Fabiola Galiana
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Noelle Leary
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sidney Wang
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adele Bubnys
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emily Ma
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Leyla A Akay
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Madison Sneve
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yong Qian
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cuixin Lai
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, and Westlake Institute for Advanced Study, Hangzhou, China
| | - Michelle M McCarthy
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Nancy Kopell
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Manolis Kellis
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kiryl D Piatkevich
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, and Westlake Institute for Advanced Study, Hangzhou, China
| | - Edward S Boyden
- Departments of Biological Engineering and Brain and Cognitive Sciences, McGovern Institute, Cambridge, MA, USA
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences and the Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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5
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Page Vizcaíno J, Symvoulidis P, Wang Z, Jelten J, Favaro P, Boyden ES, Lasser T. Fast light-field 3D microscopy with out-of-distribution detection and adaptation through conditional normalizing flows. Biomed Opt Express 2024; 15:1219-1232. [PMID: 38404325 PMCID: PMC10890860 DOI: 10.1364/boe.504039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/09/2023] [Accepted: 11/20/2023] [Indexed: 02/27/2024]
Abstract
Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring. The eXtended field-of-view light field microscope (XLFM), also known as Fourier light field microscope, is a straightforward, single snapshot solution to achieve this. The XLFM acquires spatial-angular information in a single camera exposure. In a subsequent step, a 3D volume can be algorithmically reconstructed, making it exceptionally well-suited for real-time 3D acquisition and potential analysis. Unfortunately, traditional reconstruction methods (like deconvolution) require lengthy processing times (0.0220 Hz), hampering the speed advantages of the XLFM. Neural network architectures can overcome the speed constraints but do not automatically provide a way to certify the realism of their reconstructions, which is essential in the biomedical realm. To address these shortcomings, this work proposes a novel architecture to perform fast 3D reconstructions of live immobilized zebrafish neural activity based on a conditional normalizing flow. It reconstructs volumes at 8 Hz spanning 512x512x96 voxels, and it can be trained in under two hours due to the small dataset requirements (50 image-volume pairs). Furthermore, normalizing flows provides a way to compute the exact likelihood of a sample. This allows us to certify whether the predicted output is in- or ood, and retrain the system when a novel sample is detected. We evaluate the proposed method on a cross-validation approach involving multiple in-distribution samples (genetically identical zebrafish) and various out-of-distribution ones.
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Affiliation(s)
- Josué Page Vizcaíno
- Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information and Technology, Technical University of Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Germany
| | | | - Zeguan Wang
- Synthetic Neurobiology Group, Massachusetts Institute of Technology, USA
| | - Jonas Jelten
- Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information and Technology, Technical University of Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Germany
| | - Paolo Favaro
- Computer Vision Group, University of Bern, Switzerland
| | - Edward S. Boyden
- Synthetic Neurobiology Group, Massachusetts Institute of Technology, USA
| | - Tobias Lasser
- Computational Imaging and Inverse Problems, Department of Computer Science, School of Computation, Information and Technology, Technical University of Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Germany
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6
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Valdes PA, Yu CC(J, Aronson J, Ghosh D, Zhao Y, An B, Bernstock JD, Bhere D, Felicella MM, Viapiano MS, Shah K, Chiocca EA, Boyden ES. Improved immunostaining of nanostructures and cells in human brain specimens through expansion-mediated protein decrowding. Sci Transl Med 2024; 16:eabo0049. [PMID: 38295184 PMCID: PMC10911838 DOI: 10.1126/scitranslmed.abo0049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 01/10/2024] [Indexed: 02/02/2024]
Abstract
Proteins are densely packed in cells and tissues, where they form complex nanostructures. Expansion microscopy (ExM) variants have been used to separate proteins from each other in preserved biospecimens, improving antibody access to epitopes. Here, we present an ExM variant, decrowding expansion pathology (dExPath), that can expand proteins away from each other in human brain pathology specimens, including formalin-fixed paraffin-embedded (FFPE) clinical specimens. Immunostaining of dExPath-expanded specimens reveals, with nanoscale precision, previously unobserved cellular structures, as well as more continuous patterns of staining. This enhanced molecular staining results in observation of previously invisible disease marker-positive cell populations in human glioma specimens, with potential implications for tumor aggressiveness. dExPath results in improved fluorescence signals even as it eliminates lipofuscin-associated autofluorescence. Thus, this form of expansion-mediated protein decrowding may, through improved epitope access for antibodies, render immunohistochemistry more powerful in clinical science and, perhaps, diagnosis.
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Affiliation(s)
- Pablo A. Valdes
- Department of Neurosurgery, University of Texas Medical Branch, Galveston, TX, 77555
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
| | - Chih-Chieh (Jay) Yu
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- Department of Biological Engineering, MIT, MA, USA, 02139
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- RIKEN Center for Brain Science, Saitama, Japan, 351-0198
| | - Jenna Aronson
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- RIKEN Center for Brain Science, Saitama, Japan, 351-0198
| | - Debarati Ghosh
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA, 02139
| | - Yongxin Zhao
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA, 15213
| | - Bobae An
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
| | - Joshua D. Bernstock
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Koch Institute, MIT, Cambridge, MA, USA, 02139
| | - Deepak Bhere
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Department of Pathology, Microbiology and Immunology, School of Medicine Columbia, University of South Carolina, Columbia, SC, USA, 29209
- Center for Stem Cell and Translational Immunotherapy, Harvard Medical School/Brigham and Women’s Hospital, Boston, MA, USA, 02115
| | - Michelle M. Felicella
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA, 77555
| | - Mariano S. Viapiano
- Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, NY, USA, 13210
| | - Khalid Shah
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
- Center for Stem Cell and Translational Immunotherapy, Harvard Medical School/Brigham and Women’s Hospital, Boston, MA, USA, 02115
| | - E. Antonio Chiocca
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA, 02115
| | - Edward S. Boyden
- Media Arts and Sciences, MIT, Cambridge, MA, USA, 02115
- Department of Biological Engineering, MIT, MA, USA, 02139
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA, 02139
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA, 02139
- Koch Institute, MIT, Cambridge, MA, USA, 02139
- MIT Center for Neurobiological Engineering and K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA, 02139
- Howard Hughes Medical Institute, Cambridge, MA, USA, 02139
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7
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Day JH, Della Santina CM, Maretich P, Auld AL, Schnieder KK, Shin T, Boyden ES, Boyer LA. HiExM: high-throughput expansion microscopy enables scalable super-resolution imaging. bioRxiv 2024:2023.02.07.527509. [PMID: 36798312 PMCID: PMC9934540 DOI: 10.1101/2023.02.07.527509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Expansion microscopy (ExM) enables nanoscale imaging using a standard confocal microscope through the physical, isotropic expansion of fixed immunolabeled specimens. ExM is widely employed to image proteins, nucleic acids, and lipid membranes in single cells at nanoscale resolution; however, current methods cannot be performed in multi-well cell culture plates which limits the number of samples that can be processed simultaneously. We developed High-throughput Expansion Microscopy (HiExM), a robust platform that enables expansion microscopy of cells cultured in a standard 96-well plate. Our method enables consistent ~4.2x expansion within individual wells, across multiple wells, and between plates processed in parallel. We also demonstrate that HiExM can be combined with high-throughput confocal imaging platforms greatly improve the ease and scalability of image acquisition. As an example, we analyzed the effects of doxorubicin, a known cardiotoxic agent, in human cardiomyocytes (CMs) based on Hoechst signal intensity. We show a dose dependent effect on nuclear chromatin that is not observed in unexpanded CMs, suggesting that HiExM improves the detection of cellular phenotypes in response to drug treatment. Our method broadens the application of ExM as a tool for scalable super-resolution imaging in biological research applications.
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Affiliation(s)
- John H. Day
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Catherine Marin Della Santina
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Pema Maretich
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Alexander L. Auld
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Kirsten K. Schnieder
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Tay Shin
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Edward S. Boyden
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Department of Media Arts and Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- McGovern Institute, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- K Lisa Yang Center for Bionics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Center for Neurobiological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Koch Institute, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Laurie A. Boyer
- Department of Biological Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Department of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Koch Institute, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
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8
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Wang Z, Zhang J, Symvoulidis P, Guo W, Zhang L, Wilson MA, Boyden ES. Imaging the voltage of neurons distributed across entire brains of larval zebrafish. bioRxiv 2023:2023.12.15.571964. [PMID: 38168290 PMCID: PMC10760087 DOI: 10.1101/2023.12.15.571964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Neurons interact in networks distributed throughout the brain. Although much effort has focused on whole-brain calcium imaging, recent advances in genetically encoded voltage indicators (GEVIs) raise the possibility of imaging voltage of neurons distributed across brains. To achieve this, a microscope must image at high volumetric rate and signal-to-noise ratio. We present a remote scanning light-sheet microscope capable of imaging GEVI-expressing neurons distributed throughout entire brains of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage of ∼1/3 of the neurons of the brain, distributed throughout. We observed that neurons firing at different times during a sequence were located at different brain locations, for sequences elicited by a visual stimulus, which mapped onto locations throughout the optic tectum, as well as during stimulus-independent bursts, which mapped onto locations in the cerebellum and medulla. Whole-brain voltage imaging may open up frontiers in the fundamental operation of neural systems.
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9
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Qian Y, Celiker OT, Wang Z, Guner-Ataman B, Boyden ES. Temporally multiplexed imaging of dynamic signaling networks in living cells. Cell 2023; 186:5656-5672.e21. [PMID: 38029746 PMCID: PMC10843875 DOI: 10.1016/j.cell.2023.11.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/30/2023] [Accepted: 11/05/2023] [Indexed: 12/01/2023]
Abstract
Molecular signals interact in networks to mediate biological processes. To analyze these networks, it would be useful to image many signals at once, in the same living cell, using standard microscopes and genetically encoded fluorescent reporters. Here, we report temporally multiplexed imaging (TMI), which uses genetically encoded fluorescent proteins with different clocklike properties-such as reversibly photoswitchable fluorescent proteins with different switching kinetics-to represent different cellular signals. We linearly decompose a brief (few-second-long) trace of the fluorescence fluctuations, at each point in a cell, into a weighted sum of the traces exhibited by each fluorophore expressed in the cell. The weights then represent the signal amplitudes. We use TMI to analyze relationships between different kinase activities in individual cells, as well as between different cell-cycle signals, pointing toward broad utility throughout biology in the analysis of signal transduction cascades in living systems.
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Affiliation(s)
- Yong Qian
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 01239, USA
| | - Orhan T Celiker
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 01239, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 01239, USA
| | - Zeguan Wang
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 01239, USA; Department of Media Arts and Sciences, MIT, Cambridge, MA 01239, USA
| | - Burcu Guner-Ataman
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 01239, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 01239, USA; Department of Media Arts and Sciences, MIT, Cambridge, MA 01239, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 01239, USA; Department of Biological Engineering, MIT, Cambridge, MA 01239, USA; Koch Institute, MIT, Cambridge, MA 01239, USA; Howard Hughes Medical Institute, Cambridge, MA 01239, USA; Center for Neurobiological Engineering and K. Lisa Yang Center for Bionics at MIT, Cambridge, MA 01239, USA.
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10
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Violante IR, Alania K, Cassarà AM, Neufeld E, Acerbo E, Carron R, Williamson A, Kurtin DL, Rhodes E, Hampshire A, Kuster N, Boyden ES, Pascual-Leone A, Grossman N. Publisher Correction: Non-invasive temporal interference electrical stimulation of the human hippocampus. Nat Neurosci 2023; 26:2252. [PMID: 37957321 PMCID: PMC10689236 DOI: 10.1038/s41593-023-01517-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Affiliation(s)
- Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
| | - Ketevan Alania
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Antonino M Cassarà
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Emma Acerbo
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- Department of Neurology and Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Romain Carron
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- Department of Functional and Stereotactic Neurosurgery, Timone University Hospital, Marseille, France
| | - Adam Williamson
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- International Clinical Research Center, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Danielle L Kurtin
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Edward Rhodes
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College London, London, UK
| | - Niels Kuster
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
- Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Edward S Boyden
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, McGovern and Koch Institutes, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, UK.
- UK Dementia Research Institute, Imperial College London, London, UK.
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11
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Violante IR, Alania K, Cassarà AM, Neufeld E, Acerbo E, Carron R, Williamson A, Kurtin DL, Rhodes E, Hampshire A, Kuster N, Boyden ES, Pascual-Leone A, Grossman N. Non-invasive temporal interference electrical stimulation of the human hippocampus. Nat Neurosci 2023; 26:1994-2004. [PMID: 37857775 PMCID: PMC10620081 DOI: 10.1038/s41593-023-01456-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 09/06/2023] [Indexed: 10/21/2023]
Abstract
Deep brain stimulation (DBS) via implanted electrodes is used worldwide to treat patients with severe neurological and psychiatric disorders. However, its invasiveness precludes widespread clinical use and deployment in research. Temporal interference (TI) is a strategy for non-invasive steerable DBS using multiple kHz-range electric fields with a difference frequency within the range of neural activity. Here we report the validation of the non-invasive DBS concept in humans. We used electric field modeling and measurements in a human cadaver to verify that the locus of the transcranial TI stimulation can be steerably focused in the hippocampus with minimal exposure to the overlying cortex. We then used functional magnetic resonance imaging and behavioral experiments to show that TI stimulation can focally modulate hippocampal activity and enhance the accuracy of episodic memories in healthy humans. Our results demonstrate targeted, non-invasive electrical stimulation of deep structures in the human brain.
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Affiliation(s)
- Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK.
| | - Ketevan Alania
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Antonino M Cassarà
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
| | - Emma Acerbo
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- Department of Neurology and Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Romain Carron
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- Department of Functional and Stereotactic Neurosurgery, Timone University Hospital, Marseille, France
| | - Adam Williamson
- Institut de Neurosciences des Systèmes, Aix-Marseille University, INSERM, Marseille, France
- International Clinical Research Center, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Danielle L Kurtin
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
| | - Edward Rhodes
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute, Imperial College London, London, UK
| | - Adam Hampshire
- Department of Brain Sciences, Imperial College London, London, UK
| | - Niels Kuster
- Foundation for Research on Information Technologies in Society (IT'IS), Zurich, Switzerland
- Department of Information Technology and Electrical Engineering, Swiss Federal Institute of Technology, Zurich, Switzerland
| | - Edward S Boyden
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, McGovern and Koch Institutes, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
| | - Alvaro Pascual-Leone
- Hinda and Arthur Marcus Institute for Aging Research and Deanna and Sidney Wolk Center for Memory Health, Hebrew SeniorLife, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, UK.
- UK Dementia Research Institute, Imperial College London, London, UK.
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12
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Abstract
Optogenetics, the use of microbial rhodopsins to make the electrical activity of targeted neurons controllable by light, has swept through neuroscience, enabling thousands of scientists to study how specific neuron types contribute to behaviors and pathologies, and how they might serve as novel therapeutic targets. By activating a set of neurons, one can probe what functions they can initiate or sustain, and by silencing a set of neurons, one can probe the functions they are necessary for. We here review the biophysics of these molecules, asking why they became so useful in neuroscience for the study of brain circuitry. We review the history of the field, including early thinking, early experiments, applications of optogenetics, pre-optogenetics targeted neural control tools, and the history of discovering and characterizing microbial rhodopsins. We then review the biophysical attributes of rhodopsins that make them so useful to neuroscience - their classes and structure, their photocycles, their photocurrent magnitudes and kinetics, their action spectra, and their ion selectivity. Our hope is to convey to the reader how specific biophysical properties of these molecules made them especially useful to neuroscientists for a difficult problem - the control of high-speed electrical activity, with great precision and ease, in the brain.
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Affiliation(s)
- Kiryl D Piatkevich
- School of Life Sciences, Westlake University, Hangzhou, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Edward S Boyden
- McGovern Institute and Koch Institute, Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, K. Lisa Yang Center for Bionics and Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
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13
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Cui Y, Yang G, Goodwin DR, O’Flanagan CH, Sinha A, Zhang C, Kitko KE, Shin TW, Park D, Aparicio S, Boyden ES. Expansion microscopy using a single anchor molecule for high-yield multiplexed imaging of proteins and RNAs. PLoS One 2023; 18:e0291506. [PMID: 37729182 PMCID: PMC10511132 DOI: 10.1371/journal.pone.0291506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Accepted: 08/30/2023] [Indexed: 09/22/2023] Open
Abstract
Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to existing ExM protocols is the equipping of different kinds of molecules, with different kinds of anchoring moieties, so they can all be pulled apart from each other by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables proteins and RNAs to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and reduces cost (by 2-10-fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this united ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructures, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues and may support the visualization of other kinds of biomolecular species as well. uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues.
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Affiliation(s)
- Yi Cui
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Gaojie Yang
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Daniel R. Goodwin
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Ciara H. O’Flanagan
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
| | - Anubhav Sinha
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, Massachusetts, United States of America
| | - Chi Zhang
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Kristina E. Kitko
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Tay Won Shin
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Demian Park
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
| | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer, Vancouver, British Columbia, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Edward S. Boyden
- McGovern Institute, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America
- Media Arts & Sciences, MIT, Cambridge, Massachusetts, United States of America
- Department of Biological Engineering, MIT, Cambridge, Massachusetts, United States of America
- Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts, United States of America
- Koch Institute for Cancer Research, MIT, Cambridge, Massachusetts, United States of America
- Howard Hughes Medical Institute, MIT, Cambridge, Massachusetts, United States of America
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14
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Pigoni M, Uzquiano A, Paulsen B, Kedaigle AJ, Yang SM, Symvoulidis P, Adiconis X, Velasco S, Sartore R, Kim K, Tucewicz A, Tropp SY, Tsafou K, Jin X, Barrett L, Chen F, Boyden ES, Regev A, Levin JZ, Arlotta P. Cell-type specific defects in PTEN-mutant cortical organoids converge on abnormal circuit activity. Hum Mol Genet 2023; 32:2773-2786. [PMID: 37384417 PMCID: PMC10481103 DOI: 10.1093/hmg/ddad107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 07/01/2023] Open
Abstract
De novo heterozygous loss-of-function mutations in phosphatase and tensin homolog (PTEN) are strongly associated with autism spectrum disorders; however, it is unclear how heterozygous mutations in this gene affect different cell types during human brain development and how these effects vary across individuals. Here, we used human cortical organoids from different donors to identify cell-type specific developmental events that are affected by heterozygous mutations in PTEN. We profiled individual organoids by single-cell RNA-seq, proteomics and spatial transcriptomics and revealed abnormalities in developmental timing in human outer radial glia progenitors and deep-layer cortical projection neurons, which varied with the donor genetic background. Calcium imaging in intact organoids showed that both accelerated and delayed neuronal development phenotypes resulted in similar abnormal activity of local circuits, irrespective of genetic background. The work reveals donor-dependent, cell-type specific developmental phenotypes of PTEN heterozygosity that later converge on disrupted neuronal activity.
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Affiliation(s)
- Martina Pigoni
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ana Uzquiano
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Bruna Paulsen
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Amanda J Kedaigle
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sung Min Yang
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Panagiotis Symvoulidis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Xian Adiconis
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Silvia Velasco
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rafaela Sartore
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kwanho Kim
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ashley Tucewicz
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah Yoshimi Tropp
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Kalliopi Tsafou
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xin Jin
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Society of Fellows, Harvard University, Cambridge, MA 02138, USA
| | - Lindy Barrett
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Fei Chen
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- MIT Center for Neurobiological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Harvard-MIT Health Sciences & Technology Program (HST), Harvard Medical School, Boston, MA 02115, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, MIT, Cambridge, MA 02138, USA
- Department of Brain of Cognitive Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Department of Biological Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Joshua Z Levin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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15
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Lowet E, Sheehan DJ, Chialva U, De Oliveira Pena R, Mount RA, Xiao S, Zhou SL, Tseng HA, Gritton H, Shroff S, Kondabolu K, Cheung C, Wang Y, Piatkevich KD, Boyden ES, Mertz J, Hasselmo ME, Rotstein HG, Han X. Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation. Cell Rep 2023; 42:112906. [PMID: 37540599 PMCID: PMC10530698 DOI: 10.1016/j.celrep.2023.112906] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 05/31/2023] [Accepted: 07/14/2023] [Indexed: 08/06/2023] Open
Abstract
Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3-12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30-100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.
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Affiliation(s)
- Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
| | - Daniel J Sheehan
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Ulises Chialva
- Departamento de Matemática, Universidad Nacional del Sur, Buenos Aires, Argentina
| | - Rodrigo De Oliveira Pena
- Federated Department of Biological Sciences, New Jersey Institute of Technology & Rutgers University, Newark, NJ, USA
| | - Rebecca A Mount
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Sheng Xiao
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Samuel L Zhou
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Hua-An Tseng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Howard Gritton
- Department of Comparative Biosciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Sanaya Shroff
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | - Cyrus Cheung
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yangyang Wang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Kiryl D Piatkevich
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Edward S Boyden
- McGovern Institute for Brain Research and Howard Hughes Medical Institute, MIT, Cambridge, MA, USA
| | - Jerome Mertz
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Michael E Hasselmo
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Horacio G Rotstein
- Federated Department of Biological Sciences, New Jersey Institute of Technology & Rutgers University, Newark, NJ, USA
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
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16
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Lauenburg L, Lin Z, Zhang R, Santos MD, Huang S, Arganda-Carreras I, Boyden ES, Pfister H, Wei D. 3D Domain Adaptive Instance Segmentation via Cyclic Segmentation GANs. IEEE J Biomed Health Inform 2023; 27:4018-4027. [PMID: 37252868 PMCID: PMC10481620 DOI: 10.1109/jbhi.2023.3281332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on diverse training data or sequentially conducting image translation and segmentation with two relatively independent networks. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation simultaneously using a unified network with weight sharing. Since the image translation layer can be removed at inference time, our proposed model does not introduce additional computational cost upon a standard segmentation model. For optimizing CySGAN, besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we also utilize self-supervised and segmentation-based adversarial objectives to enhance the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. The proposed CySGAN outperforms pre-trained generalist models, feature-level domain adaptation models, and the baselines that conduct image translation and segmentation sequentially. Our implementation and the newly collected, densely annotated ExM zebrafish brain nuclei dataset, named NucExM, are publicly available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html.
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17
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Vizcaíno JP, Symvoulidis P, Wang Z, Jelten J, Favaro P, Boyden ES, Lasser T. Fast light-field 3D microscopy with out-of-distribution detection and adaptation through Conditional Normalizing Flows. ArXiv 2023:arXiv:2306.06408v2. [PMID: 37396615 PMCID: PMC10312789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Real-time 3D fluorescence microscopy is crucial for the spatiotemporal analysis of live organisms, such as neural activity monitoring. The eXtended field-of-view light field microscope (XLFM), also known as Fourier light field microscope, is a straightforward, single snapshot solution to achieve this. The XLFM acquires spatial-angular information in a single camera exposure. In a subsequent step, a 3D volume can be algorithmically reconstructed, making it exceptionally well-suited for real-time 3D acquisition and potential analysis. Unfortunately, traditional reconstruction methods (like deconvolution) require lengthy processing times (0.0220 Hz), hampering the speed advantages of the XLFM. Neural network architectures can overcome the speed constraints at the expense of lacking certainty metrics, which renders them untrustworthy for the biomedical realm. This work proposes a novel architecture to perform fast 3D reconstructions of live immobilized zebrafish neural activity based on a conditional normalizing flow. It reconstructs volumes at 8 Hz spanning 512 × 512 × 96 voxels, and it can be trained in under two hours due to the small dataset requirements (10 image-volume pairs). Furthermore, normalizing flows allow for exact Likelihood computation, enabling distribution monitoring, followed by out-of-distribution detection and retraining of the system when a novel sample is detected. We evaluate the proposed method on a cross-validation approach involving multiple in-distribution samples (genetically identical zebrafish) and various out-of-distribution ones.
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Affiliation(s)
- Josué Page Vizcaíno
- Computational Imaging and Inverse Problems, Department of Informatics, School of Computation, Information and Technology, Technical University of Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Germany
| | | | - Zeguan Wang
- Synthetic Neurobiology Group, Massachusetts Institute of Technology, USA
| | - Jonas Jelten
- Computational Imaging and Inverse Problems, Department of Informatics, School of Computation, Information and Technology, Technical University of Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Germany
| | - Paolo Favaro
- Computer Vision Group, University of Bern, Switzerland
| | - Edward S. Boyden
- Synthetic Neurobiology Group, Massachusetts Institute of Technology, USA
| | - Tobias Lasser
- Computational Imaging and Inverse Problems, Department of Informatics, School of Computation, Information and Technology, Technical University of Munich, Germany
- Munich Institute of Biomedical Engineering, Technical University of Munich, Germany
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18
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Günay KA, Chang TL, Skillin NP, Rao VV, Macdougall LJ, Cutler AA, Silver JS, Brown TE, Zhang C, Yu CCJ, Olwin BB, Boyden ES, Anseth KS. Photo-expansion microscopy enables super-resolution imaging of cells embedded in 3D hydrogels. Nat Mater 2023; 22:777-785. [PMID: 37217701 PMCID: PMC10590656 DOI: 10.1038/s41563-023-01558-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 04/17/2023] [Indexed: 05/24/2023]
Abstract
Hydrogels are extensively used as tunable, biomimetic three-dimensional cell culture matrices, but optically deep, high-resolution images are often difficult to obtain, limiting nanoscale quantification of cell-matrix interactions and outside-in signalling. Here we present photopolymerized hydrogels for expansion microscopy that enable optical clearance and tunable ×4.6-6.7 homogeneous expansion of not only monolayer cell cultures and tissue sections, but cells embedded within hydrogels. The photopolymerized hydrogels for expansion microscopy formulation relies on a rapid photoinitiated thiol/acrylate mixed-mode polymerization that is not inhibited by oxygen and decouples monomer diffusion from polymerization, which is particularly beneficial when expanding cells embedded within hydrogels. Using this technology, we visualize human mesenchymal stem cells and their interactions with nascently deposited proteins at <120 nm resolution when cultured in proteolytically degradable synthetic polyethylene glycol hydrogels. Results support the notion that focal adhesion maturation requires cellular fibronectin deposition; nuclear deformation precedes cellular spreading; and human mesenchymal stem cells display cell-surface metalloproteinases for matrix remodelling.
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Affiliation(s)
- Kemal Arda Günay
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Tze-Ling Chang
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Nathaniel P Skillin
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Varsha V Rao
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Laura J Macdougall
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Alicia A Cutler
- Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO, USA
| | - Jason S Silver
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
- Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO, USA
| | - Tobin E Brown
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA
| | - Chi Zhang
- McGovern Institute, MIT, Cambridge, MA, USA
- HHMI, Cambridge, MA, USA
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT, Cambridge, MA, USA
| | - Chih-Chieh Jay Yu
- McGovern Institute, MIT, Cambridge, MA, USA
- HHMI, Cambridge, MA, USA
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT, Cambridge, MA, USA
| | - Bradley B Olwin
- Department of Molecular, Cellular and Developmental Biology, University of Colorado, Boulder, CO, USA
| | - Edward S Boyden
- McGovern Institute, MIT, Cambridge, MA, USA
- HHMI, Cambridge, MA, USA
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT, Cambridge, MA, USA
| | - Kristi S Anseth
- Department of Chemical and Biological Engineering and the BioFrontiers Institute, University of Colorado, Boulder, CO, USA.
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19
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Suk HJ, Buie N, Xu G, Banerjee A, Boyden ES, Tsai LH. Vibrotactile stimulation at gamma frequency mitigates pathology related to neurodegeneration and improves motor function. Front Aging Neurosci 2023; 15:1129510. [PMID: 37273653 PMCID: PMC10233036 DOI: 10.3389/fnagi.2023.1129510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 04/27/2023] [Indexed: 06/06/2023] Open
Abstract
The risk for neurodegenerative diseases increases with aging, with various pathological conditions and functional deficits accompanying these diseases. We have previously demonstrated that non-invasive visual stimulation using 40 Hz light flicker ameliorated pathology and modified cognitive function in mouse models of neurodegeneration, but whether 40 Hz stimulation using another sensory modality can impact neurodegeneration and motor function has not been studied. Here, we show that whole-body vibrotactile stimulation at 40 Hz leads to increased neural activity in the primary somatosensory cortex (SSp) and primary motor cortex (MOp). In two different mouse models of neurodegeneration, Tau P301S and CK-p25 mice, daily exposure to 40 Hz vibrotactile stimulation across multiple weeks also led to decreased brain pathology in SSp and MOp. Furthermore, both Tau P301S and CK-p25 mice showed improved motor performance after multiple weeks of daily 40 Hz vibrotactile stimulation. Vibrotactile stimulation at 40 Hz may thus be considered as a promising therapeutic strategy for neurodegenerative diseases with motor deficits.
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Affiliation(s)
- Ho-Jun Suk
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Nicole Buie
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Guojie Xu
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Arit Banerjee
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Edward S. Boyden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
- Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, United States
- Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States
- Howard Hughes Medical Institute, Cambridge, MA, United States
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, United States
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20
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Linghu C, An B, Shpokayte M, Celiker OT, Shmoel N, Zhang R, Zhang C, Park D, Park WM, Ramirez S, Boyden ES. Recording of cellular physiological histories along optically readable self-assembling protein chains. Nat Biotechnol 2023; 41:640-651. [PMID: 36593405 PMCID: PMC10188365 DOI: 10.1038/s41587-022-01586-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 10/21/2022] [Indexed: 01/03/2023]
Abstract
Observing cellular physiological histories is key to understanding normal and disease-related processes. Here we describe expression recording islands-a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells. The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy. We use expression recording islands to record gene expression timecourse downstream of specific pharmacological and physiological stimuli in cultured neurons and in living mouse brain, with a time resolution of a fraction of a day, over periods of days to weeks.
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Affiliation(s)
- Changyang Linghu
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Cell and Developmental Biology, Program in Single Cell Spatial Analysis, and Michigan Neuroscience Institute, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Bobae An
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Monika Shpokayte
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
- Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Orhan T Celiker
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Nava Shmoel
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Ruihan Zhang
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Chi Zhang
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Demian Park
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Biological Engineering, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Won Min Park
- Chemical Engineering, Kansas State University, Manhattan, KS, USA
| | - Steve Ramirez
- Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Edward S Boyden
- Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Biological Engineering, MIT, Cambridge, MA, USA.
- Media Arts and Sciences, MIT, Cambridge, MA, USA.
- McGovern Institute, MIT, Cambridge, MA, USA.
- Howard Hughes Medical Institute, MIT, Cambridge, MA, USA.
- K Lisa Yang Center for Bionics, MIT, Cambridge, MA, USA.
- Center for Neurobiological Engineering, MIT, Cambridge, MA, USA.
- Koch Institute, MIT, Cambridge, MA, USA.
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21
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Al Mahbuba D, Masuko S, Wang S, Syangtan D, Kang JS, Song Y, Shin TW, Xia K, Zhang F, Linhardt RJ, Boyden ES, Kiessling LL. Dynamic Changes in Heparan Sulfate Nanostructure in Human Pluripotent Stem Cell Differentiation. ACS Nano 2023; 17:7207-7218. [PMID: 37042659 DOI: 10.1021/acsnano.2c10072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Heparan sulfate (HS) is a heterogeneous, cell-surface polysaccharide critical for transducing signals essential for mammalian development. Imaging of signaling proteins has revealed how their localization influences their information transfer. In contrast, the contribution of the spatial distribution and nanostructure of information-rich, signaling polysaccharides like HS is not known. Using expansion microscopy (ExM), we found striking changes in HS nanostructure occur as human pluripotent stem (hPS) cells differentiate, and these changes correlate with growth factor signaling. Our imaging studies show that undifferentiated hPS cells are densely coated with HS displayed as hair-like protrusions. This ultrastructure can recruit fibroblast growth factor for signaling. When the hPS cells differentiate into the ectoderm lineage, HS is localized into dispersed puncta. This striking change in HS distribution coincides with a decrease in fibroblast growth factor binding to neural cells. While developmental variations in HS sequence were thought to be the primary driver of alterations in HS-mediated growth factor signaling, our high-resolution images indicate a role for the HS nanostructure. Our study highlights the utility of high-resolution glycan imaging using ExM. In the case of HS, we found that changes in how the polysaccharide is displayed link to profound differences in growth factor binding.
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Affiliation(s)
- Deena Al Mahbuba
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Sayaka Masuko
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Shiwei Wang
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts 02139, United States
| | - Deepsing Syangtan
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Jeong Seuk Kang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02139, United States
| | - Yuefan Song
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Tay Won Shin
- Media Arts and Sciences, MIT, Cambridge, Massachusetts 02139, United States
| | - Ke Xia
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Fuming Zhang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Robert J Linhardt
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Edward S Boyden
- McGovern Institute for Brain Research, MIT, Cambridge, Massachusetts 02139, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts 02139, United States
- Media Arts and Sciences, MIT, Cambridge, Massachusetts 02139, United States
- Department of Biological Engineering, MIT, Cambridge, Massachusetts 02139, United States
- Koch Institute, MIT, Cambridge, Massachusetts 02139, United States
- Howard Hughes Medical Institute, Cambridge, Massachusetts 02139, United States
- Centers for Neurobiological Engineering and Extreme Bionics, MIT, Cambridge, Massachusetts 02139, United States
| | - Laura L Kiessling
- Department of Chemistry, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Koch Institute, MIT, Cambridge, Massachusetts 02139, United States
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22
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Lillvis JL, Otsuna H, Ding X, Pisarev I, Kawase T, Colonell J, Rokicki K, Goina C, Gao R, Hu A, Wang K, Bogovic J, Milkie DE, Meienberg L, Mensh BD, Boyden ES, Saalfeld S, Tillberg PW, Dickson BJ. Rapid reconstruction of neural circuits using tissue expansion and light sheet microscopy. eLife 2022; 11:81248. [DOI: 10.7554/elife.81248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Brain function is mediated by the physiological coordination of a vast, intricately connected network of molecular and cellular components. The physiological properties of neural network components can be quantified with high throughput. The ability to assess many animals per study has been critical in relating physiological properties to behavior. By contrast, the synaptic structure of neural circuits is presently quantifiable only with low throughput. This low throughput hampers efforts to understand how variations in network structure relate to variations in behavior. For neuroanatomical reconstruction there is a methodological gulf between electron-microscopic (EM) methods, which yield dense connectomes at considerable expense and low throughput, and light-microscopic (LM) methods, which provide molecular and cell-type specificity at high throughput but without synaptic resolution. To bridge this gulf, we developed a high-throughput analysis pipeline and imaging protocol using tissue expansion and light sheet microscopy (ExLLSM) to rapidly reconstruct selected circuits across many animals with single-synapse resolution and molecular contrast. Using Drosophila to validate this approach, we demonstrate that it yields synaptic counts similar to those obtained by EM, enables synaptic connectivity to be compared across sex and experience, and can be used to correlate structural connectivity, functional connectivity, and behavior. This approach fills a critical methodological gap in studying variability in the structure and function of neural circuits across individuals within and between species.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Edward S Boyden
- MIT McGovern Institute for Brain Research, Massachusetts Institute of Technology
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23
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Sarkar D, Kang J, Wassie AT, Schroeder ME, Peng Z, Tarr TB, Tang AH, Niederst ED, Young JZ, Su H, Park D, Yin P, Tsai LH, Blanpied TA, Boyden ES. Revealing nanostructures in brain tissue via protein decrowding by iterative expansion microscopy. Nat Biomed Eng 2022; 6:1057-1073. [PMID: 36038771 PMCID: PMC9551354 DOI: 10.1038/s41551-022-00912-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 06/22/2022] [Indexed: 12/25/2022]
Abstract
Many crowded biomolecular structures in cells and tissues are inaccessible to labelling antibodies. To understand how proteins within these structures are arranged with nanoscale precision therefore requires that these structures be decrowded before labelling. Here we show that an iterative variant of expansion microscopy (the permeation of cells and tissues by a swellable hydrogel followed by isotropic hydrogel expansion, to allow for enhanced imaging resolution with ordinary microscopes) enables the imaging of nanostructures in expanded yet otherwise intact tissues at a resolution of about 20 nm. The method, which we named 'expansion revealing' and validated with DNA-probe-based super-resolution microscopy, involves gel-anchoring reagents and the embedding, expansion and re-embedding of the sample in homogeneous swellable hydrogels. Expansion revealing enabled us to use confocal microscopy to image the alignment of pre-synaptic calcium channels with post-synaptic scaffolding proteins in intact brain circuits, and to uncover periodic amyloid nanoclusters containing ion-channel proteins in brain tissue from a mouse model of Alzheimer's disease. Expansion revealing will enable the further discovery of previously unseen nanostructures within cells and tissues.
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Affiliation(s)
- Deblina Sarkar
- Media Lab, MIT, Cambridge, MA, 02139, USA.,MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, 02139, USA.,These authors contributed equally
| | - Jinyoung Kang
- MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA.,These authors contributed equally
| | - Asmamaw T Wassie
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA.,These authors contributed equally
| | - Margaret E. Schroeder
- MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA.,Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
| | - Zhuyu Peng
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.,The Picower Institute for Learning and Memory, MIT, Cambridge, MA, 02139, USA
| | - Tyler B. Tarr
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Ai-Hui Tang
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.,CAS Key Laboratory of Brain Function and Disease, Biomedical Sciences and Health Laboratory of Anhui Province, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230031, China
| | - Emily D. Niederst
- The Picower Institute for Learning and Memory, MIT, Cambridge, MA, 02139, USA
| | - Jennie, Z. Young
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.,The Picower Institute for Learning and Memory, MIT, Cambridge, MA, 02139, USA
| | - Hanquan Su
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.,Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Demian Park
- MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA
| | - Peng Yin
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.,Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Li-Huei Tsai
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA. .,The Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA. .,Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Thomas A. Blanpied
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.,Program in Neuroscience, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.,Correspondence and requests for materials should be addressed to Li-Huei Tsai, Thomas A. Blanpied or Edward S. Boyden. , ,
| | - Edward S. Boyden
- MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, 02139, USA.,MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA.,Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA.,Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.,Koch Institute, MIT, Cambridge, MA, 02139, USA.,Howard Hughes Medical Institute, Cambridge, MA, 02139, USA.,Media Arts and Sciences, MIT, Cambridge, MA, 02139, USA.,K. Lisa Yang Center for Bionics, MIT, Cambridge, MA, 02139, USA.,Correspondence and requests for materials should be addressed to Li-Huei Tsai, Thomas A. Blanpied or Edward S. Boyden. , ,
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24
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Torres Cabán C, Yang M, Lai C, Yang L, Subach FV, Smith BO, Piatkevich KD, Boyden ES. Tuning the Sensitivity of Genetically Encoded Fluorescent Potassium Indicators through Structure-Guided and Genome Mining Strategies. ACS Sens 2022; 7:1336-1346. [PMID: 35427452 PMCID: PMC9150168 DOI: 10.1021/acssensors.1c02201] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/09/2022] [Indexed: 12/31/2022]
Abstract
Genetically encoded potassium indicators lack optimal binding affinity for monitoring intracellular dynamics in mammalian cells. Through structure-guided design and genome mining of potassium binding proteins, we developed green fluorescent potassium indicators with a broad range of binding affinities. KRaION1 (K+ ratiometric indicator for optical imaging based on mNeonGreen 1), based on the insertion of a potassium binding protein, Kbp, from E. coli (Ec-Kbp) into the fluorescent protein mNeonGreen, exhibits an isotonically measured Kd of 69 ± 10 mM (mean ± standard deviation used throughout). We identified Ec-Kbp's binding site using NMR spectroscopy to detect protein-thallium scalar couplings and refined the structure of Ec-Kbp in its potassium-bound state. Guided by this structure, we modified KRaION1, yielding KRaION1/D9N and KRaION2, which exhibit isotonically measured Kd's of 138 ± 21 and 96 ± 9 mM. We identified four Ec-Kbp homologues as potassium binding proteins, which yielded indicators with isotonically measured binding affinities in the 39-112 mM range. KRaIONs functioned in HeLa cells, but the Kd values differed from the isotonically measured case. We found that, by tuning the experimental conditions, Kd values could be obtained that were consistent in vitro and in vivo. We thus recommend characterizing potassium indicator Kd in the physiological context of interest before application.
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Affiliation(s)
- Cristina
C. Torres Cabán
- McGovern
Institute for Brain Research, MIT, Cambridge, Massachusetts 02139, United States
- Department
of Biological Engineering, MIT, Cambridge, Massachusetts 02139, United States
- Department
of Media Arts & Sciences, MIT, Cambridge, Massachusetts 02139, United States
| | - Minghan Yang
- School
of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake
Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Institute
of Basic Medical Sciences, Westlake Institute
for Advanced Study, Hangzhou, Zhejiang 310024, China
- College
of Physics, Jilin University, Changchun, Jilin 130012, China
| | - Cuixin Lai
- School
of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake
Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Institute
of Basic Medical Sciences, Westlake Institute
for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Lina Yang
- School
of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake
Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Institute
of Basic Medical Sciences, Westlake Institute
for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Fedor V. Subach
- Complex
of NBICS Technologies, National Research
Center “Kurchatov Institute”, Moscow 123182, Russia
| | - Brian O. Smith
- Institute
of Molecular, Cell & Systems Biology, College of Medical Veterinary
& Life Sciences, University of Glasgow, Glasgow G128QQ, United Kingdom
| | - Kiryl D. Piatkevich
- School
of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
- Westlake
Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
- Institute
of Basic Medical Sciences, Westlake Institute
for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Edward S. Boyden
- McGovern
Institute for Brain Research, MIT, Cambridge, Massachusetts 02139, United States
- Department
of Biological Engineering, MIT, Cambridge, Massachusetts 02139, United States
- Department
of Media Arts & Sciences, MIT, Cambridge, Massachusetts 02139, United States
- Koch
Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts 02139, United States
- Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, United States
- Department
of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts 02139, United States
- K.
Lisa Yang Center for Bionics, MIT, Cambridge, Massachusetts 02139, United States
- Center
for Neurobiological Engineering, MIT, Cambridge, Massachusetts 02139, United States
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25
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Armbruster M, Naskar S, Garcia JP, Sommer M, Kim E, Adam Y, Haydon PG, Boyden ES, Cohen AE, Dulla CG. Neuronal activity drives pathway-specific depolarization of peripheral astrocyte processes. Nat Neurosci 2022; 25:607-616. [PMID: 35484406 PMCID: PMC9988390 DOI: 10.1038/s41593-022-01049-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 03/14/2022] [Indexed: 12/16/2022]
Abstract
Astrocytes are glial cells that interact with neuronal synapses via their distal processes, where they remove glutamate and potassium (K+) from the extracellular space following neuronal activity. Astrocyte clearance of both glutamate and K+ is voltage dependent, but astrocyte membrane potential (Vm) is thought to be largely invariant. As a result, these voltage dependencies have not been considered relevant to astrocyte function. Using genetically encoded voltage indicators to enable the measurement of Vm at peripheral astrocyte processes (PAPs) in mice, we report large, rapid, focal and pathway-specific depolarizations in PAPs during neuronal activity. These activity-dependent astrocyte depolarizations are driven by action potential-mediated presynaptic K+ efflux and electrogenic glutamate transporters. We find that PAP depolarization inhibits astrocyte glutamate clearance during neuronal activity, enhancing neuronal activation by glutamate. This represents a novel class of subcellular astrocyte membrane dynamics and a new form of astrocyte-neuron interaction.
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Affiliation(s)
- Moritz Armbruster
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA.
| | - Saptarnab Naskar
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
| | - Jacqueline P Garcia
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA.,Cell, Molecular, and Developmental Biology Program, Tufts Graduate School of Biomedical Sciences, Boston, MA, USA
| | - Mary Sommer
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
| | - Elliot Kim
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
| | - Yoav Adam
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.,Edmond and Lily Safra Center for Brain Sciences, the Hebrew University of Jerusalem, Jerusalem, Israel
| | - Philip G Haydon
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA
| | - Edward S Boyden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.,Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.,Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA.,Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.,Department of Physics, Harvard University, Cambridge, MA, USA
| | - Chris G Dulla
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, USA.
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26
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Babakhanova S, Jung EE, Namikawa K, Zhang H, Wang Y, Subach OM, Korzhenevskiy DA, Rakitina TV, Xiao X, Wang W, Shi J, Drobizhev M, Park D, Eisenhard L, Tang H, Köster RW, Subach FV, Boyden ES, Piatkevich KD. Rapid directed molecular evolution of fluorescent proteins in mammalian cells. Protein Sci 2022; 31:728-751. [PMID: 34913537 PMCID: PMC8862398 DOI: 10.1002/pro.4261] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 11/24/2021] [Accepted: 12/14/2021] [Indexed: 12/31/2022]
Abstract
In vivo imaging of model organisms is heavily reliant on fluorescent proteins with high intracellular brightness. Here we describe a practical method for rapid optimization of fluorescent proteins via directed molecular evolution in cultured mammalian cells. Using this method, we were able to perform screening of large gene libraries containing up to 2 × 107 independent random genes of fluorescent proteins expressed in HEK cells, completing one iteration of directed evolution in a course of 8 days. We employed this approach to develop a set of green and near-infrared fluorescent proteins with enhanced intracellular brightness. The developed near-infrared fluorescent proteins demonstrated high performance for fluorescent labeling of neurons in culture and in vivo in model organisms such as Caenorhabditis elegans, Drosophila, zebrafish, and mice. Spectral properties of the optimized near-infrared fluorescent proteins enabled crosstalk-free multicolor imaging in combination with common green and red fluorescent proteins, as well as dual-color near-infrared fluorescence imaging. The described method has a great potential to be adopted by protein engineers due to its simplicity and practicality. We also believe that the new enhanced fluorescent proteins will find wide application for in vivo multicolor imaging of small model organisms.
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Affiliation(s)
- Siranush Babakhanova
- Media Arts and SciencesMassachusetts Institute of Technology (MIT)CambridgeMassachusettsUSA,Department of PhysicsMITCambridgeMassachusettsUSA,Department of Electrical Engineering and Computer ScienceMITCambridgeMassachusettsUSA,MIT McGovern Institute for Brain Research, MITCambridgeMassachusettsUSA
| | - Erica E. Jung
- Department of Mechanical and Industrial EngineeringThe University of Illinois at ChicagoChicagoIllinoisUSA
| | - Kazuhiko Namikawa
- Division of Cellular and Molecular Neurobiology, Zoological InstituteTechnische Universität BraunschweigBraunschweigGermany
| | - Hanbin Zhang
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
| | - Yangdong Wang
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
| | - Oksana M. Subach
- National Research Center “Kurchatov Institute”MoscowRussian Federation
| | | | - Tatiana V. Rakitina
- National Research Center “Kurchatov Institute”MoscowRussian Federation,Shemyakin‐Ovchinnikov Institute of Bioorganic Chemistry, RASMoscowRussian Federation
| | - Xian Xiao
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
| | - Wenjing Wang
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
| | - Jing Shi
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
| | - Mikhail Drobizhev
- Department of Microbiology and ImmunologyMontana State UniversityBozemanMontanaUSA
| | - Demian Park
- Media Arts and SciencesMassachusetts Institute of Technology (MIT)CambridgeMassachusettsUSA,MIT McGovern Institute for Brain Research, MITCambridgeMassachusettsUSA
| | - Lea Eisenhard
- Division of Cellular and Molecular Neurobiology, Zoological InstituteTechnische Universität BraunschweigBraunschweigGermany
| | - Hongyun Tang
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
| | - Reinhard W. Köster
- Division of Cellular and Molecular Neurobiology, Zoological InstituteTechnische Universität BraunschweigBraunschweigGermany
| | - Fedor V. Subach
- National Research Center “Kurchatov Institute”MoscowRussian Federation
| | - Edward S. Boyden
- Media Arts and SciencesMassachusetts Institute of Technology (MIT)CambridgeMassachusettsUSA,MIT McGovern Institute for Brain Research, MITCambridgeMassachusettsUSA,Department of Biological EngineeringMITCambridgeMassachusettsUSA,Department of Brain and Cognitive SciencesMITCambridgeMassachusettsUSA,Howard Hughes Medical InstituteCambridgeMassachusettsUSA,Koch InstituteMITCambridgeMassachusettsUSA
| | - Kiryl D. Piatkevich
- School of Life SciencesWestlake UniversityZhejiangHangzhouChina,Westlake Laboratory of Life Sciences and BiomedicineZhejiangHangzhouChina,Institute of Basic Medical SciencesWestlake Institute for Advanced StudyZhejiangHangzhouChina
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27
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Abravanel DL, Klughammer J, Blosser T, Goltsev Y, Jiang S, Bai Y, Murray E, Alon S, Cui Y, Goodwin DR, Sinha A, Cohen O, Slyper M, Ashenberg O, Dionne D, Jané-Valbuena J, Porter CBM, Segerstolpe A, Waldman J, Vigneau S, Helvie K, Frangieh A, DelloStritto L, Patel M, We J, Pfaff K, Cullen N, Lako A, Turner M, Wakiro I, Napolitano S, Kanodia A, Ortiz R, MacKichan C, Inga S, Chen J, Thorner AR, Rotem A, Rodig S, Chen F, Boyden ES, Nolan GP, Zhuang X, Rozenblatt-Rosen O, Johnson BE, Regev A, Wagle N. Abstract PD6-03: Spatio-molecular dissection of the breast cancer metastatic microenvironment. Cancer Res 2022. [DOI: 10.1158/1538-7445.sabcs21-pd6-03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Metastatic breast cancer (MBC) remains incurable due to inevitable development of therapeutic resistance. Although tumor cell intrinsic mechanisms of resistance in MBC are beginning to be elucidated by bulk sequencing studies, the roles of the tumor microenvironment and intratumor heterogeneity in therapeutic resistance remain underexplored due to both technological barriers and limited availability of samples. To comprehensively capture these characteristics we have adapted a research biopsy protocol to collect tissue for an array of single-cell and spatio-molecular assays whose performance we have optimized for MBC, including single-cell and single-nucleus RNA sequencing, Slide-Seq, Multiplexed Error-Robust FISH (MERFISH), Expansion Sequencing (ExSEQ), Co-detection by Indexing (CODEX) and Multiplexed Ion Beam Imaging (MIBI). To date, we have successfully performed single-cell or single-nucleus RNAseq in 67 MBC biopsies and generated detailed accompanying clinical annotations for each. These samples provide a representation of the clinicopathological diversity of MBC including different breast cancer subtypes (44 HR+/HER2-, 3 HR-/HER2+, 3 HR+/HER2+, 16 TNBC, 1 unknown), common anatomic sites of metastasis (37 liver, 9 axilla, 7 breast, 5 bone, 3 chest wall, 3 neck, 1 brain, 1 lung, 1 skin), metastatic presentations (53 recurrent, 14 de novo) and histologic subtypes in the breast (45 IDC, 7 ILC, 6 mixed, 3 DCIS, 1 mucinous, 5 unknown/NA). Following optimization, both single-cell and single-nucleus RNA seq perform well in these MBC biopsies recovering all expected cell types including the malignant, stromal (e.g. fibroblasts, endothelial cells), myeloid (e.g. monocytes, macrophages) and lymphoid compartments (e.g. T cells, B cells, NK cells) as well as relevant oncogenic programs (e.g. cell cycle programs in all compartments; EMT-like and ER signaling programs in the malignant compartment, immune checkpoint programs in the lymphoid compartment; and fibroblast activation and vascular homeostasis programs in the stromal compartment). In addition to differences between the two techniques, these data demonstrate substantial intratumor heterogeneity in cell type composition. For example in liver biopsies the average number of cells per sample compartment by single nucleus RNA-seq was 6745 malignant (56%, SD 4216), 4637 stromal (41%, SD 3727), 1196 lymphoid (8%, SD 1617) and 874 myeloid (6%, SD 852); in breast biopsies the average number of cells per compartment by single nucleus RNA-seq was 6421 malignant (70%, SD 3497), 1628 stromal (24%, SD 117), 333 lymphoid (4%, SD 170) and 213 myeloid (3%, SD 117). Additionally, we find both inter- and intra-tumor heterogeneity in expression patterns and programs including, for example, expression of ER, PR and HER2 within clinical receptor subtypes (log normalized counts for ER expression in tumor cells by single cell RNA-seq: HR+/HER2- 0.921 (SD 0.714); HR+/HER2+ 0.768 (SD 0.624); HR-/HER2+ 0.018 (SD 0.122); and HR-/HER2- 0.005 (SD 0.066). For a subset of 13 biopsies we are also completing the spatiomolecular characterization methods on serial sections of a single adjacent biopsy. This unique experimental setup was designed to enable efficient comparison and integration of these assays. In spite of differences between experimental techniques and readouts, cell typing can be approached by annotation transfer from matching single cell or single nucleus RNAseq data, enabling exploratory analyses including evaluation of spatial phenotypes and cell type colocalization. Overall, these single cell and spatial data afford a comprehensive atlas including cell types, cell states/programs, cell interactions and spatial organization in MBC lesions. Future analyses will include serial biopsies over time and integration of clinicopathologic data including therapeutic response and resistance.
Citation Format: Daniel L Abravanel, Johanna Klughammer, Timothy Blosser, Yury Goltsev, Sizun Jiang, Yunjao Bai, Evan Murray, Shahar Alon, Yi Cui, Daniel R Goodwin, Anubhav Sinha, Ofir Cohen, Michal Slyper, Orr Ashenberg, Danielle Dionne, Judit Jané-Valbuena, Caroline BM Porter, Asa Segerstolpe, Julia Waldman, Sébastien Vigneau, Karla Helvie, Allison Frangieh, Laura DelloStritto, Miraj Patel, Jingyi We, Kathleen Pfaff, Nicole Cullen, Ana Lako, Madison Turner, Isaac Wakiro, Sara Napolitano, Abhay Kanodia, Rebecca Ortiz, Colin MacKichan, Stephanie Inga, Judy Chen, Aaron R Thorner, Asaf Rotem, Scott Rodig, Fei Chen, Edward S Boyden, Garry P Nolan, Xiaowei Zhuang, Orit Rozenblatt-Rosen, Bruce E Johnson, Aviv Regev, Nikhil Wagle. Spatio-molecular dissection of the breast cancer metastatic microenvironment [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD6-03.
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Affiliation(s)
| | | | | | | | | | | | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Shahar Alon
- Massachusetts Institute of Technology, Cambridge, MA
| | - Yi Cui
- Massachusetts Institute of Technology, Cambridge, MA
| | | | - Anubhav Sinha
- Massachusetts Institute of Technology, Cambridge, MA
| | - Ofir Cohen
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Jingyi We
- Dana-Farber Cancer Institute, Boston, MA
| | | | | | - Ana Lako
- Dana-Farber Cancer Institute, Boston, MA
| | | | | | | | | | | | | | | | - Judy Chen
- Dana-Farber Cancer Institute, Boston, MA
| | | | - Asaf Rotem
- Dana-Farber Cancer Institute, Boston, MA
| | | | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Edward S Boyden
- Massachusetts Institute of Technology, Howard Hughes Medical Institute, Cambridge, MA
| | | | - Xiaowei Zhuang
- Harvard University, Howard Hughes Medical Institute, Cambridge, MA
| | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA
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28
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Abstract
Studies of C. elegans will benefit from a powerful method for super-resolution imaging of proteins and mRNAs at any 3-D locations throughout the entire animal. Conventional methods of super-resolution imaging in C. elegans, such as STORM, PALM, SR-SIM and STED, are limited by imaging depths that are insufficient to map the entire depth of adult worms, and involve hardware that may not be accessible to all labs. We recently developed expansion of C. elegans (ExCel), a method for physically magnifying fixed whole animals of C. elegans with high isotropy, which provides effective resolutions finer than the diffraction limit, across the entire animal, on conventional confocal microscopes. In this chapter, we present a family of three detailed ExCel protocols. The standard ExCel protocol features simultaneous readout of diverse molecules (fluorescent proteins, RNA, DNA, and general anatomy), all at ~70 nm resolution (~3.5× linear expansion). The epitope-preserving ExCel protocol enables imaging of endogenous proteins with off-the-shelf antibodies, at a ~ 100 nm resolution (~2.8× linear expansion). The iterative ExCel protocol allows readout of fluorescent proteins at ~25 nm resolution (~20× linear expansion). The protocols described here comprise a versatile toolbox for super-resolution imaging of C. elegans.
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Affiliation(s)
- Chih-Chieh Jay Yu
- McGovern Institute for Brain Research and Koch Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Danielle M Orozco Cosio
- McGovern Institute for Brain Research and Koch Institute, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research and Koch Institute, MIT, Cambridge, MA, USA.
- Department of Biological Engineering, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Cambridge, MA, USA.
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA.
- K Lisa Yang Center for Bionics, and Center for Neurobiological Engineering, MIT, Cambridge, MA, USA.
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29
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Funnell T, O’Flanagan CH, Williams MJ, McPherson A, McKinney S, Kabeer F, Lee H, Salehi S, Vázquez-García I, Shi H, Leventhal E, Masud T, Eirew P, Yap D, Zhang AW, Lim JLP, Wang B, Brimhall J, Biele J, Ting J, Au V, Van Vliet M, Liu YF, Beatty S, Lai D, Pham J, Grewal D, Abrams D, Havasov E, Leung S, Bojilova V, Moore RA, Rusk N, Uhlitz F, Ceglia N, Weiner AC, Zaikova E, Douglas JM, Zamarin D, Weigelt B, Kim SH, Da Cruz Paula A, Reis-Filho JS, Martin SD, Li Y, Xu H, de Algara TR, Lee SR, Llanos VC, Huntsman DG, McAlpine JN, Shah SP, Aparicio S, Cannell IG, Casbolt H, Jauset C, Kovačević T, Mulvey CM, Nugent F, Ribes MP, Pearson I, Qosaj F, Sawicka K, Wild SA, Williams E, Laks E, Smith A, Lai D, Roth A, Balasubramanian S, Lee M, Bodenmiller B, Burger M, Kuett L, Tietscher S, Windhager J, Boyden ES, Alon S, Cui Y, Emenari A, Goodwin DR, Karagiannis ED, Sinha A, Wassie AT, Caldas C, Bruna A, Callari M, Greenwood W, Lerda G, Eyal-Lubling Y, Rueda OM, Shea A, Harris O, Becker R, Grimaldo F, Harris S, Vogl SL, Joyce JA, Watson SS, Tavare S, Dinh KN, Fisher E, Kunes R, Walton NA, Al Sa’d M, Chornay N, Dariush A, González-Solares EA, González-Fernández C, Yoldaş AK, Miller N, Zhuang X, Fan J, Lee H, Sepúlveda LA, Xia C, Zheng P, Shah SP, Aparicio S. Single-cell genomic variation induced by mutational processes in cancer. Nature 2022; 612:106-115. [PMID: 36289342 PMCID: PMC9712114 DOI: 10.1038/s41586-022-05249-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 08/17/2022] [Indexed: 12/15/2022]
Abstract
How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.
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Affiliation(s)
- Tyler Funnell
- grid.5386.8000000041936877XTri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA ,grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Ciara H. O’Flanagan
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Marc J. Williams
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Andrew McPherson
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Steven McKinney
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Farhia Kabeer
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Hakwoo Lee
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Sohrab Salehi
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Ignacio Vázquez-García
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Hongyu Shi
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Emily Leventhal
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Tehmina Masud
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Peter Eirew
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Damian Yap
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Allen W. Zhang
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jamie L. P. Lim
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Beixi Wang
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jazmine Brimhall
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Justina Biele
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Jerome Ting
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Vinci Au
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Michael Van Vliet
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Yi Fei Liu
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Sean Beatty
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Daniel Lai
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Jenifer Pham
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Diljot Grewal
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Douglas Abrams
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Eliyahu Havasov
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Samantha Leung
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Viktoria Bojilova
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Richard A. Moore
- grid.434706.20000 0004 0410 5424Michael Smith Genome Sciences Centre, Vancouver, British Columbia Canada
| | - Nicole Rusk
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Florian Uhlitz
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Nicholas Ceglia
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Adam C. Weiner
- grid.5386.8000000041936877XTri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY USA ,grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Elena Zaikova
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - J. Maxwell Douglas
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Dmitriy Zamarin
- grid.51462.340000 0001 2171 9952GYN Medical Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Britta Weigelt
- grid.51462.340000 0001 2171 9952Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Sarah H. Kim
- grid.51462.340000 0001 2171 9952Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Arnaud Da Cruz Paula
- grid.51462.340000 0001 2171 9952Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Jorge S. Reis-Filho
- grid.51462.340000 0001 2171 9952Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Spencer D. Martin
- grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Yangguang Li
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Hong Xu
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Teresa Ruiz de Algara
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - So Ra Lee
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - Viviana Cerda Llanos
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada
| | - David G. Huntsman
- grid.248762.d0000 0001 0702 3000Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia Canada ,grid.17091.3e0000 0001 2288 9830Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia Canada
| | - Jessica N. McAlpine
- grid.17091.3e0000 0001 2288 9830Department of Gynecology and Obstetrics, University of British Columbia, Vancouver, British Columbia Canada
| | | | - Sohrab P. Shah
- grid.51462.340000 0001 2171 9952Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Samuel Aparicio
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, British Columbia, Canada. .,Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
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30
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Chan D, Suk H, Jackson BL, Milman N, Stark D, Fernandez V, Banerjee A, Kitchener E, Klerman EB, Boyden ES, Brown EN, Dickerson BC, Tsai L. Gamma frequency sensory stimulation prevents brain atrophy, improves sleep and memory in probable mild Alzheimer’s patients. Alzheimers Dement 2021. [DOI: 10.1002/alz.054218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Diane Chan
- Massachusetts Institute of Technology Cambridge MA USA
- Massachusetts General Hospital Boston MA USA
- Harvard Medical School Boston MA USA
| | - Ho‐Jun Suk
- Massachusetts Institute of Technology Cambridge MA USA
| | | | - Noah Milman
- Massachusetts Institute of Technology Cambridge MA USA
- Northeastern University Boston MA USA
| | | | | | - Arit Banerjee
- Massachusetts Institute of Technology Cambridge MA USA
| | | | | | | | - Emery N Brown
- Massachusetts Institute of Technology Cambridge MA USA
- Massachusetts General Hospital Boston MA USA
| | - Brad C. Dickerson
- Harvard Medical School Boston MA USA
- Martinos Center for Biomedical Imaging Charlestown MA USA
- Frontotemporal Disorders Unit Department of Neurology Massachusetts General Hospital Harvard Medical School Boston MA USA
| | - Li‐Huei Tsai
- Massachusetts Institute of Technology Cambridge MA USA
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31
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Xiao S, Lowet E, Gritton HJ, Fabris P, Wang Y, Sherman J, Mount RA, Tseng HA, Man HY, Straub C, Piatkevich KD, Boyden ES, Mertz J, Han X. Large-scale voltage imaging in behaving mice using targeted illumination. iScience 2021; 24:103263. [PMID: 34761183 PMCID: PMC8567393 DOI: 10.1016/j.isci.2021.103263] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/30/2021] [Accepted: 10/11/2021] [Indexed: 11/26/2022] Open
Abstract
Recent improvements in genetically encoded voltage indicators enabled optical imaging of action potentials and subthreshold transmembrane voltage in vivo. To perform high-speed voltage imaging of many neurons simultaneously over a large anatomical area, widefield microscopy remains an essential tool. However, the lack of optical sectioning makes widefield microscopy prone to background cross-contamination. We implemented a digital-micromirror-device-based targeted illumination strategy to restrict illumination to the cells of interest and quantified the resulting improvement both theoretically and experimentally with SomArchon expressing neurons. We found that targeted illumination increased SomArchon signal contrast, decreased photobleaching, and reduced background cross-contamination. With the use of a high-speed, large-area sCMOS camera, we routinely imaged tens of spiking neurons simultaneously over minutes in behaving mice. Thus, the targeted illumination strategy described here offers a simple solution for widefield voltage imaging of many neurons over a large field of view in behaving animals. An easily implementable digital-micromirror-based targeted illumination microscope Targeted illumination improves voltage imaging signal contrast in the brain Targeted illumination decreases photobleaching and reduces background in the brain Routine voltage imaging of tens of neurons simultaneously in behaving mice
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Affiliation(s)
- Sheng Xiao
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Eric Lowet
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Howard J Gritton
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.,Department of Comparative Biosciences, University of Illinois, Urbana, IL 61802, USA
| | - Pierre Fabris
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Yangyang Wang
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Jack Sherman
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Rebecca A Mount
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Hua-An Tseng
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Heng-Ye Man
- Department of Biology, Boston University, Boston, MA 02215, USA
| | - Christoph Straub
- Department of Biomedical Sciences, College of Osteopathic Medicine, University of New England, Biddeford, ME 04005, USA
| | - Kiryl D Piatkevich
- School of Life Sciences, Westlake University, Westlake Laboratory of Life Sciences and Biomedicine, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Edward S Boyden
- MIT McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA.,Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Jerome Mertz
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
| | - Xue Han
- Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
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32
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Brzozowski CF, Hijaz BA, Singh V, Gcwensa NZ, Kelly K, Boyden ES, West AB, Sarkar D, Volpicelli-Daley LA. Inhibition of LRRK2 kinase activity promotes anterograde axonal transport and presynaptic targeting of α-synuclein. Acta Neuropathol Commun 2021; 9:180. [PMID: 34749824 PMCID: PMC8576889 DOI: 10.1186/s40478-021-01283-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 10/20/2021] [Indexed: 01/18/2023] Open
Abstract
Pathologic inclusions composed of α-synuclein called Lewy pathology are hallmarks of Parkinson’s Disease (PD). Dominant inherited mutations in leucine rich repeat kinase 2 (LRRK2) are the most common genetic cause of PD. Lewy pathology is found in the majority of individuals with LRRK2-PD, particularly those with the G2019S-LRRK2 mutation. Lewy pathology in LRRK2-PD associates with increased non-motor symptoms such as cognitive deficits, anxiety, and orthostatic hypotension. Thus, understanding the relationship between LRRK2 and α-synuclein could be important for determining the mechanisms of non-motor symptoms. In PD models, expression of mutant LRRK2 reduces membrane localization of α-synuclein, and enhances formation of pathologic α-synuclein, particularly when synaptic activity is increased. α-Synuclein and LRRK2 both localize to the presynaptic terminal. LRRK2 plays a role in membrane traffic, including axonal transport, and therefore may influence α-synuclein synaptic localization. This study shows that LRRK2 kinase activity influences α-synuclein targeting to the presynaptic terminal. We used the selective LRRK2 kinase inhibitors, MLi-2 and PF-06685360 (PF-360) to determine the impact of reduced LRRK2 kinase activity on presynaptic localization of α-synuclein. Expansion microscopy (ExM) in primary hippocampal cultures and the mouse striatum, in vivo, was used to more precisely resolve the presynaptic localization of α-synuclein. Live imaging of axonal transport of α-synuclein-GFP was used to investigate the impact of LRRK2 kinase inhibition on α-synuclein axonal transport towards the presynaptic terminal. Reduced LRRK2 kinase activity increases α-synuclein overlap with presynaptic markers in primary neurons, and increases anterograde axonal transport of α-synuclein-GFP. In vivo, LRRK2 inhibition increases α-synuclein overlap with glutamatergic, cortico-striatal terminals, and dopaminergic nigral-striatal presynaptic terminals. The findings suggest that LRRK2 kinase activity plays a role in axonal transport, and presynaptic targeting of α-synuclein. These data provide potential mechanisms by which LRRK2-mediated perturbations of α-synuclein localization could cause pathology in both LRRK2-PD, and idiopathic PD.
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33
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Bhan N, Callisto A, Strutz J, Glaser J, Kalhor R, Boyden ES, Church G, Kording K, Tyo KEJ. Recording Temporal Signals with Minutes Resolution Using Enzymatic DNA Synthesis. J Am Chem Soc 2021; 143:16630-16640. [PMID: 34591459 DOI: 10.1021/jacs.1c07331] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Employing DNA as a high-density data storage medium has paved the way for next-generation digital storage and biosensing technologies. However, the multipart architecture of current DNA-based recording techniques renders them inherently slow and incapable of recording fluctuating signals with subhour frequencies. To address this limitation, we developed a simplified system employing a single enzyme, terminal deoxynucleotidyl transferase (TdT), to transduce environmental signals into DNA. TdT adds nucleotides to the 3'-ends of single-stranded DNA (ssDNA) in a template-independent manner, selecting bases according to inherent preferences and environmental conditions. By characterizing TdT nucleotide selectivity under different conditions, we show that TdT can encode various physiologically relevant signals such as Co2+, Ca2+, and Zn2+ concentrations and temperature changes in vitro. Further, by considering the average rate of nucleotide incorporation, we show that the resulting ssDNA functions as a molecular ticker tape. With this method we accurately encode a temporal record of fluctuations in Co2+ concentration to within 1 min over a 60 min period. Finally, we engineer TdT to allosterically turn off in the presence of a physiologically relevant concentration of calcium. We use this engineered TdT in concert with a reference TdT to develop a two-polymerase system capable of recording a single-step change in the Ca2+ signal to within 1 min over a 60 min period. This work expands the repertoire of DNA-based recording techniques by developing a novel DNA synthesis-based system that can record temporal environmental signals into DNA with a resolution of minutes.
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Affiliation(s)
- Namita Bhan
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States.,Mitolab, Cambridge, Massachusetts 02139, United States
| | - Alec Callisto
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Jonathan Strutz
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Joshua Glaser
- Center for Theoretical Neuroscience, Columbia University, New York, New York 10027, United States
| | - Reza Kalhor
- Department of Biomedical Engineering, Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, United States
| | - Edward S Boyden
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,McGovern Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Howard Hughes Medical Institute, Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - George Church
- Department of Biomedical Engineering, Center for Epigenetics, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, United States
| | - Konrad Kording
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Keith E J Tyo
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, United States
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34
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Liu S, Punthambaker S, Iyer EPR, Ferrante T, Goodwin D, Fürth D, Pawlowski AC, Jindal K, Tam JM, Mifflin L, Alon S, Sinha A, Wassie AT, Chen F, Cheng A, Willocq V, Meyer K, Ling KH, Camplisson CK, Kohman RE, Aach J, Lee JH, Yankner BA, Boyden ES, Church GM. Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses. Nucleic Acids Res 2021; 49:e58. [PMID: 33693773 PMCID: PMC8191787 DOI: 10.1093/nar/gkab120] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 01/29/2021] [Accepted: 03/02/2021] [Indexed: 12/13/2022] Open
Abstract
We present barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel insitu analyses (BOLORAMIS), a reverse transcription-free method for spatially-resolved, targeted, in situ RNA identification of single or multiple targets. BOLORAMIS was demonstrated on a range of cell types and human cerebral organoids. Singleplex experiments to detect coding and non-coding RNAs in human iPSCs showed a stem-cell signature pattern. Specificity of BOLORAMIS was found to be 92% as illustrated by a clear distinction between human and mouse housekeeping genes in a co-culture system, as well as by recapitulation of subcellular localization of lncRNA MALAT1. Sensitivity of BOLORAMIS was quantified by comparing with single molecule FISH experiments and found to be 11%, 12% and 35% for GAPDH, TFRC and POLR2A, respectively. To demonstrate BOLORAMIS for multiplexed gene analysis, we targeted 96 mRNAs within a co-culture of iNGN neurons and HMC3 human microglial cells. We used fluorescence in situ sequencing to detect error-robust 8-base barcodes associated with each of these genes. We then used this data to uncover the spatial relationship among cells and transcripts by performing single-cell clustering and gene–gene proximity analyses. We anticipate the BOLORAMIS technology for in situ RNA detection to find applications in basic and translational research.
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Affiliation(s)
- Songlei Liu
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Sukanya Punthambaker
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Eswar P R Iyer
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Thomas Ferrante
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Daniel Goodwin
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Daniel Fürth
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Andrew C Pawlowski
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Kunal Jindal
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Jenny M Tam
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Lauren Mifflin
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Shahar Alon
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Anubhav Sinha
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Asmamaw T Wassie
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Fei Chen
- Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Broad Institute, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Anne Cheng
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Valerie Willocq
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Katharina Meyer
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - King-Hwa Ling
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Conor K Camplisson
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Richie E Kohman
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - John Aach
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Je Hyuk Lee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Bruce A Yankner
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Edward S Boyden
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Media Arts and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USAHoward Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
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35
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Abstract
Expansion microscopy (ExM) is a technique that physically expands preserved cells and tissues before microscope imaging, so that conventional diffraction-limited microscopes can perform nanoscale-resolution imaging. In ExM, biomolecules or their markers are linked to a dense, swellable gel network synthesized throughout a specimen. Mechanical homogenization of the sample (e.g., by protease digestion) and the addition of water enable isotropic swelling of the gel, so that the relative positions of biomolecules are preserved. We previously presented ExM protocols for analyzing proteins and RNAs in cells and tissues. Here we describe a cookbook-style ExM protocol for expanding cultured HeLa cells with immunostained microtubules, aimed to help newcomers familiarize themselves with the experimental setups and skills required to successfully perform ExM. Our aim is to help beginners, or students in a wet-lab classroom setting, learn all the key steps of ExM. © 2020 The Authors.
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Affiliation(s)
- Chi Zhang
- Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.,McGovern Institute, MIT, Cambridge, Massachusetts
| | - Jeong Seuk Kang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
| | - Shoh M Asano
- Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.,McGovern Institute, MIT, Cambridge, Massachusetts.,Current address, Internal Medicine Research Unit, Pfizer Inc., Cambridge, Massachusetts
| | - Ruixuan Gao
- Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.,McGovern Institute, MIT, Cambridge, Massachusetts
| | - Edward S Boyden
- Media Lab, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts.,McGovern Institute, MIT, Cambridge, Massachusetts.,Department of Biological Engineering, MIT, Cambridge, Massachusetts.,Department of Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts.,Koch Institute for Cancer Research, MIT, Cambridge, Massachusetts
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36
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Gao R, Yu CCJ, Gao L, Piatkevich KD, Neve RL, Munro JB, Upadhyayula S, Boyden ES. A highly homogeneous polymer composed of tetrahedron-like monomers for high-isotropy expansion microscopy. Nat Nanotechnol 2021; 16:698-707. [PMID: 33782587 PMCID: PMC8197733 DOI: 10.1038/s41565-021-00875-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/11/2021] [Indexed: 05/08/2023]
Abstract
Expansion microscopy (ExM) physically magnifies biological specimens to enable nanoscale-resolution imaging using conventional microscopes. Current ExM methods permeate specimens with free-radical-chain-growth-polymerized polyacrylate hydrogels, whose network structure limits the local isotropy of expansion as well as the preservation of morphology and shape at the nanoscale. Here we report that ExM is possible using hydrogels that have a more homogeneous network structure, assembled via non-radical terminal linking of tetrahedral monomers. As with earlier forms of ExM, such 'tetra-gel'-embedded specimens can be iteratively expanded for greater physical magnification. Iterative tetra-gel expansion of herpes simplex virus type 1 (HSV-1) virions by ~10× in linear dimension results in a median spatial error of 9.2 nm for localizing the viral envelope layer, rather than 14.3 nm from earlier versions of ExM. Moreover, tetra-gel-based expansion better preserves the virion spherical shape. Thus, tetra-gels may support ExM with reduced spatial errors and improved local isotropy, pointing the way towards single-biomolecule accuracy ExM.
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Affiliation(s)
- Ruixuan Gao
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Chih-Chieh Jay Yu
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Linyi Gao
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Broad Institute, MIT, Cambridge, MA, USA
| | - Kiryl D Piatkevich
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Rachael L Neve
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA, USA
| | - James B Munro
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA
| | - Srigokul Upadhyayula
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Advanced Bioimaging Center, University of California at Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.
- Media Arts and Sciences, MIT, Cambridge, MA, USA.
- Department of Biological Engineering, MIT, Cambridge, MA, USA.
- MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Koch Institute, MIT, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Cambridge, MA, USA.
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37
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Payne AC, Chiang ZD, Reginato PL, Mangiameli SM, Murray EM, Yao CC, Markoulaki S, Earl AS, Labade AS, Jaenisch R, Church GM, Boyden ES, Buenrostro JD, Chen F. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 2021; 371:eaay3446. [PMID: 33384301 PMCID: PMC7962746 DOI: 10.1126/science.aay3446] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 08/17/2020] [Accepted: 12/14/2020] [Indexed: 12/11/2022]
Abstract
Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei. Using these data, we characterized parent-specific changes in genome structure across embryonic stages, revealed single-cell chromatin domains in zygotes, and uncovered epigenetic memory of global chromosome positioning within individual embryos. These results demonstrate how IGS can directly connect sequence and structure across length scales from single base pairs to whole organisms.
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Affiliation(s)
- Andrew C Payne
- Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Zachary D Chiang
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Paul L Reginato
- Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | | | - Evan M Murray
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
| | - Chun-Chen Yao
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | | | - Andrew S Earl
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Ajay S Labade
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Rudolf Jaenisch
- Whitehead Institute for Biomedical Research, Cambridge, MA 02139, USA
- Department of Biology, MIT, Cambridge, MA 02139, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Edward S Boyden
- Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
- McGovern Institute, MIT, Cambridge, MA 02139, USA
- Koch Institute, MIT, Cambridge, MA 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA 02139, USA
- Centers for Neurobiological Engineering and Extreme Bionics, MIT, Cambridge, MA 02139, USA
| | - Jason D Buenrostro
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA
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38
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Alon S, Goodwin DR, Sinha A, Wassie AT, Chen F, Daugharthy ER, Bando Y, Kajita A, Xue AG, Marrett K, Prior R, Cui Y, Payne AC, Yao CC, Suk HJ, Wang R, Yu CCJ, Tillberg P, Reginato P, Pak N, Liu S, Punthambaker S, Iyer EPR, Kohman RE, Miller JA, Lein ES, Lako A, Cullen N, Rodig S, Helvie K, Abravanel DL, Wagle N, Johnson BE, Klughammer J, Slyper M, Waldman J, Jané-Valbuena J, Rozenblatt-Rosen O, Regev A, Church GM, Marblestone AH, Boyden ES. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. Science 2021; 371:eaax2656. [PMID: 33509999 PMCID: PMC7900882 DOI: 10.1126/science.aax2656] [Citation(s) in RCA: 157] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/13/2020] [Accepted: 11/20/2020] [Indexed: 12/12/2022]
Abstract
Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.
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Affiliation(s)
- Shahar Alon
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Faculty of Engineering, Gonda Brain Research Center and Institute of Nanotechnology, Bar-Ilan University, Ramat Gan, Israel
| | - Daniel R Goodwin
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Anubhav Sinha
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Asmamaw T Wassie
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Fei Chen
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan R Daugharthy
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Yosuke Bando
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Kioxia Corporation, Minato-ku, Tokyo, Japan
| | | | - Andrew G Xue
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | | | | | - Yi Cui
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Andrew C Payne
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chun-Chen Yao
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ho-Jun Suk
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Ru Wang
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
| | - Chih-Chieh Jay Yu
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Paul Tillberg
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Paul Reginato
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Nikita Pak
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Mechanical Engineering, MIT, Cambridge, MA, USA
| | - Songlei Liu
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Sukanya Punthambaker
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Eswar P R Iyer
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | - Richie E Kohman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Scott Rodig
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Daniel L Abravanel
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
| | - Nikhil Wagle
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Michal Slyper
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - George M Church
- Department of Genetics, Harvard Medical School, Boston, MA, USA.
- Wyss Institute for Biologically Inspired Engineering, Boston, MA, USA
| | | | - Edward S Boyden
- Department of Media Arts and Sciences, MIT, Cambridge, MA, USA.
- McGovern Institute, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
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39
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Sunshine MD, Cassarà AM, Neufeld E, Grossman N, Mareci TH, Otto KJ, Boyden ES, Fuller DD. Restoration of breathing after opioid overdose and spinal cord injury using temporal interference stimulation. Commun Biol 2021; 4:107. [PMID: 33495588 PMCID: PMC7835220 DOI: 10.1038/s42003-020-01604-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 12/14/2020] [Indexed: 01/05/2023] Open
Abstract
Respiratory insufficiency is a leading cause of death due to drug overdose or neuromuscular disease. We hypothesized that a stimulation paradigm using temporal interference (TI) could restore breathing in such conditions. Following opioid overdose in rats, two high frequency (5000 Hz and 5001 Hz), low amplitude waveforms delivered via intramuscular wires in the neck immediately activated the diaphragm and restored ventilation in phase with waveform offset (1 Hz or 60 breaths/min). Following cervical spinal cord injury (SCI), TI stimulation via dorsally placed epidural electrodes uni- or bilaterally activated the diaphragm depending on current and electrode position. In silico modeling indicated that an interferential signal in the ventral spinal cord predicted the evoked response (left versus right diaphragm) and current-ratio-based steering. We conclude that TI stimulation can activate spinal motor neurons after SCI and prevent fatal apnea during drug overdose by restoring ventilation with minimally invasive electrodes.
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Affiliation(s)
- Michael D Sunshine
- Rehabilitation Science PhD Program, University of Florida, Gainesville, FL, 32611, USA
- Department of Physical Therapy, University of Florida, Gainesville, FL, 32611, USA
- Breathing Research and Therapeutics Center, University of Florida, Gainesville, FL, 32611, USA
- McKnight Brain Institute, University of Florida, Gainesville, FL, 32611, USA
| | - Antonino M Cassarà
- Foundation for Research on Information Technologies in Society (IT'IS), 8004, Zurich, Switzerland
| | - Esra Neufeld
- Foundation for Research on Information Technologies in Society (IT'IS), 8004, Zurich, Switzerland
| | - Nir Grossman
- Division of Brain Sciences, Imperial College London, London, SW7 2BU, United Kingdom
- United Kingdom Dementia Research Institute, Imperial College London, London, SW7 2BU, United Kingdom
| | - Thomas H Mareci
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, 32611, USA
| | - Kevin J Otto
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA
- Department of Neuroscience, University of Florida, Gainesville, FL, 32611, USA
- Department of Neurology, University of Florida, Gainesville, FL, 32611, USA
- Department of Materials Science and Engineering, University of Florida, Gainesville, FL, 32611, USA
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Edward S Boyden
- Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, McGovern and Koch Institutes, MIT, Cambridge, MA, 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA, 02138, USA
| | - David D Fuller
- Department of Physical Therapy, University of Florida, Gainesville, FL, 32611, USA.
- Breathing Research and Therapeutics Center, University of Florida, Gainesville, FL, 32611, USA.
- McKnight Brain Institute, University of Florida, Gainesville, FL, 32611, USA.
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40
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Schreglmann SR, Wang D, Peach RL, Li J, Zhang X, Latorre A, Rhodes E, Panella E, Cassara AM, Boyden ES, Barahona M, Santaniello S, Rothwell J, Bhatia KP, Grossman N. Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence. Nat Commun 2021; 12:363. [PMID: 33441542 PMCID: PMC7806740 DOI: 10.1038/s41467-020-20581-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/04/2020] [Indexed: 12/16/2022] Open
Abstract
Aberrant neural oscillations hallmark numerous brain disorders. Here, we first report a method to track the phase of neural oscillations in real-time via endpoint-corrected Hilbert transform (ecHT) that mitigates the characteristic Gibbs distortion. We then used ecHT to show that the aberrant neural oscillation that hallmarks essential tremor (ET) syndrome, the most common adult movement disorder, can be transiently suppressed via transcranial electrical stimulation of the cerebellum phase-locked to the tremor. The tremor suppression is sustained shortly after the end of the stimulation and can be phenomenologically predicted. Finally, we use feature-based statistical-learning and neurophysiological-modelling to show that the suppression of ET is mechanistically attributed to a disruption of the temporal coherence of the aberrant oscillations in the olivocerebellar loop, thus establishing its causal role. The suppression of aberrant neural oscillation via phase-locked driven disruption of temporal coherence may in the future represent a powerful neuromodulatory strategy to treat brain disorders.
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Affiliation(s)
- Sebastian R Schreglmann
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK
| | - David Wang
- Computer Science and Artificial Intelligence Laboratory, Massachussetts Institute of Technology (MIT), Cambridge, MA, 02139, USA
- NuVu studio Inc, Cambridge, MA, 02139, USA
| | - Robert L Peach
- Department of Mathematics and EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, London, SW7 2AZ, UK
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK
| | - Junheng Li
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK
| | - Xu Zhang
- Biomedical Engineering Department, University of Connecticut, Storrs, CT, 06269, USA
- CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Anna Latorre
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK
| | - Edward Rhodes
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK
| | - Emanuele Panella
- Department of Physics, Imperial College London, London, SW7 2AZ, UK
| | - Antonino M Cassara
- IT'IS Foundation for Research on Information Technologies in Society, 8004, Zurich, Switzerland
| | - Edward S Boyden
- Department of Media Arts and Sciences, MIT, Cambridge, MA, 02139, USA
- McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA
- Howard Hughes Medical Institute, Cambridge, MA, 02142, USA
- Department of Biological Engineering, MIT, Cambridge, MA, 02139, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA
- Centre for Neurobiological Engineering, MIT, Cambridge, MA, 02139, USA
- Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, 02139, USA
| | - Mauricio Barahona
- Department of Mathematics and EPSRC Centre for Mathematics of Precision Healthcare, Imperial College London, London, SW7 2AZ, UK
| | - Sabato Santaniello
- Biomedical Engineering Department, University of Connecticut, Storrs, CT, 06269, USA
- CT Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - John Rothwell
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK
| | - Kailash P Bhatia
- Institute of Neurology, Department of Clinical and Movement Neuroscience, Queen Square, University College London (UCL), London, WC1N 3BG, UK.
| | - Nir Grossman
- Department of Brain Sciences, Imperial College London, London, W12 0HS, UK.
- UK Dementia Research Institute (UK DRI) at Imperial College London, London, W12 0NN, UK.
- Department of Media Arts and Sciences, MIT, Cambridge, MA, 02139, USA.
- McGovern Institute for Brain Research, MIT, Cambridge, MA, 02139, USA.
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, UK.
- Centre for Neurotechnology, Imperial College London, London, SW7 2AZ, UK.
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41
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Qian Y, Cosio DMO, Piatkevich KD, Aufmkolk S, Su WC, Celiker OT, Schohl A, Murdock MH, Aggarwal A, Chang YF, Wiseman PW, Ruthazer ES, Boyden ES, Campbell RE. Improved genetically encoded near-infrared fluorescent calcium ion indicators for in vivo imaging. PLoS Biol 2020; 18:e3000965. [PMID: 33232322 PMCID: PMC7723245 DOI: 10.1371/journal.pbio.3000965] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 12/08/2020] [Accepted: 10/29/2020] [Indexed: 12/15/2022] Open
Abstract
Near-infrared (NIR) genetically encoded calcium ion (Ca2+) indicators (GECIs) can provide advantages over visible wavelength fluorescent GECIs in terms of reduced phototoxicity, minimal spectral cross talk with visible light excitable optogenetic tools and fluorescent probes, and decreased scattering and absorption in mammalian tissues. Our previously reported NIR GECI, NIR-GECO1, has these advantages but also has several disadvantages including lower brightness and limited fluorescence response compared to state-of-the-art visible wavelength GECIs, when used for imaging of neuronal activity. Here, we report 2 improved NIR GECI variants, designated NIR-GECO2 and NIR-GECO2G, derived from NIR-GECO1. We characterized the performance of the new NIR GECIs in cultured cells, acute mouse brain slices, and Caenorhabditis elegans and Xenopus laevis in vivo. Our results demonstrate that NIR-GECO2 and NIR-GECO2G provide substantial improvements over NIR-GECO1 for imaging of neuronal Ca2+ dynamics. This study describes improved genetically encoded near-infrared fluorescent calcium ion indicators, demonstrating that they enable robust detection of neuronal activity in cultured cells, rodent brain slices, Caenorhabditis elegans, and Xenopus laevis.
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Affiliation(s)
- Yong Qian
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
- Departments of Biological Engineering, Media Arts and Sciences, Brain and Cognitive Sciences, McGovern Institute, Koch Institute, Center for Neurobiological Engineering, MIT, and Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
| | - Danielle M. Orozco Cosio
- Departments of Biological Engineering, Media Arts and Sciences, Brain and Cognitive Sciences, McGovern Institute, Koch Institute, Center for Neurobiological Engineering, MIT, and Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
| | - Kiryl D. Piatkevich
- Departments of Biological Engineering, Media Arts and Sciences, Brain and Cognitive Sciences, McGovern Institute, Koch Institute, Center for Neurobiological Engineering, MIT, and Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Sarah Aufmkolk
- Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
- Department of Chemistry, McGill University, Montreal, Quebec, Canada
- Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Wan-Chi Su
- LumiSTAR Biotechnology, Nangang District, Taipei City, Taiwan
| | - Orhan T. Celiker
- Departments of Biological Engineering, Media Arts and Sciences, Brain and Cognitive Sciences, McGovern Institute, Koch Institute, Center for Neurobiological Engineering, MIT, and Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
| | - Anne Schohl
- Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Mitchell H. Murdock
- Departments of Biological Engineering, Media Arts and Sciences, Brain and Cognitive Sciences, McGovern Institute, Koch Institute, Center for Neurobiological Engineering, MIT, and Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
| | - Abhi Aggarwal
- Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, United States of America
| | - Yu-Fen Chang
- LumiSTAR Biotechnology, Nangang District, Taipei City, Taiwan
| | - Paul W. Wiseman
- Department of Chemistry, McGill University, Montreal, Quebec, Canada
- Department of Physics, McGill University, Montreal, Quebec, Canada
| | - Edward S. Ruthazer
- Montreal Neurological Institute-Hospital, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Edward S. Boyden
- Departments of Biological Engineering, Media Arts and Sciences, Brain and Cognitive Sciences, McGovern Institute, Koch Institute, Center for Neurobiological Engineering, MIT, and Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America
| | - Robert E. Campbell
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
- Department of Chemistry, The University of Tokyo, Tokyo, Japan
- * E-mail:
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42
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Linghu C, Johnson SL, Valdes PA, Shemesh OA, Park WM, Park D, Piatkevich KD, Wassie AT, Liu Y, An B, Barnes SA, Celiker OT, Yao CC, Yu CCJ, Wang R, Adamala KP, Bear MF, Keating AE, Boyden ES. Spatial Multiplexing of Fluorescent Reporters for Imaging Signaling Network Dynamics. Cell 2020; 183:1682-1698.e24. [PMID: 33232692 DOI: 10.1016/j.cell.2020.10.035] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 10/05/2020] [Accepted: 10/21/2020] [Indexed: 12/23/2022]
Abstract
In order to analyze how a signal transduction network converts cellular inputs into cellular outputs, ideally one would measure the dynamics of many signals within the network simultaneously. We found that, by fusing a fluorescent reporter to a pair of self-assembling peptides, it could be stably clustered within cells at random points, distant enough to be resolved by a microscope but close enough to spatially sample the relevant biology. Because such clusters, which we call signaling reporter islands (SiRIs), can be modularly designed, they permit a set of fluorescent reporters to be efficiently adapted for simultaneous measurement of multiple nodes of a signal transduction network within single cells. We created SiRIs for indicators of second messengers and kinases and used them, in hippocampal neurons in culture and intact brain slices, to discover relationships between the speed of calcium signaling, and the amplitude of PKA signaling, upon receiving a cAMP-driving stimulus.
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Affiliation(s)
- Changyang Linghu
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - Shannon L Johnson
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA
| | - Pablo A Valdes
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Department of Neurosurgery, Harvard Medical School, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Or A Shemesh
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Department of Neurobiology and Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Won Min Park
- Tim Taylor Department of Chemical Engineering, Kansas State University, Manhattan, KS 66506, USA
| | - Demian Park
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA
| | - Kiryl D Piatkevich
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Asmamaw T Wassie
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
| | - Yixi Liu
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA
| | - Bobae An
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Stephanie A Barnes
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Orhan T Celiker
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
| | - Chun-Chen Yao
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Chih-Chieh Jay Yu
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA
| | - Ru Wang
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA
| | - Katarzyna P Adamala
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mark F Bear
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Picower Institute for Learning and Memory, MIT, Cambridge, MA 02139, USA
| | - Amy E Keating
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Biology, MIT, Cambridge, MA 02139, USA; Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Edward S Boyden
- Department of Media Arts and Sciences, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, USA; Center for Neurobiological Engineering, MIT, Cambridge, MA 02139, USA; Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Koch Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, Cambridge, MA 02139, USA.
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43
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Moreaux LC, Yatsenko D, Sacher WD, Choi J, Lee C, Kubat NJ, Cotton RJ, Boyden ES, Lin MZ, Tian L, Tolias AS, Poon JKS, Shepard KL, Roukes ML. Integrated Neurophotonics: Toward Dense Volumetric Interrogation of Brain Circuit Activity-at Depth and in Real Time. Neuron 2020; 108:66-92. [PMID: 33058767 PMCID: PMC8061790 DOI: 10.1016/j.neuron.2020.09.043] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 08/18/2020] [Accepted: 09/28/2020] [Indexed: 12/17/2022]
Abstract
We propose a new paradigm for dense functional imaging of brain activity to surmount the limitations of present methodologies. We term this approach "integrated neurophotonics"; it combines recent advances in microchip-based integrated photonic and electronic circuitry with those from optogenetics. This approach has the potential to enable lens-less functional imaging from within the brain itself to achieve dense, large-scale stimulation and recording of brain activity with cellular resolution at arbitrary depths. We perform a computational study of several prototype 3D architectures for implantable probe-array modules that are designed to provide fast and dense single-cell resolution (e.g., within a 1-mm3 volume of mouse cortex comprising ∼100,000 neurons). We describe progress toward realizing integrated neurophotonic imaging modules, which can be produced en masse with current semiconductor foundry protocols for chip manufacturing. Implantation of multiple modules can cover extended brain regions.
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Affiliation(s)
- Laurent C Moreaux
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA.
| | - Dimitri Yatsenko
- Vathes LLC, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wesley D Sacher
- Kavli Nanoscience Institute, California Institute of Technology, Pasadena, CA 91125, USA; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; Max Planck Institute for Microstructure Physics, Halle, Germany
| | - Jaebin Choi
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Changhyuk Lee
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA; Center for BioMicrosystems, Brain Science Institute, Korea Institute of Science and Technology, Korea
| | - Nicole J Kubat
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA
| | - R James Cotton
- Shirley Ryan AbilityLab, Northwestern University, Chicago, IL 60611, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Edward S Boyden
- Howard Hughes Medical Institute, Cambridge, MA, USA; McGovern Institute, MIT, Cambridge, USA; Koch Institute, MIT, Cambridge, USA; Departments of Brain and Cognitive Sciences, Media Arts and Sciences, and Biological Engineering, MIT, Cambridge, USA
| | - Michael Z Lin
- Departments of Neurobiology and Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Lin Tian
- Department of Biochemistry and Molecular Medicine, School of Medicine, University of California, Davis, CA 95616, USA
| | - Andreas S Tolias
- Vathes LLC, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence and Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Joyce K S Poon
- Max Planck Institute for Microstructure Physics, Halle, Germany; Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, ON M5S 3G4, Canada
| | - Kenneth L Shepard
- Departments of Electrical Engineering and Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Michael L Roukes
- Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA; Kavli Nanoscience Institute, California Institute of Technology, Pasadena, CA 91125, USA; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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44
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Shen FY, Harrington MM, Walker LA, Cheng HPJ, Boyden ES, Cai D. Light microscopy based approach for mapping connectivity with molecular specificity. Nat Commun 2020; 11:4632. [PMID: 32934230 PMCID: PMC7493953 DOI: 10.1038/s41467-020-18422-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 08/21/2020] [Indexed: 11/28/2022] Open
Abstract
Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy.
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Affiliation(s)
- Fred Y Shen
- Medical Scientist Training Program, University of Michigan, Ann Arbor, MI, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA
| | - Margaret M Harrington
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Logan A Walker
- LS & A, Program in Biophysics, University of Michigan, Ann Arbor, MI, USA
| | - Hon Pong Jimmy Cheng
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Edward S Boyden
- McGovern Institute, Koch Institute, Department of Media Arts and Sciences, Department of Biological Engineering, and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
| | - Dawen Cai
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- LS & A, Program in Biophysics, University of Michigan, Ann Arbor, MI, USA.
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Gong X, Mendoza-Halliday D, Ting JT, Kaiser T, Sun X, Bastos AM, Wimmer RD, Guo B, Chen Q, Zhou Y, Pruner M, Wu CWH, Park D, Deisseroth K, Barak B, Boyden ES, Miller EK, Halassa MM, Fu Z, Bi G, Desimone R, Feng G. An Ultra-Sensitive Step-Function Opsin for Minimally Invasive Optogenetic Stimulation in Mice and Macaques. Neuron 2020; 107:197. [PMID: 32645306 DOI: 10.1016/j.neuron.2020.06.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Shemesh OA, Linghu C, Piatkevich KD, Goodwin D, Celiker OT, Gritton HJ, Romano MF, Gao R, Yu CCJ, Tseng HA, Bensussen S, Narayan S, Yang CT, Freifeld L, Siciliano CA, Gupta I, Wang J, Pak N, Yoon YG, Ullmann JFP, Guner-Ataman B, Noamany H, Sheinkopf ZR, Park WM, Asano S, Keating AE, Trimmer JS, Reimer J, Tolias AS, Bear MF, Tye KM, Han X, Ahrens MB, Boyden ES. Precision Calcium Imaging of Dense Neural Populations via a Cell-Body-Targeted Calcium Indicator. Neuron 2020; 107:470-486.e11. [PMID: 32592656 DOI: 10.1016/j.neuron.2020.05.029] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 05/09/2019] [Accepted: 05/20/2020] [Indexed: 01/11/2023]
Abstract
Methods for one-photon fluorescent imaging of calcium dynamics can capture the activity of hundreds of neurons across large fields of view at a low equipment complexity and cost. In contrast to two-photon methods, however, one-photon methods suffer from higher levels of crosstalk from neuropil, resulting in a decreased signal-to-noise ratio and artifactual correlations of neural activity. We address this problem by engineering cell-body-targeted variants of the fluorescent calcium indicators GCaMP6f and GCaMP7f. We screened fusions of GCaMP to natural, as well as artificial, peptides and identified fusions that localized GCaMP to within 50 μm of the cell body of neurons in mice and larval zebrafish. One-photon imaging of soma-targeted GCaMP in dense neural circuits reported fewer artifactual spikes from neuropil, an increased signal-to-noise ratio, and decreased artifactual correlation across neurons. Thus, soma-targeting of fluorescent calcium indicators facilitates usage of simple, powerful, one-photon methods for imaging neural calcium dynamics.
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Affiliation(s)
- Or A Shemesh
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Department of Neurobiology and Pittsburgh Institute for Neurodegenerative Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Changyang Linghu
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Kiryl D Piatkevich
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Daniel Goodwin
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Orhan Tunc Celiker
- MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Howard J Gritton
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
| | - Michael F Romano
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
| | - Ruixuan Gao
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Chih-Chieh Jay Yu
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Hua-An Tseng
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
| | - Seth Bensussen
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
| | - Sujatha Narayan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Chao-Tsung Yang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Limor Freifeld
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Faculty of Biomedical Engineering, Technion, Haifa, Israel
| | - Cody A Siciliano
- Vanderbilt Center for Addiction Research, Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Ishan Gupta
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
| | - Joyce Wang
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Nikita Pak
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Department of Mechanical Engineering, MIT, Cambridge, MA, USA
| | - Young-Gyu Yoon
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA; School of Electrical Engineering, KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Jeremy F P Ullmann
- Epilepsy Genetics Program, Department of Neurology, Boston Children's Hospital & Harvard Medical School, Boston, MA, USA
| | - Burcu Guner-Ataman
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Habiba Noamany
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Zoe R Sheinkopf
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Shoh Asano
- Internal Medicine Research Unit, Pfizer, Cambridge, MA, USA
| | - Amy E Keating
- Department of Biological Engineering, MIT, Cambridge, MA, USA; Department of Biology, MIT, Cambridge, MA, USA; Koch Institute, MIT, Cambridge, MA 02139, USA
| | - James S Trimmer
- Department of Physiology and Membrane Biology, University of California, Davis School of Medicine, Davis, CA, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Center for Neuroscience and AI, Baylor College of Medicine, Houston, TX, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Center for Neuroscience and AI, Baylor College of Medicine, Houston, TX, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Mark F Bear
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA
| | - Kay M Tye
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, USA; Salk Institute for Biological Studies, La Jolla, CA 92037, USA
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, MA 02215, USA
| | - Misha B Ahrens
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Edward S Boyden
- The MIT Media Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; Department of Biological Engineering, MIT, Cambridge, MA, USA; MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA; Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA; MIT McGovern Institute for Brain Research, MIT, Cambridge, MA, USA; Koch Institute, MIT, Cambridge, MA 02139, USA.
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Yu CC(J, Barry NC, Wassie AT, Sinha A, Bhattacharya A, Asano S, Zhang C, Chen F, Hobert O, Goodman MB, Haspel G, Boyden ES. Expansion microscopy of C. elegans. eLife 2020; 9:e46249. [PMID: 32356725 PMCID: PMC7195193 DOI: 10.7554/elife.46249] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 03/30/2020] [Indexed: 12/20/2022] Open
Abstract
We recently developed expansion microscopy (ExM), which achieves nanoscale-precise imaging of specimens at ~70 nm resolution (with ~4.5x linear expansion) by isotropic swelling of chemically processed, hydrogel-embedded tissue. ExM of C. elegans is challenged by its cuticle, which is stiff and impermeable to antibodies. Here we present a strategy, expansion of C. elegans (ExCel), to expand fixed, intact C. elegans. ExCel enables simultaneous readout of fluorescent proteins, RNA, DNA location, and anatomical structures at resolutions of ~65-75 nm (3.3-3.8x linear expansion). We also developed epitope-preserving ExCel, which enables imaging of endogenous proteins stained by antibodies, and iterative ExCel, which enables imaging of fluorescent proteins after 20x linear expansion. We demonstrate the utility of the ExCel toolbox for mapping synaptic proteins, for identifying previously unreported proteins at cell junctions, and for gene expression analysis in multiple individual neurons of the same animal.
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Affiliation(s)
- Chih-Chieh (Jay) Yu
- Department of Biological Engineering, Massachusetts Institute of TechnologyCambridgeUnited States
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Nicholas C Barry
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Asmamaw T Wassie
- Department of Biological Engineering, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Anubhav Sinha
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
- Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Abhishek Bhattacharya
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia UniversityNew YorkUnited States
| | - Shoh Asano
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Chi Zhang
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Fei Chen
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Oliver Hobert
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia UniversityNew YorkUnited States
| | - Miriam B Goodman
- Department of Molecular and Cellular Physiology, Stanford UniversityStanfordUnited States
| | - Gal Haspel
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University-NewarkNewarkUnited States
- The Brain Research Institute, New Jersey Institute of TechnologyNewarkUnited States
| | - Edward S Boyden
- Department of Biological Engineering, Massachusetts Institute of TechnologyCambridgeUnited States
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
- Koch Institute, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
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48
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Gong X, Mendoza-Halliday D, Ting JT, Kaiser T, Sun X, Bastos AM, Wimmer RD, Guo B, Chen Q, Zhou Y, Pruner M, Wu CWH, Park D, Deisseroth K, Barak B, Boyden ES, Miller EK, Halassa MM, Fu Z, Bi G, Desimone R, Feng G. An Ultra-Sensitive Step-Function Opsin for Minimally Invasive Optogenetic Stimulation in Mice and Macaques. Neuron 2020; 107:38-51.e8. [PMID: 32353253 PMCID: PMC7351618 DOI: 10.1016/j.neuron.2020.03.032] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 02/26/2020] [Accepted: 03/27/2020] [Indexed: 01/27/2023]
Abstract
Optogenetics is among the most widely employed techniques to manipulate neuronal activity. However, a major drawback is the need for invasive implantation of optical fibers. To develop a minimally invasive optogenetic method that overcomes this challenge, we engineered a new step-function opsin with ultra-high light sensitivity (SOUL). We show that SOUL can activate neurons located in deep mouse brain regions via transcranial optical stimulation and elicit behavioral changes in SOUL knock-in mice. Moreover, SOUL can be used to modulate neuronal spiking and induce oscillations reversibly in macaque cortex via optical stimulation from outside the dura. By enabling external light delivery, our new opsin offers a minimally invasive tool for manipulating neuronal activity in rodent and primate models with fewer limitations on the depth and size of target brain regions and may further facilitate the development of minimally invasive optogenetic tools for the treatment of neurological disorders.
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Affiliation(s)
- Xin Gong
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China; McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Diego Mendoza-Halliday
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jonathan T Ting
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Human Cell Types, Allen Institute for Brain Science, Seattle, WA 98109, USA; Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Tobias Kaiser
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xuyun Sun
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang 310027, China
| | - André M Bastos
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ralf D Wimmer
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Baolin Guo
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Qian Chen
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yang Zhou
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Maxwell Pruner
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Carolyn W-H Wu
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Demian Park
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Boaz Barak
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Earl K Miller
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Michael M Halassa
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Zhanyan Fu
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Guoqiang Bi
- Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Robert Desimone
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Guoping Feng
- McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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50
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Bucur O, Fu F, Calderon M, Mylvaganam GH, Ly NL, Day J, Watkin S, Walker BD, Boyden ES, Zhao Y. Nanoscale imaging of clinical specimens using conventional and rapid-expansion pathology. Nat Protoc 2020; 15:1649-1672. [PMID: 32238952 DOI: 10.1038/s41596-020-0300-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 01/16/2020] [Indexed: 11/09/2022]
Abstract
In pathology, microscopy is an important tool for the analysis of human tissues, both for the scientific study of disease states and for diagnosis. However, the microscopes commonly used in pathology are limited in resolution by diffraction. Recently, we discovered that it was possible, through a chemical process, to isotropically expand preserved cells and tissues by 4-5× in linear dimension. We call this process expansion microscopy (ExM). ExM enables nanoscale resolution imaging on conventional microscopes. Here we describe protocols for the simple and effective physical expansion of a variety of human tissues and clinical specimens, including paraffin-embedded, fresh frozen and chemically stained human tissues. These protocols require only inexpensive, commercially available reagents and hardware commonly found in a routine pathology laboratory. Our protocols are written for researchers and pathologists experienced in conventional fluorescence microscopy. The conventional protocol, expansion pathology, can be completed in ~1 d with immunostained tissue sections and 2 d with unstained specimens. We also include a new, fast variant, rapid expansion pathology, that can be performed on <5-µm-thick tissue sections, taking <4 h with immunostained tissue sections and <8 h with unstained specimens.
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Affiliation(s)
- Octavian Bucur
- Department of Pathology and Cancer Research Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Ludwig Center at Harvard Medical School, Boston, MA, USA.,Institute of Biochemistry of the Romanian Academy, Bucharest, Romania.,QPathology, Boston, MA, USA
| | - Feifei Fu
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Mike Calderon
- Center for Biologic Imaging, Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Geetha H Mylvaganam
- Ragon Institute of MGH, MIT and Harvard and Harvard University Center for AIDS Research, Cambridge, MA, USA.,Massachussetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ngoc L Ly
- Ragon Institute of MGH, MIT and Harvard and Harvard University Center for AIDS Research, Cambridge, MA, USA.,Massachussetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jimmy Day
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Simon Watkin
- Center for Biologic Imaging, Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce D Walker
- Ragon Institute of MGH, MIT and Harvard and Harvard University Center for AIDS Research, Cambridge, MA, USA.,Massachussetts General Hospital, Harvard Medical School, Boston, MA, USA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Edward S Boyden
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. .,McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Koch Institute, Massachusetts Institute of Technology, Cambridge, MA, USA. .,Center for Neurobiological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yongxin Zhao
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA. .,Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
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