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Mao Y, Jin Z, Yang J, Xu D, Zhao L, Kiram A, Yin Y, Zhou D, Sun Z, Xiao L, Zhou Z, Yang L, Fu T, Xu Z, Jia Y, Chen X, Niu FN, Li X, Zhu Z, Gan Z. Muscle-bone cross-talk through the FNIP1-TFEB-IGF2 axis is associated with bone metabolism in human and mouse. Sci Transl Med 2024; 16:eadk9811. [PMID: 38838134 DOI: 10.1126/scitranslmed.adk9811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/13/2024] [Indexed: 06/07/2024]
Abstract
Clinical evidence indicates a close association between muscle dysfunction and bone loss; however, the underlying mechanisms remain unclear. Here, we report that muscle dysfunction-related bone loss in humans with limb-girdle muscular dystrophy is associated with decreased expression of folliculin-interacting protein 1 (FNIP1) in muscle tissue. Supporting this finding, murine gain- and loss-of-function genetic models demonstrated that muscle-specific ablation of FNIP1 caused decreased bone mass, increased osteoclastic activity, and mechanical impairment that could be rescued by myofiber-specific expression of FNIP1. Myofiber-specific FNIP1 deficiency stimulated expression of nuclear translocation of transcription factor EB, thereby activating transcription of insulin-like growth factor 2 (Igf2) at a conserved promoter-binding site and subsequent IGF2 secretion. Muscle-derived IGF2 stimulated osteoclastogenesis through IGF2 receptor signaling. AAV9-mediated overexpression of IGF2 was sufficient to decrease bone volume and impair bone mechanical properties in mice. Further, we found that serum IGF2 concentration was negatively correlated with bone health in humans in the context of osteoporosis. Our findings elucidate a muscle-bone cross-talk mechanism bridging the gap between muscle dysfunction and bone loss. This cross-talk represents a potential target to treat musculoskeletal diseases and osteoporosis.
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Affiliation(s)
- Yan Mao
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Zhen Jin
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China
| | - Jing Yang
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Dengqiu Xu
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Lei Zhao
- Department of Neurology, Children,s Hospital of Fudan University, Shanghai 201102, China
| | - Abdukahar Kiram
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yujing Yin
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Danxia Zhou
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Zongchao Sun
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Liwei Xiao
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Zheng Zhou
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Likun Yang
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Tingting Fu
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Zhisheng Xu
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Yuhuan Jia
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Xinyi Chen
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
| | - Feng-Nan Niu
- Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Xihua Li
- Department of Neurology, Children,s Hospital of Fudan University, Shanghai 201102, China
| | - Zezhang Zhu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Zhenji Gan
- State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center, Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Key Laboratory of Molecular Medicine, Chemistry and Biomedicine Innovation Center (ChemBIC), Medical School of Nanjing University, Nanjing University, Nanjing 210061, China
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Pandurangan K, Jayakumar J, Savoia S, Nanda R, Lata S, Kumar EH, S S, Vasudevan S, Srinivasan C, Joseph J, Sivaprakasam M, Verma R. Systematic development of immunohistochemistry protocol for large cryosections-specific to non-perfused fetal brain. J Neurosci Methods 2024; 405:110085. [PMID: 38387804 DOI: 10.1016/j.jneumeth.2024.110085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/01/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Immunohistochemistry (IHC) is an important technique in understanding the expression of neurochemical molecules in the developing human brain. Despite its routine application in the research and clinical setup, the IHC protocol specific for soft fragile fetal brains that are fixed using the non-perfusion method is still limited in studying the whole brain. NEW METHOD This study shows that the IHC protocols, using a chromogenic detection system, used in animals and adult humans are not optimal in the fetal brains. We have optimized key steps from Antigen retrieval (AR) to chromogen visualization for formalin-fixed whole-brain cryosections (20 µm) mounted on glass slides. RESULTS We show the results from six validated, commonly used antibodies to study the fetal brain. We achieved optimal antigen retrieval with 0.1 M Boric Acid, pH 9.0 at 70°C for 20 minutes. We also present the optimal incubation duration and temperature for protein blocking and the primary antibody that results in specific antigen labeling with minimal tissue damage. COMPARISON WITH EXISTING METHODS The IHC protocol commonly used for adult human and animal brains results in significant tissue damage in the fetal brains with little or suboptimal antigen expression. Our new method with important modifications including the temperature, duration, and choice of the alkaline buffer for AR addresses these pitfalls and provides high-quality results. CONCLUSION The optimized IHC protocol for the developing human brain (13-22 GW) provides a high-quality, repeatable, and reliable method for studying chemoarchitecture in neurotypical and pathological conditions across different gestational ages.
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Affiliation(s)
- Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | | | - Reetuparna Nanda
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | - S Lata
- Mediscan Systems, Chennai, Tamil Nadu, India.
| | | | - Suresh S
- Mediscan Systems, Chennai, Tamil Nadu, India.
| | - Sudha Vasudevan
- Department of Obstetrics & Gynaecology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India.
| | - Chitra Srinivasan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India.
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India.
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India.
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
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3
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Verma R, Jayakumar J, Folkerth R, Manger PR, Bota M, Majumder M, Pandurangan K, Savoia S, Karthik S, Kumarasami R, Joseph J, Rohini G, Vasudevan S, Srinivasan C, Lata S, Kumar EH, Rangasami R, Kumutha J, Suresh S, Šimić G, Mitra PP, Sivaprakasam M. Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology. J Comp Neurol 2024; 532:e25612. [PMID: 38591638 DOI: 10.1002/cne.25612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/06/2024] [Accepted: 03/24/2024] [Indexed: 04/10/2024]
Abstract
Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.
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Affiliation(s)
- Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Rebecca Folkerth
- Department of Forensic Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Moitrayee Majumder
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | | | - Srinivasa Karthik
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Ramdayalan Kumarasami
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
| | - G Rohini
- Department of Obstetrics & Gynaecology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - Sudha Vasudevan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - Chitra Srinivasan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - S Lata
- Mediscan Systems, Chennai, Tamil Nadu, India
| | | | - Rajeswaran Rangasami
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Jayaraman Kumutha
- Department of Neonatology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - S Suresh
- Mediscan Systems, Chennai, Tamil Nadu, India
| | - Goran Šimić
- Department of Neuroscience, Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, Hrvatska, Croatia
| | - Partha P Mitra
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Cold Spring Harbor Laboratory, New York, New York, USA
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
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Zhang A, Jin L, Yao S, Matsuyama M, van Velthoven CTJ, Sullivan HA, Sun N, Kellis M, Tasic B, Wickersham I, Chen X. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. eLife 2024; 12:RP87866. [PMID: 38319699 PMCID: PMC10942611 DOI: 10.7554/elife.87866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024] Open
Abstract
Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4130 retrogradely labeled cells and 2914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.
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Affiliation(s)
- Aixin Zhang
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lei Jin
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shenqin Yao
- Allen Institute for Brain ScienceSeattleUnited States
| | - Makoto Matsuyama
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | | | - Heather Anne Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Na Sun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Broad Institute of MIT and HarvardCambridgeUnited States
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Broad Institute of MIT and HarvardCambridgeUnited States
- Broad Institute of MIT and HarvardCambridgeUnited States
| | | | - Ian Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Xiaoyin Chen
- Allen Institute for Brain ScienceSeattleUnited States
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5
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Qian K, Friedman B, Takatoh J, Wang F, Kleinfeld D, Freund Y. CellBoost: A pipeline for machine assisted annotation in Neuroanatomy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.13.557658. [PMID: 38293051 PMCID: PMC10827062 DOI: 10.1101/2023.09.13.557658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
One of the important yet labor intensive tasks in neuroanatomy is the identification of select populations of cells. Current high-throughput techniques enable marking cells with histochemical fluorescent molecules as well as through the genetic expression of fluorescent proteins. Modern scanning microscopes allow high resolution multi-channel imaging of the mechanically or optically sectioned brain with thousands of marked cells per square millimeter. Manual identification of all marked cells is prohibitively time consuming. At the same time, simple segmentation algorithms suffer from high error rates and sensitivity to variation in fluorescent intensity and spatial distribution. We present a methodology that combines human judgement and machine learning that serves to significantly reduce the labor of the anatomist while improving the consistency of the annotation. As a demonstration, we analyzed murine brains with marked premotor neurons in the brainstem. We compared the error rate of our method to the disagreement rate among human anatomists. This comparison shows that our method can reduce the time to annotate by as much as ten-fold without significantly increasing the rate of errors. We show that our method achieves significant reduction in labor while achieving an accuracy that is similar to the level of agreement between different anatomists.
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Affiliation(s)
- Kui Qian
- Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Beth Friedman
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jun Takatoh
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Fan Wang
- Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - David Kleinfeld
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA
| | - Yoav Freund
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093, USA
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA
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6
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Zhou G, Tward D, Lange K. A Majorization-Minimization Algorithm for Neuroimage Registration. SIAM JOURNAL ON IMAGING SCIENCES 2024; 17:273-300. [PMID: 38550750 PMCID: PMC10977051 DOI: 10.1137/22m1516907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
Intensity-based image registration is critical for neuroimaging tasks, such as 3D reconstruction, times-series alignment, and common coordinate mapping. The gradient-based optimization methods commonly used to solve this problem require a careful selection of step-length. This limitation imposes substantial time and computational costs. Here we propose a gradient-independent rigid-motion registration algorithm based on the majorization-minimization (MM) principle. Each iteration of our intensity-based MM algorithm reduces to a simple point-set rigid registration problem with a closed form solution that avoids the step-length issue altogether. The details of the algorithm are presented, and an error bound for its more practical truncated form is derived. The performance of the MM algorithm is shown to be more effective than gradient descent on simulated images and Nissl stained coronal slices of mouse brain. We also compare and contrast the similarities and differences between the MM algorithm and another gradient-free registration algorithm called the block-matching method. Finally, extensions of this algorithm to more complex problems are discussed.
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Affiliation(s)
- Gaiting Zhou
- Computational Medicine, UCLA, Los Angeles, CA 90024 USA
| | - Daniel Tward
- Computational Medicine, UCLA, Los Angeles, CA 90024 USA
| | - Kenneth Lange
- Computational Medicine, UCLA, Los Angeles, CA 90024 USA
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7
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James RI, Verma R, Johnson LR, Manesh A, Jayakumar J, Sen M, Joseph J, Kumarasami R, Mitra PP, Sivaprakasam M, Varghese GM. A Standardized Protocol for the Safe Retrieval of Infectious Postmortem Human Brain for Studying Whole-Brain Pathology. Am J Forensic Med Pathol 2023; 44:303-310. [PMID: 37490584 PMCID: PMC10662599 DOI: 10.1097/paf.0000000000000871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT We describe a safe and standardized perfusion protocol for studying brain pathology in high-risk autopsies using a custom-designed low-cost infection containment chamber and high-resolution histology. The output quality was studied using the histological data from the whole cerebellum and brain stem processed using a high-resolution cryohistology pipeline at 0.5 μm per pixel, in-plane resolution with serial sections at 20-μm thickness. To understand the pathophysiology of highly infectious diseases, it is necessary to have a safe and cost-effective method of performing high-risk autopsies and a standardized perfusion protocol for preparing high-quality tissues. Using the low-cost infection containment chamber, we detail the cranial autopsy protocol and ex situ perfusion-fixation of 4 highly infectious adult human brains. The digitized high-resolution histology images of the Nissl-stained series reveal that most of the sections were free of processing artifacts, such as fixation damage, freezing artifacts, and osmotic shock, at the macrocellular and microcellular level. The quality of our protocol was also tested with the highly sensitive immunohistochemistry staining for specific protein markers. Our protocol provides a safe and effective method in high-risk autopsies that allows for the evaluation of pathogen-host interaction, the underlying pathophysiology, and the extent of the infection across the whole brain at microscopic resolutions.
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Affiliation(s)
- Ranjit Immanuel James
- From the Department of Forensic Medicine and Toxicology, Christian Medical College, Vellore
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
| | - Latif Rajesh Johnson
- From the Department of Forensic Medicine and Toxicology, Christian Medical College, Vellore
| | - Abi Manesh
- Department of Infectious Diseases, Christian Medical College, Vellore
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Center for Computational Brain Research
| | - Mousumi Sen
- From the Department of Forensic Medicine and Toxicology, Christian Medical College, Vellore
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Department of Electrical Engineering
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Ramdayalan Kumarasami
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Partha P. Mitra
- Center for Computational Brain Research
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai
- Department of Electrical Engineering
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
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Zhang A, Jin L, Yao S, Matsuyama M, van Velthoven C, Sullivan H, Sun N, Kellis M, Tasic B, Wickersham IR, Chen X. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532873. [PMID: 36993334 PMCID: PMC10055146 DOI: 10.1101/2023.03.16.532873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4,130 retrogradely labeled cells and 2,914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.
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Affiliation(s)
- Aixin Zhang
- Allen Institute for Brain Science, Seattle, WA
| | - Lei Jin
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Current address: Lingang Laboratory, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA
| | - Makoto Matsuyama
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Current address: Metcela Inc., Kawasaki, Kanagawa, Japan
| | | | - Heather Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Na Sun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Ian R. Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
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Kumarasami R, Verma R, Pandurangan K, Ramesh JJ, Pandidurai S, Savoia S, Jayakumar J, Bota M, Mitra P, Joseph J, Sivaprakasam M. A technology platform for standardized cryoprotection and freezing of large-volume brain tissues for high-resolution histology. Front Neuroanat 2023; 17:1292655. [PMID: 38020211 PMCID: PMC10651725 DOI: 10.3389/fnana.2023.1292655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Understanding and mapping the human connectome is a long-standing endeavor of neuroscience, yet the significant challenges associated with the large size of the human brain during cryosectioning remain unsolved. While smaller brains, such as rodents and marmosets, have been the focus of previous connectomics projects, the processing of the larger human brain requires significant technological advancements. This study addresses the problem of freezing large brains in aligned neuroanatomical coordinates with minimal tissue damage, facilitating large-scale distortion-free cryosectioning. We report the most effective and stable freezing technique utilizing an appropriate choice of cryoprotection and leveraging engineering tools such as brain master patterns, custom-designed molds, and a continuous temperature monitoring system. This standardized approach to freezing enables high-quality, distortion-free histology, allowing researchers worldwide to explore the complexities of the human brain at a cellular level. Our approach combines neuroscience and engineering technologies to address this long-standing challenge with limited resources, enhancing accessibility of large-scale scientific endeavors beyond developed countries, promoting diverse approaches, and fostering collaborations.
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Affiliation(s)
- Ramdayalan Kumarasami
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Jivitha Jyothi Ramesh
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Sathish Pandidurai
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Stephen Savoia
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, United States
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, India
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Partha Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, United States
| | - Jayaraj Joseph
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Mohanasankar Sivaprakasam
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
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10
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Karthik S, Joseph J, Jayakumar J, Manoj R, Shetty M, Bota M, Verma R, Mitra P, Sivaprakasam M. Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain. J Neurosci Methods 2023; 397:109921. [PMID: 37459898 DOI: 10.1016/j.jneumeth.2023.109921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/26/2023] [Accepted: 07/13/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Imaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3). NEW METHOD We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features. RESULTS The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition. COMPARISON WITH EXISTING METHOD(S) The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI. CONCLUSIONS The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.
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Affiliation(s)
- Srinivasa Karthik
- Healthcare Technology Innovation Centre, No. 1, 5th Floor, 'C' Block, Phase-II, IIT Madras Research Park, Kanagam Road, Taramani, Chennai 600113, India; Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India.
| | - Jayaraj Joseph
- Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India; Center for Computational Brain Research, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India
| | - Rahul Manoj
- Healthcare Technology Innovation Centre, No. 1, 5th Floor, 'C' Block, Phase-II, IIT Madras Research Park, Kanagam Road, Taramani, Chennai 600113, India; Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India
| | - Mahesh Shetty
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
| | - Partha Mitra
- Center for Computational Brain Research, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India; Cold Spring Harbor Laboratory, 1, Bungtown Road, Cold Spring Harbor, New York 11724, United States
| | - Mohanasankar Sivaprakasam
- Healthcare Technology Innovation Centre, No. 1, 5th Floor, 'C' Block, Phase-II, IIT Madras Research Park, Kanagam Road, Taramani, Chennai 600113, India; Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India; Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
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11
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Sithambaram P, Kumarasami R, Pandidurai S, Sekar S, Vasan JK, Sivaprakasam M, Joseph J. Automation of slide staining for large tissue sections. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083015 DOI: 10.1109/embc40787.2023.10339963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Staining is a critical step in tissue analysis as it enhances the visibility and contrast of tissue structures for microscopic examination. Large tissue sections such as the human brain, heart, and liver are becoming increasingly important in studying complex tissue structures, providing critical information about the tissue's normal or abnormal development, function, and disease processes. Manual staining methods are still widely used and are prone to inconsistencies and inaccuracies, leading to unreliable results. Commercially available automated staining systems offer a more efficient alternative, but currently, these systems are only available for smaller 1" x 3" slides which are ill-suited for examining larger tissue sections. To address this challenge, we present a custom-designed Large format Automated Slide Stainer that can handle various glass slides, from the standard 1" x 3" slides to the custom-sized 2" x 3", 5" x 7", and 6" x 8" glass slides. The system uses a Cartesian robotic arm to stain the slides and has a user-friendly and intuitive interface for creating and modifying custom staining protocols. Safety features include chemical isolation, a ventilation system, an emergency shutdown, and a protective shield to minimize hazards from handling chemicals and biological materials. The automated stainer showed little variability in positioning with a mean offset error of 1.65 ± 0.65 mm and 1.73 ± 0.76 mm in the X and Y axes, respectively. In addition, the automated staining process showed better uniformity than manual staining. A pairwise distance was used to evaluate how well image histograms matched within a batch. The automated staining had a mean pairwise distance of 0.0070 ± 0.0017 (Nissl) and 0.0060 ± 0.0003 (Hematoxylin and Eosin(H&E)), which were far superior to the manual staining distances (Nissl: 0.0173 ± 0.0107 and H&E: 0.0185 ± 0.0067). This system represents a substantial advancement in tissue staining and has the potential to improve the reliability of tissue analysis significantly.Clinical relevance - Automated system for providing accurate, reproducible, and high-throughput staining of large tissue sections for use in histopathology and research.
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12
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Arnold KM, Sicard D, Tschumperlin DJ, Westendorf JJ. Atomic Force Microscopy Micro-Indentation Methods for Determining the Elastic Modulus of Murine Articular Cartilage. SENSORS (BASEL, SWITZERLAND) 2023; 23:1835. [PMID: 36850434 PMCID: PMC9967621 DOI: 10.3390/s23041835] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/20/2023] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
The mechanical properties of biological tissues influence their function and can predict degenerative conditions before gross histological or physiological changes are detectable. This is especially true for structural tissues such as articular cartilage, which has a primarily mechanical function that declines after injury and in the early stages of osteoarthritis. While atomic force microscopy (AFM) has been used to test the elastic modulus of articular cartilage before, there is no agreement or consistency in methodologies reported. For murine articular cartilage, methods differ in two major ways: experimental parameter selection and sample preparation. Experimental parameters that affect AFM results include indentation force and cantilever stiffness; these are dependent on the tip, sample, and instrument used. The aim of this project was to optimize these experimental parameters to measure murine articular cartilage elastic modulus by AFM micro-indentation. We first investigated the effects of experimental parameters on a control material, polydimethylsiloxane gel (PDMS), which has an elastic modulus on the same order of magnitude as articular cartilage. Experimental parameters were narrowed on this control material, and then finalized on wildtype C57BL/6J murine articular cartilage samples that were prepared with a novel technique that allows for cryosectioning of epiphyseal segments of articular cartilage and long bones without decalcification. This technique facilitates precise localization of AFM measurements on the murine articular cartilage matrix and eliminates the need to separate cartilage from underlying bone tissues, which can be challenging in murine bones because of their small size. Together, the new sample preparation method and optimized experimental parameters provide a reliable standard operating procedure to measure microscale variations in the elastic modulus of murine articular cartilage.
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Affiliation(s)
- Katherine M. Arnold
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
- Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Delphine Sicard
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Daniel J. Tschumperlin
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
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13
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Ohno Y, Murphy R, Choi M, Ou W, Sumbria RK. Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections. JOURNAL OF VISUALIZED EXPERIMENTS : JOVE 2022:10.3791/63669. [PMID: 35661689 PMCID: PMC9851888 DOI: 10.3791/63669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Extracellular accumulation of amyloid-beta (Aβ) plaques is one of the major pathological hallmarks of Alzheimer's disease (AD), and is the target of the only FDA-approved disease-modifying treatment for AD. Accordingly, the use of transgenic mouse models that overexpress the amyloid precursor protein and thereby accumulate cerebral Aβ plaques are widely used to model human AD in mice. Therefore, immunoassays, including enzyme-linked immunosorbent assay (ELISA) and immunostaining, commonly measure the Aβ load in brain tissues derived from AD transgenic mice. Though the methods for Aβ detection and quantification have been well established and documented, the impact of the size of the region of interest selected in the brain tissue on Aβ load measurements following immunostaining has not been reported. Therefore, the current protocol aimed to compare the Aβ load measurements across the full- and sub-regions of interest using an image analysis software. The steps involved in brain tissue preparation, free-floating brain section immunostaining, imaging, and quantification of Aβ load in full- versus sub-regions of interest are described using brain sections derived from 13-month-old APP/PS1 double transgenic male mice. The current protocol and the results provide valuable information about the impact of the size of the region of interest on Aβ-positive area quantification, and show a strong correlation between the Aβ-positive area obtained using the full- and sub-regions of interest analyses for brain sections derived from 13-month-old male APP/PS1 mice that show widespread Aβ deposition.
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Affiliation(s)
- Yuu Ohno
- Henry E. Riggs School of Applied Life Sciences, Keck Graduate Institute, Claremont, CA, USA
| | - Riley Murphy
- Crean College of Health and Behavioral Sciences, Chapman University, Irvine, CA, USA
| | - Matthew Choi
- Keck Science Department, Claremont McKenna College, Claremont, CA, USA
| | - Weijun Ou
- Department of Biomedical and Pharmaceutical Sciences, School of Pharmacy, Chapman University, Irvine, CA, USA
| | - Rachita K. Sumbria
- Department of Biomedical and Pharmaceutical Sciences, School of Pharmacy, Chapman University, Irvine, CA, USA,Department of Neurology, University of California, Irvine, Irvine, CA, USA,corresponding author: Rachita K. Sumbria, ()
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14
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Mass spectrometry imaging in drug distribution and drug metabolism studies – Principles, applications and perspectives. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2021.116482] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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15
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Banerjee S, Magee L, Wang D, Li X, Huo BX, Jayakumar J, Matho K, Lin MK, Ram K, Sivaprakasam M, Huang J, Wang Y, Mitra PP. Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. NAT MACH INTELL 2020; 2:585-594. [PMID: 34604701 PMCID: PMC8486300 DOI: 10.1038/s42256-020-0227-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 08/09/2020] [Indexed: 11/09/2022]
Abstract
Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains.
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Affiliation(s)
| | - Lucas Magee
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH, USA 43210
| | - Dingkang Wang
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH, USA 43210
| | - Xu Li
- Cold Spring Harbor Laboratory, NY, USA 11724
| | | | - Jaikishan Jayakumar
- Center for Computational Brain Research, Indian Institute of Technology, Chennai, Tamil Nadu, India 600036
| | | | | | - Keerthi Ram
- Center for Computational Brain Research, Indian Institute of Technology, Chennai, Tamil Nadu, India 600036
| | - Mohanasankar Sivaprakasam
- Center for Computational Brain Research, Indian Institute of Technology, Chennai, Tamil Nadu, India 600036
| | - Josh Huang
- Cold Spring Harbor Laboratory, NY, USA 11724
| | - Yusu Wang
- Computer Science and Engineering Department, The Ohio State University, Columbus, OH, USA 43210
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16
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Pollatou A, Ferrante DD. Out-of-focus brain image detection in serial tissue sections. J Neurosci Methods 2020; 345:108852. [PMID: 32771371 DOI: 10.1016/j.jneumeth.2020.108852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 07/01/2020] [Accepted: 07/03/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND A large part of image processing workflow in brain imaging is quality control which is typically done visually. One of the most time consuming steps of the quality control process is classifying an image as in-focus or out-of-focus (OOF). NEW METHOD In this paper we introduce an automated way of identifying OOF brain images from serial tissue sections in large datasets (>1.5 PB). The method utilizes steerable filters (STF) to derive a focus value (FV) for each image. The FV combined with an outlier detection that applies a dynamic threshold allows for the focus classification of the images. RESULTS The method was tested by comparing the results of our algorithm with a visual inspection of the same images. The results support that the method works extremely well by successfully identifying OOF images within serial tissue sections with a minimal number of false positives. COMPARISON WITH EXISTING METHODS Our algorithm was also compared to other methods and metrics and successfully tested in different stacks of images consisting solely of simulated OOF images in order to demonstrate the applicability of the method to other large datasets. CONCLUSIONS We have presented a practical method to distinguish OOF images from large datasets that include serial tissue sections that can be included in an automated pre-processing image analysis pipeline.
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Affiliation(s)
- Angeliki Pollatou
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY 11794-3800, USA; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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17
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Pollatou A. An automated method for removal of striping artifacts in fluorescent whole-slide microscopy. J Neurosci Methods 2020; 341:108781. [DOI: 10.1016/j.jneumeth.2020.108781] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/31/2020] [Accepted: 05/11/2020] [Indexed: 11/25/2022]
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18
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Lee BC, Lin MK, Fu Y, Hata J, Miller MI, Mitra PP. Multimodal cross-registration and quantification of metric distortions in marmoset whole brain histology using diffeomorphic mappings. J Comp Neurol 2020; 529:281-295. [PMID: 32406083 DOI: 10.1002/cne.24946] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 03/23/2020] [Accepted: 04/30/2020] [Indexed: 11/08/2022]
Abstract
Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. A fundamental problem in this field is the mapping of individual brain data sets to a reference space. Previous work has not rigorously quantified in-vivo to ex-vivo distortions in brain geometry from tissue processing. Further, existing approaches focus on registering unimodal volumetric data; however, given the increasing interest in the marmoset model for neuroscience research and the importance of addressing individual brain architecture variations, new algorithms are necessary to cross-register multimodal data sets including MRIs and multiple histological series. Here we present a computational approach for same-subject multimodal MRI-guided reconstruction of a series of consecutive histological sections, jointly with diffeomorphic mapping to a reference atlas. We quantify the scale change during different stages of brain histological processing using the Jacobian determinant of the diffeomorphic transformations involved. By mapping the final image stacks to the ex-vivo post-fixation MRI, we show that (a) tape-transfer assisted histological sections can be reassembled accurately into 3D volumes with a local scale change of 2.0 ± 0.4% per axis dimension; in contrast, (b) tissue perfusion/fixation as assessed by mapping the in-vivo MRIs to the ex-vivo post fixation MRIs shows a larger median absolute scale change of 6.9 ± 2.1% per axis dimension. This is the first systematic quantification of local metric distortions associated with whole-brain histological processing, and we expect that the results will generalize to other species. These local scale changes will be important for computing local properties to create reference brain maps.
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Affiliation(s)
- Brian C Lee
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Meng K Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Yan Fu
- Shanghai Jiaotong University, Shanghai, China
| | | | - Michael I Miller
- Center for Imaging Science, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
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19
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Griffin AD, Turtzo LC, Parikh GY, Tolpygo A, Lodato Z, Moses AD, Nair G, Perl DP, Edwards NA, Dardzinski BJ, Armstrong RC, Ray-Chaudhury A, Mitra PP, Latour LL. Traumatic microbleeds suggest vascular injury and predict disability in traumatic brain injury. Brain 2020; 142:3550-3564. [PMID: 31608359 DOI: 10.1093/brain/awz290] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 07/15/2019] [Accepted: 07/28/2019] [Indexed: 12/14/2022] Open
Abstract
Traumatic microbleeds are small foci of hypointensity seen on T2*-weighted MRI in patients following head trauma that have previously been considered a marker of axonal injury. The linear appearance and location of some traumatic microbleeds suggests a vascular origin. The aims of this study were to: (i) identify and characterize traumatic microbleeds in patients with acute traumatic brain injury; (ii) determine whether appearance of traumatic microbleeds predict clinical outcome; and (iii) describe the pathology underlying traumatic microbleeds in an index patient. Patients presenting to the emergency department following acute head trauma who received a head CT were enrolled within 48 h of injury and received a research MRI. Disability was defined using Glasgow Outcome Scale-Extended ≤6 at follow-up. All magnetic resonance images were interpreted prospectively and were used for subsequent analysis of traumatic microbleeds. Lesions on T2* MRI were stratified based on 'linear' streak-like or 'punctate' petechial-appearing traumatic microbleeds. The brain of an enrolled subject imaged acutely was procured following death for evaluation of traumatic microbleeds using MRI targeted pathology methods. Of the 439 patients enrolled over 78 months, 31% (134/439) had evidence of punctate and/or linear traumatic microbleeds on MRI. Severity of injury, mechanism of injury, and CT findings were associated with traumatic microbleeds on MRI. The presence of traumatic microbleeds was an independent predictor of disability (P < 0.05; odds ratio = 2.5). No differences were found between patients with punctate versus linear appearing microbleeds. Post-mortem imaging and histology revealed traumatic microbleed co-localization with iron-laden macrophages, predominately seen in perivascular space. Evidence of axonal injury was not observed in co-localized histopathological sections. Traumatic microbleeds were prevalent in the population studied and predictive of worse outcome. The source of traumatic microbleed signal on MRI appeared to be iron-laden macrophages in the perivascular space tracking a network of injured vessels. While axonal injury in association with traumatic microbleeds cannot be excluded, recognizing traumatic microbleeds as a form of traumatic vascular injury may aid in identifying patients who could benefit from new therapies targeting the injured vasculature and secondary injury to parenchyma.
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Affiliation(s)
- Allison D Griffin
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Acute Cerebrovasular Diagnostics Unit of the National Institute of Neurologic Disorders and Stroke, Bethesda, Maryland, USA
| | - L Christine Turtzo
- Acute Cerebrovasular Diagnostics Unit of the National Institute of Neurologic Disorders and Stroke, Bethesda, Maryland, USA
| | - Gunjan Y Parikh
- R. Adams Cowley Shock Trauma Center, Program in Trauma, University of Maryland School of Medicine, Baltimore, USA.,Division of Neurocritical Care and Emergency Neurology, Department of Neurology, University of Maryland School of Medicine, Baltimore, USA
| | | | - Zachary Lodato
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Anita D Moses
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Acute Cerebrovasular Diagnostics Unit of the National Institute of Neurologic Disorders and Stroke, Bethesda, Maryland, USA
| | - Govind Nair
- National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Daniel P Perl
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Nancy A Edwards
- Surgical Neurology Branch of the National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Bernard J Dardzinski
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Regina C Armstrong
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Abhik Ray-Chaudhury
- Surgical Neurology Branch of the National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | - Lawrence L Latour
- Center for Neuroscience and Regenerative Medicine, Bethesda, Maryland, USA.,Acute Cerebrovasular Diagnostics Unit of the National Institute of Neurologic Disorders and Stroke, Bethesda, Maryland, USA
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20
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Yang Y, Liu Q, Zhang L, Fu X, Chen J, Hong D. A modified tape transfer approach for rapidly preparing high-quality cryosections of undecalcified adult rodent bones. J Orthop Translat 2020; 26:92-100. [PMID: 33437628 PMCID: PMC7773961 DOI: 10.1016/j.jot.2020.03.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 02/18/2020] [Accepted: 03/02/2020] [Indexed: 12/28/2022] Open
Abstract
Background/Objective Histology-based analyses are important tools to dissect cellular and molecular mechanisms of skeletal homeostasis, diseases, and regeneration. The success of these efforts is highly dependent on rapidly obtaining high-quality sections of mineralized skeletal tissues suitable for various analyses. However, the current techniques for preparing such sections are still far from satisfactory. This study aimed to develop a new approach for preparing high-quality undecalcified bone sections applicable to various histological analyses. Methods Two important modifications were made to the conventional Cryojane Tape-Transfer System, including utilization of an optimized adhesive to prepare adhesive glass slides for improving the transfer efficiency, and a cheap conventional benchtop UV transilluminator for UV curing. Cryosections of undecalcified rodent bones were prepared using this modified tape transfer approach, and their tissue morphology and structural integrity were visually examined. A variety of histological analyses, including calcein labeling, Von kossa staining, immunofluorescence, and enzymatic activity staining as well as 5-Ethynyl-2’-deoxyuridine (EdU) and TUNEL assays, were performed on these sections. Results We developed a modified version of tape transfer approach that can prepare cryosections of undecalcified rodent adult bones within 4 days at a low cost. Bone sections prepared by this approach exhibited good tissue morphology and structural integrity. Moreover, these sections were applicable to a variety of histological analyses, including calcein labeling, Von kossa staining, immunofluorescence, and enzymatic activity staining as well as EdU and TUNEL assays. Conclusion The tape transfer approach we developed provides a rapid, affordable, and easy learning method for preparing high-quality undecalcified bone sections valuable for bone research. The translational potential of this article Our research provides a rapid, affordable, and easy learning method for preparing high-quality undecalcified bone sections that can be potentially used for accurate diagnosis of various bone disorders and evaluation of the efficacy of different therapies in the treatment of these diseases.
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Affiliation(s)
- Yanjun Yang
- Orthopedic Institute, Medical College, Soochow University, Suzhou, Jiangsu, China.,Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Qingbai Liu
- Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.,Department of Orthopedics, Lianshui County People's Hospital, Huaian, Jiangsu, China
| | - Liwei Zhang
- Orthopedic Institute, Medical College, Soochow University, Suzhou, Jiangsu, China.,Orthopedic Department, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, Zhejiang, China
| | - Xuejie Fu
- Orthopedic Institute, Medical College, Soochow University, Suzhou, Jiangsu, China.,Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Jianquan Chen
- Orthopedic Institute, Medical College, Soochow University, Suzhou, Jiangsu, China.,Department of Orthopedics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Dun Hong
- Orthopedic Department, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, Zhejiang, China
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21
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Sticker method for preparation of frozen section using adhesive film. J Neurosci Methods 2019; 328:108436. [PMID: 31526765 DOI: 10.1016/j.jneumeth.2019.108436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/12/2019] [Accepted: 09/12/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND In basic research, especially animal experiments, tissue histology with preserved organ morphology is essential, and the reliability of experiments depends on the quality of tissue sections. Frozen sections adequately maintain the antigenicity of tissues and are suitable for immunohistochemistry. However, thin frozen sections are often difficult to prepare from specimens. Therefore, a simple and fast method with a high success rate of specimen preparation is desired. NEW METHOD In this study, we propose the "Sticker method," for preparing frozen sections using adhesive film, which is easy and maintains the whole organ morphology even in frozen section. RESULTS This method requires a simple adhesive film, and other components of general tissue fixation method with embedding medium. The present sticker method showed a higher success rate than the conventional method in preparing frozen sections. COMPARISON WITH EXISTING METHOD Fragile frozen sections can be prepared with intact whole organ morphology without wrinkles. The advantage of the present method is that tissues can be embedded using only a common embedding medium and besides the adhesive film, no other special equipment is required. Furthermore, this method can be easily used by virtually every lab performing cryosections. CONCLUSIONS The sticker method using adhesive films is characterized by a shortened preparation time and increased success rate of frozen section compared with conventional method.
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22
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Chen X, Sun YC, Zhan H, Kebschull JM, Fischer S, Matho K, Huang ZJ, Gillis J, Zador AM. High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing. Cell 2019; 179:772-786.e19. [PMID: 31626774 PMCID: PMC7836778 DOI: 10.1016/j.cell.2019.09.023] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 07/30/2019] [Accepted: 09/20/2019] [Indexed: 01/08/2023]
Abstract
Understanding neural circuits requires deciphering interactions among myriad cell types defined by spatial organization, connectivity, gene expression, and other properties. Resolving these cell types requires both single-neuron resolution and high throughput, a challenging combination with conventional methods. Here, we introduce barcoded anatomy resolved by sequencing (BARseq), a multiplexed method based on RNA barcoding for mapping projections of thousands of spatially resolved neurons in a single brain and relating those projections to other properties such as gene or Cre expression. Mapping the projections to 11 areas of 3,579 neurons in mouse auditory cortex using BARseq confirmed the laminar organization of the three top classes (intratelencephalic [IT], pyramidal tract-like [PT-like], and corticothalamic [CT]) of projection neurons. In depth analysis uncovered a projection type restricted almost exclusively to transcriptionally defined subtypes of IT neurons. By bridging anatomical and transcriptomic approaches at cellular resolution with high throughput, BARseq can potentially uncover the organizing principles underlying the structure and formation of neural circuits.
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Affiliation(s)
- Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Justus M Kebschull
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Katherine Matho
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.
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23
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Huo BX, Zeater N, Lin MK, Takahashi YS, Hanada M, Nagashima J, Lee BC, Hata J, Zaheer A, Grünert U, Miller MI, Rosa MGP, Okano H, Martin PR, Mitra PP. Relation of koniocellular layers of dorsal lateral geniculate to inferior pulvinar nuclei in common marmosets. Eur J Neurosci 2019; 50:4004-4017. [PMID: 31344282 DOI: 10.1111/ejn.14529] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 07/11/2019] [Accepted: 07/15/2019] [Indexed: 11/27/2022]
Abstract
Traditionally, the dorsal lateral geniculate nucleus (LGN) and the inferior pulvinar (IPul) nucleus are considered as anatomically and functionally distinct thalamic nuclei. However, in several primate species it has also been established that the koniocellular (K) layers of LGN and parts of the IPul have a shared pattern of immunoreactivity for the calcium-binding protein calbindin. These calbindin-rich cells constitute a thalamic matrix system which is implicated in thalamocortical synchronisation. Further, the K layers and IPul are both involved in visual processing and have similar connections with retina and superior colliculus. Here, we confirmed the continuity between calbindin-rich cells in LGN K layers and the central lateral division of IPul (IPulCL) in marmoset monkeys. By employing a high-throughput neuronal tracing method, we found that both the K layers and IPulCL form comparable patterns of connections with striate and extrastriate cortices; these connections are largely different to those of the parvocellular and magnocellular laminae of LGN. Retrograde tracer-labelled cells and anterograde tracer-labelled axon terminals merged seamlessly from IPulCL into LGN K layers. These results support continuity between LGN K layers and IPulCL, providing an anatomical basis for functional congruity of this region of the dorsal thalamic matrix and calling into question the traditional segregation between LGN and the inferior pulvinar nucleus.
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Affiliation(s)
- Bing-Xing Huo
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Natalie Zeater
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Sydney University Node, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Meng Kuan Lin
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yeonsook S Takahashi
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Integra Life Science Japan, Minato-Ku, Akasaka, Japan
| | - Mitsutoshi Hanada
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Systems Neuroscience Institute and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jaimi Nagashima
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Systems Neuroscience Institute and Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brian C Lee
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Junichi Hata
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan
| | - Afsah Zaheer
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Ulrike Grünert
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Sydney University Node, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Marcello G P Rosa
- Department of Physiology and Biomedicine Research Institute, Monash University, Clayton, Vic., Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, Vic., Australia
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Paul R Martin
- Faculty of Medicine and Health, Save Sight Institute, The University of Sydney, Sydney, NSW, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Sydney University Node, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Partha P Mitra
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Wako, Japan.,Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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24
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Schiller HB, Montoro DT, Simon LM, Rawlins EL, Meyer KB, Strunz M, Vieira Braga FA, Timens W, Koppelman GH, Budinger GRS, Burgess JK, Waghray A, van den Berge M, Theis FJ, Regev A, Kaminski N, Rajagopal J, Teichmann SA, Misharin AV, Nawijn MC. The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. Am J Respir Cell Mol Biol 2019; 61:31-41. [PMID: 30995076 PMCID: PMC6604220 DOI: 10.1165/rcmb.2018-0416tr] [Citation(s) in RCA: 128] [Impact Index Per Article: 25.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/17/2019] [Indexed: 12/13/2022] Open
Abstract
Lung disease accounts for every sixth death globally. Profiling the molecular state of all lung cell types in health and disease is currently revolutionizing the identification of disease mechanisms and will aid the design of novel diagnostic and personalized therapeutic regimens. Recent progress in high-throughput techniques for single-cell genomic and transcriptomic analyses has opened up new possibilities to study individual cells within a tissue, classify these into cell types, and characterize variations in their molecular profiles as a function of genetics, environment, cell-cell interactions, developmental processes, aging, or disease. Integration of these cell state definitions with spatial information allows the in-depth molecular description of cellular neighborhoods and tissue microenvironments, including the tissue resident structural and immune cells, the tissue matrix, and the microbiome. The Human Cell Atlas consortium aims to characterize all cells in the healthy human body and has prioritized lung tissue as one of the flagship projects. Here, we present the rationale, the approach, and the expected impact of a Human Lung Cell Atlas.
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Affiliation(s)
- Herbert B. Schiller
- Helmholtz Zentrum München, Institute of Lung Biology and Disease, Group Systems Medicine of Chronic Lung Disease, Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Daniel T. Montoro
- Harvard Stem Cell Institute, Cambridge, Massachusetts
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Lukas M. Simon
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
| | - Emma L. Rawlins
- Wellcome Trust/Cancer Research UK Gurdon Institute, University of Cambridge, Cambridge, United Kingdom
| | | | - Maximilian Strunz
- Helmholtz Zentrum München, Institute of Lung Biology and Disease, Group Systems Medicine of Chronic Lung Disease, Member of the German Center for Lung Research (DZL), Munich, Germany
| | | | - Wim Timens
- Department of Pathology and Medical Biology
- Groningen Research Institute for Asthma and COPD at the University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Gerard H. Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children’s Hospital, and
- Groningen Research Institute for Asthma and COPD at the University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - G. R. Scott Budinger
- Division of Pulmonary and Critical Care Medicine, Northwestern University, Chicago, Illinois
| | - Janette K. Burgess
- Department of Pathology and Medical Biology
- Groningen Research Institute for Asthma and COPD at the University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Avinash Waghray
- Harvard Stem Cell Institute, Cambridge, Massachusetts
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Maarten van den Berge
- Department of Pulmonology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD at the University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Fabian J. Theis
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany
- Department of Mathematics, Technische Universität München, Munich, Germany
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
- Department of Biology, Howard Hughes Medical Institute and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts; and
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut
| | - Jayaraj Rajagopal
- Harvard Stem Cell Institute, Cambridge, Massachusetts
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | | | - Alexander V. Misharin
- Division of Pulmonary and Critical Care Medicine, Northwestern University, Chicago, Illinois
| | - Martijn C. Nawijn
- Department of Pathology and Medical Biology
- Groningen Research Institute for Asthma and COPD at the University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
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25
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Habbal O, Farhat A, Khalil R. A mechanized device for mounting histological tissue sections. J Neurosci Methods 2019; 320:72-78. [DOI: 10.1016/j.jneumeth.2019.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/16/2019] [Accepted: 03/19/2019] [Indexed: 11/16/2022]
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26
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An active texture-based digital atlas enables automated mapping of structures and markers across brains. Nat Methods 2019; 16:341-350. [PMID: 30858600 DOI: 10.1038/s41592-019-0328-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 11/27/2018] [Accepted: 01/25/2019] [Indexed: 11/08/2022]
Abstract
Brain atlases enable the mapping of labeled cells and projections from different brains onto a standard coordinate system. We address two issues in the construction and use of atlases. First, expert neuroanatomists ascertain the fine-scale pattern of brain tissue, the 'texture' formed by cellular organization, to define cytoarchitectural borders. We automate the processes of localizing landmark structures and alignment of brains to a reference atlas using machine learning and training data derived from expert annotations. Second, we construct an atlas that is active; that is, augmented with each use. We show that the alignment of new brains to a reference atlas can continuously refine the coordinate system and associated variance. We apply this approach to the adult murine brainstem and achieve a precise alignment of projections in cytoarchitecturally ill-defined regions across brains from different animals.
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27
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Lin MK, Takahashi YS, Huo BX, Hanada M, Nagashima J, Hata J, Tolpygo AS, Ram K, Lee BC, Miller MI, Rosa MGP, Sasaki E, Iriki A, Okano H, Mitra P. A high-throughput neurohistological pipeline for brain-wide mesoscale connectivity mapping of the common marmoset. eLife 2019; 8:e40042. [PMID: 30720427 PMCID: PMC6384052 DOI: 10.7554/elife.40042] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 02/04/2019] [Indexed: 11/13/2022] Open
Abstract
Understanding the connectivity architecture of entire vertebrate brains is a fundamental but difficult task. Here we present an integrated neuro-histological pipeline as well as a grid-based tracer injection strategy for systematic mesoscale connectivity mapping in the common marmoset (Callithrix jacchus). Individual brains are sectioned into ~1700 20 µm sections using the tape transfer technique, permitting high quality 3D reconstruction of a series of histochemical stains (Nissl, myelin) interleaved with tracer labeled sections. Systematic in-vivo MRI of the individual animals facilitates injection placement into reference-atlas defined anatomical compartments. Further, by combining the resulting 3D volumes, containing informative cytoarchitectonic markers, with in-vivo and ex-vivo MRI, and using an integrated computational pipeline, we are able to accurately map individual brains into a common reference atlas despite the significant individual variation. This approach will facilitate the systematic assembly of a mesoscale connectivity matrix together with unprecedented 3D reconstructions of brain-wide projection patterns in a primate brain.
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Affiliation(s)
- Meng Kuan Lin
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
| | | | - Bing-Xing Huo
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
| | - Mitsutoshi Hanada
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
| | - Jaimi Nagashima
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
| | - Junichi Hata
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
| | | | | | - Brian C Lee
- Center for Imaging ScienceJohns Hopkins UniversityMarylandUnited States
| | - Michael I Miller
- Center for Imaging ScienceJohns Hopkins UniversityMarylandUnited States
| | - Marcello GP Rosa
- Department of Physiology and Biomedicine, Discovery InstituteMonash UniversityMelbourneAustralia
- Australian Research Council Centre of Excellence for Integrative Brain FunctionClaytonAustralia
| | - Erika Sasaki
- Central Institute for Experimental AnimalsKawasakiJapan
| | - Atsushi Iriki
- Laboratory for Symbolic Cognitive DevelopmentRIKEN Center for Brain ScienceWakoJapan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
- Department of PhysiologyKeio University School of MedicineTokyoJapan
| | - Partha Mitra
- Laboratory for Marmoset Neural ArchitectureRIKEN Center for Brain ScienceWakoJapan
- Cold Spring Harbor LaboratoryCold Spring HarborUnited States
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28
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Lee BC, Tward DJ, Mitra PP, Miller MI. On variational solutions for whole brain serial-section histology using a Sobolev prior in the computational anatomy random orbit model. PLoS Comput Biol 2018; 14:e1006610. [PMID: 30586384 PMCID: PMC6324828 DOI: 10.1371/journal.pcbi.1006610] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Revised: 01/08/2019] [Accepted: 10/27/2018] [Indexed: 11/23/2022] Open
Abstract
This paper presents a variational framework for dense diffeomorphic atlas-mapping onto high-throughput histology stacks at the 20 μm meso-scale. The observed sections are modelled as Gaussian random fields conditioned on a sequence of unknown section by section rigid motions and unknown diffeomorphic transformation of a three-dimensional atlas. To regularize over the high-dimensionality of our parameter space (which is a product space of the rigid motion dimensions and the diffeomorphism dimensions), the histology stacks are modelled as arising from a first order Sobolev space smoothness prior. We show that the joint maximum a-posteriori, penalized-likelihood estimator of our high dimensional parameter space emerges as a joint optimization interleaving rigid motion estimation for histology restacking and large deformation diffeomorphic metric mapping to atlas coordinates. We show that joint optimization in this parameter space solves the classical curvature non-identifiability of the histology stacking problem. The algorithms are demonstrated on a collection of whole-brain histological image stacks from the Mouse Brain Architecture Project. New developments in neural tracing techniques have motivated the widespread use of histology as a modality for exploring the circuitry of the brain. Automated mapping of pre-labeled atlases onto modern large datasets of histological imagery is a critical step for elucidating the brain’s neural circuitry and shape. This task is challenging as histological sections are imaged independently and the reconstruction of the unsectioned volume is nontrivial. Typically, neuroanatomists use reference volumes of the same subject (e.g. MRI) to guide reconstruction. However, obtaining reference imagery is often non-standard, as in high-throughput animal models like mouse histology. Others have proposed using anatomical atlases as guides, but have not accounted for the intrinsic nonlinear shape difference from atlas to subject. Our method addresses these limitations by jointly optimizing reconstruction informed by an atlas simultaneously with the nonlinear change of coordinates that encapsulates anatomical variation. This accounts for intrinsic shape differences and enables rigorous, direct comparisons of atlas and subject coordinates. Using simulations, we demonstrate that our method recovers the reconstruction parameters more accurately than atlas-free models and innately produces accurate segmentations from simultaneous atlas mapping. We also demonstrate our method on the Mouse Brain Architecture dataset, successfully mapping and reconstructing over 1000 brains.
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Affiliation(s)
- Brian C. Lee
- Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- * E-mail:
| | - Daniel J. Tward
- Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | | | - Michael I. Miller
- Center for Imaging Science, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
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29
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Majka P, Rosa MGP, Bai S, Chan JM, Huo BX, Jermakow N, Lin MK, Takahashi YS, Wolkowicz IH, Worthy KH, Rajan R, Reser DH, Wójcik DK, Okano H, Mitra PP. Unidirectional monosynaptic connections from auditory areas to the primary visual cortex in the marmoset monkey. Brain Struct Funct 2018; 224:111-131. [PMID: 30288557 PMCID: PMC6373361 DOI: 10.1007/s00429-018-1764-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 09/27/2018] [Indexed: 11/26/2022]
Abstract
Until the late twentieth century, it was believed that different sensory modalities were processed by largely independent pathways in the primate cortex, with cross-modal integration only occurring in specialized polysensory areas. This model was challenged by the finding that the peripheral representation of the primary visual cortex (V1) receives monosynaptic connections from areas of the auditory cortex in the macaque. However, auditory projections to V1 have not been reported in other primates. We investigated the existence of direct interconnections between V1 and auditory areas in the marmoset, a New World monkey. Labelled neurons in auditory cortex were observed following 4 out of 10 retrograde tracer injections involving V1. These projections to V1 originated in the caudal subdivisions of auditory cortex (primary auditory cortex, caudal belt and parabelt areas), and targeted parts of V1 that represent parafoveal and peripheral vision. Injections near the representation of the vertical meridian of the visual field labelled few or no cells in auditory cortex. We also placed 8 retrograde tracer injections involving core, belt and parabelt auditory areas, none of which revealed direct projections from V1. These results confirm the existence of a direct, nonreciprocal projection from auditory areas to V1 in a different primate species, which has evolved separately from the macaque for over 30 million years. The essential similarity of these observations between marmoset and macaque indicate that early-stage audiovisual integration is a shared characteristic of primate sensory processing.
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Affiliation(s)
- Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093, Warsaw, Poland
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia
| | - Marcello G P Rosa
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia.
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia.
| | - Shi Bai
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Jonathan M Chan
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Bing-Xing Huo
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
| | - Natalia Jermakow
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093, Warsaw, Poland
| | - Meng K Lin
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
| | - Yeonsook S Takahashi
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
| | - Ianina H Wolkowicz
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Katrina H Worthy
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - Ramesh Rajan
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia
- Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC, 3800, Australia
| | - David H Reser
- School of Rural Health, Monash University, Churchill, VIC, 3842, Australia
| | - Daniel K Wójcik
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 02-093, Warsaw, Poland
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, 160-8582, Japan
| | - Partha P Mitra
- Monash University Node, Australian Research Council, Centre of Excellence for Integrative Brain Function, Clayton, VIC, 3800, Australia.
- Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, 351-0106, Japan.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
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30
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Majka P, Chaplin TA, Yu HH, Tolpygo A, Mitra PP, Wójcik DK, Rosa MGP. Towards a comprehensive atlas of cortical connections in a primate brain: Mapping tracer injection studies of the common marmoset into a reference digital template. J Comp Neurol 2017; 524:2161-81. [PMID: 27099164 PMCID: PMC4892968 DOI: 10.1002/cne.24023] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Revised: 04/11/2016] [Accepted: 04/18/2016] [Indexed: 02/02/2023]
Abstract
The marmoset is an emerging animal model for large‐scale attempts to understand primate brain connectivity, but achieving this aim requires the development and validation of procedures for normalization and integration of results from many neuroanatomical experiments. Here we describe a computational pipeline for coregistration of retrograde tracing data on connections of cortical areas into a 3D marmoset brain template, generated from Nissl‐stained sections. The procedure results in a series of spatial transformations that are applied to the coordinates of labeled neurons in the different cases, bringing them into common stereotaxic space. We applied this procedure to 17 injections, placed in the frontal lobe of nine marmosets as part of earlier studies. Visualizations of cortical patterns of connections revealed by these injections are supplied as Supplementary Materials. Comparison between the results of the automated and human‐based processing of these cases reveals that the centers of injection sites can be reconstructed, on average, to within 0.6 mm of coordinates estimated by an experienced neuroanatomist. Moreover, cell counts obtained in different areas by the automated approach are highly correlated (r = 0.83) with those obtained by an expert, who examined in detail histological sections for each individual. The present procedure enables comparison and visualization of large datasets, which in turn opens the way for integration and analysis of results from many animals. Its versatility, including applicability to archival materials, may reduce the number of additional experiments required to produce the first detailed cortical connectome of a primate brain. J. Comp. Neurol. 524:2161–2181, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Piotr Majka
- Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.,Department of Physiology, Monash University, Clayton, VIC, Australia.,Nencki Institute of Experimental Biology, Warsaw, Poland.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, Australia
| | - Tristan A Chaplin
- Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.,Department of Physiology, Monash University, Clayton, VIC, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, Australia.,Monash Vision Group, Monash University, Clayton, VIC, Australia
| | - Hsin-Hao Yu
- Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.,Department of Physiology, Monash University, Clayton, VIC, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, Australia.,Monash Vision Group, Monash University, Clayton, VIC, Australia
| | | | - Partha P Mitra
- Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, Australia.,Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA
| | | | - Marcello G P Rosa
- Neuroscience Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia.,Department of Physiology, Monash University, Clayton, VIC, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC, Australia.,Monash Vision Group, Monash University, Clayton, VIC, Australia
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31
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Hunnicutt BJ, Jongbloets BC, Birdsong WT, Gertz KJ, Zhong H, Mao T. A comprehensive excitatory input map of the striatum reveals novel functional organization. eLife 2016; 5. [PMID: 27892854 PMCID: PMC5207773 DOI: 10.7554/elife.19103] [Citation(s) in RCA: 277] [Impact Index Per Article: 34.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 11/25/2016] [Indexed: 01/02/2023] Open
Abstract
The striatum integrates excitatory inputs from the cortex and the thalamus to control diverse functions. Although the striatum is thought to consist of sensorimotor, associative and limbic domains, their precise demarcations and whether additional functional subdivisions exist remain unclear. How striatal inputs are differentially segregated into each domain is also poorly understood. This study presents a comprehensive map of the excitatory inputs to the mouse striatum. The input patterns reveal boundaries between the known striatal domains. The most posterior striatum likely represents the 4th functional subdivision, and the dorsomedial striatum integrates highly heterogeneous, multimodal inputs. The complete thalamo-cortico-striatal loop is also presented, which reveals that the thalamic subregions innervated by the basal ganglia preferentially interconnect with motor-related cortical areas. Optogenetic experiments show the subregion-specific heterogeneity in the synaptic properties of striatal inputs from both the cortex and the thalamus. This projectome will guide functional studies investigating diverse striatal functions. DOI:http://dx.doi.org/10.7554/eLife.19103.001 To fully understand how the brain works, we need to understand how different brain structures are organized and how information flows between these structures. For example, the cortex and thalamus communicate with another structure known as the basal ganglia, which is essential for controlling voluntary movement, emotions and reward behaviour in humans and other mammals. Information from the cortex and the thalamus enters the basal ganglia at an area called the striatum. This area is further divided into smaller functional regions known as domains that sort sensorimotor, emotion and executive information into the basal ganglia to control different types of behaviour. Three such domains have been identified in the striatum of mice. However, the boundaries between these domains are vague and it is not clear whether any other domains exist or if the domains can actually be divided into even smaller areas with more precise roles. Information entering the striatum from other parts of the brain can either stimulate activity in the striatum (known as an “excitatory input”) or alter existing excitatory inputs. Now, Hunnicutt et al. present the first comprehensive map of excitatory inputs into the striatum of mice. The experiments show that while many of the excitatory inputs flowing into the striatum from the cortex and thalamus are sorted into the three known domains, a unique combination of the excitatory inputs are sorted into a new domain instead. One of the original three domains of the striatum is known to relay information related to associative learning, for example, linking an emotion to a person or place. Hunnicutt et al. show that this domain has a more complex architecture than the other domains, being made up of many distinct areas. This complexity may help it to process the various types of information required to make such associations. The findings of Hunnicutt et al. provide a framework for understanding how the striatum works in healthy and diseased brains. Since faulty information processing in the striatum is a direct cause of Parkinson’s disease, Huntington’s disease and other neurological disorders in humans, this framework may aid the development of new treatments for these disorders. DOI:http://dx.doi.org/10.7554/eLife.19103.002
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Affiliation(s)
- Barbara J Hunnicutt
- Vollum Institute, Oregon Health and Science University, Portland, United States
| | - Bart C Jongbloets
- Vollum Institute, Oregon Health and Science University, Portland, United States
| | - William T Birdsong
- Vollum Institute, Oregon Health and Science University, Portland, United States
| | - Katrina J Gertz
- Vollum Institute, Oregon Health and Science University, Portland, United States
| | - Haining Zhong
- Vollum Institute, Oregon Health and Science University, Portland, United States
| | - Tianyi Mao
- Vollum Institute, Oregon Health and Science University, Portland, United States
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