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Ma J, Xie R, Ayyadhury S, Ge C, Gupta A, Gupta R, Gu S, Zhang Y, Lee G, Kim J, Lou W, Li H, Upschulte E, Dickscheid T, de Almeida JG, Wang Y, Han L, Yang X, Labagnara M, Gligorovski V, Scheder M, Rahi SJ, Kempster C, Pollitt A, Espinosa L, Mignot T, Middeke JM, Eckardt JN, Li W, Li Z, Cai X, Bai B, Greenwald NF, Van Valen D, Weisbart E, Cimini BA, Cheung T, Brück O, Bader GD, Wang B. The multimodality cell segmentation challenge: toward universal solutions. Nat Methods 2024:10.1038/s41592-024-02233-6. [PMID: 38532015 DOI: 10.1038/s41592-024-02233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
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Affiliation(s)
- Jun Ma
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Ronald Xie
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Shamini Ayyadhury
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Cheng Ge
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, China
| | - Anubha Gupta
- Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology Delhi (IIITD), New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. BRAIRCH, All India Institute of Medical Sciences, New Delhi, India
| | - Song Gu
- Department of Image Reconstruction, Nanjing Anke Medical Technology Co., Nanjing, China
| | - Yao Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Gihun Lee
- Graduate School of AI, KAIST, Seoul, South Korea
| | - Joonkee Kim
- Graduate School of AI, KAIST, Seoul, South Korea
| | - Wei Lou
- Shenzhen Research Institute of Big Data, Shenzhen, China
- Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Haofeng Li
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Eric Upschulte
- Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences - Institute of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - José Guilherme de Almeida
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Champalimaud Foundation - Centre for the Unknown, Lisbon, Portugal
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Lin Han
- Tandon School of Engineering, New York University, New York, NY, USA
| | - Xin Yang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Marco Labagnara
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vojislav Gligorovski
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Scheder
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Carly Kempster
- School of Biological Sciences, University of Reading, Reading, UK
| | - Alice Pollitt
- School of Biological Sciences, University of Reading, Reading, UK
| | - Leon Espinosa
- Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France
| | - Tâm Mignot
- Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Wangkai Li
- Department of Automation, University of Science and Technology of China, Hefei, China
| | - Zhaoyang Li
- Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
| | - Xiaochen Cai
- Department of Computer Science and Technology, Nanjing University, Nanjing, China
| | - Bizhe Bai
- School of EECS, The University of Queensland, Brisbane, Queensland, Australia
| | | | - David Van Valen
- Division of Computing and Mathematical Science, Caltech, Pasadena, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Trevor Cheung
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Oscar Brück
- Hematoscope Laboratory, Comprehensive Cancer Center & Center of Diagnostics, Helsinki University Hospital, Helsinki, Finland
- Department of Oncology, University of Helsinki, Helsinki, Finland
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- CIFAR Multiscale Human Program, CIFAR, Toronto, Ontario, Canada
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- UHN AI Hub, University Health Network, Toronto, Ontario, Canada.
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2
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Israel U, Marks M, Dilip R, Li Q, Yu C, Laubscher E, Li S, Schwartz M, Pradhan E, Ates A, Abt M, Brown C, Pao E, Pearson-Goulart A, Perona P, Gkioxari G, Barnowski R, Yue Y, Valen DV. A Foundation Model for Cell Segmentation. bioRxiv 2024:2023.11.17.567630. [PMID: 38045277 PMCID: PMC10690226 DOI: 10.1101/2023.11.17.567630] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models in use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. We also show that CellSAM can unify bioimaging analysis workflows such as spatial transcriptomics and cell tracking. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.
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Affiliation(s)
- Uriah Israel
- Division of Biology and Biological Engineering, Caltech
- Division of Computing and Mathematical Science, Caltech
| | - Markus Marks
- Division of Engineering and Applied Science, Caltech
- Division of Computing and Mathematical Science, Caltech
| | - Rohit Dilip
- Division of Computing and Mathematical Science, Caltech
| | - Qilin Li
- Division of Engineering and Applied Science, Caltech
| | - Changhua Yu
- Division of Biology and Biological Engineering, Caltech
| | | | - Shenyi Li
- Division of Biology and Biological Engineering, Caltech
| | | | - Elora Pradhan
- Division of Biology and Biological Engineering, Caltech
| | - Ada Ates
- Division of Biology and Biological Engineering, Caltech
| | - Martin Abt
- Division of Biology and Biological Engineering, Caltech
| | - Caitlin Brown
- Division of Biology and Biological Engineering, Caltech
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech
| | | | - Pietro Perona
- Division of Engineering and Applied Science, Caltech
- Division of Computing and Mathematical Science, Caltech
| | | | | | - Yisong Yue
- Division of Computing and Mathematical Science, Caltech
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech
- Howard Hughes Medical Institute
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3
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Laubscher E, Wang X(J, Razin N, Dougherty T, Xu RJ, Ombelets L, Pao E, Graf W, Moffitt JR, Yue Y, Van Valen D. Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning. bioRxiv 2024:2023.09.03.556122. [PMID: 37732188 PMCID: PMC10508757 DOI: 10.1101/2023.09.03.556122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually-tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial transcriptomics that combines deep learning models for cell segmentation and spot detection with a probabilistic gene decoder to quantify single-cell gene expression accurately. Polaris offers a unifying, turnkey solution for analyzing spatial transcriptomics data from MERFSIH, seqFISH, or ISS experiments. Polaris is available through the DeepCell software library (https://github.com/vanvalenlab/deepcell-spots) and https://www.deepcell.org.
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Affiliation(s)
- Emily Laubscher
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA
| | | | - Nitzan Razin
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Tom Dougherty
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Rosalind J. Xu
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston MA
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
| | - Lincoln Ombelets
- Division of Chemistry and Chemical Engineering, Caltech, Pasadena, CA
| | - Edward Pao
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - William Graf
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
| | - Jeffrey R. Moffitt
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston MA
- Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA
- Broad Institute of Harvard and MIT, Cambridge, MA
| | - Yisong Yue
- Division of Computational and Mathematical Sciences, Caltech, Pasadena, CA
| | - David Van Valen
- Division of Biology and Biological Engineering, Caltech, Pasadena, CA
- Howard Hughes Medical Institute, Chevy Chase, MD
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4
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Sockell A, Wong W, Longwell S, Vu T, Karlsson K, Mokhtari D, Schaepe J, Lo YH, Cornelius V, Kuo C, Van Valen D, Curtis C, Fordyce PM. A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids. Cell Syst 2023; 14:764-776.e6. [PMID: 37734323 DOI: 10.1016/j.cels.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 02/24/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023]
Abstract
Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Alexandra Sockell
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Wing Wong
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Scott Longwell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Thy Vu
- Department of Biochemistry, UT Austin, Austin, TX 78712, USA
| | - Kasper Karlsson
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Daniel Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - Julia Schaepe
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yuan-Hung Lo
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Vincent Cornelius
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Calvin Kuo
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Christina Curtis
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA.
| | - Polly M Fordyce
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA.
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5
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Greenbaum S, Averbukh I, Soon E, Rizzuto G, Baranski A, Greenwald NF, Kagel A, Bosse M, Jaswa EG, Khair Z, Kwok S, Warshawsky S, Piyadasa H, Goldston M, Spence A, Miller G, Schwartz M, Graf W, Van Valen D, Winn VD, Hollmann T, Keren L, van de Rijn M, Angelo M. A spatially resolved timeline of the human maternal-fetal interface. Nature 2023; 619:595-605. [PMID: 37468587 PMCID: PMC10356615 DOI: 10.1038/s41586-023-06298-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/08/2023] [Indexed: 07/21/2023]
Abstract
Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodelling (SAR)1-3. However, it remains a matter of debate regarding which immune and stromal cells participate in these interactions and how this evolves with respect to gestational age. Here we used a multiomics approach, combining the strengths of spatial proteomics and transcriptomics, to construct a spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time-of-flight and a 37-plex antibody panel to analyse around 500,000 cells and 588 arteries within intact decidua from 66 individuals between 6 and 20 weeks of gestation, integrating this dataset with co-registered transcriptomics profiles. Gestational age substantially influenced the frequency of maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 and IDO-1 becoming increasingly enriched and colocalized at later time points. By contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting distinct monotonic and biphasic trends. Last, we developed an integrated model of SAR whereby invasion is accompanied by the upregulation of pro-angiogenic, immunoregulatory EVT programmes that promote interactions with the vascular endothelium while avoiding the activation of maternal immune cells.
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Affiliation(s)
- Shirley Greenbaum
- Department of Pathology, Stanford University, Stanford, CA, USA.
- Department of Obstetrics and Gynecology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
| | - Inna Averbukh
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Erin Soon
- Department of Pathology, Stanford University, Stanford, CA, USA
- Immunology Program, Stanford University, Stanford, CA, USA
| | - Gabrielle Rizzuto
- Department of Pathology, University of Californica San Francisco, San Francisco, CA, USA
| | - Alex Baranski
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Noah F Greenwald
- Department of Pathology, Stanford University, Stanford, CA, USA
- Cancer Biology Program, Stanford University, Stanford, CA, USA
| | - Adam Kagel
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Eleni G Jaswa
- Department of Obstetrics Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Zumana Khair
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Shirley Kwok
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | | | - Mako Goldston
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Angie Spence
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Geneva Miller
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Morgan Schwartz
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Will Graf
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Virginia D Winn
- Department of Obstetrics and Gynecology, Stanford University, Stanford, CA, USA
| | - Travis Hollmann
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Michael Angelo
- Department of Pathology, Stanford University, Stanford, CA, USA.
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6
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Schwartz M, Israel U, Wang XJ, Laubscher E, Yu C, Dilip R, Li Q, Mari J, Soro J, Yu K, Pradhan E, Ates A, Gallandt D, Barnowski R, Pao E, Van Valen D. Scaling biological discovery at the interface of deep learning and cellular imaging. Nat Methods 2023; 20:956-957. [PMID: 37434003 DOI: 10.1038/s41592-023-01931-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Affiliation(s)
- Morgan Schwartz
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Uriah Israel
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Xuefei Julie Wang
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Emily Laubscher
- Department of Chemistry, California Institute of Technology, Pasadena, CA, USA
| | - Changhua Yu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Rohit Dilip
- Department of Computer Science, California Institute of Technology, Pasadena, CA, USA
| | - Qilin Li
- Department of Electrical Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Joud Mari
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Johnathon Soro
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Kevin Yu
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Elora Pradhan
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Ada Ates
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Danielle Gallandt
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Ross Barnowski
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Edward Pao
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
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7
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Sanders LM, Scott RT, Yang JH, Qutub AA, Garcia Martin H, Berrios DC, Hastings JJA, Rask J, Mackintosh G, Hoarfrost AL, Chalk S, Kalantari J, Khezeli K, Antonsen EL, Babdor J, Barker R, Baranzini SE, Beheshti A, Delgado-Aparicio GM, Glicksberg BS, Greene CS, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Mason CE, Matar M, Mias GI, Miller J, Myers JG, Nelson C, Oribello J, Park SM, Parsons-Wingerter P, Prabhu RK, Reynolds RJ, Saravia-Butler A, Saria S, Sawyer A, Singh NK, Snyder M, Soboczenski F, Soman K, Theriot CA, Van Valen D, Venkateswaran K, Warren L, Worthey L, Zitnik M, Costes SV. Biological research and self-driving labs in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00618-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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8
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Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park SM, Mackintosh G, Reynolds RJ, Hoarfrost AL, Sawyer A, Greene CS, Glicksberg BS, Theriot CA, Berrios DC, Miller J, Babdor J, Barker R, Baranzini SE, Beheshti A, Chalk S, Delgado-Aparicio GM, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Kalantari J, Khezeli K, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Garcia Martin H, Mason CE, Matar M, Mias GI, Myers JG, Nelson C, Oribello J, Parsons-Wingerter P, Prabhu RK, Qutub AA, Rask J, Saravia-Butler A, Saria S, Singh NK, Snyder M, Soboczenski F, Soman K, Van Valen D, Venkateswaran K, Warren L, Worthey L, Yang JH, Zitnik M, Costes SV. Biomonitoring and precision health in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00617-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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9
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McCaffrey EF, Donato M, Keren L, Chen Z, Delmastro A, Fitzpatrick MB, Gupta S, Greenwald NF, Baranski A, Graf W, Kumar R, Bosse M, Fullaway CC, Ramdial PK, Forgó E, Jojic V, Van Valen D, Mehra S, Khader SA, Bendall SC, van de Rijn M, Kalman D, Kaushal D, Hunter RL, Banaei N, Steyn AJC, Khatri P, Angelo M. Author Correction: The immunoregulatory landscape of human tuberculosis granulomas. Nat Immunol 2022; 23:814. [PMID: 35277696 PMCID: PMC9098386 DOI: 10.1038/s41590-022-01178-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Erin F McCaffrey
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Michele Donato
- Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Zhenghao Chen
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Alea Delmastro
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Sanjana Gupta
- Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Noah F Greenwald
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Alex Baranski
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - William Graf
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Rashmi Kumar
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Marc Bosse
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Pratista K Ramdial
- Africa Health Research Institute, University of KwaZulu-Natal, Durban, South Africa
| | - Erna Forgó
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Smriti Mehra
- Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Shabaana A Khader
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Matt van de Rijn
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel Kalman
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Deepak Kaushal
- Southwest National Primate Research Center, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Robert L Hunter
- Department of Pathology and Laboratory Medicine, University of Texas Health Sciences Center at Houston, Houston, TX, USA
| | - Niaz Banaei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Division of Infectious Diseases & Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Adrie J C Steyn
- Africa Health Research Institute, University of KwaZulu-Natal, Durban, South Africa
- Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Purvesh Khatri
- Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael Angelo
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
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10
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Hartmann FJ, Mrdjen D, McCaffrey E, Glass DR, Greenwald NF, Bharadwaj A, Khair Z, Verberk SGS, Baranski A, Baskar R, Graf W, Van Valen D, Van den Bossche J, Angelo M, Bendall SC. Single-cell metabolic profiling of human cytotoxic T cells. Nat Biotechnol 2021; 39:186-197. [PMID: 32868913 PMCID: PMC7878201 DOI: 10.1038/s41587-020-0651-8] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 07/23/2020] [Indexed: 12/12/2022]
Abstract
Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.
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Affiliation(s)
- Felix J Hartmann
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Dunja Mrdjen
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Erin McCaffrey
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
- Immunology Graduate Program, Stanford University, Palo Alto, CA, USA
| | - David R Glass
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
- Immunology Graduate Program, Stanford University, Palo Alto, CA, USA
| | - Noah F Greenwald
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Anusha Bharadwaj
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Zumana Khair
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sanne G S Verberk
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Alex Baranski
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Reema Baskar
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - William Graf
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Jan Van den Bossche
- Department of Molecular Cell Biology and Immunology, Amsterdam Cardiovascular Sciences, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Michael Angelo
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sean C Bendall
- Department of Pathology, School of Medicine, Stanford University, Palo Alto, CA, USA.
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11
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Bannon D, Moen E, Schwartz M, Borba E, Kudo T, Greenwald N, Vijayakumar V, Chang B, Pao E, Osterman E, Graf W, Van Valen D. Publisher Correction: DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes. Nat Methods 2021; 18:219. [PMID: 33437045 DOI: 10.1038/s41592-021-01059-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Dylan Bannon
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Erick Moen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Morgan Schwartz
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Enrico Borba
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Takamasa Kudo
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Noah Greenwald
- Department of Cancer Biology, Stanford University, Stanford, CA, USA
| | - Vibha Vijayakumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Brian Chang
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Edward Pao
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | | | - William Graf
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
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12
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Abstract
Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.
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Affiliation(s)
- Erick Moen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Dylan Bannon
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Takamasa Kudo
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - William Graf
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA
| | - Markus Covert
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
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13
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Keren L, Bosse M, Marquez D, Angoshtari R, Jain S, Varma S, Yang SR, Kurian A, Van Valen D, West R, Bendall SC, Angelo M. A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging. Cell 2018; 174:1373-1387.e19. [PMID: 30193111 PMCID: PMC6132072 DOI: 10.1016/j.cell.2018.08.039] [Citation(s) in RCA: 563] [Impact Index Per Article: 93.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 06/13/2018] [Accepted: 08/17/2018] [Indexed: 12/12/2022]
Abstract
The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology.
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Affiliation(s)
- Leeat Keren
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Marc Bosse
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Diana Marquez
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Roshan Angoshtari
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Samir Jain
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Sushama Varma
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Soo-Ryum Yang
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Allison Kurian
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - David Van Valen
- Department of Biology, Caltech, Pasadena, CA 91125, USA; Department of Bioengineering, Caltech, Pasadena, CA 91125, USA
| | - Robert West
- Department of Pathology, Stanford University, Stanford CA, 94305, USA
| | - Sean C Bendall
- Department of Pathology, Stanford University, Stanford CA, 94305, USA.
| | - Michael Angelo
- Department of Pathology, Stanford University, Stanford CA, 94305, USA.
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14
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Lane K, Van Valen D, DeFelice MM, Macklin DN, Kudo T, Jaimovich A, Carr A, Meyer T, Pe'er D, Boutet SC, Covert MW. Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation. Cell Syst 2017; 4:458-469.e5. [PMID: 28396000 DOI: 10.1016/j.cels.2017.03.010] [Citation(s) in RCA: 102] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 12/16/2016] [Accepted: 03/15/2017] [Indexed: 02/02/2023]
Abstract
Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell.
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Affiliation(s)
- Keara Lane
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - David Van Valen
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Mialy M DeFelice
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Derek N Macklin
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Takamasa Kudo
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ariel Jaimovich
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
| | - Ambrose Carr
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Tobias Meyer
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA 94305, USA
| | - Dana Pe'er
- Program in Computational and Systems Biology, Sloan Kettering Institute, New York, NY 10065, USA
| | - Stéphane C Boutet
- R&D Department, Fluidigm Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, CA 94080, USA
| | - Markus W Covert
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
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15
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Van Valen D, Wu D, Chen YJ, Tuson H, Wiggins P, Phillips R. A single-molecule Hershey-Chase experiment. Curr Biol 2012; 22:1339-43. [PMID: 22727695 DOI: 10.1016/j.cub.2012.05.023] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 04/18/2012] [Accepted: 05/10/2012] [Indexed: 02/02/2023]
Abstract
Ever since Hershey and Chase used phages to establish DNA as the carrier of genetic information in 1952, the precise mechanisms of phage DNA translocation have been a mystery. Although bulk measurements have set a timescale for in vivo DNA translocation during bacteriophage infection, measurements of DNA ejection by single bacteriophages have only been made in vitro. Here, we present direct visualization of single bacteriophages infecting individual Escherichia coli cells. For bacteriophage λ, we establish a mean ejection time of roughly 5 min with significant cell-to-cell variability, including pausing events. In contrast, corresponding in vitro single-molecule ejections are more uniform and finish within 10 s. Our data reveal that when plotted against the amount of DNA ejected, the velocity of ejection for two different genome lengths collapses onto a single curve. This suggests that in vivo ejections are controlled by the amount of DNA ejected. In contrast, in vitro DNA ejections are governed by the amount of DNA left inside the capsid. This analysis provides evidence against a purely intrastrand repulsion-based mechanism and suggests that cell-internal processes dominate. This provides a picture of the early stages of phage infection and sheds light on the problem of polymer translocation.
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Affiliation(s)
- David Van Valen
- Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA
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16
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Wu D, Van Valen D, Hu Q, Phillips R. Ion-dependent dynamics of DNA ejections for bacteriophage lambda. Biophys J 2010; 99:1101-9. [PMID: 20712993 DOI: 10.1016/j.bpj.2010.06.024] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2010] [Revised: 06/08/2010] [Accepted: 06/09/2010] [Indexed: 10/19/2022] Open
Abstract
We studied the control parameters that govern the dynamics of in vitro DNA ejection in bacteriophage lambda. Previous work demonstrated that bacteriophage DNA is highly pressurized, and this pressure has been hypothesized to help drive DNA ejection. Ions influence this process by screening charges on DNA; however, a systematic variation of salt concentrations to explore these effects has not been undertaken. To study the nature of the forces driving DNA ejection, we performed in vitro measurements of DNA ejection in bulk and at the single-phage level. We present measurements on the dynamics of ejection and on the self-repulsion force driving ejection. We examine the role of ion concentration and identity in both measurements, and show that the charge of counterions is an important control parameter. These measurements show that the mobility of ejecting DNA is independent of ionic concentrations for a given amount of DNA in the capsid. We also present evidence that phage DNA forms loops during ejection, and confirm that this effect occurs using optical tweezers.
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Affiliation(s)
- David Wu
- Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, California, USA
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17
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Van Valen D, Haataja M, Phillips R. Biochemistry on a leash: the roles of tether length and geometry in signal integration proteins. Biophys J 2009; 96:1275-92. [PMID: 19217847 DOI: 10.1016/j.bpj.2008.10.052] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2008] [Accepted: 10/31/2008] [Indexed: 01/14/2023] Open
Abstract
We use statistical mechanics and simple ideas from polymer physics to develop a quantitative model of proteins whose activity is controlled by flexibly tethered ligands and receptors. We predict how the properties of tethers influence the function of these proteins and demonstrate how their tether length dependence can be exploited to construct proteins whose integration of multiple signals can be tuned. One case study to which we apply these ideas is that of the Wiskott-Aldrich Syndrome Proteins as activators of actin polymerization. More generally, tethered ligands competing with those free in solution are common phenomena in biology, making this an important specific example of a widespread biological idea.
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Affiliation(s)
- David Van Valen
- Department of Applied Physics, California Institute of Technology, Pasadena, California, USA
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