1
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Zhou P, Bocci F, Li T, Nie Q. Spatial transition tensor of single cells. Nat Methods 2024; 21:1053-1062. [PMID: 38755322 PMCID: PMC11166574 DOI: 10.1038/s41592-024-02266-x] [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: 07/03/2023] [Accepted: 04/02/2024] [Indexed: 05/18/2024]
Abstract
Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial-mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT's capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.
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Affiliation(s)
- Peijie Zhou
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- Center for Machine Learning Research, Peking University, Beijing, China
- AI for Science Institute, Beijing, China
- National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China
| | - Federico Bocci
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
| | - Tiejun Li
- LMAM and School of Mathematical Sciences, Peking University, Beijing, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Department of Cell and Developmental Biology, University of California, Irvine, Irvine, CA, USA.
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2
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Maizels RJ, Snell DM, Briscoe J. Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics. Cell Syst 2024; 15:411-424.e9. [PMID: 38754365 DOI: 10.1016/j.cels.2024.04.004] [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: 09/19/2023] [Revised: 02/01/2024] [Accepted: 04/17/2024] [Indexed: 05/18/2024]
Abstract
The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.
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Affiliation(s)
- Rory J Maizels
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK; University College, London, UK
| | - Daniel M Snell
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - James Briscoe
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK.
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3
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Maizels RJ. A dynamical perspective: moving towards mechanism in single-cell transcriptomics. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230049. [PMID: 38432314 PMCID: PMC10909508 DOI: 10.1098/rstb.2023.0049] [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: 07/10/2023] [Accepted: 10/31/2023] [Indexed: 03/05/2024] Open
Abstract
As the field of single-cell transcriptomics matures, research is shifting focus from phenomenological descriptions of cellular phenotypes to a mechanistic understanding of the gene regulation underneath. This perspective considers the value of capturing dynamical information at single-cell resolution for gaining mechanistic insight; reviews the available technologies for recording and inferring temporal information in single cells; and explores whether better dynamical resolution is sufficient to adequately capture the causal relationships driving complex biological systems. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.
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Affiliation(s)
- Rory J. Maizels
- The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- University College London, London WC1E 6BT, UK
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4
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Mitic N, Neuschulz A, Spanjaard B, Schneider J, Fresmann N, Novoselc KT, Strunk T, Münster L, Olivares-Chauvet P, Ninkovic J, Junker JP. Dissecting the spatiotemporal diversity of adult neural stem cells. Mol Syst Biol 2024; 20:321-337. [PMID: 38365956 PMCID: PMC10987636 DOI: 10.1038/s44320-024-00022-z] [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: 05/04/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024] Open
Abstract
Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.
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Affiliation(s)
- Nina Mitic
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Anika Neuschulz
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Humboldt Universität zu Berlin, Institute for Biology, Berlin, Germany
| | - Bastiaan Spanjaard
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Julia Schneider
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU, Munich, Germany
| | - Nora Fresmann
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klara Tereza Novoselc
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
- Graduate School of Systemic Neurosciences, LMU, Munich, Germany
| | - Taraneh Strunk
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Lisa Münster
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Pedro Olivares-Chauvet
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Jovica Ninkovic
- Helmholtz Center Munich - German Research Center for Environmental Health, Institute of Stem Cell Research, Munich, Germany
- Biomedical Center Munich (BMC), Department of Cell Biology and Anatomy, Medical Faculty, LMU, Munich, Germany
| | - Jan Philipp Junker
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
- Charité - Universitätsmedizin Berlin, Berlin, Germany.
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5
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Bouman BJ, Demerdash Y, Sood S, Grünschläger F, Pilz F, Itani AR, Kuck A, Marot-Lassauzaie V, Haas S, Haghverdi L, Essers MA. Single-cell time series analysis reveals the dynamics of HSPC response to inflammation. Life Sci Alliance 2024; 7:e202302309. [PMID: 38110222 PMCID: PMC10728485 DOI: 10.26508/lsa.202302309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/20/2023] Open
Abstract
Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.
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Affiliation(s)
- Brigitte J Bouman
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Yasmin Demerdash
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
| | - Shubhankar Sood
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
| | - Florian Grünschläger
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
- Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Franziska Pilz
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
| | - Abdul R Itani
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
| | - Andrea Kuck
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
| | - Valérie Marot-Lassauzaie
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
- Charité-Universitätsmedizin, Berlin, Germany
| | - Simon Haas
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Charité-Universitätsmedizin, Berlin, Germany
| | - Laleh Haghverdi
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
| | - Marieke Ag Essers
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- DKFZ-ZMBH Alliance, Heidelberg, Germany
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6
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Gayoso A, Weiler P, Lotfollahi M, Klein D, Hong J, Streets A, Theis FJ, Yosef N. Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells. Nat Methods 2024; 21:50-59. [PMID: 37735568 PMCID: PMC10776389 DOI: 10.1038/s41592-023-01994-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 08/08/2023] [Indexed: 09/23/2023]
Abstract
RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.
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Affiliation(s)
- Adam Gayoso
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Philipp Weiler
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Dominik Klein
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Justin Hong
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Aaron Streets
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Nir Yosef
- Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA.
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7
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Zheng SC, Stein-O’Brien G, Boukas L, Goff LA, Hansen KD. Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates. Genome Biol 2023; 24:246. [PMID: 37885016 PMCID: PMC10601342 DOI: 10.1186/s13059-023-03065-x] [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: 10/31/2022] [Accepted: 09/19/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND RNA velocity analysis of single cells offers the potential to predict temporal dynamics from gene expression. In many systems, RNA velocity has been observed to produce a vector field that qualitatively reflects known features of the system. However, the limitations of RNA velocity estimates are still not well understood. RESULTS We analyze the impact of different steps in the RNA velocity workflow on direction and speed. We consider both high-dimensional velocity estimates and low-dimensional velocity vector fields mapped onto an embedding. We conclude the transition probability method for mapping velocity estimates onto an embedding is effectively interpolating in the embedding space. Our findings reveal a significant dependence of the RNA velocity workflow on smoothing via the k-nearest-neighbors (k-NN) graph of the observed data. This reliance results in considerable estimation errors for both direction and speed in both high- and low-dimensional settings when the k-NN graph fails to accurately represent the true data structure; this is an unknown feature of real data. RNA velocity performs poorly at estimating speed in both low- and high-dimensional spaces, except in very low noise settings. We introduce a novel quality measure that can identify when RNA velocity should not be used. CONCLUSIONS Our findings emphasize the importance of choices in the RNA velocity workflow and highlight critical limitations of data analysis. We advise against over-interpreting expression dynamics using RNA velocity, particularly in terms of speed. Finally, we emphasize that the use of RNA velocity in assessing the correctness of a low-dimensional embedding is circular.
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Affiliation(s)
- Shijie C. Zheng
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Genevieve Stein-O’Brien
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD USA
- Kavli Neurodiscovery Institute, Johns Hopkins University, Baltimore, MD USA
- Quantitative Sciences Division, Department of Oncology, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Leandros Boukas
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
| | - Loyal A. Goff
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD USA
- Kavli Neurodiscovery Institute, Johns Hopkins University, Baltimore, MD USA
| | - Kasper D. Hansen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD USA
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8
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Li Q. scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics. Genome Biol 2023; 24:149. [PMID: 37353848 PMCID: PMC10290357 DOI: 10.1186/s13059-023-02988-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 06/13/2023] [Indexed: 06/25/2023] Open
Abstract
Despite the continued efforts, a batch-insensitive tool that can both infer and predict the developmental dynamics using single-cell genomics is lacking. Here, I present scTour, a novel deep learning architecture to perform robust inference and accurate prediction of cellular dynamics with minimal influence from batch effects. For inference, scTour simultaneously estimates the developmental pseudotime, delineates the vector field, and maps the transcriptomic latent space under a single, integrated framework. For prediction, scTour precisely reconstructs the underlying dynamics of unseen cellular states or a new independent dataset. scTour's functionalities are demonstrated in a variety of biological processes from 19 datasets.
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Affiliation(s)
- Qian Li
- Department of Pathology, University of Cambridge, Cambridge, UK.
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9
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Haghverdi L, Ludwig LS. Single-cell multi-omics and lineage tracing to dissect cell fate decision-making. Stem Cell Reports 2023; 18:13-25. [PMID: 36630900 PMCID: PMC9860164 DOI: 10.1016/j.stemcr.2022.12.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 01/12/2023] Open
Abstract
The concept of cell fate relates to the future identity of a cell, and its daughters, which is obtained via cell differentiation and division. Understanding, predicting, and manipulating cell fate has been a long-sought goal of developmental and regenerative biology. Recent insights obtained from single-cell genomic and integrative lineage-tracing approaches have further aided to identify molecular features predictive of cell fate. In this perspective, we discuss these approaches with a focus on theoretical concepts and future directions of the field to dissect molecular mechanisms underlying cell fate.
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Affiliation(s)
- Laleh Haghverdi
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany.
| | - Leif S Ludwig
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
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