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Emili E, Pérez-Posada A, Vanni V, Salamanca-Díaz D, Ródriguez-Fernández D, Christodoulou MD, Solana J. Allometry of cell types in planarians by single-cell transcriptomics. SCIENCE ADVANCES 2025; 11:eadm7042. [PMID: 40333969 PMCID: PMC12057665 DOI: 10.1126/sciadv.adm7042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 04/02/2025] [Indexed: 05/09/2025]
Abstract
Allometry explores the relationship between an organism's body size and its various components, offering insights into ecology, physiology, metabolism, and disease. The cell is the basic unit of biological systems, and yet the study of cell-type allometry remains relatively unexplored. Single-cell RNA sequencing (scRNA-seq) provides a promising tool for investigating cell-type allometry. Planarians, capable of growing and degrowing following allometric scaling rules, serve as an excellent model for these studies. We used scRNA-seq to examine cell-type allometry in asexual planarians of different sizes, revealing that they consist of the same basic cell types but in varying proportions. Notably, the gut basal cells are the most responsive to changes in size, suggesting a role in energy storage. We capture the regulated gene modules of distinct cell types in response to body size. This research sheds light on the molecular and cellular aspects of cell-type allometry in planarians and underscores the utility of scRNA-seq in these investigations.
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Affiliation(s)
- Elena Emili
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
| | - Alberto Pérez-Posada
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Department of Biosciences, University of Exeter, Exeter, UK
| | - Virginia Vanni
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Department of Biosciences, University of Exeter, Exeter, UK
| | - David Salamanca-Díaz
- Living Systems Institute, University of Exeter, Exeter, UK
- Department of Biosciences, University of Exeter, Exeter, UK
| | | | | | - Jordi Solana
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- Department of Biosciences, University of Exeter, Exeter, UK
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2
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Chen AD, Kroehling L, Ennis C, Denis GV, Monti S. A highly resolved integrated transcriptomic atlas of human breast cancers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.13.643025. [PMID: 40161579 PMCID: PMC11952505 DOI: 10.1101/2025.03.13.643025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
In this study, we developed an integrated single cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling > 600,000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment we were able to characterize heterogeneity of CD4, CD8 T cells and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune cell types which conferred a survival advantage in both The Cancer Genome Atlas (TCGA) and METABRIC. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.
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3
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Jansma A, Yao Y, Wolfe J, Del Debbio L, Beentjes SV, Ponting CP, Khamseh A. High order expression dependencies finely resolve cryptic states and subtypes in single cell data. Mol Syst Biol 2025; 21:173-207. [PMID: 39748128 PMCID: PMC11790937 DOI: 10.1038/s44320-024-00074-1] [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: 01/09/2024] [Revised: 10/24/2024] [Accepted: 10/31/2024] [Indexed: 01/04/2025] Open
Abstract
Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells' local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator's finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator's fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.
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Affiliation(s)
- Abel Jansma
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Higgs Centre for Theoretical Physics, School of Physics & Astronomy, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - Yuelin Yao
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Jareth Wolfe
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Luigi Del Debbio
- Higgs Centre for Theoretical Physics, School of Physics & Astronomy, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - Sjoerd V Beentjes
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
- School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD, UK
| | - Chris P Ponting
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
| | - Ava Khamseh
- MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
- Higgs Centre for Theoretical Physics, School of Physics & Astronomy, University of Edinburgh, Edinburgh, EH9 3FD, UK.
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.
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4
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Hrovatin K, Sikkema L, Shitov VA, Heimberg G, Shulman M, Oliver AJ, Mueller MF, Ibarra IL, Wang H, Ramírez-Suástegui C, He P, Schaar AC, Teichmann SA, Theis FJ, Luecken MD. Considerations for building and using integrated single-cell atlases. Nat Methods 2025; 22:41-57. [PMID: 39672979 DOI: 10.1038/s41592-024-02532-y] [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: 10/31/2023] [Accepted: 10/22/2024] [Indexed: 12/15/2024]
Abstract
The rapid adoption of single-cell technologies has created an opportunity to build single-cell 'atlases' integrating diverse datasets across many laboratories. Such atlases can serve as a reference for analyzing and interpreting current and future data. However, it has become apparent that atlasing approaches differ, and the impact of these differences are often unclear. Here we review the current atlasing literature and present considerations for building and using atlases. Importantly, we find that no one-size-fits-all protocol for atlas building exists, but rather we discuss context-specific considerations and workflows, including atlas conceptualization, data collection, curation and integration, atlas evaluation and atlas sharing. We further highlight the benefits of integrated atlases for analyses of new datasets and deriving biological insights beyond what is possible from individual datasets. Our overview of current practices and associated recommendations will improve the quality of atlases to come, facilitating the shift to a unified, reference-based understanding of single-cell biology.
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Affiliation(s)
- Karin Hrovatin
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Lisa Sikkema
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Vladimir A Shitov
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- Comprehensive Pneumology Center (CPC) with the CPC-M bioArchive / Institute of Lung Health and Immunity (LHI), Helmholtz Zentrum München; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Graham Heimberg
- Department of OMNI Bioinformatics, Genentech, South San Francisco, CA, USA
- Department of Biological Research | AI Development, Genentech, South San Francisco, CA, USA
| | - Maiia Shulman
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Amanda J Oliver
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Michaela F Mueller
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
| | - Ignacio L Ibarra
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
| | - Hanchen Wang
- Department of Biological Research | AI Development, Genentech, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Palo Alto, CA, USA
| | - Ciro Ramírez-Suástegui
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Peng He
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Anna C Schaar
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
- Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK
- Cambridge Stem Cell Institute and Department of Medicine, University of Cambridge, Cambridge, UK
- CIFAR MacMillan Multiscale Human Programme, Toronto, Ontario, Canada
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Department of Mathematics, Technical University of Munich, Garching, Germany.
| | - Malte D Luecken
- Department of Computational Health, Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
- Comprehensive Pneumology Center (CPC) with the CPC-M bioArchive / Institute of Lung Health and Immunity (LHI), Helmholtz Zentrum München; Member of the German Center for Lung Research (DZL), Munich, Germany.
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5
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Proks M, Salehin N, Brickman JM. Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing. Nat Methods 2025; 22:207-216. [PMID: 39543284 PMCID: PMC11725497 DOI: 10.1038/s41592-024-02511-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 10/15/2024] [Indexed: 11/17/2024]
Abstract
The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process of lineage specification has meant it has become increasingly difficult to define specific cell types or states in vivo, and compare these with in vitro differentiation. Here we utilize a set of deep learning tools to integrate and classify multiple datasets. This allows the definition of both mouse and human embryo cell types, lineages and states, thereby maximizing the information one can garner from these precious experimental resources. Our approaches are built on recent initiatives for large-scale human organ atlases, but here we focus on material that is difficult to obtain and process, spanning early mouse and human development. Using publicly available data for these stages, we test different deep learning approaches and develop a model to classify cell types in an unbiased fashion at the same time as defining the set of genes used by the model to identify lineages, cell types and states. We used our models trained on in vivo development to classify pluripotent stem cell models for both mouse and human development, showcasing the importance of this resource as a dynamic reference for early embryogenesis.
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Affiliation(s)
- Martin Proks
- The Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nazmus Salehin
- The Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joshua M Brickman
- The Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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6
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Dong M, Agrawal K, Fan R, Sefik E, Flavell RA, Kluger Y. Scaling deep identifiable models enables zero-shot characterization of single-cell biological states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.11.566161. [PMID: 38014345 PMCID: PMC10680588 DOI: 10.1101/2023.11.11.566161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
How to identify true biological differences across samples while overcoming batch effects has been a persistent challenge in single-cell RNA-seq data analysis, hindering analyses across datasets for transferable biological findings. In this work, we show that scaling up deep identifiable models leads to a surprisingly effective solution for this challenging task. We developed scShift, a deep variational inference framework with theoretical support in disentangling batch-dependent and independent variations. By training the model with compendiums of scRNA-seq atlases, scShift shows remarkable zero-shot capabilities in revealing representations of cell types and biological states in single-cell data while overcoming batch effects. We employed scShift to systematically compare lung fibrosis states across different datasets, tissues and experimental systems. scShift uniquely extrapolates lung fibrosis states to previously unseen post-COVID-19 fibrosis, characterizing universal myeloid-fibrosis signatures, potential repurposing drug targets and fibrosis-associated cell interactions. Evaluations of over 200 trained scShift models demonstrate emergent zero-shot capabilities and a scaling law beyond a transition threshold, with respect to dataset diversity. With its scaling performance on massive single-cell compendiums and exceptional zero-shot capabilities, scShift represents an important advance toward next-generation computational models for single-cell analysis.
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Affiliation(s)
- Mingze Dong
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Kriti Agrawal
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale University, New Haven, CT, USA
| | - Rong Fan
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale University, New Haven, CT, USA
- Human and Translational Immunology, Yale University, New Haven, CT, USA
| | - Esen Sefik
- Department of Immunobiology, Yale University, New Haven, CT, USA
| | - Richard A. Flavell
- Department of Immunobiology, Yale University, New Haven, CT, USA
- Howard Hughes Medical Institute, Yale University, New Haven, CT, USA
| | - Yuval Kluger
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
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7
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Liu T, Long W, Cao Z, Wang Y, He CH, Zhang L, Strittmatter SM, Zhao H. CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis. Brief Bioinform 2024; 26:bbae626. [PMID: 39592241 PMCID: PMC11596696 DOI: 10.1093/bib/bbae626] [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: 07/12/2024] [Revised: 10/07/2024] [Accepted: 11/14/2024] [Indexed: 11/28/2024] Open
Abstract
MOTIVATION Selecting representative genes or marker genes to distinguish cell types is an important task in single-cell sequencing analysis. Although many methods have been proposed to select marker genes, the genes selected may have redundancy and/or do not show cell-type-specific expression patterns to distinguish cell types. RESULTS Here, we present a novel model, named CosGeneGate, to select marker genes for more effective marker selections. CosGeneGate is inspired by combining the advantages of selecting marker genes based on both cell-type classification accuracy and marker gene specific expression patterns. We demonstrate the better performance of the marker genes selected by CosGeneGate for various downstream analyses than the existing methods with both public datasets and newly sequenced datasets. The non-redundant marker genes identified by CosGeneGate for major cell types and tissues in human can be found at the website as follows: https://github.com/VivLon/CosGeneGate/blob/main/marker gene list.xlsx.
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Affiliation(s)
- Tianyu Liu
- Department of Biostatistics, Yale University, New Haven, CT, 06520, United States
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06520, United States
| | - Wenxin Long
- Department of Biostatistics, Yale University, New Haven, CT, 06520, United States
- Department of Statistics, The Pennsylvania State University, University Park, PA, 16820, United States
| | - Zhiyuan Cao
- Department of Biostatistics, Yale University, New Haven, CT, 06520, United States
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06520, United States
- Program of Health Informatics, Yale University, New Haven, CT, 06520, United States
| | - Yuge Wang
- Department of Biostatistics, Yale University, New Haven, CT, 06520, United States
| | - Chuan Hua He
- Department of Neurology, Yale University School of Medicine, New Haven, CT, 06520, United States
| | - Le Zhang
- Department of Neurology, Yale University School of Medicine, New Haven, CT, 06520, United States
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, 06520, United States
| | - Stephen M Strittmatter
- Department of Neurology, Yale University School of Medicine, New Haven, CT, 06520, United States
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, 06520, United States
- Cellular Neuroscience, Neurodegeneration and Repair Program, Yale University School of Medicine, New Haven, CT, 06520, United States
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT, 06520, United States
- Interdepartmental Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, 06520, United States
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8
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Li R, Qu R, Parisi F, Strino F, Lam H, Stanley JS, Cheng X, Myung P, Kluger Y. LMD: Cluster-Independent Multiscale Marker Identification in Single-cell RNA-seq Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.12.566780. [PMID: 38014159 PMCID: PMC10680591 DOI: 10.1101/2023.11.12.566780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Identifying accurate cell markers in single-cell RNA-seq data is crucial for understanding cellular diversity and function. Localized Marker Detector (LMD) is a novel tool to identify "localized genes" - genes exclusively expressed in groups of highly similar cells - thereby characterizing cellular diversity in a multi-resolution and fine-grained manner. LMD constructs a cell-cell affinity graph, diffuses the gene expression value across the cell graph, and assigns a score to each gene based on its diffusion dynamics. LMD's candidate markers can be grouped into functional gene modules, which accurately reflect cell types, subtypes, and other sources of variation such as cell cycle status. We apply LMD to mouse bone marrow and hair follicle dermal condensate datasets, where LMD facilitates cross-sample comparisons, identifying shared and sample-specific gene signatures and novel cell populations without requiring batch effect correction or integration methods. Furthermore, we assessed the performance of LMD across nine single-cell RNA sequencing datasets, compared it with six other methods aimed at achieving similar objectives, and found that LMD outperforms the other methods evaluated.
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9
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Rusnak B, Clark FK, Vadde BVL, Roeder AHK. What Is a Plant Cell Type in the Age of Single-Cell Biology? It's Complicated. Annu Rev Cell Dev Biol 2024; 40:301-328. [PMID: 38724025 DOI: 10.1146/annurev-cellbio-111323-102412] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2024]
Abstract
One of the fundamental questions in developmental biology is how a cell is specified to differentiate as a specialized cell type. Traditionally, plant cell types were defined based on their function, location, morphology, and lineage. Currently, in the age of single-cell biology, researchers typically attempt to assign plant cells to cell types by clustering them based on their transcriptomes. However, because cells are dynamic entities that progress through the cell cycle and respond to signals, the transcriptome also reflects the state of the cell at a particular moment in time, raising questions about how to define a cell type. We suggest that these complexities and dynamics of cell states are of interest and further consider the roles signaling, stochasticity, cell cycle, and mechanical forces play in plant cell fate specification. Once established, cell identity must also be maintained. With the wealth of single-cell data coming out, the field is poised to elucidate both the complexity and dynamics of cell states.
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Affiliation(s)
- Byron Rusnak
- Weill Institute for Cell and Molecular Biology and School of Integrative Plant Science, Section of Plant Biology, Cornell University, Ithaca, New York, USA; , ,
| | - Frances K Clark
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York, USA
- Weill Institute for Cell and Molecular Biology and School of Integrative Plant Science, Section of Plant Biology, Cornell University, Ithaca, New York, USA; , ,
| | - Batthula Vijaya Lakshmi Vadde
- Plant Molecular and Cellular Biology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA;
- Weill Institute for Cell and Molecular Biology and School of Integrative Plant Science, Section of Plant Biology, Cornell University, Ithaca, New York, USA; , ,
| | - Adrienne H K Roeder
- Weill Institute for Cell and Molecular Biology and School of Integrative Plant Science, Section of Plant Biology, Cornell University, Ithaca, New York, USA; , ,
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10
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Qin X, Tape CJ. Functional analysis of cell plasticity using single-cell technologies. Trends Cell Biol 2024; 34:854-864. [PMID: 38355348 DOI: 10.1016/j.tcb.2024.01.006] [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: 11/02/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.
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Affiliation(s)
- Xiao Qin
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Oxford, OX3 9DS, UK.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, 72 Huntley Street, London, WC1E 6DD, UK.
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11
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Rood JE, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas. Cell 2024; 187:4520-4545. [PMID: 39178831 DOI: 10.1016/j.cell.2024.07.035] [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: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/21/2024] [Indexed: 08/26/2024]
Abstract
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
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Affiliation(s)
| | | | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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12
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Szałata A, Hrovatin K, Becker S, Tejada-Lapuerta A, Cui H, Wang B, Theis FJ. Transformers in single-cell omics: a review and new perspectives. Nat Methods 2024; 21:1430-1443. [PMID: 39122952 DOI: 10.1038/s41592-024-02353-z] [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: 12/19/2023] [Accepted: 06/07/2024] [Indexed: 08/12/2024]
Abstract
Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.
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Affiliation(s)
- Artur Szałata
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Sören Becker
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Munich Center of Machine Learning, Munich, Germany
| | - Alejandro Tejada-Lapuerta
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Haotian Cui
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- AI Hub, University Health Network, Toronto, Ontario, Canada
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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13
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del Giudice G, Serra A, Pavel A, Torres Maia M, Saarimäki LA, Fratello M, Federico A, Alenius H, Fadeel B, Greco D. A Network Toxicology Approach for Mechanistic Modelling of Nanomaterial Hazard and Adverse Outcomes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400389. [PMID: 38923832 PMCID: PMC11348149 DOI: 10.1002/advs.202400389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 05/10/2024] [Indexed: 06/28/2024]
Abstract
Hazard assessment is the first step in evaluating the potential adverse effects of chemicals. Traditionally, toxicological assessment has focused on the exposure, overlooking the impact of the exposed system on the observed toxicity. However, systems toxicology emphasizes how system properties significantly contribute to the observed response. Hence, systems theory states that interactions store more information than individual elements, leading to the adoption of network based models to represent complex systems in many fields of life sciences. Here, they develop a network-based approach to characterize toxicological responses in the context of a biological system, inferring biological system specific networks. They directly link molecular alterations to the adverse outcome pathway (AOP) framework, establishing direct connections between omics data and toxicologically relevant phenotypic events. They apply this framework to a dataset including 31 engineered nanomaterials with different physicochemical properties in two different in vitro and one in vivo models and demonstrate how the biological system is the driving force of the observed response. This work highlights the potential of network-based methods to significantly improve their understanding of toxicological mechanisms from a systems biology perspective and provides relevant considerations and future data-driven approaches for the hazard assessment of nanomaterials and other advanced materials.
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Affiliation(s)
- Giusy del Giudice
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
| | - Angela Serra
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33100Finland
| | - Alisa Pavel
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Marcella Torres Maia
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Laura Aliisa Saarimäki
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
| | - Michele Fratello
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
| | - Antonio Federico
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33100Finland
| | - Harri Alenius
- Human Microbiome Research Program (HUMI)University of HelsinkiHelsinki00014Finland
- Institute of Environmental MedicineKarolinska InstitutetStockholm171 77Sweden
| | - Bengt Fadeel
- Institute of Environmental MedicineKarolinska InstitutetStockholm171 77Sweden
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE), Faculty of Medicine and Health TechnologyTampere UniversityTampere33520Finland
- Division of Pharmaceutical Biosciences, Faculty of PharmacyUniversity of HelsinkiHelsinki00790Finland
- Tampere Institute for Advanced StudyTampere UniversityTampere33100Finland
- Institute of BiotechnologyUniversity of HelsinkiHelsinki00790Finland
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14
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Rebboah E, Rezaie N, Williams BA, Weimer AK, Shi M, Yang X, Liang HY, Dionne LA, Reese F, Trout D, Jou J, Youngworth I, Reinholdt L, Morabito S, Snyder MP, Wold BJ, Mortazavi A. The ENCODE mouse postnatal developmental time course identifies regulatory programs of cell types and cell states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598567. [PMID: 38915583 PMCID: PMC11195270 DOI: 10.1101/2024.06.12.598567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Postnatal genomic regulation significantly influences tissue and organ maturation but is under-studied relative to existing genomic catalogs of adult tissues or prenatal development in mouse. The ENCODE4 consortium generated the first comprehensive single-nucleus resource of postnatal regulatory events across a diverse set of mouse tissues. The collection spans seven postnatal time points, mirroring human development from childhood to adulthood, and encompasses five core tissues. We identified 30 cell types, further subdivided into 69 subtypes and cell states across adrenal gland, left cerebral cortex, hippocampus, heart, and gastrocnemius muscle. Our annotations cover both known and novel cell differentiation dynamics ranging from early hippocampal neurogenesis to a new sex-specific adrenal gland population during puberty. We used an ensemble Latent Dirichlet Allocation strategy with a curated vocabulary of 2,701 regulatory genes to identify regulatory "topics," each of which is a gene vector, linked to cell type differentiation, subtype specialization, and transitions between cell states. We find recurrent regulatory topics in tissue-resident macrophages, neural cell types, endothelial cells across multiple tissues, and cycling cells of the adrenal gland and heart. Cell-type-specific topics are enriched in transcription factors and microRNA host genes, while chromatin regulators dominate mitosis topics. Corresponding chromatin accessibility data reveal dynamic and sex-specific regulatory elements, with enriched motifs matching transcription factors in regulatory topics. Together, these analyses identify both tissue-specific and common regulatory programs in postnatal development across multiple tissues through the lens of the factors regulating transcription.
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Affiliation(s)
- Elisabeth Rebboah
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Narges Rezaie
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Brian A. Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, USA
| | - Annika K. Weimer
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, USA
| | - Minyi Shi
- Department of Next Generation Sequencing and Microchemistry, Proteomics and Lipidomics, Genentech, San Francisco, USA
| | - Xinqiong Yang
- Department of Genetics, Stanford University School of Medicine, Palo Alto, USA
| | - Heidi Yahan Liang
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
| | | | - Fairlie Reese
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
| | - Diane Trout
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, USA
| | - Jennifer Jou
- Department of Genetics, Stanford University School of Medicine, Palo Alto, USA
| | - Ingrid Youngworth
- Department of Genetics, Stanford University School of Medicine, Palo Alto, USA
| | | | - Samuel Morabito
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Palo Alto, USA
| | - Barbara J. Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, USA
| | - Ali Mortazavi
- Developmental and Cell Biology, University of California Irvine, Irvine, USA
- Center for Complex Biological Systems, University of California Irvine, Irvine, USA
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15
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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16
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Kosik KS. Tau and the hard problem faceoff. Cytoskeleton (Hoboken) 2024; 81:63-65. [PMID: 37772747 DOI: 10.1002/cm.21791] [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: 08/31/2023] [Revised: 09/13/2023] [Accepted: 09/14/2023] [Indexed: 09/30/2023]
Abstract
Tau has attracted the attention of fundamental cell biologists for its control over microtubules and Alzheimer biologists for its tendency to form pathological inclusions. While an extensive and insightful literature exists on the tauopathies and vulnerable cell populations, we should acknowledge that a core problem remains-how the individually variable experience of dementia is embodied, how it is felt.
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Affiliation(s)
- Kenneth S Kosik
- Department of Molecular, Cellular, and Development Biology, University of California Santa Barbara, Santa Barbara, California, USA
- Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, California, USA
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