1
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Ganier C. Unlocking Skin's Symphony: Single-Cell and Spatial Transcriptomics Reveal the Harmonious Interplay among Diverse Cell Populations. J Invest Dermatol 2024; 144:1917-1920. [PMID: 38678464 DOI: 10.1016/j.jid.2024.01.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 05/01/2024]
Affiliation(s)
- Clarisse Ganier
- Centre for Gene Therapy and Regenerative Medicine, Guy's Hospital, King's College London, London, United Kingdom.
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2
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Kang S, Chen EC, Cifuentes H, Co JY, Cole G, Graham J, Hsia R, Kiyota T, Klein JA, Kroll KT, Nieves Lopez LM, Norona LM, Peiris H, Potla R, Romero-Lopez M, Roth JG, Tseng M, Fullerton AM, Homan KA. Complex in vitromodels positioned for impact to drug testing in pharma: a review. Biofabrication 2024; 16:042006. [PMID: 39189069 DOI: 10.1088/1758-5090/ad6933] [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: 12/22/2023] [Accepted: 07/30/2024] [Indexed: 08/28/2024]
Abstract
Recent years have seen the creation and popularization of various complexin vitromodels (CIVMs), such as organoids and organs-on-chip, as a technology with the potential to reduce animal usage in pharma while also enhancing our ability to create safe and efficacious drugs for patients. Public awareness of CIVMs has increased, in part, due to the recent passage of the FDA Modernization Act 2.0. This visibility is expected to spur deeper investment in and adoption of such models. Thus, end-users and model developers alike require a framework to both understand the readiness of current models to enter the drug development process, and to assess upcoming models for the same. This review presents such a framework for model selection based on comparative -omics data (which we term model-omics), and metrics for qualification of specific test assays that a model may support that we term context-of-use (COU) assays. We surveyed existing healthy tissue models and assays for ten drug development-critical organs of the body, and provide evaluations of readiness and suggestions for improving model-omics and COU assays for each. In whole, this review comes from a pharma perspective, and seeks to provide an evaluation of where CIVMs are poised for maximum impact in the drug development process, and a roadmap for realizing that potential.
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Affiliation(s)
- Serah Kang
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Eugene C Chen
- Department of Drug Metabolism and Pharmacokinetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Helen Cifuentes
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Julia Y Co
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Gabrielle Cole
- Investigative Toxicology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Jessica Graham
- Product Quality & Occupational Toxicology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of Americaica
| | - Rebecca Hsia
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Tomomi Kiyota
- Investigative Toxicology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Jessica A Klein
- Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Katharina T Kroll
- Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Lenitza M Nieves Lopez
- Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Leah M Norona
- Investigative Toxicology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Heshan Peiris
- Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Ratnakar Potla
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Monica Romero-Lopez
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Julien G Roth
- Human Genetics, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Min Tseng
- Investigative Toxicology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Aaron M Fullerton
- Investigative Toxicology, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
| | - Kimberly A Homan
- Complex in vitro Systems Group, Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, United States of America
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3
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [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: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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4
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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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5
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Johnston KG, Grieco SF, Nie Q, Theis FJ, Xu X. Small data methods in omics: the power of one. Nat Methods 2024:10.1038/s41592-024-02390-8. [PMID: 39174710 DOI: 10.1038/s41592-024-02390-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 07/24/2024] [Indexed: 08/24/2024]
Abstract
Over the last decade, biology has begun utilizing 'big data' approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with 'small data' approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.
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Affiliation(s)
- Kevin G Johnston
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Steven F Grieco
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA
- Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
| | - Fabian J Theis
- Helmholtz Center Munich-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA.
- Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA, USA.
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6
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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7
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Mulenge F, Gern OL, Busker LM, Aringo A, Ghita L, Waltl I, Pavlou A, Kalinke U. Transcriptomic analysis unveils bona fide molecular signatures of microglia under conditions of homeostasis and viral encephalitis. J Neuroinflammation 2024; 21:203. [PMID: 39153993 PMCID: PMC11330067 DOI: 10.1186/s12974-024-03197-2] [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: 04/24/2024] [Accepted: 08/06/2024] [Indexed: 08/19/2024] Open
Abstract
Microglia serve as a front-line defense against neuroinvasive viral infection, however, determination of their actual transcriptional profiles under conditions of health and disease is challenging. Here, we used various experimental approaches to delineate the transcriptional landscape of microglia during viral infection. Intriguingly, multiple activation genes were found to be artificially induced in sorted microglia and we demonstrated that shear stress encountered during cell sorting was one of the key inducers. Post-hoc analysis revealed that publicly available large-scale single-cell RNA sequencing datasets were significantly tainted by aberrant signatures that are associated with cell sorting. By exploiting the ribosomal tagging approach, we developed a strategy to enrich microglia-specific transcripts by comparing immunoprecipitated RNA with total RNA. Such enriched transcripts were instrumental in defining bona fide signatures of microglia under conditions of health and virus infection. These unified microglial signatures may serve as a benchmark to retrospectively assess ex vivo artefacts from available atlases. Leveraging the microglial translatome, we found enrichment of genes implicated in T-cell activation and cytokine production during the course of VSV infection. These data linked microglia with T-cell re-stimulation and further underscored that microglia are involved in shaping antiviral T-cell responses in the brain. Collectively, our study defines the transcriptional landscape of microglia under steady state and during viral encephalitis and highlights cellular interactions between microglia and T cells that contribute to the control of virus dissemination.
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Affiliation(s)
- Felix Mulenge
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
| | - Olivia Luise Gern
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
| | - Lena Mareike Busker
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
- Department of Pathology, University of Veterinary Medicine Hannover, 30559, Foundation, Hannover, Germany
| | - Angela Aringo
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
| | - Luca Ghita
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
- , Genentech, South San Francisco, CA, 94080, USA
| | - Inken Waltl
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
| | - Andreas Pavlou
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany
| | - Ulrich Kalinke
- Institute for Experimental Infection Research, Centre for Experimental and Clinical Infection Research, a joint venture between The Helmholtz-Centre for Infection Research, Hannover Medical School, TWINCORE, Feodor-Lynen-Str. 7, 30625, Hannover, Germany.
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, 30625, Hannover, Germany.
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8
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Gao H, Hua K, Wu X, Wei L, Chen S, Yin Q, Jiang R, Zhang X. Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model. Commun Biol 2024; 7:977. [PMID: 39134617 PMCID: PMC11319358 DOI: 10.1038/s42003-024-06564-0] [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: 08/17/2023] [Accepted: 07/05/2024] [Indexed: 08/15/2024] Open
Abstract
A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.
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Affiliation(s)
- Haoxiang Gao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Kui Hua
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Xinze Wu
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Lei Wei
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
| | - Sijie Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Qijin Yin
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
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9
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Southard KM, Ardy RC, Tang A, O’Sullivan DD, Metzner E, Guruvayurappan K, Norman TM. Comprehensive transcription factor perturbations recapitulate fibroblast transcriptional states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606073. [PMID: 39131349 PMCID: PMC11312553 DOI: 10.1101/2024.07.31.606073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Cell atlas projects have nominated recurrent transcriptional states as drivers of biological processes and disease, but their origins, regulation, and properties remain unclear. To enable complementary functional studies, we developed a scalable approach for recapitulating cell states in vitro using CRISPR activation (CRISPRa) Perturb-seq. Aided by a novel multiplexing method, we activated 1,836 transcription factors in two cell types. Measuring 21,958 perturbations showed that CRISPRa activated targets within physiological ranges, that epigenetic features predicted activatable genes, and that the protospacer seed region drove an off-target effect. Perturbations recapitulated in vivo fibroblast states, including universal and inflammatory states, and identified KLF4 and KLF5 as key regulators of the universal state. Inducing the universal state suppressed disease-associated states, highlighting its therapeutic potential. Our findings cement CRISPRa as a tool for perturbing differentiated cells and indicate that in vivo states can be elicited via perturbation, enabling studies of clinically relevant states ex vivo.
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Affiliation(s)
- Kaden M. Southard
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Rico C. Ardy
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anran Tang
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Deirdre D. O’Sullivan
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Eli Metzner
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Karthik Guruvayurappan
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Thomas M. Norman
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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10
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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11
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A cellular reference atlas across human brain regions. Nat Med 2024:10.1038/s41591-024-03151-y. [PMID: 39095600 DOI: 10.1038/s41591-024-03151-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
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12
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Chen X, Huang Y, Huang L, Huang Z, Hao ZZ, Xu L, Xu N, Li Z, Mou Y, Ye M, You R, Zhang X, Liu S, Miao Z. A brain cell atlas integrating single-cell transcriptomes across human brain regions. Nat Med 2024:10.1038/s41591-024-03150-z. [PMID: 39095595 DOI: 10.1038/s41591-024-03150-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 06/24/2024] [Indexed: 08/04/2024]
Abstract
While single-cell technologies have greatly advanced our comprehension of human brain cell types and functions, studies including large numbers of donors and multiple brain regions are needed to extend our understanding of brain cell heterogeneity. Integrating atlas-level single-cell data presents a chance to reveal rare cell types and cellular heterogeneity across brain regions. Here we present the Brain Cell Atlas, a comprehensive reference atlas of brain cells, by assembling single-cell data from 70 human and 103 mouse studies of the brain throughout major developmental stages across brain regions, covering over 26.3 million cells or nuclei from both healthy and diseased tissues. Using machine-learning based algorithms, the Brain Cell Atlas provides a consensus cell type annotation, and it showcases the identification of putative neural progenitor cells and a cell subpopulation of PCDH9high microglia in the human brain. We demonstrate the gene regulatory difference of PCDH9high microglia between hippocampus and prefrontal cortex and elucidate the cell-cell communication network. The Brain Cell Atlas presents an atlas-level integrative resource for comparing brain cells in different environments and conditions within the Human Cell Atlas.
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Affiliation(s)
- Xinyue Chen
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Yin Huang
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Liangfeng Huang
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Ziliang Huang
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Lahong Xu
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Zhi Li
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yonggao Mou
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Mingli Ye
- Tsinghua Fuzhou Institute for Data Technology, Fuzhou, China
| | - Renke You
- Tsinghua Fuzhou Institute for Data Technology, Fuzhou, China
| | - Xuegong Zhang
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing, China
- School of Medicine, Tsinghua University, Beijing, China
- School of Life Sciences, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, China.
| | - Zhichao Miao
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou International Bio Island, Guangzhou, China.
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou International Bio Island, Guangzhou, China.
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13
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Rosen Y, Brbić M, Roohani Y, Swanson K, Li Z, Leskovec J. Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN. Nat Methods 2024; 21:1492-1500. [PMID: 38366243 PMCID: PMC11310084 DOI: 10.1038/s41592-024-02191-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: 02/03/2023] [Accepted: 01/22/2024] [Indexed: 02/18/2024]
Abstract
Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint analysis of cross-species datasets to homologous genes. Here we present SATURN, a deep learning method for learning universal cell embeddings that encodes genes' biological properties using protein language models. By coupling protein embeddings from language models with RNA expression, SATURN integrates datasets profiled from different species regardless of their genomic similarity. SATURN can detect functionally related genes coexpressed across species, redefining differential expression for cross-species analysis. Applying SATURN to three species whole-organism atlases and frog and zebrafish embryogenesis datasets, we show that SATURN can effectively transfer annotations across species, even when they are evolutionarily remote. We also demonstrate that SATURN can be used to find potentially divergent gene functions between glaucoma-associated genes in humans and four other species.
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Affiliation(s)
- Yanay Rosen
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Maria Brbić
- School of Computer and Communication Sciences, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Yusuf Roohani
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Kyle Swanson
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziang Li
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA.
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14
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Kuhn TM, Paulsen M, Cuylen-Haering S. Accessible high-speed image-activated cell sorting. Trends Cell Biol 2024; 34:657-670. [PMID: 38789300 DOI: 10.1016/j.tcb.2024.04.007] [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: 09/06/2023] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024]
Abstract
Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.
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Affiliation(s)
- Terra M Kuhn
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Malte Paulsen
- Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Sara Cuylen-Haering
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
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15
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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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16
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Altieri B, Secener AK, Sai S, Fischer C, Sbiera S, Arampatzi P, Kircher S, Herterich S, Landwehr L, Vitcetz SN, Braeuning C, Fassnacht M, Ronchi CL, Sauer S. Single-nucleus and spatial transcriptome reveal adrenal homeostasis in normal and tumoural adrenal glands. Clin Transl Med 2024; 14:e1798. [PMID: 39167619 PMCID: PMC11338279 DOI: 10.1002/ctm2.1798] [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: 11/21/2023] [Revised: 07/11/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024] Open
Abstract
The human adrenal gland is a complex endocrine tissue. Studies on adrenal renewal have been limited to animal models or human foetuses. Enhancing our understanding of adult human adrenal homeostasis is crucial for gaining insights into the pathogenesis of adrenal diseases, such as adrenocortical tumours. Here, we present a comprehensive cellular genomics analysis of the adult human normal adrenal gland, combining single-nuclei RNA sequencing and spatial transcriptome data to reconstruct adrenal gland homeostasis. As expected, we identified primary cells of the various zones of the adrenal cortex and medulla, but we also uncovered additional cell types. They constitute the adrenal microenvironment, including immune cells, mostly composed of a large population of M2 macrophages, and new cell populations, including different subpopulations of vascular-endothelial cells and cortical-neuroendocrine cells. Utilizing spatial transcriptome and pseudotime trajectory analysis, we support evidence of the centripetal dynamics of adrenocortical cell maintenance and the essential role played by Wnt/β-catenin, sonic hedgehog, and fibroblast growth factor pathways in the adult adrenocortical homeostasis. Furthermore, we compared single-nuclei transcriptional profiles obtained from six healthy adrenal glands and twelve adrenocortical adenomas. This analysis unveiled a notable heterogeneity in cell populations within the adenoma samples. In addition, we identified six distinct adenoma-specific clusters, each with varying distributions based on steroid profiles and tumour mutational status. Overall, our results provide novel insights into adrenal homeostasis and molecular mechanisms potentially underlying early adrenocortical tumorigenesis and/or autonomous steroid secretion. Our cell atlas represents a powerful resource to investigate other adrenal-related pathologies.
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Affiliation(s)
- Barbara Altieri
- Division of Endocrinology and DiabetesDepartment of Internal Medicine IUniversity HospitalUniversity of WürzburgWürzburgGermany
| | - A. Kerim Secener
- Max Delbrück Center for Molecular MedicineBerlinGermany
- Berlin Institute of HealthBerlinGermany
- Department of BiologyChemistry and PharmacyInstitute of BiochemistryFree University BerlinBerlinGermany
| | - Somesh Sai
- Max Delbrück Center for Molecular MedicineBerlinGermany
- Berlin Institute of HealthBerlinGermany
- Department of BiologyChemistry and PharmacyInstitute of BiochemistryFree University BerlinBerlinGermany
| | - Cornelius Fischer
- Max Delbrück Center for Molecular MedicineBerlinGermany
- Berlin Institute of HealthBerlinGermany
| | - Silviu Sbiera
- Division of Endocrinology and DiabetesDepartment of Internal Medicine IUniversity HospitalUniversity of WürzburgWürzburgGermany
| | | | - Stefan Kircher
- Institute of PathologyUniversity of WürzburgWürzburgGermany
| | | | - Laura‐Sophie Landwehr
- Division of Endocrinology and DiabetesDepartment of Internal Medicine IUniversity HospitalUniversity of WürzburgWürzburgGermany
| | - Sarah N. Vitcetz
- Max Delbrück Center for Molecular MedicineBerlinGermany
- Berlin Institute of HealthBerlinGermany
| | | | - Martin Fassnacht
- Division of Endocrinology and DiabetesDepartment of Internal Medicine IUniversity HospitalUniversity of WürzburgWürzburgGermany
- Central Laboratory University Hospital WürzburgWürzburgGermany
| | - Cristina L. Ronchi
- Division of Endocrinology and DiabetesDepartment of Internal Medicine IUniversity HospitalUniversity of WürzburgWürzburgGermany
- Institute of Metabolism and System ResearchUniversity of BirminghamEdgabston, BirminghamUK
| | - Sascha Sauer
- Max Delbrück Center for Molecular MedicineBerlinGermany
- Berlin Institute of HealthBerlinGermany
- Core Unit SysMedUniversity of WürzburgWürzburgGermany
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17
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Perez-Gutierrez L, Li P, Ferrara N. Endothelial cell diversity: the many facets of the crystal. FEBS J 2024; 291:3287-3302. [PMID: 36266750 DOI: 10.1111/febs.16660] [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: 07/17/2022] [Revised: 10/03/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022]
Abstract
Endothelial cells (ECs) form the inner lining of blood vessels and play crucial roles in angiogenesis. While it has been known for a long time that there are considerable differences among ECs from lymphatic and blood vessels, as well as among arteries, veins and capillaries, the full repertoire of endothelial diversity is only beginning to be elucidated. It has become apparent that the role of ECs is not just limited to their exchange functions. Indeed, a multitude of organ-specific functions, including release of growth factors, regulation of immune functions, have been linked to ECs. Recent years have seen a surge into the identification of spatiotemporal molecular and functional heterogeneity of ECs, supported by technologies such as single-cell RNA sequencing (scRNA-seq), lineage tracing and intersectional genetics. Together, these techniques have spurred the generation of epigenomic, transcriptomic and proteomic signatures of ECs. It is now clear that ECs across organs and in different vascular beds, but even within the same vessel, have unique molecular identities and employ specialized molecular mechanisms to fulfil highly specialized needs. Here, we focus on the molecular heterogeneity of the endothelium in different organs and pathological conditions.
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Affiliation(s)
- Lorena Perez-Gutierrez
- Department of Pathology, Moores Cancer Center, University of California, San Diego, CA, USA
| | - Pin Li
- Department of Pathology, Moores Cancer Center, University of California, San Diego, CA, USA
| | - Napoleone Ferrara
- Department of Pathology, Moores Cancer Center, University of California, San Diego, CA, USA
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18
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Qin L, Zhang G, Zhang S, Chen Y. Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308934. [PMID: 38778573 PMCID: PMC11304254 DOI: 10.1002/advs.202308934] [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: 11/20/2023] [Revised: 03/14/2024] [Indexed: 05/25/2024]
Abstract
Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.
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Affiliation(s)
- Lu Qin
- College of Computer and Information EngineeringTianjin Normal UniversityTianjin300387China
| | - Guangya Zhang
- College of Computer and Information EngineeringTianjin Normal UniversityTianjin300387China
| | - Shaoqiang Zhang
- College of Computer and Information EngineeringTianjin Normal UniversityTianjin300387China
| | - Yong Chen
- Department of Biological and Biomedical SciencesRowan UniversityNJ08028USA
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19
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Cousins HC, Nayar G, Altman RB. Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities. Annu Rev Biomed Data Sci 2024; 7:15-29. [PMID: 38598857 DOI: 10.1146/annurev-biodatasci-110123-025333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, drug repurposing enables more efficient treatment of rare diseases, stratified patient populations, and urgent threats to public health. However, prioritizing well-suited drug candidates from among a nearly infinite number of repurposing options continues to represent a significant challenge in drug development. Over the past decade, advances in genomic profiling, database curation, and machine learning techniques have enabled more accurate identification of drug repurposing candidates for subsequent clinical evaluation. This review outlines the major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) genomic signatures, (c) biological networks, and (d) real-world clinical data. We propose that realizing the full impact of drug repurposing methodologies requires a multidisciplinary understanding of each method's advantages and limitations with respect to clinical practice.
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Affiliation(s)
- Henry C Cousins
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA;
| | - Gowri Nayar
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA;
| | - Russ B Altman
- Departments of Genetics, Medicine, and Bioengineering, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA;
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20
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Hao M, Gong J, Zeng X, Liu C, Guo Y, Cheng X, Wang T, Ma J, Zhang X, Song L. Large-scale foundation model on single-cell transcriptomics. Nat Methods 2024; 21:1481-1491. [PMID: 38844628 DOI: 10.1038/s41592-024-02305-7] [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: 06/02/2023] [Accepted: 05/10/2024] [Indexed: 08/10/2024]
Abstract
Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.
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Affiliation(s)
- Minsheng Hao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
- BioMap, Beijing, China
| | | | | | | | | | | | | | - Jianzhu Ma
- Department of Electrical Engineering, Tsinghua University, Beijing, China.
- Institute for AI Industry Research, Tsinghua University, Beijing, China.
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
| | - Le Song
- BioMap, Beijing, China.
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
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21
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Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, Wang B. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 2024; 21:1470-1480. [PMID: 38409223 DOI: 10.1038/s41592-024-02201-0] [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: 07/12/2023] [Accepted: 01/30/2024] [Indexed: 02/28/2024]
Abstract
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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Affiliation(s)
- Haotian Cui
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Chloe Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Hassaan Maan
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Kuan Pang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Fengning Luo
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Nan Duan
- Microsoft Research, Redmond, WA, USA
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- AI Hub, University Health Network, Toronto, Ontario, Canada.
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22
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Cesnik A, Schaffer LV, Gaur I, Jain M, Ideker T, Lundberg E. Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes. Annu Rev Biomed Data Sci 2024; 7:369-389. [PMID: 38748859 PMCID: PMC11343683 DOI: 10.1146/annurev-biodatasci-102423-113534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.
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Affiliation(s)
- Anthony Cesnik
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Leah V Schaffer
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Ishan Gaur
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Mayank Jain
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Trey Ideker
- Departments of Computer Science and Engineering and Bioengineering, University of California San Diego, La Jolla, California, USA
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Emma Lundberg
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Pathology, Stanford University, Palo Alto, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA;
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23
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Smail C, Montgomery SB. RNA Sequencing in Disease Diagnosis. Annu Rev Genomics Hum Genet 2024; 25:353-367. [PMID: 38360541 DOI: 10.1146/annurev-genom-021623-121812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
RNA sequencing (RNA-seq) enables the accurate measurement of multiple transcriptomic phenotypes for modeling the impacts of disease variants. Advances in technologies, experimental protocols, and analysis strategies are rapidly expanding the application of RNA-seq to identify disease biomarkers, tissue- and cell-type-specific impacts, and the spatial localization of disease-associated mechanisms. Ongoing international efforts to construct biobank-scale transcriptomic repositories with matched genomic data across diverse population groups are further increasing the utility of RNA-seq approaches by providing large-scale normative reference resources. The availability of these resources, combined with improved computational analysis pipelines, has enabled the detection of aberrant transcriptomic phenotypes underlying rare diseases. Further expansion of these resources, across both somatic and developmental tissues, is expected to soon provide unprecedented insights to resolve disease origin, mechanism of action, and causal gene contributions, suggesting the continued high utility of RNA-seq in disease diagnosis.
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Affiliation(s)
- Craig Smail
- Genomic Medicine Center, Children's Mercy Research Institute, Children's Mercy Kansas City, Kansas City, Missouri, USA;
| | - Stephen B Montgomery
- Department of Biomedical Data Science, Department of Genetics, and Department of Pathology, Stanford University School of Medicine, Stanford, California, USA;
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24
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Ghosh G, Shannon AE, Searle BC. Data acquisition approaches for single cell proteomics. Proteomics 2024:e2400022. [PMID: 39088833 DOI: 10.1002/pmic.202400022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/03/2024]
Abstract
Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.
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Affiliation(s)
- Gautam Ghosh
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Ariana E Shannon
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
| | - Brian C Searle
- Ohio State Biochemistry Program, The Ohio State University, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
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25
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Sarfraz I, Wang Y, Shastry A, Teh WK, Sokolov A, Herb BR, Creasy HH, Virshup I, Dries R, Degatano K, Mahurkar A, Schnell DJ, Madrigal P, Hilton J, Gehlenborg N, Tickle T, Campbell JD. MAMS: matrix and analysis metadata standards to facilitate harmonization and reproducibility of single-cell data. Genome Biol 2024; 25:205. [PMID: 39090672 PMCID: PMC11292877 DOI: 10.1186/s13059-024-03349-w] [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: 03/06/2023] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
Many datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While biospecimen and experimental information is often captured, detailed metadata standards related to data matrices and analysis workflows are currently lacking. To address this, we develop the matrix and analysis metadata standards (MAMS) to serve as a resource for data centers, repositories, and tool developers. We define metadata fields for matrices and parameters commonly utilized in analytical workflows and developed the rmams package to extract MAMS from single-cell objects. Overall, MAMS promotes the harmonization, integration, and reproducibility of single-cell data across platforms.
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Affiliation(s)
- Irzam Sarfraz
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Yichen Wang
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Amulya Shastry
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Wei Kheng Teh
- European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, Cambridgeshire, UK
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Brian R Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather H Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Isaac Virshup
- Department of Computational Health, Helmholtz Munich, Oberschleißheim, Germany
| | - Ruben Dries
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Kylee Degatano
- Data Sciences Platform, Broad Institute, Cambridge, MA, USA
| | - Anup Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Daniel J Schnell
- Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Pedro Madrigal
- European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, Cambridgeshire, UK
| | - Jason Hilton
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Nils Gehlenborg
- Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Timothy Tickle
- Data Sciences Platform, Broad Institute, Cambridge, MA, USA
| | - Joshua D Campbell
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
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26
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Huang J, Mao L, Lei Q, Guo AY. Bioinformatics tools and resources for cancer and application. Chin Med J (Engl) 2024:00029330-990000000-01162. [PMID: 39075637 DOI: 10.1097/cm9.0000000000003254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Indexed: 07/31/2024] Open
Abstract
ABSTRACT Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.
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Affiliation(s)
- Jin Huang
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lingzi Mao
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qian Lei
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - An-Yuan Guo
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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27
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Bernadskaya YY, Kuan A, Tjärnberg A, Brandenburg J, Zheng P, Wiechecki K, Kaplan N, Failla M, Bikou M, Madilian O, Wang W, Christiaen L. Cell cycle-driven transcriptome maturation confers multilineage competence to cardiopharyngeal progenitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.23.604718. [PMID: 39091743 PMCID: PMC11291048 DOI: 10.1101/2024.07.23.604718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
During development, stem and progenitor cells divide and transition through germ layer- and lineage-specific multipotent states to generate the diverse cell types that compose an animal. Defined changes in biomolecular composition underlie the progressive loss of potency and acquisition of lineage-specific characteristics. For example, multipotent cardiopharyngeal progenitors display multilineage transcriptional priming, whereby both the cardiac and pharyngeal muscle programs are partially active and coexist in the same progenitor cells, while their daughter cells engage in a cardiac or pharyngeal muscle differentiation path only after cell division. Here, using the tunicate Ciona, we studied the acquisition of multilineage competence and the coupling between fate decisions and cell cycle progression. We showed that multipotent cardiopharyngeal progenitors acquire the competence to produce distinct Tbx1/10 (+) and (-) daughter cells shortly before mitosis, which is necessary for Tbx1/10 activation. By combining transgene-based sample barcoding with single cell RNA-seq (scRNA-seq), we uncovered transcriptome-wide dynamics in migrating cardiopharyngeal progenitors as cells progress through G1, S and G2 phases. We termed this process "transcriptome maturation", and identified candidate "mature genes", including the Rho GAP-coding gene Depdc1 , which peak in late G2. Functional assays indicated that transcriptome maturation fosters cardiopharyngeal competence, in part through multilineage priming and proper oriented and asymmetric division that influences subsequent fate decisions, illustrating the concept of "behavioral competence". Both classic feedforward circuits and coupling with cell cycle progression drive transcriptome maturation, uncovering distinct levels of coupling between cell cycle progression and fateful molecular transitions. We propose that coupling competence and fate decision with the G2 and G1 phases, respectively, ensures the timely deployment of lineage-specific programs.
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28
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Feng S, Tellaetxe-Abete M, Zhang Y, Peng Y, Zhou H, Dong M, Larrea E, Xue L, Zhang L, Koziol MJ. Single-cell discovery of m 6A RNA modifications in the hippocampus. Genome Res 2024; 34:822-836. [PMID: 39009472 PMCID: PMC11293556 DOI: 10.1101/gr.278424.123] [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: 08/21/2023] [Accepted: 06/11/2024] [Indexed: 07/17/2024]
Abstract
N 6-Methyladenosine (m6A) is a prevalent and highly regulated RNA modification essential for RNA metabolism and normal brain function. It is particularly important in the hippocampus, where m6A is implicated in neurogenesis and learning. Although extensively studied, its presence in specific cell types remains poorly understood. We investigated m6A in the hippocampus at a single-cell resolution, revealing a comprehensive landscape of m6A modifications within individual cells. Through our analysis, we uncovered transcripts exhibiting a dense m6A profile, notably linked to neurological disorders such as Alzheimer's disease. Our findings suggest a pivotal role of m6A-containing transcripts, particularly in the context of CAMK2A neurons. Overall, this work provides new insights into the molecular mechanisms underlying hippocampal physiology and lays the foundation for future studies investigating the dynamic nature of m6A RNA methylation in the healthy and diseased brain.
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Affiliation(s)
- Shuangshuang Feng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
- Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing 102206, China
| | - Maitena Tellaetxe-Abete
- Intelligent Systems Group, Computer Science Faculty, University of the Basque Country, Donostia/San Sebastian 20018, Spain
| | - Yujie Zhang
- Chinese Institute for Brain Research, Beijing 102206, China
- Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing 102206, China
| | - Yan Peng
- Chinese Institute for Brain Research, Beijing 102206, China
- Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing 102206, China
- Peking University, Beijing, 100871, China
| | - Han Zhou
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Mingjie Dong
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Erika Larrea
- Chinese Institute for Brain Research, Beijing 102206, China
- Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing 102206, China
- Tsinghua University, Beijing 100084, China
| | - Liang Xue
- Chinese Institute for Brain Research, Beijing 102206, China
- Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing 102206, China
| | - Li Zhang
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Magdalena J Koziol
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China;
- Chinese Institute for Brain Research, Beijing 102206, China
- Research Unit of Medical Neurobiology, Chinese Academy of Medical Sciences, Beijing 102206, China
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29
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Wertman E. Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology. J Clin Med 2024; 13:4202. [PMID: 39064242 PMCID: PMC11277671 DOI: 10.3390/jcm13144202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/12/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024] Open
Abstract
Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper.
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Affiliation(s)
- Eli Wertman
- Department of Neurology, Hadassah University Hospital, The Hebrew University, Jerusalem 9190500, Israel;
- Section of Neuropsychology, Department of Psychology, The Hebrew University, Jerusalem 9190500, Israel
- Or’ad: Organization for Cognitive and Behavioral Changes in the Elderly, Jerusalem 9458118, Israel
- Merhav Neuropsychogeriatric Clinics, Nehalim 4995000, Israel
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30
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Grobecker P, Sakoparnig T, van Nimwegen E. Identifying cell states in single-cell RNA-seq data at statistically maximal resolution. PLoS Comput Biol 2024; 20:e1012224. [PMID: 38995959 DOI: 10.1371/journal.pcbi.1012224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/04/2024] [Indexed: 07/14/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.
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Affiliation(s)
- Pascal Grobecker
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Thomas Sakoparnig
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Erik van Nimwegen
- Biozentrum, University of Basel and Swiss Institute of Bioinformatics, Basel, Switzerland
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31
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Nitz AA, Giraldez Chavez JH, Eliason ZG, Payne SH. Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses. J Proteome Res 2024. [PMID: 38981598 DOI: 10.1021/acs.jproteome.4c00091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.
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Affiliation(s)
- Alyssa A Nitz
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | | | - Zachary G Eliason
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah 84602, United States
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32
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Ctortecka C, Clark NM, Boyle BW, Seth A, Mani DR, Udeshi ND, Carr SA. Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications. Nat Commun 2024; 15:5707. [PMID: 38977691 PMCID: PMC11231172 DOI: 10.1038/s41467-024-49651-w] [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: 01/24/2024] [Accepted: 06/14/2024] [Indexed: 07/10/2024] Open
Abstract
The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.
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Affiliation(s)
| | | | - Brian W Boyle
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - D R Mani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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33
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Verhey TB, Seo H, Gillmor A, Thoppey-Manoharan V, Schriemer D, Morrissy S. mosaicMPI: a framework for modular data integration across cohorts and -omics modalities. Nucleic Acids Res 2024; 52:e53. [PMID: 38813827 PMCID: PMC11229337 DOI: 10.1093/nar/gkae442] [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: 09/02/2023] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.
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Affiliation(s)
- Theodore B Verhey
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Heewon Seo
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Aaron Gillmor
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Varsha Thoppey-Manoharan
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - David Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Sorana Morrissy
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
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34
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Curion F, Rich-Griffin C, Agarwal D, Ouologuem S, Rue-Albrecht K, May L, Garcia GEL, Heumos L, Thomas T, Lason W, Sims D, Theis FJ, Dendrou CA. Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysis. Genome Biol 2024; 25:181. [PMID: 38978088 PMCID: PMC11229213 DOI: 10.1186/s13059-024-03322-7] [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: 03/14/2023] [Accepted: 06/25/2024] [Indexed: 07/10/2024] Open
Abstract
Single-cell multiomic analysis of the epigenome, transcriptome, and proteome allows for comprehensive characterization of the molecular circuitry that underpins cell identity and state. However, the holistic interpretation of such datasets presents a challenge given a paucity of approaches for systematic, joint evaluation of different modalities. Here, we present Panpipes, a set of computational workflows designed to automate multimodal single-cell and spatial transcriptomic analyses by incorporating widely-used Python-based tools to perform quality control, preprocessing, integration, clustering, and reference mapping at scale. Panpipes allows reliable and customizable analysis and evaluation of individual and integrated modalities, thereby empowering decision-making before downstream investigations.
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Affiliation(s)
- Fabiola Curion
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Charlotte Rich-Griffin
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Devika Agarwal
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
| | - Sarah Ouologuem
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Kevin Rue-Albrecht
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Lilly May
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
| | - Giulia E L Garcia
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Doctoral Training Centre, University of Oxford, Oxford, UK
| | - Lukas Heumos
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany
- Comprehensive Pneumology Center With the CPC-M bioArchive, Helmholtz Zentrum Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Tom Thomas
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Translational Gastroenterology Unit, University of Oxford, Oxford, UK
| | - Wojciech Lason
- Nuffield Department of Medicine, Respiratory Medicine Unit, Experimental Medicine Division, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - David Sims
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
| | - Calliope A Dendrou
- Nuffield Department of Medicine, Centre for Human Genetics, University of Oxford, Oxford, UK.
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
- NIHR Oxford Biomedical Research Centre, Oxford, UK.
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35
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Gondal MN, Shah SUR, Chinnaiyan AM, Cieslik M. A systematic overview of single-cell transcriptomics databases, their use cases, and limitations. FRONTIERS IN BIOINFORMATICS 2024; 4:1417428. [PMID: 39040140 PMCID: PMC11260681 DOI: 10.3389/fbinf.2024.1417428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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Affiliation(s)
- Mahnoor N. Gondal
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
| | - Saad Ur Rehman Shah
- Gies College of Business, University of Illinois Business College, Champaign, MI, United States
| | - Arul M. Chinnaiyan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Urology, University of Michigan, Ann Arbor, MI, United States
- Howard Hughes Medical Institute, Ann Arbor, MI, United States
- University of Michigan Rogel Cancer Center, Ann Arbor, MI, United States
| | - Marcin Cieslik
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI, United States
- Department of Pathology, University of Michigan, Ann Arbor, MI, United States
- University of Michigan Rogel Cancer Center, Ann Arbor, MI, United States
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36
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Alayoubi Y, Bentsen M, Looso M. Scanpro is a tool for robust proportion analysis of single-cell resolution data. Sci Rep 2024; 14:15581. [PMID: 38971877 PMCID: PMC11227528 DOI: 10.1038/s41598-024-66381-7] [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: 09/21/2023] [Accepted: 07/01/2024] [Indexed: 07/08/2024] Open
Abstract
In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.
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Affiliation(s)
- Yousef Alayoubi
- Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Mette Bentsen
- Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Mario Looso
- Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany.
- Cardio-Pulmonary Institute (CPI), Bad Nauheim, Germany.
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37
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Aguirre M, Spence JP, Sella G, Pritchard JK. Gene regulatory network structure informs the distribution of perturbation effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602130. [PMID: 39005431 PMCID: PMC11245109 DOI: 10.1101/2024.07.04.602130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.
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Affiliation(s)
- Matthew Aguirre
- Department of Biomedical Data Science, Stanford University, Stanford CA
| | | | - Guy Sella
- Department of Biological Sciences, Columbia University, New York NY
- Program for Mathematical Genomics, Columbia University, New York NY
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford CA
- Department of Biology, Stanford University, Stanford CA
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Nussinov R, Yavuz BR, Demirel HC, Arici MK, Jang H, Tuncbag N. Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide. Front Cell Dev Biol 2024; 12:1376639. [PMID: 39015651 PMCID: PMC11249571 DOI: 10.3389/fcell.2024.1376639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/10/2024] [Indexed: 07/18/2024] Open
Abstract
The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel
| | - Bengi Ruken Yavuz
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | | | - M. Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Türkiye
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD, United States
| | - Nurcan Tuncbag
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Türkiye
- School of Medicine, Koc University, Istanbul, Türkiye
- Koc University Research Center for Translational Medicine (KUTTAM), Istanbul, Türkiye
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Derisoud E, Jiang H, Zhao A, Chavatte-Palmer P, Deng Q. Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies. Hum Reprod Update 2024; 30:410-441. [PMID: 38478759 PMCID: PMC11215163 DOI: 10.1093/humupd/dmae006] [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: 05/18/2023] [Revised: 02/12/2024] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta's role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types. OBJECTIVE AND RATIONALE We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases. SEARCH METHODS We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types. OUTCOMES Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation. WIDER IMPLICATIONS This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.
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Affiliation(s)
- Emilie Derisoud
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Hong Jiang
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Allan Zhao
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Pascale Chavatte-Palmer
- INRAE, BREED, Université Paris-Saclay, UVSQ, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
| | - Qiaolin Deng
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Solna, Stockholm, Sweden
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Wu X, Xu W, Deng L, Li Y, Wang Z, Sun L, Gao A, Wang H, Yang X, Wu C, Zou Y, Yan K, Liu Z, Zhang L, Du G, Yang L, Lin D, Yue J, Wang P, Han Y, Fu Z, Dai J, Cao G. Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing. Nat Biomed Eng 2024; 8:872-889. [PMID: 38745110 DOI: 10.1038/s41551-024-01205-7] [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: 12/29/2022] [Accepted: 04/01/2024] [Indexed: 05/16/2024]
Abstract
Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.
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Affiliation(s)
- Xiaofeng Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Weize Xu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lulu Deng
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yue Li
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Zhongchao Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Leqiang Sun
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Anran Gao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Haoqi Wang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Xiaodan Yang
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chengchao Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Yanyan Zou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Keji Yan
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Zhixiang Liu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Lingkai Zhang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Guohua Du
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Liyao Yang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Da Lin
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China
| | - Junqiu Yue
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ping Wang
- Britton Chance Centre for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Centre for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yunyun Han
- School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhenfang Fu
- Department of Pathology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
| | - Jinxia Dai
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
| | - Gang Cao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, China.
- College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, China.
- Faculty of Life and Health Sciences, and Shenzhen-Hong Kong Institute of Brain Science and The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
- NMPA Key Laboratory for Research and Evaluation of Viral Vector Technology in Cell and Gene Therapy Medicinal Products, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [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: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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Otto DJ, Jordan C, Dury B, Dien C, Setty M. Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon. Nat Methods 2024; 21:1185-1195. [PMID: 38890426 DOI: 10.1038/s41592-024-02302-w] [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: 07/11/2023] [Accepted: 05/08/2024] [Indexed: 06/20/2024]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive diverse biological processes. Here, we present Mellon, an algorithm for estimation of cell-state densities from high-dimensional representations of single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. We present evidence implicating enhancer priming and the activation of master regulators in emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during developmental processes. Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide insights into mechanisms guiding biological trajectories.
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Affiliation(s)
- Dominik J Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Gao SM, Qi Y, Zhang Q, Guan Y, Lee YT, Ding L, Wang L, Mohammed AS, Li H, Fu Y, Wang MC. Aging atlas reveals cell-type-specific effects of pro-longevity strategies. NATURE AGING 2024; 4:998-1013. [PMID: 38816550 PMCID: PMC11257944 DOI: 10.1038/s43587-024-00631-1] [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: 04/12/2023] [Accepted: 04/10/2024] [Indexed: 06/01/2024]
Abstract
Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues and how those lifespan-extending strategies slow tissue aging in distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) of wild-type and long-lived strains. We discovered cell-specific, age-related molecular and functional signatures across all somatic and germ cell types. We developed transcriptomic aging clocks for different tissues and quantitatively determined how three different pro-longevity strategies slow tissue aging distinctively. Furthermore, through genome-wide profiling of alternative polyadenylation (APA) events in different tissues, we discovered cell-type-specific APA changes during aging and revealed how these changes are differentially affected by the pro-longevity strategies. Together, this study offers fundamental molecular insights into both somatic and reproductive aging and provides a valuable resource for in-depth understanding of the diversity of pro-longevity mechanisms.
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Affiliation(s)
- Shihong Max Gao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Program in Developmental Biology, Baylor College of Medicine, Houston, TX, USA
| | - Yanyan Qi
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Qinghao Zhang
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA
| | - Youchen Guan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Molecular and Cellular Biology Graduate Program, Baylor College of Medicine, Houston, TX, USA
| | - Yi-Tang Lee
- Integrative Program of Molecular and Biochemical Science, Baylor College of Medicine, Houston, TX, USA
| | - Lang Ding
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Graduate Program in Chemical, Physical & Structural Biology, Graduate School of Biomedical Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Lihua Wang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Aaron S Mohammed
- Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE, USA
| | - Hongjie Li
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA.
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Yusi Fu
- Huffington Center on Aging, Baylor College of Medicine, Houston, TX, USA.
- Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE, USA.
| | - Meng C Wang
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.
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Weber S, Unger K, Alunni-Fabbroni M, Hirner-Eppeneder H, Öcal E, Zitzelsberger H, Mayerle J, Malfertheiner P, Ricke J. Metabolomic Analysis of Human Cirrhosis and Hepatocellular Carcinoma: A Pilot Study. Dig Dis Sci 2024; 69:2488-2501. [PMID: 38652389 PMCID: PMC11258188 DOI: 10.1007/s10620-024-08446-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Molecular changes in HCC development are largely unknown. As the liver plays a fundamental role in the body's metabolism, metabolic changes are to be expected. AIMS We aimed to identify metabolomic changes in HCC in comparison to liver cirrhosis (LC) patients, which could potentially serve as novel biomarkers for HCC diagnosis and prognosis. METHODS Metabolite expression from 38 HCC from the SORAMIC trial and 32 LC patients were analyzed by mass spectrometry. Metabolites with significant differences between LC and HCC at baseline were analyzed regarding expression over follow-up. In addition, association with overall survival was tested using univariate Cox proportional-hazard analysis. RESULTS 41 metabolites showed differential expression between LC and HCC patients. 14 metabolites demonstrated significant changes in HCC patients during follow-up. Campesterol, lysophosphatidylcholine, octadecenoic and octadecadienoic acid, and furoylglycine showed a differential expression in the local ablation vs. palliative care group. High expression of eight metabolites (octadecenoic acid, 2-hydroxybutyrate, myo-inositol, isocitrate, erythronic acid, creatinine, pseudouridine, and erythrol) were associated with poor overall survival. The association between poor OS and octadecenoic acid and creatinine remained statistically significant even after adjusting for tumor burden and LC severity. CONCLUSION Our findings give promising insides into the metabolic changes during HCC carcinogenesis and provide candidate biomarkers for future studies. Campesterol and furoylglycine in particular were identified as possible biomarkers for HCC progression. Moreover, eight metabolites were detected as predictors for poor overall survival.
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Affiliation(s)
- Sabine Weber
- Department of Medicine II, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - Kristian Unger
- Research Unit Radiation Cytogenetics, Helmholtz Centre Munich, 85622, Neuherberg, Germany
- Department of Radiation Oncology, University Hospital, LMU Munich, 81377, Munich, Germany
| | | | | | - Elif Öcal
- Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany
| | - Horst Zitzelsberger
- Research Unit Radiation Cytogenetics, Helmholtz Centre Munich, 85622, Neuherberg, Germany
| | - Julia Mayerle
- Department of Medicine II, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Peter Malfertheiner
- Department of Medicine II, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Jens Ricke
- Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany
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Mitani TT, Susaki EA, Matsumoto K, Ueda HR. Realization of cellomics to dive into the whole-body or whole-organ cell cloud. Nat Methods 2024; 21:1138-1142. [PMID: 38871985 DOI: 10.1038/s41592-024-02307-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Affiliation(s)
- Tomoki T Mitani
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
- Department of Systems Biology, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Neurology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Etsuo A Susaki
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
- Department of Biochemistry and Systems Biomedicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Nakatani Biomedical Spatialomics Hub, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Katsuhiko Matsumoto
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan
- Department of Systems Pharmacology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Hiroki R Ueda
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Osaka, Japan.
- Department of Systems Pharmacology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan.
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Petrova K, Tretiakov M, Kotov A, Monsoro-Burq AH, Peshkin L. A new atlas to study embryonic cell types in Xenopus. Dev Biol 2024; 511:76-83. [PMID: 38614285 PMCID: PMC11315121 DOI: 10.1016/j.ydbio.2024.04.003] [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: 01/25/2024] [Revised: 04/06/2024] [Accepted: 04/08/2024] [Indexed: 04/15/2024]
Abstract
This paper introduces a single-cell atlas for pivotal developmental stages in Xenopus, encompassing gastrulation, neurulation, and early tailbud. Notably surpassing its predecessors, the new atlas enhances gene mapping, read counts, and gene/cell type nomenclature. Leveraging the latest Xenopus tropicalis genome version, alongside advanced alignment pipelines and machine learning for cell type assignment, this release maintains consistency with previous cell type annotations while rectifying nomenclature issues. Employing an unbiased approach for cell type assignment proves especially apt for embryonic contexts, given the considerable number of non-terminally differentiated cell types. An alternative cell type attribution here adopts a fuzzy, non-deterministic stance, capturing the transient nature of early embryo progenitor cells by presenting an ensemble of types in superposition. The value of the new resource is emphasized through numerous examples, with a focus on previously unexplored germ cell populations where we uncover novel transcription onset features. Offering interactive exploration via a user-friendly web portal and facilitating complete data downloads, this atlas serves as a comprehensive and accessible reference.
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Affiliation(s)
- Kseniya Petrova
- Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | | | - Aleksandr Kotov
- Université Paris Saclay, Faculté des Sciences d'Orsay, CNRS UMR 3347, INSERM, U1021, Orsay, France; Institut Curie, PSL Research University, CNRS UMR 3347, INSERM U1021, F-91405, Orsay, France
| | - Anne H Monsoro-Burq
- Université Paris Saclay, Faculté des Sciences d'Orsay, CNRS UMR 3347, INSERM, U1021, Orsay, France; Institut Curie, PSL Research University, CNRS UMR 3347, INSERM U1021, F-91405, Orsay, France; Institut Universitaire de France, F-75005, Paris, France
| | - Leonid Peshkin
- Systems Biology, Harvard Medical School, Boston, MA, 02115, USA; Marine Biological Laboratory, Woods Hole, MA, 02543, USA.
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Ertürk A. Deep 3D histology powered by tissue clearing, omics and AI. Nat Methods 2024; 21:1153-1165. [PMID: 38997593 DOI: 10.1038/s41592-024-02327-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 05/28/2024] [Indexed: 07/14/2024]
Abstract
To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.
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Affiliation(s)
- Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany.
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University, Munich, Germany.
- School of Medicine, Koç University, İstanbul, Turkey.
- Deep Piction GmbH, Munich, Germany.
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Hoekx CA, Martinez-Tellez B, Straat ME, Verkleij MMA, Kemmeren M, Kooijman S, Uhrbom M, de Jager SCA, Rensen PCN, Boon MR. Cold exposure increases circulating fibroblast growth factor 21 in the evening in males and females. Endocr Connect 2024; 13:e240074. [PMID: 38781402 PMCID: PMC11227058 DOI: 10.1530/ec-24-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 05/23/2024] [Indexed: 05/25/2024]
Abstract
Objectives Cold exposure is linked to cardiometabolic benefits. Cold activates brown adipose tissue (BAT), increases energy expenditure, and induces secretion of the hormones fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15). The cold-induced increase in energy expenditure exhibits a diurnal rhythm in men. Therefore, we aimed to investigate the effect of cold exposure on serum FGF21 and GDF15 levels in humans and whether cold-induced changes in FGF21 and GDF15 levels differ between morning and evening in males and females. Method In this randomized cross-over study, serum FGF21 and GDF15 levels were measured in healthy lean males (n = 12) and females (n = 12) before, during, and after 90 min of stable cold exposure in the morning (07:45 h) and evening (19:45 h) with a 1-day washout period in between. Results Cold exposure increased FGF21 levels in the evening compared to the morning both in males (+61% vs -13%; P < 0.001) and in females (+58% vs +8%; P < 0.001). In contrast, cold exposure did not significantly modify serum GDF15 levels, and no diurnal variation was found. Changes in FGF21 and GDF15 levels did not correlate with changes in cold-induced energy expenditure in the morning and evening. Conclusion Cold exposure increased serum FGF21 levels in the evening, but not in the morning, in both males and females. GDF15 levels were not affected by cold exposure. Thus, this study suggests that the timing of cold exposure may influence cold-induced changes in FGF21 levels but not GDF15 levels and seems to be independent of changes in energy expenditure.
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Affiliation(s)
- Carlijn A Hoekx
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Borja Martinez-Tellez
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Nursing Physiotherapy and Medicine, SPORT Research Group (CTS-1024), CERNEP Research Center, University of Almería, Almería, Spain
- Biomedical Research Unit, Torrecárdenas University Hospital, Almería, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Granada, Spain
| | - Maaike E Straat
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Magdalena M A Verkleij
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Mirjam Kemmeren
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Sander Kooijman
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Martin Uhrbom
- Bioscience Metabolism, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca Gothenburg, Sweden
- Department of Medicine, Huddinge, Karolinska Institutet Campus Flemingsberg, Neo Building, Huddinge, Sweden
| | - Saskia C A de Jager
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Patrick C N Rensen
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Mariëtte R Boon
- Division of Endocrinology, Department of Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
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Liu A, Peng B, Pankajam AV, Duong TE, Pryhuber G, Scheuermann RH, Zhang Y. Discovery of optimal cell type classification marker genes from single cell RNA sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590194. [PMID: 38712147 PMCID: PMC11071431 DOI: 10.1101/2024.04.22.590194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.
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Affiliation(s)
- Angela Liu
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
| | - Beverly Peng
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
| | - Ajith V Pankajam
- Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of America
| | - Thu Elizabeth Duong
- Department of Pediatrics, Division of Respiratory Medicine, University of California, San Diego, La Jolla, CA, United States of America
| | - Gloria Pryhuber
- Department of Pediatrics, University of Rochester Medical Center, Rochester, NY, United States of America
| | - Richard H Scheuermann
- Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of America
| | - Yun Zhang
- Department of Informatics, J. Craig Venter Institute, La Jolla, CA, United States of America
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Nishide M, Shimagami H, Kumanogoh A. Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice. Nat Rev Immunol 2024:10.1038/s41577-024-01043-3. [PMID: 38914790 DOI: 10.1038/s41577-024-01043-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/26/2024]
Abstract
Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.
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Affiliation(s)
- Masayuki Nishide
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
| | - Hiroshi Shimagami
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan.
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Osaka, Japan.
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Osaka, Japan.
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