1
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Sun S, Jiang M, Ma S, Ren J, Liu GH. Exploring the heterogeneous targets of metabolic aging at single-cell resolution. Trends Endocrinol Metab 2024:S1043-2760(24)00190-5. [PMID: 39181730 DOI: 10.1016/j.tem.2024.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 07/09/2024] [Accepted: 07/12/2024] [Indexed: 08/27/2024]
Abstract
Our limited understanding of metabolic aging poses major challenges to comprehending the diverse cellular alterations that contribute to age-related decline, and to devising targeted interventions. This review provides insights into the heterogeneous nature of cellular metabolism during aging and its response to interventions, with a specific focus on cellular heterogeneity and its implications. By synthesizing recent findings using single-cell approaches, we explored the vulnerabilities of distinct cell types and key metabolic pathways. Delving into the cell type-specific alterations underlying the efficacy of systemic interventions, we also discuss the complexity of integrating single-cell data and advocate for leveraging computational tools and artificial intelligence to harness the full potential of these data, develop effective strategies against metabolic aging, and promote healthy aging.
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Affiliation(s)
- Shuhui Sun
- Beijing Anzhen Hospital, Capital Medical University, Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing 100029, China.
| | - Mengmeng Jiang
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China
| | - Shuai Ma
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Aging Biomarker Consortium, Beijing 100101, China.
| | - Jie Ren
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Aging Biomarker Consortium, Beijing 100101, China; Key Laboratory of RNA Innovation, Science and Engineering, China National Center for Bioinformation, Beijing 100101, China; Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 101408, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Guang-Hui Liu
- Key Laboratory of Organ Regeneration and Reconstruction, State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China; Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Aging Biomarker Consortium, Beijing 100101, China; Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China; Aging Translational Medicine Center, Xuanwu Hospital, Capital Medical University, Beijing 100053, China.
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2
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Cao X, Huang YA, You ZH, Shang X, Hu L, Hu PW, Huang ZA. scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data. Genome Biol 2024; 25:207. [PMID: 39103856 DOI: 10.1186/s13059-024-03357-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: 10/26/2023] [Accepted: 07/29/2024] [Indexed: 08/07/2024] Open
Abstract
Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.
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Affiliation(s)
- Xiyue Cao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zhi-An Huang
- Research Office, City University of Hong Kong (Dongguan), Dongguan, 523000, China
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3
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Fischer F, Fischer DS, Mukhin R, Isaev A, Biederstedt E, Villani AC, Theis FJ. scTab: Scaling cross-tissue single-cell annotation models. Nat Commun 2024; 15:6611. [PMID: 39098889 PMCID: PMC11298532 DOI: 10.1038/s41467-024-51059-5] [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: 11/26/2023] [Accepted: 07/25/2024] [Indexed: 08/06/2024] Open
Abstract
Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.
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Affiliation(s)
- Felix Fischer
- Department of Computational Health, Institute of Computational Biology, Helmholtz, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - David S Fischer
- Department of Computational Health, Institute of Computational Biology, Helmholtz, Munich, Germany
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | | | | | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA
| | - Fabian J Theis
- Department of Computational Health, Institute of Computational Biology, Helmholtz, 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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4
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Saiki N, Nio Y, Yoneyama Y, Kawamura S, Iwasawa K, Kawakami E, Araki K, Fukumura J, Sakairi T, Kono T, Ohmura R, Koido M, Funata M, Thompson WL, Cruz-Encarnacion P, Chen YW, Takebe T. Self-Organization of Sinusoidal Vessels in Pluripotent Stem Cell-derived Human Liver Bud Organoids. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.02.601804. [PMID: 39005378 PMCID: PMC11245015 DOI: 10.1101/2024.07.02.601804] [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
The induction of tissue-specific vessels in in vitro living tissue systems remains challenging. Here, we directly differentiated human pluripotent stem cells into CD32b+ putative liver sinusoidal progenitors (iLSEP) by dictating developmental pathways. By devising an inverted multilayered air-liquid interface (IMALI) culture, hepatic endoderm, septum mesenchyme, arterial and sinusoidal quadruple progenitors self-organized to generate and sustain hepatocyte-like cells neighbored by divergent endothelial subsets composed of CD32blowCD31high, LYVE1+STAB1+CD32bhighCD31lowTHBD-vWF-, and LYVE1-THBD+vWF+ cells. Wnt2 mediated sinusoidal-to-hepatic intercellular crosstalk potentiates hepatocyte differentiation and branched endothelial network formation. Intravital imaging revealed iLSEP developed fully patent human vessels with functional sinusoid-like features. Organoid-derived hepatocyte- and sinusoid-derived coagulation factors enabled correction of in vitro clotting time with Factor V, VIII, IX, and XI deficient patients' plasma and rescued the severe bleeding phenotype in hemophilia A mice upon transplantation. Advanced organoid vascularization technology allows for interrogating key insights governing organ-specific vessel development, paving the way for coagulation disorder therapeutics.
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Affiliation(s)
- Norikazu Saiki
- Institute of Research, Tokyo Medical and Dental University (TMDU), Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Yasunori Nio
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Yosuke Yoneyama
- Institute of Research, Tokyo Medical and Dental University (TMDU), Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Shuntaro Kawamura
- Institute of Research, Tokyo Medical and Dental University (TMDU), Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kentaro Iwasawa
- Division of Gastroenterology, Hepatology and Nutrition & Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, USA
| | - Eri Kawakami
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Kohei Araki
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Junko Fukumura
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Tsuyoshi Sakairi
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Tamaki Kono
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Rio Ohmura
- Institute of Research, Tokyo Medical and Dental University (TMDU), Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Masaru Koido
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Masaaki Funata
- T-CiRA Discovery, Takeda Pharmaceutical Company Ltd, Fujisawa, Kanagawa 251-8555, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
| | - Wendy L. Thompson
- Division of Gastroenterology, Hepatology and Nutrition & Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, USA
| | | | - Ya-Wen Chen
- Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY
- Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY
- Institute for Regenerative Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- Institute for Airway Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
- Center for Epithelial and Airway Biology and Regeneration, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Takanori Takebe
- Institute of Research, Tokyo Medical and Dental University (TMDU), Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Organoid Medicine project, T-CiRA joint program, Fujisawa, Kanagawa 251-8555, Japan
- Division of Gastroenterology, Hepatology and Nutrition & Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, USA
- The Center for Stem Cell and Organoid Medicine (CuSTOM), Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, 3333 Burnet Avenue, Cincinnati, OH 45229-3039, USA
- Communication Design Center, Advanced Medical Research Center, Yokohama City University, Yokohama, Kanagawa, Japan
- Department of Genome Biology, Graduate School of Medicine, and Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka 565-0871, Japan
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5
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Huang K, Xu Y, Feng T, Lan H, Ling F, Xiang H, Liu Q. The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research. BIOLOGY 2024; 13:451. [PMID: 38927331 PMCID: PMC11200756 DOI: 10.3390/biology13060451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
Single-cell RNA sequencing technology (scRNA-seq) has been steadily developing since its inception in 2009. Unlike bulk RNA-seq, scRNA-seq identifies the heterogeneity of tissue cells and reveals gene expression changes in individual cells at the microscopic level. Here, we review the development of scRNA-seq, which has gone through iterations of reverse transcription, in vitro transcription, smart-seq, drop-seq, 10 × Genomics, and spatial single-cell transcriptome technologies. The technology of 10 × Genomics has been widely applied in medicine and biology, producing rich research results. Furthermore, this review presents a summary of the analytical process for single-cell transcriptome data and its integration with other omics analyses, including genomes, epigenomes, proteomes, and metabolomics. The single-cell transcriptome has a wide range of applications in biology and medicine. This review analyzes the applications of scRNA-seq in cancer, stem cell research, developmental biology, microbiology, and other fields. In essence, scRNA-seq provides a means of elucidating gene expression patterns in single cells, thereby offering a valuable tool for scientific research. Nevertheless, the current single-cell transcriptome technology is still imperfect, and this review identifies its shortcomings and anticipates future developments. The objective of this review is to facilitate a deeper comprehension of scRNA-seq technology and its applications in biological and medical research, as well as to identify avenues for its future development in alignment with practical needs.
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Affiliation(s)
- Kongwei Huang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan 528225, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510641, China
| | - Yixue Xu
- Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Animal Science and Technology, Guangxi University, Nanning 530005, China;
| | - Tong Feng
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular Imaging, Center for Artificial Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hong Lan
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan 528225, China
| | - Fei Ling
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510641, China
| | - Hai Xiang
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan 528225, China
| | - Qingyou Liu
- Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan 528225, China
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6
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Lotfollahi M, Yuhan Hao, Theis FJ, Satija R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 2024; 187:2343-2358. [PMID: 38729109 PMCID: PMC11184658 DOI: 10.1016/j.cell.2024.03.009] [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: 01/05/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 05/12/2024]
Abstract
As the number of single-cell datasets continues to grow rapidly, workflows that map new data to well-curated reference atlases offer enormous promise for the biological community. In this perspective, we discuss key computational challenges and opportunities for single-cell reference-mapping algorithms. We discuss how mapping algorithms will enable the integration of diverse datasets across disease states, molecular modalities, genetic perturbations, and diverse species and will eventually replace manual and laborious unsupervised clustering pipelines.
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Affiliation(s)
- Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich - German Research Center for Environmental Health, Neuherberg, Germany; Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK; Department of Mathematics, Technical University of Munich, Garching, Germany.
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA; New York Genome Center, New York, NY, USA.
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7
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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8
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Shen X, Li X. Deep-learning methods for unveiling large-scale single-cell transcriptomes. Cancer Biol Med 2024; 20:j.issn.2095-3941.2023.0436. [PMID: 38318925 PMCID: PMC10845931 DOI: 10.20892/j.issn.2095-3941.2023.0436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 12/20/2023] [Indexed: 02/07/2024] Open
Affiliation(s)
- Xilin Shen
- Tianjin Cancer Institute, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin 300060, China
| | - Xiangchun Li
- Tianjin Cancer Institute, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin 300060, China
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9
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Møller AF, Madsen JGS. JOINTLY: interpretable joint clustering of single-cell transcriptomes. Nat Commun 2023; 14:8473. [PMID: 38123569 PMCID: PMC10733431 DOI: 10.1038/s41467-023-44279-8] [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: 07/14/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can be masked by technical variation. Here, we present JOINTLY, an algorithm enabling joint clustering of sxRNA-seq datasets across batches. JOINTLY performs on par or better than state-of-the-art batch integration methods in clustering tasks and outperforms other intrinsically interpretable methods. We demonstrate that JOINTLY is robust against over-correction while retaining subtle cell state differences between biological conditions and highlight how the interpretation of JOINTLY can be used to annotate cell types and identify active signalling programs across cell types and pseudo-time. Finally, we use JOINTLY to construct a reference atlas of white adipose tissue (WATLAS), an expandable and comprehensive community resource, in which we describe four adipocyte subpopulations and map compositional changes in obesity and between depots.
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Affiliation(s)
- Andreas Fønss Møller
- Institute of Biochemistry and Molecular Biology, University of Southern, Odense, Denmark
- Sino-Danish College (SDC), University of Chinese Academy of Sciences, Beijing, China
| | - Jesper Grud Skat Madsen
- Institute of Biochemistry and Molecular Biology, University of Southern, Odense, Denmark.
- Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
- Center for Functional Genomics and Tissue Plasticity (ATLAS), Odense M, 5230, Denmark.
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
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10
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Ramirez Flores RO, Lanzer JD, Dimitrov D, Velten B, Saez-Rodriguez J. Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease. eLife 2023; 12:e93161. [PMID: 37991480 PMCID: PMC10718529 DOI: 10.7554/elife.93161] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023] Open
Abstract
Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
| | - Jan David Lanzer
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
| | - Britta Velten
- Heidelberg University, Centre for Organismal Studies, Centre for Scientific ComputingHeidelbergGermany
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, BioQuantHeidelbergGermany
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