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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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2
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Hasanaj E, Mathur S, Bar-Joseph Z. Integrating patients in time series clinical transcriptomics data. Bioinformatics 2024; 40:i151-i159. [PMID: 38940139 PMCID: PMC11256926 DOI: 10.1093/bioinformatics/btae241] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Analysis of time series transcriptomics data from clinical trials is challenging. Such studies usually profile very few time points from several individuals with varying response patterns and dynamics. Current methods for these datasets are mainly based on linear, global orderings using visit times which do not account for the varying response rates and subgroups within a patient cohort. RESULTS We developed a new method that utilizes multi-commodity flow algorithms for trajectory inference in large scale clinical studies. Recovered trajectories satisfy individual-based timing restrictions while integrating data from multiple patients. Testing the method on multiple drug datasets demonstrated an improved performance compared to prior approaches suggested for this task, while identifying novel disease subtypes that correspond to heterogeneous patient response patterns. AVAILABILITY AND IMPLEMENTATION The source code and instructions to download the data have been deposited on GitHub at https://github.com/euxhenh/Truffle.
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Affiliation(s)
- Euxhen Hasanaj
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Sachin Mathur
- R&D Data and Computational Sciences, Sanofi, Cambridge, MA 02141, United States
| | - Ziv Bar-Joseph
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- R&D Data and Computational Sciences, Sanofi, Cambridge, MA 02141, United States
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
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3
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Pellecchia S, Franchini M, Viscido G, Arnese R, Gambardella G. Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer. Genome Med 2024; 16:55. [PMID: 38605363 PMCID: PMC11008053 DOI: 10.1186/s13073-024-01327-2] [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: 05/02/2023] [Accepted: 03/29/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses. METHODS To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR. RESULTS Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells. CONCLUSIONS Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.
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Affiliation(s)
- Simona Pellecchia
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Scuola Superiore Meridionale, Genomics and Experimental Medicine Program, Naples, Italy
| | - Melania Franchini
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
| | - Gaetano Viscido
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Chemical, Materials and Industrial Engineering , University of Naples Federico II, Naples, Italy
| | - Riccardo Arnese
- Telethon Institute of Genetics and Medicine, Naples, Italy
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
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4
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Bouman BJ, Demerdash Y, Sood S, Grünschläger F, Pilz F, Itani AR, Kuck A, Marot-Lassauzaie V, Haas S, Haghverdi L, Essers MA. Single-cell time series analysis reveals the dynamics of HSPC response to inflammation. Life Sci Alliance 2024; 7:e202302309. [PMID: 38110222 PMCID: PMC10728485 DOI: 10.26508/lsa.202302309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/08/2023] [Accepted: 12/11/2023] [Indexed: 12/20/2023] Open
Abstract
Hematopoietic stem and progenitor cells (HSPCs) are known to respond to acute inflammation; however, little is understood about the dynamics and heterogeneity of these stress responses in HSPCs. Here, we performed single-cell sequencing during the sensing, response, and recovery phases of the inflammatory response of HSPCs to treatment (a total of 10,046 cells from four time points spanning the first 72 h of response) with the pro-inflammatory cytokine IFNα to investigate the HSPCs' dynamic changes during acute inflammation. We developed the essential novel computational approaches to process and analyze the resulting single-cell time series dataset. This includes an unbiased cell type annotation and abundance analysis post inflammation, tools for identification of global and cell type-specific responding genes, and a semi-supervised linear regression approach for response pseudotime reconstruction. We discovered a variety of different gene responses of the HSPCs to the treatment. Interestingly, we were able to associate a global reduced myeloid differentiation program and a locally enhanced pyroptosis activity with reduced myeloid progenitor and differentiated cells after IFNα treatment. Altogether, the single-cell time series analyses have allowed us to unbiasedly study the heterogeneous and dynamic impact of IFNα on the HSPCs.
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Affiliation(s)
- Brigitte J Bouman
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Yasmin Demerdash
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
| | - Shubhankar Sood
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
| | - Florian Grünschläger
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
- Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Franziska Pilz
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
| | - Abdul R Itani
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Faculty of Biosciences, University of Heidelberg, Heidelberg, Germany
| | - Andrea Kuck
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
| | - Valérie Marot-Lassauzaie
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
- Charité-Universitätsmedizin, Berlin, Germany
| | - Simon Haas
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- Department of Hematology, Oncology and Cancer Immunology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
- Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
- Charité-Universitätsmedizin, Berlin, Germany
| | - Laleh Haghverdi
- Berlin Institute for Medical Systems Biology, Max Delbrück Center in the Helmholtz Association, Berlin, Germany
| | - Marieke Ag Essers
- Division Inflammatory Stress in Stem Cells, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM gGMBH), Heidelberg, Germany
- DKFZ-ZMBH Alliance, Heidelberg, Germany
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5
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Yakubovich E, Cook DP, Rodriguez GM, Vanderhyden BC. Mesenchymal ovarian cancer cells promote CD8 + T cell exhaustion through the LGALS3-LAG3 axis. NPJ Syst Biol Appl 2023; 9:61. [PMID: 38086828 PMCID: PMC10716312 DOI: 10.1038/s41540-023-00322-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Cancer cells often metastasize by undergoing an epithelial-mesenchymal transition (EMT). Although abundance of CD8+ T-cells in the tumor microenvironment correlates with improved survival, mesenchymal cancer cells acquire greater resistance to antitumor immunity in some cancers. We hypothesized the EMT modulates the immune response to ovarian cancer. Here we show that cancer cells from infiltrated/inflamed tumors possess more mesenchymal cells, than excluded and desert tumors. We also noted high expression of LGALS3 is associated with EMT in vivo, a finding validated with in vitro EMT models. Dissecting the cellular communications among populations in the tumor revealed that mesenchymal cancer cells in infiltrated tumors communicate through LGALS3 to LAG3 receptor expressed by CD8+ T cells. We found CD8+ T cells express high levels of LAG3, a marker of T cell exhaustion. The results indicate that EMT in ovarian cancer cells promotes interactions between cancer cells and T cells through the LGALS3 - LAG3 axis, which could increase T cell exhaustion in infiltrated tumors, dampening antitumor immunity.
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Affiliation(s)
- Edward Yakubovich
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.
- Cancer Therapeutics Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
- Center for Infection, Immunity and Inflammation, University of Ottawa, Ottawa, ON, Canada.
| | - David P Cook
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
- Cancer Therapeutics Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Galaxia M Rodriguez
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
- Cancer Therapeutics Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Center for Infection, Immunity and Inflammation, University of Ottawa, Ottawa, ON, Canada
| | - Barbara C Vanderhyden
- Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
- Cancer Therapeutics Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
- Center for Infection, Immunity and Inflammation, University of Ottawa, Ottawa, ON, Canada
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6
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Otto D, Jordan C, Dury B, Dien C, Setty M. Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.09.548272. [PMID: 37502954 PMCID: PMC10369887 DOI: 10.1101/2023.07.09.548272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the 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 the inherently continuous developmental processes. Scalable and adaptable, 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 new insights into the regulatory mechanisms guiding cellular fate decisions.
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Affiliation(s)
- Dominik Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
- Molecular and Cellular Biology Program, University of Washington, Seattle WA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
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7
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Cai C, Yue Y, Yue B. Single-cell RNA sequencing in skeletal muscle developmental biology. Biomed Pharmacother 2023; 162:114631. [PMID: 37003036 DOI: 10.1016/j.biopha.2023.114631] [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: 02/12/2023] [Revised: 03/29/2023] [Accepted: 03/29/2023] [Indexed: 04/01/2023] Open
Abstract
Skeletal muscle is the most extensive tissue in mammals, and they perform several functions; it is derived from paraxial mesodermal somites and undergoes hyperplasia and hypertrophy to form multinucleated, contractile, and functional muscle fibers. Skeletal muscle is a complex heterogeneous tissue composed of various cell types that establish communication strategies to exchange biological information; therefore, characterizing the cellular heterogeneity and transcriptional signatures of skeletal muscle is central to understanding its ontogeny's details. Studies of skeletal myogenesis have focused primarily on myogenic cells' proliferation, differentiation, migration, and fusion and ignored the intricate network of cells with specific biological functions. The rapid development of single-cell sequencing technology has recently enabled the exploration of skeletal muscle cell types and molecular events during development. This review summarizes the progress in single-cell RNA sequencing and its applications in skeletal myogenesis, which will provide insights into skeletal muscle pathophysiology.
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Affiliation(s)
- Cuicui Cai
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu 610225, China; Guyuan Branch, Ningxia Academy of Agriculture and Forestry Sciences, Guyuan 7560000, China
| | - Yuan Yue
- Department of Pathobiology and Immunology, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
| | - Binglin Yue
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu 610225, China.
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8
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Guo PC, Zuo J, Huang KK, Lai GY, Zhang X, An J, Li JX, Li L, Wu L, Lin YT, Wang DY, Xu JS, Hao SJ, Wang Y, Li RH, Ma W, Song YM, Liu C, Liu CY, Dai Z, Xu Y, Sharma AD, Ott M, Ou-Yang Q, Huo F, Fan R, Li YY, Hou JL, Volpe G, Liu LQ, Esteban MA, Lai YW. Cell atlas of CCl 4-induced progressive liver fibrosis reveals stage-specific responses. Zool Res 2023; 44:451-466. [PMID: 36994536 PMCID: PMC10236302 DOI: 10.24272/j.issn.2095-8137.2023.031] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/11/2023] [Indexed: 03/12/2023] Open
Abstract
Chronic liver injury leads to progressive liver fibrosis and ultimately cirrhosis, a major cause of morbidity and mortality worldwide. However, there are currently no effective anti-fibrotic therapies available, especially for late-stage patients, which is partly attributed to the major knowledge gap regarding liver cell heterogeneity and cell-specific responses in different fibrosis stages. To reveal the multicellular networks regulating mammalian liver fibrosis from mild to severe phenotypes, we generated a single-nucleus transcriptomic atlas encompassing 49 919 nuclei corresponding to all main liver cell types at different stages of murine carbon tetrachloride (CCl 4)-induced progressive liver fibrosis. Integrative analysis distinguished the sequential responses to injury of hepatocytes, hepatic stellate cells and endothelial cells. Moreover, we reconstructed cell-cell interactions and gene regulatory networks implicated in these processes. These integrative analyses uncovered previously overlooked aspects of hepatocyte proliferation exhaustion and disrupted pericentral metabolic functions, dysfunction for clearance by apoptosis of activated hepatic stellate cells, accumulation of pro-fibrotic signals, and the switch from an anti-angiogenic to a pro-angiogenic program during CCl 4-induced progressive liver fibrosis. Our dataset thus constitutes a useful resource for understanding the molecular basis of progressive liver fibrosis using a relevant animal model.
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Affiliation(s)
- Peng-Cheng Guo
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, Jilin 130062, China
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Jing Zuo
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Ke-Ke Huang
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510799, China
| | - Guang-Yao Lai
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, Guangdong 510530, China
| | - Xiao Zhang
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, Jilin 130062, China
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Juan An
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jin-Xiu Li
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Liang Wu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Yi-Ting Lin
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Dong-Ye Wang
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Jiang-Shan Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Shi-Jie Hao
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yang Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Rong-Hai Li
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Wen Ma
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Yu-Mo Song
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Chang Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Chuan-Yu Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Zhen Dai
- Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
| | - Yan Xu
- Biotherapy Centre, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510630, China
| | - Amar Deep Sharma
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover 30625, Germany
| | - Michael Ott
- Department of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, Hannover 30625, Germany
| | - Qing Ou-Yang
- Department of Hepatobiliary Surgery and Liver Transplant Center, General Hospital of Southern Theater Command, Guangzhou, Guangdong 510010, China
| | - Feng Huo
- Department of Hepatobiliary Surgery and Liver Transplant Center, General Hospital of Southern Theater Command, Guangzhou, Guangdong 510010, China
| | - Rong Fan
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, Guangdong 510515, China
| | - Yong-Yin Li
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, Guangdong 510515, China
| | - Jin-Lin Hou
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Guangzhou, Guangdong 510515, China
| | - Giacomo Volpe
- Hematology and Cell Therapy Unit, IRCCS-Istituto Tumori 'Giovanni Paolo II', Bari 70124, Italy
| | - Long-Qi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
| | - Miguel A Esteban
- State Key Laboratory for Zoonotic Diseases, Key Laboratory for Zoonosis Research of Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Changchun, Jilin 130062, China
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510799, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, Guangdong 510530, China
- Institute of Experimental Hematology, Hannover Medical School, Hannover 30625, Germany. E-mail:
| | - Yi-Wei Lai
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China. E-mail:
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9
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Franchini M, Pellecchia S, Viscido G, Gambardella G. Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data. NAR Genom Bioinform 2023; 5:lqad024. [PMID: 36879897 PMCID: PMC9985338 DOI: 10.1093/nargab/lqad024] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/16/2023] [Accepted: 02/20/2023] [Indexed: 03/07/2023] Open
Abstract
Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
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Affiliation(s)
- Melania Franchini
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy.,Department of Electrical Engineering and Information Technologies, University of Naples Federico II, 80125 Naples, Italy
| | - Simona Pellecchia
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy
| | - Gaetano Viscido
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy
| | - Gennaro Gambardella
- Telethon Institute of Genetics and Medicine, Pozzuoli 80078 Naples, Italy.,Department of Chemical Materials and Industrial Engineering, University of Naples Federico II, 80125 Naples, Italy
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