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Yang B, Zhang M, Shi Y, Zheng BQ, Shi C, Lu D, Yang ZZ, Dong YM, Zhu L, Ma X, Zhang J, He J, Zhang Y, Hu K, Lin H, Liao JY, Yin D. PerturbDB for unraveling gene functions and regulatory networks. Nucleic Acids Res 2024:gkae777. [PMID: 39265120 DOI: 10.1093/nar/gkae777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/26/2024] [Accepted: 09/05/2024] [Indexed: 09/14/2024] Open
Abstract
Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-based genetic screens with single-cell RNA sequencing readouts for high-content phenotypic screens. Despite the rapid accumulation of Perturb-Seq datasets, there remains a lack of a user-friendly platform for their efficient reuse. Here, we developed PerturbDB (http://research.gzsys.org.cn/perturbdb), a platform to help users unveil gene functions using Perturb-Seq datasets. PerturbDB hosts 66 Perturb-Seq datasets, which encompass 4 518 521 single-cell transcriptomes derived from the knockdown of 10 194 genes across 19 different cell lines. All datasets were uniformly processed using the Mixscape algorithm. Genes were clustered by their perturbed transcriptomic phenotypes derived from Perturb-Seq data, resulting in 421 gene clusters, 157 of which were stable across different cellular contexts. Through integrating chemically perturbed transcriptomes with Perturb-Seq data, we identified 552 potential inhibitors targeting 1409 genes, including an mammalian target of rapamycin (mTOR) signaling inhibitor, retinol, which was experimentally verified. Moreover, we developed a 'Cancer' module to facilitate the understanding of the regulatory role of genes in cancer using Perturb-Seq data. An interactive web interface has also been developed, enabling users to visualize, analyze and download all the comprehensive datasets available in PerturbDB. PerturbDB will greatly drive gene functional studies and enhance our understanding of the regulatory roles of genes in diseases such as cancer.
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Affiliation(s)
- Bing Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Man Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Yanmei Shi
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Bing-Qi Zheng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Chuanping Shi
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Daning Lu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Zhi-Zhi Yang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Yi-Ming Dong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Liwen Zhu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Xingyu Ma
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Jingyuan Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Jiehua He
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Yin Zhang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Kaishun Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Haoming Lin
- HBP Surgery Department, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
| | - Jian-You Liao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
- Center for Precision Medicine, Shenshan Central Hospital, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 1 Heng Er Road, Dongyong Town, Shanwei, Guangdong, 516621, China
| | - Dong Yin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yan Jiang West Road, Guangzhou, Guangdong, 510120, China
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Trapnell C. Revealing gene function with statistical inference at single-cell resolution. Nat Rev Genet 2024; 25:623-638. [PMID: 38951690 DOI: 10.1038/s41576-024-00750-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 07/03/2024]
Abstract
Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.
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Affiliation(s)
- Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
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Nicol PB, Paulson D, Qian G, Liu XS, Irizarry R, Sahu AD. Robust identification of perturbed cell types in single-cell RNA-seq data. Nat Commun 2024; 15:7610. [PMID: 39218971 PMCID: PMC11366752 DOI: 10.1038/s41467-024-51649-3] [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: 12/14/2023] [Accepted: 08/09/2024] [Indexed: 09/04/2024] Open
Abstract
Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms current methods in both simulated and real datasets, even with limited sample sizes. Through the analysis of COVID-19 and immunotherapy datasets, scDist uncovers transcriptomic perturbations in dendritic cells, plasmacytoid dendritic cells, and FCER1G+NK cells, that provide new insights into disease mechanisms and treatment responses. As single-cell datasets continue to expand, our faster and statistically rigorous method offers a robust and versatile tool for a wide range of research and clinical applications, enabling the investigation of cellular perturbations with implications for human health and disease.
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Affiliation(s)
| | | | - Gege Qian
- University of California San Diego School of Medicine, San Diego, CA, USA
| | | | | | - Avinash D Sahu
- University of New Mexico Comprehensive Cancer Center, Albuquerque, NM, USA.
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4
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Almet AA, Tsai YC, Watanabe M, Nie Q. Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics. Nat Methods 2024:10.1038/s41592-024-02380-w. [PMID: 39187683 DOI: 10.1038/s41592-024-02380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 07/23/2024] [Indexed: 08/28/2024]
Abstract
From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.
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Affiliation(s)
- Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
| | - Yuan-Chen Tsai
- Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Momoko Watanabe
- Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
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5
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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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6
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Foerster S, Floriddia EM, van Bruggen D, Kukanja P, Hervé B, Cheng S, Kim E, Phillips BU, Heath CJ, Tripathi RB, Call C, Bartels T, Ridley K, Neumann B, López-Cruz L, Crawford AH, Lynch CJ, Serrano M, Saksida L, Rowitch DH, Möbius W, Nave KA, Rasband MN, Bergles DE, Kessaris N, Richardson WD, Bussey TJ, Zhao C, Castelo-Branco G, Franklin RJM. Developmental origin of oligodendrocytes determines their function in the adult brain. Nat Neurosci 2024; 27:1545-1554. [PMID: 38849524 PMCID: PMC11303253 DOI: 10.1038/s41593-024-01666-8] [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: 12/13/2022] [Accepted: 04/25/2024] [Indexed: 06/09/2024]
Abstract
In the mouse embryonic forebrain, developmentally distinct oligodendrocyte progenitor cell populations and their progeny, oligodendrocytes, emerge from three distinct regions in a spatiotemporal gradient from ventral to dorsal. However, the functional importance of this oligodendrocyte developmental heterogeneity is unknown. Using a genetic strategy to ablate dorsally derived oligodendrocyte lineage cells (OLCs), we show here that the areas in which dorsally derived OLCs normally reside in the adult central nervous system become populated and myelinated by OLCs of ventral origin. These ectopic oligodendrocytes (eOLs) have a distinctive gene expression profile as well as subtle myelination abnormalities. The failure of eOLs to fully assume the role of the original dorsally derived cells results in locomotor and cognitive deficits in the adult animal. This study reveals the importance of developmental heterogeneity within the oligodendrocyte lineage and its importance for homeostatic brain function.
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Affiliation(s)
- Sarah Foerster
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Elisa M Floriddia
- Laboratory of Molecular Neurobiology, Karolinska Institutet, Stockholm, Sweden
| | - David van Bruggen
- Laboratory of Molecular Neurobiology, Karolinska Institutet, Stockholm, Sweden
| | - Petra Kukanja
- Laboratory of Molecular Neurobiology, Karolinska Institutet, Stockholm, Sweden
| | - Bastien Hervé
- Laboratory of Molecular Neurobiology, Karolinska Institutet, Stockholm, Sweden
| | - Shangli Cheng
- Ming Wai Lau Centre for Reparative Medicine, Stockholm and Hong Kong nodes, Karolinska Institutet, Stockholm, Sweden
| | - Eosu Kim
- Behavioral and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Department of Psychiatry, Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Benjamin U Phillips
- Behavioral and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK
| | - Christopher J Heath
- School of Life, Health and Chemical Sciences, The Open University, Milton Keynes, UK
| | - Richa B Tripathi
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Cody Call
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Theresa Bartels
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Katherine Ridley
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Björn Neumann
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Laura López-Cruz
- Behavioral and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Abbe H Crawford
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Cian J Lynch
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Manuel Serrano
- Altos Labs, Cambridge Institute of Science, Cambridge, UK
| | - Lisa Saksida
- Department of Physiology and Pharmacology and Robarts Research Institute, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada
| | - David H Rowitch
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Wiebke Möbius
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Klaus-Armin Nave
- Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Matthew N Rasband
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Dwight E Bergles
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicoletta Kessaris
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - William D Richardson
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Timothy J Bussey
- Behavioral and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
- Department of Physiology and Pharmacology and Robarts Research Institute, Schulich School of Medicine & Dentistry, University of Western Ontario, London, ON, Canada
| | - Chao Zhao
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- Altos Labs, Cambridge Institute of Science, Cambridge, UK.
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Karolinska Institutet, Stockholm, Sweden.
- Ming Wai Lau Centre for Reparative Medicine, Stockholm and Hong Kong nodes, Karolinska Institutet, Stockholm, Sweden.
| | - Robin J M Franklin
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- Altos Labs, Cambridge Institute of Science, Cambridge, UK.
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7
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Jakobsen NA, Turkalj S, Zeng AGX, Stoilova B, Metzner M, Rahmig S, Nagree MS, Shah S, Moore R, Usukhbayar B, Angulo Salazar M, Gafencu GA, Kennedy A, Newman S, Kendrick BJL, Taylor AH, Afinowi-Luitz R, Gundle R, Watkins B, Wheway K, Beazley D, Murison A, Aguilar-Navarro AG, Flores-Figueroa E, Dakin SG, Carr AJ, Nerlov C, Dick JE, Xie SZ, Vyas P. Selective advantage of mutant stem cells in human clonal hematopoiesis is associated with attenuated response to inflammation and aging. Cell Stem Cell 2024; 31:1127-1144.e17. [PMID: 38917807 DOI: 10.1016/j.stem.2024.05.010] [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: 06/25/2023] [Revised: 01/29/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024]
Abstract
Clonal hematopoiesis (CH) arises when hematopoietic stem cells (HSCs) acquire mutations, most frequently in the DNMT3A and TET2 genes, conferring a competitive advantage through mechanisms that remain unclear. To gain insight into how CH mutations enable gradual clonal expansion, we used single-cell multi-omics with high-fidelity genotyping on human CH bone marrow (BM) samples. Most of the selective advantage of mutant cells occurs within HSCs. DNMT3A- and TET2-mutant clones expand further in early progenitors, while TET2 mutations accelerate myeloid maturation in a dose-dependent manner. Unexpectedly, both mutant and non-mutant HSCs from CH samples are enriched for inflammatory and aging transcriptomic signatures, compared with HSCs from non-CH samples, revealing a non-cell-autonomous effect. However, DNMT3A- and TET2-mutant HSCs have an attenuated inflammatory response relative to wild-type HSCs within the same sample. Our data support a model whereby CH clones are gradually selected because they are resistant to the deleterious impact of inflammation and aging.
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Affiliation(s)
- Niels Asger Jakobsen
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Sven Turkalj
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Andy G X Zeng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Bilyana Stoilova
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Marlen Metzner
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Susann Rahmig
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Murtaza S Nagree
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Sayyam Shah
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Rachel Moore
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Batchimeg Usukhbayar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Mirian Angulo Salazar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Grigore-Aristide Gafencu
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Alison Kennedy
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Wellcome - MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Simon Newman
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Benjamin J L Kendrick
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Adrian H Taylor
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Rasheed Afinowi-Luitz
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Roger Gundle
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Bridget Watkins
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Kim Wheway
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Debra Beazley
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Alex Murison
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Alicia G Aguilar-Navarro
- Unidad de Investigación Médica en Enfermedades Oncológicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eugenia Flores-Figueroa
- Unidad de Investigación Médica en Enfermedades Oncológicas, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Stephanie G Dakin
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK
| | - Andrew J Carr
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK; Nuffield Orthopaedic Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Claus Nerlov
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - John E Dick
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Stephanie Z Xie
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Paresh Vyas
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
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8
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Ramirez A, Orcutt-Jahns BT, Pascoe S, Abraham A, Remigio B, Thomas N, Meyer AS. Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.29.605698. [PMID: 39131377 PMCID: PMC11312543 DOI: 10.1101/2024.07.29.605698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Effective tools for exploration and analysis are needed to extract insights from large-scale single-cell measurement data. However, current techniques for handling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that the tensor decomposition method PARAFAC2 (Pf2) enables the dimensionality reduction of single-cell data across conditions. We demonstrate these benefits across two distinct contexts of single-cell RNA-sequencing (scRNA-seq) experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. By isolating relevant gene modules across cells and conditions, Pf2 enables straightforward associations of gene variation patterns across specific patients or perturbations while connecting each coordinated change to certain cells without pre-defining cell types. The theoretical grounding of Pf2 suggests a unified framework for many modeling tasks associated with single-cell data. Thus, Pf2 provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts.
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Affiliation(s)
- Andrew Ramirez
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
| | | | - Sean Pascoe
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
- Department of Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Armaan Abraham
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
| | | | | | - Aaron S. Meyer
- Department of Bioengineering, University of California, Los Angeles (UCLA), CA, USA
- Jonsson Comprehensive Cancer Center, UCLA, CA, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, CA, USA
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9
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Chari T, Gorin G, Pachter L. Stochastic Modeling of Biophysical Responses to Perturbation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.04.602131. [PMID: 39005347 PMCID: PMC11245117 DOI: 10.1101/2024.07.04.602131] [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
Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the 'how' behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.
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Affiliation(s)
- Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
| | | | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
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10
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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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11
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Enk LUB, Hellmig M, Riecken K, Kilian C, Datlinger P, Jauch-Speer SL, Neben T, Sultana Z, Sivayoganathan V, Borchers A, Paust HJ, Zhao Y, Asada N, Liu S, Agalioti T, Pelczar P, Wiech T, Bock C, Huber TB, Huber S, Bonn S, Gagliani N, Fehse B, Panzer U, Krebs CF. Targeting T cell plasticity in kidney and gut inflammation by pooled single-cell CRISPR screening. Sci Immunol 2024; 9:eadd6774. [PMID: 38875317 DOI: 10.1126/sciimmunol.add6774] [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: 06/27/2022] [Accepted: 05/23/2024] [Indexed: 06/16/2024]
Abstract
Pro-inflammatory CD4+ T cells are major drivers of autoimmune diseases, yet therapies modulating T cell phenotypes to promote an anti-inflammatory state are lacking. Here, we identify T helper 17 (TH17) cell plasticity in the kidneys of patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis on the basis of single-cell (sc) T cell receptor analysis and scRNA velocity. To uncover molecules driving T cell polarization and plasticity, we established an in vivo pooled scCRISPR droplet sequencing (iCROP-seq) screen and applied it to mouse models of glomerulonephritis and colitis. CRISPR-based gene targeting in TH17 cells could be ranked according to the resulting transcriptional perturbations, and polarization biases into T helper 1 (TH1) and regulatory T cells could be quantified. Furthermore, we show that iCROP-seq can facilitate the identification of therapeutic targets by efficient functional stratification of genes and pathways in a disease- and tissue-specific manner. These findings uncover TH17 to TH1 cell plasticity in the human kidney in the context of renal autoimmunity.
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Affiliation(s)
- Leon U B Enk
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Malte Hellmig
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kristoffer Riecken
- Department of Stem Cell Transplantation, Research Department Cell and Gene Therapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Kilian
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Paul Datlinger
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Saskia L Jauch-Speer
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tobias Neben
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Zeba Sultana
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Varshi Sivayoganathan
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alina Borchers
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hans-Joachim Paust
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Yu Zhao
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nariaki Asada
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Shuya Liu
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Theodora Agalioti
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Penelope Pelczar
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Thorsten Wiech
- Institute of Pathology, Division of Nephropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Institute of Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Samuel Huber
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Stefan Bonn
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical AI, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nicola Gagliani
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department for General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Boris Fehse
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Stem Cell Transplantation, Research Department Cell and Gene Therapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ulf Panzer
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian F Krebs
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Hamburg Center for Kidney Health (HCKH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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12
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Brunet-Ratnasingham E, Morin S, Randolph HE, Labrecque M, Bélair J, Lima-Barbosa R, Pagliuzza A, Marchitto L, Hultström M, Niessl J, Cloutier R, Sreng Flores AM, Brassard N, Benlarbi M, Prévost J, Ding S, Anand SP, Sannier G, Marks A, Wågsäter D, Bareke E, Zeberg H, Lipcsey M, Frithiof R, Larsson A, Zhou S, Nakanishi T, Morrison D, Vezina D, Bourassa C, Gendron-Lepage G, Medjahed H, Point F, Richard J, Larochelle C, Prat A, Cunningham JL, Arbour N, Durand M, Richards JB, Moon K, Chomont N, Finzi A, Tétreault M, Barreiro L, Wolf G, Kaufmann DE. Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity. Nat Commun 2024; 15:4177. [PMID: 38755196 DOI: 10.1038/s41467-024-48556-y] [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: 06/01/2023] [Accepted: 05/06/2024] [Indexed: 05/18/2024] Open
Abstract
Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized patients with COVID-19. Integrated analysis using k-means reveals four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors segregate into high and low early antibody responders. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the "Interferon paradox" previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.
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Affiliation(s)
- Elsa Brunet-Ratnasingham
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
- Department of Medicine, University of California, San Francisco, CA, USA
| | - Sacha Morin
- Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada
- Mila-Quebec AI Institute, Montreal, QC, Canada
| | - Haley E Randolph
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Marjorie Labrecque
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Bioinformatics Program, Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, Canada
| | - Justin Bélair
- Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada
- Independent Data Scientist, JB Consulting, Montreal, QC, H3S1K8, Canada
| | - Raphaël Lima-Barbosa
- Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada
- Independent Data Scientist, JB Consulting, Montreal, QC, H3S1K8, Canada
| | - Amélie Pagliuzza
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Lorie Marchitto
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Michael Hultström
- Anaesthesiology and Intensive Care Medicine, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Integrative Physiology, Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.
| | - Julia Niessl
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
- BioNTech SE, Mainz, Germany
| | - Rose Cloutier
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Alina M Sreng Flores
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Nathalie Brassard
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Mehdi Benlarbi
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Jérémie Prévost
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Shilei Ding
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Sai Priya Anand
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Gérémy Sannier
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Amanda Marks
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Dick Wågsäter
- Integrative Physiology, Department of Medical Cell Biology, Uppsala University, Uppsala, Sweden
| | - Eric Bareke
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Hugo Zeberg
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany
| | - Miklos Lipcsey
- Anaesthesiology and Intensive Care Medicine, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Robert Frithiof
- Anaesthesiology and Intensive Care Medicine, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Anders Larsson
- Clinical Chemistry, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Gaduate School of Medicine, Kyoto University, Kyoto, Japan
- Research Fellow, Japan Society for the Promotion of Science, Tokyo, Japan
| | - David Morrison
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Dani Vezina
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Catherine Bourassa
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Gabrielle Gendron-Lepage
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Halima Medjahed
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Floriane Point
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Jonathan Richard
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Catherine Larochelle
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neurosciences, Université de Montréal, Montreal, QC, Canada
| | - Alexandre Prat
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neurosciences, Université de Montréal, Montreal, QC, Canada
| | - Janet L Cunningham
- Department of Medical Sciences, Psychiatry, Uppsala University, Uppsala, Sweden
| | - Nathalie Arbour
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neurosciences, Université de Montréal, Montreal, QC, Canada
| | - Madeleine Durand
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, QC, Canada
| | - J Brent Richards
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Department of Twin Research, King's College London, London, UK
| | - Kevin Moon
- Department of Mathematics and Statistics, Utah State University, Logan, UT, USA
| | - Nicolas Chomont
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
| | - Andrés Finzi
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montreal, QC, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, QC, H3A 2B4, Canada
| | - Martine Tétreault
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Neurosciences, Université de Montréal, Montreal, QC, Canada
| | - Luis Barreiro
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Committee on Immunology, University of Chicago, Chicago, IL, USA
| | - Guy Wolf
- Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada.
- Mila-Quebec AI Institute, Montreal, QC, Canada.
- Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada.
| | - Daniel E Kaufmann
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada.
- Département de Médecine, Université de Montréal, Montreal, QC, Canada.
- Division of Infectious Diseases, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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13
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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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14
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Jiang Q, Chen S, Chen X, Jiang R. scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism. Bioinformatics 2024; 40:btae265. [PMID: 38625746 PMCID: PMC11076148 DOI: 10.1093/bioinformatics/btae265] [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: 12/07/2023] [Revised: 04/06/2024] [Accepted: 04/13/2024] [Indexed: 04/17/2024] Open
Abstract
MOTIVATION With the rapid advancement of single-cell sequencing technology, it becomes gradually possible to delve into the cellular responses to various external perturbations at the gene expression level. However, obtaining perturbed samples in certain scenarios may be considerably challenging, and the substantial costs associated with sequencing also curtail the feasibility of large-scale experimentation. A repertoire of methodologies has been employed for forecasting perturbative responses in single-cell gene expression. However, existing methods primarily focus on the average response of a specific cell type to perturbation, overlooking the single-cell specificity of perturbation responses and a more comprehensive prediction of the entire perturbation response distribution. RESULTS Here, we present scPRAM, a method for predicting perturbation responses in single-cell gene expression based on attention mechanisms. Leveraging variational autoencoders and optimal transport, scPRAM aligns cell states before and after perturbation, followed by accurate prediction of gene expression responses to perturbations for unseen cell types through attention mechanisms. Experiments on multiple real perturbation datasets involving drug treatments and bacterial infections demonstrate that scPRAM attains heightened accuracy in perturbation prediction across cell types, species, and individuals, surpassing existing methodologies. Furthermore, scPRAM demonstrates outstanding capability in identifying differentially expressed genes under perturbation, capturing heterogeneity in perturbation responses across species, and maintaining stability in the presence of data noise and sample size variations. AVAILABILITY AND IMPLEMENTATION https://github.com/jiang-q19/scPRAM and https://doi.org/10.5281/zenodo.10935038.
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Affiliation(s)
- Qun Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Xiaoyang Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
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15
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Luecken MD, Gigante S, Burkhardt DB, Cannoodt R, Strobl DC, Markov NS, Zappia L, Palla G, Lewis W, Dimitrov D, Vinyard ME, Magruder DS, Andersson A, Dann E, Qin Q, Otto DJ, Klein M, Botvinnik OB, Deconinck L, Waldrant K, Bloom JM, Pisco AO, Saez-Rodriguez J, Wulsin D, Pinello L, Saeys Y, Theis FJ, Krishnaswamy S. Defining and benchmarking open problems in single-cell analysis. RESEARCH SQUARE 2024:rs.3.rs-4181617. [PMID: 38645152 PMCID: PMC11030530 DOI: 10.21203/rs.3.rs-4181617/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.
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Affiliation(s)
- Malte D Luecken
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Institute of Lung Health & Immunity, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | | | | | - Robrecht Cannoodt
- Data Intuitive, Lebbeke, Belgium
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | - Daniel C Strobl
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Nikolay S Markov
- Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University
| | - Luke Zappia
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Giovanni Palla
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Wesley Lewis
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Michael E Vinyard
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - D S Magruder
- Department of Computer Science, Yale University, New Haven CT, USA
| | - Alma Andersson
- Genentech Inc
- Royal Institute of Technology (KTH), Gene Technology
- Science for Life Laboratory (SciLifeLab)
| | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Qian Qin
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dominik J 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
| | | | - Olga Borisovna Botvinnik
- Data Sciences Platform, Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158
- Bridge Bio Pharma, 3160 Porter Drive, Suite 250, Palo Alto, CA, 94304
| | - Louise Deconinck
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | | | | | - Angela Oliveira Pisco
- Data Sciences Platform, Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158
- Insitro, South San Francisco
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | | | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI & Computational Biology (VIB.AI), Gent, Belgium
| | - Fabian J Theis
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, UK (associated faculty)
| | - Smita Krishnaswamy
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Department of Computer Science, Yale University, New Haven CT, USA
- Department of Genetics, Yale University, New Haven CT, USA
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16
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Rauber S, Mohammadian H, Schmidkonz C, Atzinger A, Soare A, Treutlein C, Kemble S, Mahony CB, Geisthoff M, Angeli MR, Raimondo MG, Xu C, Yang KT, Lu L, Labinsky H, Saad MSA, Gwellem CA, Chang J, Huang K, Kampylafka E, Knitza J, Bilyy R, Distler JHW, Hanlon MM, Fearon U, Veale DJ, Roemer FW, Bäuerle T, Maric HM, Maschauer S, Ekici AB, Buckley CD, Croft AP, Kuwert T, Prante O, Cañete JD, Schett G, Ramming A. CD200 + fibroblasts form a pro-resolving mesenchymal network in arthritis. Nat Immunol 2024; 25:682-692. [PMID: 38396288 DOI: 10.1038/s41590-024-01774-4] [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: 03/15/2023] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.
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Affiliation(s)
- Simon Rauber
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hashem Mohammadian
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christian Schmidkonz
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Department of Industrial Engineering and Health, Technical University Amberg-Weiden, Institute of Medical Engineering, Weiden, Germany
| | - Armin Atzinger
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Alina Soare
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Christoph Treutlein
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Samuel Kemble
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Christopher B Mahony
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Manuel Geisthoff
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mario R Angeli
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Maria G Raimondo
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Cong Xu
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Kai-Ting Yang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Le Lu
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hannah Labinsky
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Mina S A Saad
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Charles A Gwellem
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Jiyang Chang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Kaiyue Huang
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Eleni Kampylafka
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Johannes Knitza
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Rostyslav Bilyy
- Department of Histology, Cytology, Embryology, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
- Institute of Cellular Biology and Pathology 'Nicolae Simionescu', Bucharest, Romania
| | - Jörg H W Distler
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Clinic for Rheumatology, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Hiller Research Center, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Megan M Hanlon
- Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Ursula Fearon
- Molecular Rheumatology, School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
| | - Douglas J Veale
- EULAR Centre for Arthritis & Rheumatic Diseases, St. Vincent's University Hospital, University College Dublin, Dublin, Ireland
| | - Frank W Roemer
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Tobias Bäuerle
- Institute of Radiology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Hans M Maric
- Rudolf-Virchow-Center for Integrative and Translational Imaging, University of Würzburg, Würzburg, Germany
| | - Simone Maschauer
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Arif B Ekici
- Institute of Human Genetics, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Adam P Croft
- Rheumatology Research Group, Institute for Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
- NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK
| | - Torsten Kuwert
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Olaf Prante
- Department of Nuclear Medicine, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | | | - Georg Schett
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany
| | - Andreas Ramming
- Department of Internal Medicine 3 - Rheumatology and Immunology, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
- Deutsches Zentrum für Immuntherapie (DZI), Friedrich-Alexander-University (FAU) Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany.
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17
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Ranek JS, Stallaert W, Milner JJ, Redick M, Wolff SC, Beltran AS, Stanley N, Purvis JE. DELVE: feature selection for preserving biological trajectories in single-cell data. Nat Commun 2024; 15:2765. [PMID: 38553455 PMCID: PMC10980758 DOI: 10.1038/s41467-024-46773-z] [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: 05/18/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .
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Affiliation(s)
- Jolene S Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Margaret Redick
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel C Wolff
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana S Beltran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Human Pluripotent Cell Core, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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18
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Childs JE, Morabito S, Das S, Santelli C, Pham V, Kusche K, Vera VA, Reese F, Campbell RR, Matheos DP, Swarup V, Wood MA. Relapse to cocaine seeking is regulated by medial habenula NR4A2/NURR1 in mice. Cell Rep 2024; 43:113956. [PMID: 38489267 PMCID: PMC11100346 DOI: 10.1016/j.celrep.2024.113956] [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: 09/28/2022] [Revised: 09/11/2023] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
Drugs of abuse can persistently change the reward circuit in ways that contribute to relapse behavior, partly via mechanisms that regulate chromatin structure and function. Nuclear orphan receptor subfamily4 groupA member2 (NR4A2, also known as NURR1) is an important effector of histone deacetylase 3 (HDAC3)-dependent mechanisms in persistent memory processes and is highly expressed in the medial habenula (MHb), a region that regulates nicotine-associated behaviors. Here, expressing the Nr4a2 dominant negative (Nurr2c) in the MHb blocks reinstatement of cocaine seeking in mice. We use single-nucleus transcriptomics to characterize the molecular cascade following Nr4a2 manipulation, revealing changes in transcriptional networks related to addiction, neuroplasticity, and GABAergic and glutamatergic signaling. The network controlled by NR4A2 is characterized using a transcription factor regulatory network inference algorithm. These results identify the MHb as a pivotal regulator of relapse behavior and demonstrate the importance of NR4A2 as a key mechanism driving the MHb component of relapse.
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Affiliation(s)
- Jessica E Childs
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Samuel Morabito
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA 92697, USA; Mathematical, Computational, and Systems Biology (MCSB) Program, University of California, Irvine, Irvine, CA 92697, USA; Center for Complex Biological Systems, University of California, Irvine, Irvine, CA 92697, USA
| | - Sudeshna Das
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA 92697, USA
| | - Caterina Santelli
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Victoria Pham
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Kelly Kusche
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Vanessa Alizo Vera
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Fairlie Reese
- Center for Complex Biological Systems, University of California, Irvine, Irvine, CA 92697, USA; Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA 92697, USA
| | - Rianne R Campbell
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Dina P Matheos
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA
| | - Vivek Swarup
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA 92697, USA.
| | - Marcelo A Wood
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA 92697, USA; UC Irvine Center for Addiction Neuroscience, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Center for the Neurobiology of Learning and Memory, School of Biological Sciences, University of California, Irvine, Irvine, CA 92697, USA; Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA 92697, USA.
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19
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [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: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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20
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Tejwani L, Ravindra NG, Lee C, Cheng Y, Nguyen B, Luttik K, Ni L, Zhang S, Morrison LM, Gionco J, Xiang Y, Yoon J, Ro H, Haidery F, Grijalva RM, Bae E, Kim K, Martuscello RT, Orr HT, Zoghbi HY, McLoughlin HS, Ranum LPW, Shakkottai VG, Faust PL, Wang S, van Dijk D, Lim J. Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1. Neuron 2024; 112:362-383.e15. [PMID: 38016472 PMCID: PMC10922326 DOI: 10.1016/j.neuron.2023.10.039] [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: 12/15/2022] [Revised: 09/10/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023]
Abstract
Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately results in the death of vulnerable neuronal populations. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellar tissue, establishing continuous dynamic trajectories of each cell population. Importantly, we defined the precise transcriptional changes that precede loss of Purkinje cells and, for the first time, identified robust early transcriptional dysregulation in unipolar brush cells and oligodendroglia. Finally, we applied a deep learning method to predict disease state accurately and identified specific features that enable accurate distinction of wild-type and SCA1 cells. Together, this work reveals new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.
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Affiliation(s)
- Leon Tejwani
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Neal G Ravindra
- Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA; Department of Computer Science, Yale University, New Haven, CT 06510, USA
| | - Changwoo Lee
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Yubao Cheng
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Billy Nguyen
- University of California, San Francisco School of Medicine, San Francisco, CA 94143, USA
| | - Kimberly Luttik
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Luhan Ni
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Shupei Zhang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA
| | - Logan M Morrison
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - John Gionco
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, New York, NY 10032, USA
| | - Yangfei Xiang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA; Yale Stem Cell Center, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - Hannah Ro
- Yale College, New Haven, CT 06510, USA
| | | | - Rosalie M Grijalva
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | | | - Kristen Kim
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Psychiatry, Yale School of Medicine, New Haven, CT 06510, USA
| | - Regina T Martuscello
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, New York, NY 10032, USA
| | - Harry T Orr
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Huda Y Zoghbi
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Howard Hughes Medical Institute, Baylor College of Medicine, Houston, TX 77030, USA
| | - Hayley S McLoughlin
- Department of Neurology, University of Michigan, Ann Arbor, MI 48109-2200, USA
| | - Laura P W Ranum
- Department of Molecular Genetics and Microbiology, Center for Neurogenetics, College of Medicine, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL 32610, USA
| | - Vikram G Shakkottai
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Phyllis L Faust
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center and the New York Presbyterian Hospital, New York, NY 10032, USA
| | - Siyuan Wang
- Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA; Department of Cell Biology, Yale School of Medicine, New Haven, CT 06510, USA.
| | - David van Dijk
- Cardiovascular Research Center, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA; Department of Computer Science, Yale University, New Haven, CT 06510, USA.
| | - Janghoo Lim
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Genetics, Yale School of Medicine, New Haven, CT 06510, USA; Yale Stem Cell Center, Yale School of Medicine, New Haven, CT 06510, USA; Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale School of Medicine, New Haven, CT 06510, USA; Wu Tsai Institute, Yale School of Medicine, New Haven, CT 06510, USA.
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21
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Haage V, Tuddenham JF, Comandante-Lou N, Bautista A, Monzel A, Chiu R, Fujita M, Garcia FG, Bhattarai P, Patel R, Buonfiglioli A, Idiarte J, Herman M, Rinderspacher A, Mela A, Zhao W, Argenziano MG, Furnari JL, Banu MA, Landry DW, Bruce JN, Canoll P, Zhang Y, Nuriel T, Kizil C, Sproul AA, de Witte LD, Sims PA, Menon V, Picard M, De Jager PL. A pharmacological toolkit for human microglia identifies Topoisomerase I inhibitors as immunomodulators for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579103. [PMID: 38370689 PMCID: PMC10871172 DOI: 10.1101/2024.02.06.579103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
While efforts to identify microglial subtypes have recently accelerated, the relation of transcriptomically defined states to function has been largely limited to in silico annotations. Here, we characterize a set of pharmacological compounds that have been proposed to polarize human microglia towards two distinct states - one enriched for AD and MS genes and another characterized by increased expression of antigen presentation genes. Using different model systems including HMC3 cells, iPSC-derived microglia and cerebral organoids, we characterize the effect of these compounds in mimicking human microglial subtypes in vitro. We show that the Topoisomerase I inhibitor Camptothecin induces a CD74high/MHChigh microglial subtype which is specialized in amyloid beta phagocytosis. Camptothecin suppressed amyloid toxicity and restored microglia back to their homeostatic state in a zebrafish amyloid model. Our work provides avenues to recapitulate human microglial subtypes in vitro, enabling functional characterization and providing a foundation for modulating human microglia in vivo.
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Affiliation(s)
- Verena Haage
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - John F. Tuddenham
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Natacha Comandante-Lou
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Alex Bautista
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Anna Monzel
- Department of Psychiatry, Division of Behavioral Medicine, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA
| | - Rebecca Chiu
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Masashi Fujita
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Frankie G. Garcia
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Prabesh Bhattarai
- Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Ronak Patel
- Department of Pathology and Cell Biology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Alice Buonfiglioli
- Department of Psychiatry, Icahn School of Medicine, 1460 Madison Avenue, New York, NY, 10029, United States
| | - Juan Idiarte
- Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Mathieu Herman
- Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | | | - Angeliki Mela
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenting Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Michael G. Argenziano
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Julia L. Furnari
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Matei A. Banu
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Donald W. Landry
- Department of Medicine, Columbia University, New York, NY 10032, United States
| | - Jeffrey N. Bruce
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Peter Canoll
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Ya Zhang
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Tal Nuriel
- Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Caghan Kizil
- Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Andrew A. Sproul
- Department of Pathology and Cell Biology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Lotje D. de Witte
- Department of Psychiatry, Icahn School of Medicine, 1460 Madison Avenue, New York, NY, 10029, United States
| | - Peter A. Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
| | - Martin Picard
- Department of Psychiatry, Division of Behavioral Medicine, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA
- Department of Neurology, H. Houston Merritt Center, Columbia Translational Neuroscience Initiative, College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, USA
- New York State Psychiatric Institute, New York, USA
- Robert N Butler Columbia Aging Center, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Neuroimmunology Division, Department of Neurology and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY 10032, United States
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22
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Morris JA, Sun JS, Sanjana NE. Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis. Trends Genet 2024; 40:118-133. [PMID: 37989654 PMCID: PMC10872607 DOI: 10.1016/j.tig.2023.10.012] [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: 08/01/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping.
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Affiliation(s)
- John A Morris
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Jennifer S Sun
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA
| | - Neville E Sanjana
- New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
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23
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Roux de Bézieux H, Van den Berge K, Street K, Dudoit S. Trajectory inference across multiple conditions with condiments. Nat Commun 2024; 15:833. [PMID: 38280860 PMCID: PMC10821945 DOI: 10.1038/s41467-024-44823-0] [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: 10/13/2023] [Accepted: 01/08/2024] [Indexed: 01/29/2024] Open
Abstract
In single-cell RNA sequencing (scRNA-Seq), gene expression is assessed individually for each cell, allowing the investigation of developmental processes, such as embryogenesis and cellular differentiation and regeneration, at unprecedented resolution. In such dynamic biological systems, cellular states form a continuum, e.g., for the differentiation of stem cells into mature cell types. This process is often represented via a trajectory in a reduced-dimensional representation of the scRNA-Seq dataset. While many methods have been suggested for trajectory inference, it is often unclear how to handle multiple biological groups or conditions, e.g., inferring and comparing the differentiation trajectories of wild-type and knock-out stem cell populations. In this manuscript, we present condiments, a method for the inference and downstream interpretation of cell trajectories across multiple conditions. Our framework allows the interpretation of differences between conditions at the trajectory, cell population, and gene expression levels. We start by integrating datasets from multiple conditions into a single trajectory. By comparing the cell's conditions along the trajectory's path, we can detect large-scale changes, indicative of differential progression or fate selection. We also demonstrate how to detect subtler changes by finding genes that exhibit different behaviors between these conditions along a differentiation path.
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Affiliation(s)
- Hector Roux de Bézieux
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
- Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Koen Van den Berge
- Department of Statistics, University of California, Berkeley, CA, USA
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- Statistics and Decision Sciences, J&J Innovative Medicine, Beerse, Belgium
| | - Kelly Street
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA.
| | - Sandrine Dudoit
- Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
- Center for Computational Biology, University of California, Berkeley, CA, USA.
- Department of Statistics, University of California, Berkeley, CA, USA.
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24
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Shahir JA, Stanley N, Purvis JE. Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks. BMC Bioinformatics 2024; 25:25. [PMID: 38221640 PMCID: PMC10788980 DOI: 10.1186/s12859-024-05641-9] [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: 10/31/2023] [Accepted: 01/04/2024] [Indexed: 01/16/2024] Open
Abstract
With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.
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Affiliation(s)
- Jamshaid A Shahir
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Natalie Stanley
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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25
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Yi H, Plotkin A, Stanley N. Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets. Genome Biol 2024; 25:9. [PMID: 38172966 PMCID: PMC10762948 DOI: 10.1186/s13059-023-03143-0] [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: 02/17/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND To analyze the large volume of data generated by single-cell technologies and to identify cellular correlates of particular clinical or experimental outcomes, differential abundance analyses are often applied. These algorithms identify subgroups of cells whose abundances change significantly in response to disease progression, or to an experimental perturbation. Despite the effectiveness of differential abundance analyses in identifying critical cell-states, there is currently no systematic benchmarking study to compare their applicability, usefulness, and accuracy in practice across single-cell modalities. RESULTS Here, we perform a comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art differential abundance testing methods. We benchmarked six single-cell testing methods on several practical tasks, using both synthetic and real single-cell datasets. The tasks evaluated include effectiveness in identifying true differentially abundant subpopulations, accuracy in the adequate handling of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the practical use of differential abundance testing approaches. CONCLUSIONS Based on our benchmarking study, we provide a set of recommendations for the optimal usage of single-cell DA testing methods in practice, particularly with respect to factors such as the presence of technical noise (for example batch effects), dataset size, and hyperparameter sensitivity.
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Affiliation(s)
- Haidong Yi
- Department of Computer Science, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA
| | - Alec Plotkin
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA
| | - Natalie Stanley
- Department of Computer Science, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA.
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26
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [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: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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27
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Isobe T, Kucinski I, Barile M, Wang X, Hannah R, Bastos HP, Chabra S, Vijayabaskar MS, Sturgess KHM, Williams MJ, Giotopoulos G, Marando L, Li J, Rak J, Gozdecka M, Prins D, Shepherd MS, Watcham S, Green AR, Kent DG, Vassiliou GS, Huntly BJP, Wilson NK, Göttgens B. Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes. CELL GENOMICS 2023; 3:100426. [PMID: 38116120 PMCID: PMC10726426 DOI: 10.1016/j.xgen.2023.100426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/13/2023] [Accepted: 09/29/2023] [Indexed: 12/21/2023]
Abstract
Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.
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Affiliation(s)
- Tomoya Isobe
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Iwo Kucinski
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Melania Barile
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Xiaonan Wang
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Rebecca Hannah
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Hugo P Bastos
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Shirom Chabra
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - M S Vijayabaskar
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Katherine H M Sturgess
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Matthew J Williams
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - George Giotopoulos
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Ludovica Marando
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Juan Li
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Justyna Rak
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK; Hematological Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Malgorzata Gozdecka
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK; Hematological Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Daniel Prins
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Mairi S Shepherd
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Sam Watcham
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Anthony R Green
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - David G Kent
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK; York Biomedical Research Institute, Department of Biology, University of York, York, UK
| | - George S Vassiliou
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK; Hematological Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK
| | - Brian J P Huntly
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK
| | - Nicola K Wilson
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK.
| | - Berthold Göttgens
- Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK.
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28
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Ramos Zapatero M, Tong A, Opzoomer JW, O'Sullivan R, Cardoso Rodriguez F, Sufi J, Vlckova P, Nattress C, Qin X, Claus J, Hochhauser D, Krishnaswamy S, Tape CJ. Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses. Cell 2023; 186:5606-5619.e24. [PMID: 38065081 DOI: 10.1016/j.cell.2023.11.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 07/27/2023] [Accepted: 11/02/2023] [Indexed: 12/18/2023]
Abstract
Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.
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Affiliation(s)
- María Ramos Zapatero
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA; Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada; Mila - Quebec AI Institute, Montréal, QC, Canada
| | - James W Opzoomer
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Rhianna O'Sullivan
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ferran Cardoso Rodriguez
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Jahangir Sufi
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Petra Vlckova
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Callum Nattress
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Xiao Qin
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Jeroen Claus
- Phospho Biomedical Animation, The Greenhouse Studio 6, London N17 9QU, UK
| | - Daniel Hochhauser
- Drug-DNA Interactions Group, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA; Department of Genetics, Yale University, New Haven, CT, USA; Program for Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Program for Applied Math, Yale University, New Haven, CT, USA; Wu-Tsai Institute, Yale University, New Haven, CT, USA.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK.
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29
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Golan N, Ehrlich D, Bonanno J, O'Brien RF, Murillo M, Kauer SD, Ravindra N, Van Dijk D, Cafferty WB. Anatomical Diversity of the Adult Corticospinal Tract Revealed by Single-Cell Transcriptional Profiling. J Neurosci 2023; 43:7929-7945. [PMID: 37748862 PMCID: PMC10669816 DOI: 10.1523/jneurosci.0811-22.2023] [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: 04/26/2022] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 09/27/2023] Open
Abstract
The corticospinal tract (CST) forms a central part of the voluntary motor apparatus in all mammals. Thus, injury, disease, and subsequent degeneration within this pathway result in chronic irreversible functional deficits. Current strategies to repair the damaged CST are suboptimal in part because of underexplored molecular heterogeneity within the adult tract. Here, we combine spinal retrograde CST tracing with single-cell RNA sequencing (scRNAseq) in adult male and female mice to index corticospinal neuron (CSN) subtypes that differentially innervate the forelimb and hindlimb. We exploit publicly available datasets to confer anatomic specialization among CSNs and show that CSNs segregate not only along the forelimb and hindlimb axis but also by supraspinal axon collateralization. These anatomically defined transcriptional data allow us to use machine learning tools to build classifiers that discriminate between CSNs and cortical layer 2/3 and nonspinally terminating layer 5 neurons in M1 and separately identify limb-specific CSNs. Using these tools, CSN subtypes can be differentially identified to study postnatal patterning of the CST in vivo, leveraged to screen for novel limb-specific axon growth survival and growth activators in vitro, and ultimately exploited to repair the damaged CST after injury and disease.SIGNIFICANCE STATEMENT Therapeutic interventions designed to repair the damaged CST after spinal cord injury have remained functionally suboptimal in part because of an incomplete understanding of the molecular heterogeneity among subclasses of CSNs. Here, we combine spinal retrograde labeling with scRNAseq and annotate a CSN index by the termination pattern of their primary axon in the cervical or lumbar spinal cord and supraspinal collateral terminal fields. Using machine learning we have confirmed the veracity of our CSN gene lists to train classifiers to identify CSNs among all classes of neurons in primary motor cortex to study the development, patterning, homeostasis, and response to injury and disease, and ultimately target streamlined repair strategies to this critical motor pathway.
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Affiliation(s)
- Noa Golan
- Interdepartmental Neuroscience Program, Yale University School, New Haven, Connecticut 06511
- Department of Neurology, Yale University School, New Haven, Connecticut 06511
| | - Daniel Ehrlich
- Interdepartmental Neuroscience Program, Yale University School, New Haven, Connecticut 06511
- Department of Psychiatry, Yale University School, New Haven, Connecticut 06511
| | - James Bonanno
- Interdepartmental Neuroscience Program, Yale University School, New Haven, Connecticut 06511
- Department of Neurology, Yale University School, New Haven, Connecticut 06511
| | - Rory F O'Brien
- Department of Neurology, Yale University School, New Haven, Connecticut 06511
| | - Matias Murillo
- Interdepartmental Neuroscience Program, Yale University School, New Haven, Connecticut 06511
- Department of Neurology, Yale University School, New Haven, Connecticut 06511
| | - Sierra D Kauer
- Department of Neurology, Yale University School, New Haven, Connecticut 06511
| | - Neal Ravindra
- Department of Internal Medicine, Yale University School, New Haven, Connecticut 06511
- Department of Computer Science, Yale University School, New Haven, Connecticut 06511
| | - David Van Dijk
- Department of Internal Medicine, Yale University School, New Haven, Connecticut 06511
- Department of Computer Science, Yale University School, New Haven, Connecticut 06511
| | - William B Cafferty
- Department of Neurology, Yale University School, New Haven, Connecticut 06511
- Department of Neuroscience, Yale University School, New Haven, Connecticut 06511
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30
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Dann E, Cujba AM, Oliver AJ, Meyer KB, Teichmann SA, Marioni JC. Precise identification of cell states altered in disease using healthy single-cell references. Nat Genet 2023; 55:1998-2008. [PMID: 37828140 PMCID: PMC10632138 DOI: 10.1038/s41588-023-01523-7] [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: 11/09/2022] [Accepted: 09/05/2023] [Indexed: 10/14/2023]
Abstract
Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use.
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Affiliation(s)
- Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Ana-Maria Cujba
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Amanda J Oliver
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Kerstin B Meyer
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- Theory of Condensed Matter Group, The Cavendish Laboratory, University of Cambridge, Cambridge, UK.
| | - John C Marioni
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
- Genentech, San Francisco, CA, USA.
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31
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Dong M, Wang B, Wei J, de O Fonseca AH, Perry CJ, Frey A, Ouerghi F, Foxman EF, Ishizuka JJ, Dhodapkar RM, van Dijk D. Causal identification of single-cell experimental perturbation effects with CINEMA-OT. Nat Methods 2023; 20:1769-1779. [PMID: 37919419 PMCID: PMC10630139 DOI: 10.1038/s41592-023-02040-5] [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: 11/14/2022] [Accepted: 09/08/2023] [Indexed: 11/04/2023]
Abstract
Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. Here we present a causal-inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (causal independent effect module attribution + optimal transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment-effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment-effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly generated datasets: (1) rhinovirus and cigarette-smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette-smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.
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Affiliation(s)
- Mingze Dong
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Bao Wang
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Jessica Wei
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | | | - Curtis J Perry
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Alexander Frey
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
- Department of Surgery, Yale School of Medicine, New Haven, CT, USA
| | - Feriel Ouerghi
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA
| | - Ellen F Foxman
- Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
| | - Jeffrey J Ishizuka
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA.
- Department of Internal Medicine (Oncology), Yale School of Medicine, New Haven, CT, USA.
| | - Rahul M Dhodapkar
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - David van Dijk
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
- Department of Internal Medicine (Cardiology), Yale School of Medicine, New Haven, CT, USA.
- Department of Computer Science, Yale University, New Haven, CT, USA.
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32
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Hrovatin K, Bastidas-Ponce A, Bakhti M, Zappia L, Büttner M, Salinno C, Sterr M, Böttcher A, Migliorini A, Lickert H, Theis FJ. Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas. Nat Metab 2023; 5:1615-1637. [PMID: 37697055 PMCID: PMC10513934 DOI: 10.1038/s42255-023-00876-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 07/26/2023] [Indexed: 09/13/2023]
Abstract
Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.
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Affiliation(s)
- Karin Hrovatin
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Aimée Bastidas-Ponce
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Mostafa Bakhti
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Luke Zappia
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Ciro Salinno
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Michael Sterr
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Anika Böttcher
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Adriana Migliorini
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- McEwen Stem Cell Institute, University Health Network (UHN), Toronto, Ontario, Canada
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Medical Faculty, Technical University of Munich, Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Department of Mathematics, Technical University of Munich, Garching, Germany.
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33
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Ghersi JJ, Baldissera G, Hintzen J, Luff SA, Cheng S, Xia IF, Sturgeon CM, Nicoli S. Haematopoietic stem and progenitor cell heterogeneity is inherited from the embryonic endothelium. Nat Cell Biol 2023; 25:1135-1145. [PMID: 37460694 PMCID: PMC10415179 DOI: 10.1038/s41556-023-01187-9] [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: 08/04/2022] [Accepted: 06/09/2023] [Indexed: 08/12/2023]
Abstract
Definitive haematopoietic stem and progenitor cells (HSPCs) generate erythroid, lymphoid and myeloid lineages. HSPCs are produced in the embryo via transdifferentiation of haemogenic endothelial cells in the aorta-gonad-mesonephros (AGM). HSPCs in the AGM are heterogeneous in differentiation and proliferative output, but how these intrinsic differences are acquired remains unanswered. Here we discovered that loss of microRNA (miR)-128 in zebrafish leads to an expansion of HSPCs in the AGM with different cell cycle states and a skew towards erythroid and lymphoid progenitors. Manipulating miR-128 in differentiating haemogenic endothelial cells, before their transition to HSPCs, recapitulated the lineage skewing in both zebrafish and human pluripotent stem cells. miR-128 promotes Wnt and Notch signalling in the AGM via post-transcriptional repression of the Wnt inhibitor csnk1a1 and the Notch ligand jag1b. De-repression of cskn1a1 resulted in replicative and erythroid-biased HSPCs, whereas de-repression of jag1b resulted in G2/M and lymphoid-biased HSPCs with long-term consequence on the respective blood lineages. We propose that HSPC heterogeneity arises in the AGM endothelium and is programmed in part by Wnt and Notch signalling.
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Affiliation(s)
- Joey J Ghersi
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA
| | - Gabriel Baldissera
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA
| | - Jared Hintzen
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA
| | - Stephanie A Luff
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advancement of Blood Cancer Therapies, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Siyuan Cheng
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA
| | - Ivan Fan Xia
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA
| | - Christopher M Sturgeon
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Advancement of Blood Cancer Therapies, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cell, Developmental and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stefania Nicoli
- Yale Cardiovascular Research Center, Department of Internal Medicine, Section of Cardiology, Yale University School of Medicine, New Haven, CT, USA.
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA.
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, USA.
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34
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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35
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 168] [Impact Index Per Article: 168.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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36
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Sinner P, Peckert-Maier K, Mohammadian H, Kuhnt C, Draßner C, Panagiotakopoulou V, Rauber S, Linnerbauer M, Haimon Z, Royzman D, Kronenberg-Versteeg D, Ramming A, Steinkasserer A, Wild AB. Microglial expression of CD83 governs cellular activation and restrains neuroinflammation in experimental autoimmune encephalomyelitis. Nat Commun 2023; 14:4601. [PMID: 37528070 PMCID: PMC10394088 DOI: 10.1038/s41467-023-40370-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 07/21/2023] [Indexed: 08/03/2023] Open
Abstract
Microglial activation during neuroinflammation is crucial for coordinating the immune response against neuronal tissue, and the initial response of microglia determines the severity of neuro-inflammatory diseases. The CD83 molecule has been recently shown to modulate the activation status of dendritic cells and macrophages. Although the expression of CD83 is associated with early microglia activation in various disease settings, its functional relevance for microglial biology has been elusive. Here, we describe a thorough assessment of CD83 regulation in microglia and show that CD83 expression in murine microglia is not only associated with cellular activation but also with pro-resolving functions. Using single-cell RNA-sequencing, we reveal that conditional deletion of CD83 results in an over-activated state during neuroinflammation in the experimental autoimmune encephalomyelitis model. Subsequently, CD83-deficient microglia recruit more pathogenic immune cells to the central nervous system, deteriorating resolving mechanisms and exacerbating the disease. Thus, CD83 in murine microglia orchestrates cellular activation and, consequently, also the resolution of neuroinflammation.
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Affiliation(s)
- Pia Sinner
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany
| | - Katrin Peckert-Maier
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany
| | - Hashem Mohammadian
- Department of Internal Medicine 3, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Christine Kuhnt
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany
| | - Christina Draßner
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany
| | - Vasiliki Panagiotakopoulou
- Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, 72076, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, 72076, Germany
| | - Simon Rauber
- Department of Internal Medicine 3, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Mathias Linnerbauer
- Department of Neurology, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Zhana Haimon
- Departments of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Dmytro Royzman
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany
| | - Deborah Kronenberg-Versteeg
- Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, 72076, Germany
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, 72076, Germany
| | - Andreas Ramming
- Department of Internal Medicine 3, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91054, Erlangen, Germany
| | - Alexander Steinkasserer
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany
| | - Andreas B Wild
- Department of Immune Modulation, Uniklinikum Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg, 91052, Erlangen, Germany.
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37
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Pichler AC, Carrié N, Cuisinier M, Ghazali S, Voisin A, Axisa PP, Tosolini M, Mazzotti C, Golec DP, Maheo S, do Souto L, Ekren R, Blanquart E, Lemaitre L, Feliu V, Joubert MV, Cannons JL, Guillerey C, Avet-Loiseau H, Watts TH, Salomon BL, Joffre O, Grinberg-Bleyer Y, Schwartzberg PL, Lucca LE, Martinet L. TCR-independent CD137 (4-1BB) signaling promotes CD8 +-exhausted T cell proliferation and terminal differentiation. Immunity 2023; 56:1631-1648.e10. [PMID: 37392737 PMCID: PMC10649891 DOI: 10.1016/j.immuni.2023.06.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 03/29/2023] [Accepted: 06/08/2023] [Indexed: 07/03/2023]
Abstract
CD137 (4-1BB)-activating receptor represents a promising cancer immunotherapeutic target. Yet, the cellular program driven by CD137 and its role in cancer immune surveillance remain unresolved. Using T cell-specific deletion and agonist antibodies, we found that CD137 modulates tumor infiltration of CD8+-exhausted T (Tex) cells expressing PD1, Lag-3, and Tim-3 inhibitory receptors. T cell-intrinsic, TCR-independent CD137 signaling stimulated the proliferation and the terminal differentiation of Tex precursor cells through a mechanism involving the RelA and cRel canonical NF-κB subunits and Tox-dependent chromatin remodeling. While Tex cell accumulation induced by prophylactic CD137 agonists favored tumor growth, anti-PD1 efficacy was improved with subsequent CD137 stimulation in pre-clinical mouse models. Better understanding of T cell exhaustion has crucial implications for the treatment of cancer and infectious diseases. Our results identify CD137 as a critical regulator of Tex cell expansion and differentiation that holds potential for broad therapeutic applications.
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Affiliation(s)
- Andrea C Pichler
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Cell Signaling and Immunity Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Nadège Carrié
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Marine Cuisinier
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France
| | - Samira Ghazali
- Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), UPS, INSERM, CNRS, Toulouse, France
| | - Allison Voisin
- Centre de Recherche en Cancérologie de Lyon, Labex DEVweCAN, INSERM, CNRS, Université Claude Bernard Lyon 1, Centre Léon Bérard, Lyon, France
| | - Pierre-Paul Axisa
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Marie Tosolini
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Céline Mazzotti
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France
| | - Dominic P Golec
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Sabrina Maheo
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France
| | - Laura do Souto
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France
| | - Rüçhan Ekren
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Eve Blanquart
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Lea Lemaitre
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Virginie Feliu
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France
| | - Marie-Véronique Joubert
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France
| | - Jennifer L Cannons
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Camille Guillerey
- Cancer Immunotherapies Group, The University of Queensland, Brisbane, QLD, Australia
| | - Hervé Avet-Loiseau
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France
| | - Tania H Watts
- Department of Immunology, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Benoit L Salomon
- Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), UPS, INSERM, CNRS, Toulouse, France; Sorbonne Université, INSERM, CNRS, Centre d'Immunologie et des Maladies Infectieuses (CIMI-Paris), Paris, France
| | - Olivier Joffre
- Toulouse Institute for Infectious and Inflammatory Diseases (Infinity), UPS, INSERM, CNRS, Toulouse, France
| | - Yenkel Grinberg-Bleyer
- Centre de Recherche en Cancérologie de Lyon, Labex DEVweCAN, INSERM, CNRS, Université Claude Bernard Lyon 1, Centre Léon Bérard, Lyon, France
| | - Pamela L Schwartzberg
- Cell Signaling and Immunity Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Liliana E Lucca
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France.
| | - Ludovic Martinet
- Cancer Research Center of Toulouse (CRCT), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Université Toulouse III-Paul Sabatier (UPS), Toulouse, France; Institut Universitaire du Cancer, CHU Toulouse, Toulouse, France.
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38
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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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39
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Moore JL, Bhaskar D, Gao F, Matte-Martone C, Du S, Lathrop E, Ganesan S, Shao L, Norris R, Campamà Sanz N, Annusver K, Kasper M, Cox A, Hendry C, Rieck B, Krishnaswamy S, Greco V. Cell cycle controls long-range calcium signaling in the regenerating epidermis. J Cell Biol 2023; 222:e202302095. [PMID: 37102999 PMCID: PMC10140546 DOI: 10.1083/jcb.202302095] [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: 02/23/2023] [Revised: 03/27/2023] [Accepted: 03/31/2023] [Indexed: 04/28/2023] Open
Abstract
Skin homeostasis is maintained by stem cells, which must communicate to balance their regenerative behaviors. Yet, how adult stem cells signal across regenerative tissue remains unknown due to challenges in studying signaling dynamics in live mice. We combined live imaging in the mouse basal stem cell layer with machine learning tools to analyze patterns of Ca2+ signaling. We show that basal cells display dynamic intercellular Ca2+ signaling among local neighborhoods. We find that these Ca2+ signals are coordinated across thousands of cells and that this coordination is an emergent property of the stem cell layer. We demonstrate that G2 cells are required to initiate normal levels of Ca2+ signaling, while connexin43 connects basal cells to orchestrate tissue-wide coordination of Ca2+ signaling. Lastly, we find that Ca2+ signaling drives cell cycle progression, revealing a communication feedback loop. This work provides resolution into how stem cells at different cell cycle stages coordinate tissue-wide signaling during epidermal regeneration.
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Affiliation(s)
- Jessica L. Moore
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Dhananjay Bhaskar
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Feng Gao
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | | | - Shuangshuang Du
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Elizabeth Lathrop
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Smirthy Ganesan
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Lin Shao
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Rachael Norris
- Department of Cell Biology, UConn Health, Farmington, CT, USA
| | - Nil Campamà Sanz
- Department of Cell and Molecular Biology (CMB), Karolinska Institutet, Stockholm, Sweden
| | - Karl Annusver
- Department of Cell and Molecular Biology (CMB), Karolinska Institutet, Stockholm, Sweden
| | - Maria Kasper
- Department of Cell and Molecular Biology (CMB), Karolinska Institutet, Stockholm, Sweden
| | - Andy Cox
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Caroline Hendry
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
| | - Bastian Rieck
- Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany
| | - Smita Krishnaswamy
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Computer Science, Yale University, New Haven, CT, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
- Program for Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Valentina Greco
- Department of Genetics, Yale University School of Medicine, New Haven, CT, USA
- Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
- Yale Stem Cell Center, Yale University School of Medicine, New Haven, CT, USA
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA
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40
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Venkat A, Bhaskar D, Krishnaswamy S. Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data. Trends Immunol 2023; 44:551-563. [PMID: 37301677 DOI: 10.1016/j.it.2023.05.003] [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: 04/02/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 06/12/2023]
Abstract
Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels. Such a multigranular structure corresponds to key geometric and topological features. Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology. Ultimately, multiscale approaches go beyond classical clustering, revealing a more comprehensive picture of cellular heterogeneity.
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Affiliation(s)
- Aarthi Venkat
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA
| | | | - Smita Krishnaswamy
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA; Department of Genetics, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
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41
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Damo M, Hornick NI, Venkat A, William I, Clulo K, Venkatesan S, He J, Fagerberg E, Loza JL, Kwok D, Tal A, Buck J, Cui C, Singh J, Damsky WE, Leventhal JS, Krishnaswamy S, Joshi NS. PD-1 maintains CD8 T cell tolerance towards cutaneous neoantigens. Nature 2023; 619:151-159. [PMID: 37344588 PMCID: PMC10989189 DOI: 10.1038/s41586-023-06217-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/12/2023] [Indexed: 06/23/2023]
Abstract
The peripheral T cell repertoire of healthy individuals contains self-reactive T cells1,2. Checkpoint receptors such as PD-1 are thought to enable the induction of peripheral tolerance by deletion or anergy of self-reactive CD8 T cells3-10. However, this model is challenged by the high frequency of immune-related adverse events in patients with cancer who have been treated with checkpoint inhibitors11. Here we developed a mouse model in which skin-specific expression of T cell antigens in the epidermis caused local infiltration of antigen-specific CD8 T cells with an effector gene-expression profile. In this setting, PD-1 enabled the maintenance of skin tolerance by preventing tissue-infiltrating antigen-specific effector CD8 T cells from (1) acquiring a fully functional, pathogenic differentiation state, (2) secreting significant amounts of effector molecules, and (3) gaining access to epidermal antigen-expressing cells. In the absence of PD-1, epidermal antigen-expressing cells were eliminated by antigen-specific CD8 T cells, resulting in local pathology. Transcriptomic analysis of skin biopsies from two patients with cutaneous lichenoid immune-related adverse events showed the presence of clonally expanded effector CD8 T cells in both lesional and non-lesional skin. Thus, our data support a model of peripheral T cell tolerance in which PD-1 allows antigen-specific effector CD8 T cells to co-exist with antigen-expressing cells in tissues without immunopathology.
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Affiliation(s)
- Martina Damo
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Noah I Hornick
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Aarthi Venkat
- Departments of Genetics and of Computer Science, Yale University School of Medicine, New Haven, CT, USA
| | - Ivana William
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Kathryn Clulo
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Srividhya Venkatesan
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Jiaming He
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Eric Fagerberg
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Jennifer L Loza
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Darwin Kwok
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Aya Tal
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Jessica Buck
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Can Cui
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Jaiveer Singh
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - William E Damsky
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Jonathan S Leventhal
- Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
| | - Smita Krishnaswamy
- Departments of Genetics and of Computer Science, Yale University School of Medicine, New Haven, CT, USA
| | - Nikhil S Joshi
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.
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42
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Wang M, Jiang R, Mohanty S, Meng H, Shaw AC, Kleinstein SH. High-throughput single-cell profiling of B cell responses following inactivated influenza vaccination in young and older adults. Aging (Albany NY) 2023; 15:9250-9274. [PMID: 37367734 PMCID: PMC10564424 DOI: 10.18632/aging.204778] [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: 11/16/2022] [Accepted: 05/03/2023] [Indexed: 06/28/2023]
Abstract
Seasonal influenza contributes to a substantial disease burden, resulting in approximately 10 million hospital visits and 50 thousand deaths in a typical year in the United States. 70 - 85% of the mortality occurs in people over the age of 65. Influenza vaccination is the best protection against the virus, but it is less effective for the elderly, which may be in part due to differences in the quantity or type of B cells induced by vaccination. To investigate this possibility, we sorted pre- and post-vaccination peripheral blood B cells from three young and three older adults with strong antibody responses to the inactivated influenza vaccine and employed single-cell technology to simultaneously profile the gene expression and the B cell receptor (BCR) of the B cells. Prior to vaccination, we observed a higher somatic hypermutation frequency and a higher abundance of activated B cells in older adults than in young adults. Following vaccination, young adults mounted a more clonal response than older adults. The expanded clones included a mix of plasmablasts, activated B cells, and resting memory B cells in both age groups, with a decreased proportion of plasmablasts in older adults. Differential abundance analysis identified additional vaccine-responsive cells that were not part of expanded clones, especially in older adults. We observed broadly consistent gene expression changes in vaccine-responsive plasmablasts and greater heterogeneity among activated B cells between age groups. These quantitative and qualitative differences in the B cells provide insights into age-related changes in influenza vaccination response.
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Affiliation(s)
- Meng Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA
| | - Ruoyi Jiang
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Subhasis Mohanty
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Hailong Meng
- Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA
| | - Albert C. Shaw
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT 06510, USA
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43
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Maity AK, Teschendorff AE. Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data. Nat Commun 2023; 14:3244. [PMID: 37277399 DOI: 10.1038/s41467-023-39017-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: 10/24/2022] [Accepted: 05/25/2023] [Indexed: 06/07/2023] Open
Abstract
Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.
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Affiliation(s)
- Alok K Maity
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
- UCL Cancer Institute, Paul O'Gorman Building, University College London, 72 Huntley Street, London, WC1E 6BT, United Kingdom.
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44
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Pierce CA, Loh LN, Steach HR, Cheshenko N, Preston-Hurlburt P, Zhang F, Stransky S, Kravets L, Sidoli S, Philbrick W, Nassar M, Krishnaswamy S, Herold KC, Herold BC. HSV-2 triggers upregulation of MALAT1 in CD4+ T cells and promotes HIV latency reversal. J Clin Invest 2023; 133:e164317. [PMID: 37079384 PMCID: PMC10232005 DOI: 10.1172/jci164317] [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: 08/08/2022] [Accepted: 04/17/2023] [Indexed: 04/21/2023] Open
Abstract
Herpes simplex virus type 2 (HSV-2) coinfection is associated with increased HIV-1 viral loads and expanded tissue reservoirs, but the mechanisms are not well defined. HSV-2 recurrences result in an influx of activated CD4+ T cells to sites of viral replication and an increase in activated CD4+ T cells in peripheral blood. We hypothesized that HSV-2 induces changes in these cells that facilitate HIV-1 reactivation and replication and tested this hypothesis in human CD4+ T cells and 2D10 cells, a model of HIV-1 latency. HSV-2 promoted latency reversal in HSV-2-infected and bystander 2D10 cells. Bulk and single-cell RNA-Seq studies of activated primary human CD4+ T cells identified decreased expression of HIV-1 restriction factors and increased expression of transcripts including MALAT1 that could drive HIV replication in both the HSV-2-infected and bystander cells. Transfection of 2D10 cells with VP16, an HSV-2 protein that regulates transcription, significantly upregulated MALAT1 expression, decreased trimethylation of lysine 27 on histone H3 protein, and triggered HIV latency reversal. Knockout of MALAT1 from 2D10 cells abrogated the response to VP16 and reduced the response to HSV-2 infection. These results demonstrate that HSV-2 contributes to HIV-1 reactivation through diverse mechanisms, including upregulation of MALAT1 to release epigenetic silencing.
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Affiliation(s)
- Carl A. Pierce
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | - Lip Nam Loh
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | | | - Natalia Cheshenko
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | | | - Fengrui Zhang
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Leah Kravets
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
| | | | - William Philbrick
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Michel Nassar
- Department of Otorhinolaryngology–Head and Neck Surgery, Albert Einstein College of Medicine, New York, New York, USA
| | - Smita Krishnaswamy
- Department of Computational Biology
- Department of Genetics, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kevan C. Herold
- Department of Immunobiology, and
- Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA
| | - Betsy C. Herold
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, New York, USA
- Department of Pediatrics, Albert Einstein College of Medicine, New York, New York, USA
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45
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Ranek JS, Stallaert W, Milner J, Stanley N, Purvis JE. Feature selection for preserving biological trajectories in single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.09.540043. [PMID: 37214963 PMCID: PMC10197710 DOI: 10.1101/2023.05.09.540043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at: https://github.com/jranek/delve.
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Affiliation(s)
- Jolene S. Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jeremy E. Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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46
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Kuchroo M, DiStasio M, Song E, Calapkulu E, Zhang L, Ige M, Sheth AH, Majdoubi A, Menon M, Tong A, Godavarthi A, Xing Y, Gigante S, Steach H, Huang J, Huguet G, Narain J, You K, Mourgkos G, Dhodapkar RM, Hirn MJ, Rieck B, Wolf G, Krishnaswamy S, Hafler BP. Single-cell analysis reveals inflammatory interactions driving macular degeneration. Nat Commun 2023; 14:2589. [PMID: 37147305 PMCID: PMC10162998 DOI: 10.1038/s41467-023-37025-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 02/27/2023] [Indexed: 05/07/2023] Open
Abstract
Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer's disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.
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Affiliation(s)
- Manik Kuchroo
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | | | - Eric Song
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Eda Calapkulu
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Le Zhang
- Department of Neuroscience, Yale University, New Haven, CT, USA
- Department of Neurology, Yale University, New Haven, CT, USA
| | - Maryam Ige
- Yale School of Medicine, New Haven, CT, USA
| | | | - Abdelilah Majdoubi
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Madhvi Menon
- Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK
| | - Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA
| | | | - Yu Xing
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | - Scott Gigante
- Computational Biology, Bioinformatics Program, Yale University, New Haven, CT, USA
| | - Holly Steach
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Jessie Huang
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Guillaume Huguet
- Mila-Quebec AI institute, Montréal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
| | - Janhavi Narain
- Department of Computer Science, Rutgers University, New Brunswick, NJ, USA
| | - Kisung You
- Department of Genetics, Yale University, New Haven, CT, USA
| | - George Mourgkos
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA
| | | | - Matthew J Hirn
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
- Department of Mathematics, Michigan State University, East Lansing, MI, USA
| | - Bastian Rieck
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Guy Wolf
- Mila-Quebec AI institute, Montréal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA.
- Department of Genetics, Yale University, New Haven, CT, USA.
| | - Brian P Hafler
- Department of Pathology, Yale University, New Haven, CT, USA.
- Department of Ophthalmology and Visual Science, Yale University, New Haven, CT, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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47
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Satterlee JW, Evans LJ, Conlon BR, Conklin P, Martinez-Gomez J, Yen JR, Wu H, Sylvester AW, Specht CD, Cheng J, Johnston R, Coen E, Scanlon MJ. A Wox3-patterning module organizes planar growth in grass leaves and ligules. NATURE PLANTS 2023; 9:720-732. [PMID: 37142751 DOI: 10.1038/s41477-023-01405-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 03/28/2023] [Indexed: 05/06/2023]
Abstract
Grass leaves develop from a ring of primordial initial cells within the periphery of the shoot apical meristem, a pool of organogenic stem cells that generates all of the organs of the plant shoot. At maturity, the grass leaf is a flattened, strap-like organ comprising a proximal supportive sheath surrounding the stem and a distal photosynthetic blade. The sheath and blade are partitioned by a hinge-like auricle and the ligule, a fringe of epidermally derived tissue that grows from the adaxial (top) leaf surface. Together, the ligule and auricle comprise morphological novelties that are specific to grass leaves. Understanding how the planar outgrowth of grass leaves and their adjoining ligules is genetically controlled can yield insight into their evolutionary origins. Here we use single-cell RNA-sequencing analyses to identify a 'rim' cell type present at the margins of maize leaf primordia. Cells in the leaf rim have a distinctive identity and share transcriptional signatures with proliferating ligule cells, suggesting that a shared developmental genetic programme patterns both leaves and ligules. Moreover, we show that rim function is regulated by genetically redundant Wuschel-like homeobox3 (WOX3) transcription factors. Higher-order mutations in maize Wox3 genes greatly reduce leaf width and disrupt ligule outgrowth and patterning. Together, these findings illustrate the generalizable use of a rim domain during planar growth of maize leaves and ligules, and suggest a parsimonious model for the homology of the grass ligule as a distal extension of the leaf sheath margin.
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Affiliation(s)
- James W Satterlee
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Lukas J Evans
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Brianne R Conlon
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Phillip Conklin
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | | | - Jeffery R Yen
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Hao Wu
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Anne W Sylvester
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- Marine Biological Laboratory, Woods Hole, MA, USA
| | - Chelsea D Specht
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | - Jie Cheng
- John Innes Centre, Norwich Research Park, Norwich, UK
- State Key Laboratory of Systematic and Evolutionary Botany, Chinese Academy of Sciences, Beijing, China
| | - Robyn Johnston
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
- The Elshire Group Ltd., Palmerston North, New Zealand
| | - Enrico Coen
- John Innes Centre, Norwich Research Park, Norwich, UK
| | - Michael J Scanlon
- School of Integrative Plant Science, Cornell University, Ithaca, NY, USA.
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48
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Weigel B, Tegethoff JF, Grieder SD, Lim B, Nagarajan B, Liu YC, Truberg J, Papageorgiou D, Adrian-Segarra JM, Schmidt LK, Kaspar J, Poisel E, Heinzelmann E, Saraswat M, Christ M, Arnold C, Ibarra IL, Campos J, Krijgsveld J, Monyer H, Zaugg JB, Acuna C, Mall M. MYT1L haploinsufficiency in human neurons and mice causes autism-associated phenotypes that can be reversed by genetic and pharmacologic intervention. Mol Psychiatry 2023; 28:2122-2135. [PMID: 36782060 PMCID: PMC10575775 DOI: 10.1038/s41380-023-01959-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 02/15/2023]
Abstract
MYT1L is an autism spectrum disorder (ASD)-associated transcription factor that is expressed in virtually all neurons throughout life. How MYT1L mutations cause neurological phenotypes and whether they can be targeted remains enigmatic. Here, we examine the effects of MYT1L deficiency in human neurons and mice. Mutant mice exhibit neurodevelopmental delays with thinner cortices, behavioural phenotypes, and gene expression changes that resemble those of ASD patients. MYT1L target genes, including WNT and NOTCH, are activated upon MYT1L depletion and their chemical inhibition can rescue delayed neurogenesis in vitro. MYT1L deficiency also causes upregulation of the main cardiac sodium channel, SCN5A, and neuronal hyperactivity, which could be restored by shRNA-mediated knockdown of SCN5A or MYT1L overexpression in postmitotic neurons. Acute application of the sodium channel blocker, lamotrigine, also rescued electrophysiological defects in vitro and behaviour phenotypes in vivo. Hence, MYT1L mutation causes both developmental and postmitotic neurological defects. However, acute intervention can normalise resulting electrophysiological and behavioural phenotypes in adulthood.
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Affiliation(s)
- Bettina Weigel
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
- Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
| | - Jana F Tegethoff
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
- Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
| | - Sarah D Grieder
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Bryce Lim
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
- Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
| | - Bhuvaneswari Nagarajan
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Yu-Chao Liu
- Department of Clinical Neurobiology, University Hospital Heidelberg and DKFZ, Heidelberg, Germany
| | - Jule Truberg
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
- Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
| | - Dimitris Papageorgiou
- Division of Proteomics of Stem Cells and Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Medical Faculty, Heidelberg University, 69120, Heidelberg, Germany
| | - Juan M Adrian-Segarra
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Laura K Schmidt
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Janina Kaspar
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Eric Poisel
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Elisa Heinzelmann
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Manu Saraswat
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
- Faculty of Biosciences, Heidelberg University, 69120, Heidelberg, Germany
| | - Marleen Christ
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Christian Arnold
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69115, Heidelberg, Germany
| | - Ignacio L Ibarra
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69115, Heidelberg, Germany
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Joaquin Campos
- Chica and Heinz Schaller Research Group, Institute for Anatomy and Cell Biology, Heidelberg University, 69120, Heidelberg, Germany
| | - Jeroen Krijgsveld
- Division of Proteomics of Stem Cells and Cancer, German Cancer Research Center (DKFZ), 69120, Heidelberg, Germany
- Medical Faculty, Heidelberg University, 69120, Heidelberg, Germany
| | - Hannah Monyer
- Department of Clinical Neurobiology, University Hospital Heidelberg and DKFZ, Heidelberg, Germany
| | - Judith B Zaugg
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, 69115, Heidelberg, Germany
| | - Claudio Acuna
- Chica and Heinz Schaller Research Group, Institute for Anatomy and Cell Biology, Heidelberg University, 69120, Heidelberg, Germany
| | - Moritz Mall
- Cell Fate Engineering and Disease Modeling Group, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, 69120, Heidelberg, Germany.
- HITBR Hector Institute for Translational Brain Research gGmbH, 69120, Heidelberg, Germany.
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany.
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49
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Ren T, Chen C, Danilov AV, Liu S, Guan X, Du S, Wu X, Sherman MH, Spellman PT, Coussens LM, Adey AC, Mills GB, Wu LY, Xia Z. Supervised learning of high-confidence phenotypic subpopulations from single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.23.533712. [PMID: 36993424 PMCID: PMC10055361 DOI: 10.1101/2023.03.23.533712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Accurately identifying phenotype-relevant cell subsets from heterogeneous cell populations is crucial for delineating the underlying mechanisms driving biological or clinical phenotypes. Here, by deploying a learning with rejection strategy, we developed a novel supervised learning framework called PENCIL to identify subpopulations associated with categorical or continuous phenotypes from single-cell data. By embedding a feature selection function into this flexible framework, for the first time, we were able to select informative features and identify cell subpopulations simultaneously, which enables the accurate identification of phenotypic subpopulations otherwise missed by methods incapable of concurrent gene selection. Furthermore, the regression mode of PENCIL presents a novel ability for supervised phenotypic trajectory learning of subpopulations from single-cell data. We conducted comprehensive simulations to evaluate PENCIĽs versatility in simultaneous gene selection, subpopulation identification and phenotypic trajectory prediction. PENCIL is fast and scalable to analyze 1 million cells within 1 hour. Using the classification mode, PENCIL detected T-cell subpopulations associated with melanoma immunotherapy outcomes. Moreover, when applied to scRNA-seq of a mantle cell lymphoma patient with drug treatment across multiple time points, the regression mode of PENCIL revealed a transcriptional treatment response trajectory. Collectively, our work introduces a scalable and flexible infrastructure to accurately identify phenotype-associated subpopulations from single-cell data.
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Affiliation(s)
- Tao Ren
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Canping Chen
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | | | - Susan Liu
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Xiangnan Guan
- Department of Oncology Biomarker Development, Genentech Inc, South San Francisco, CA, USA
| | - Shunyi Du
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
| | - Xiwei Wu
- City of Hope National Medical Center, Duarte, CA, USA
| | - Mara H. Sherman
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Paul T. Spellman
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Lisa M. Coussens
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Andrew C. Adey
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Gordon B. Mills
- Division of Oncological Sciences Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Ling-Yun Wu
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Xia
- Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
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50
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Bogaert D, van Beveren GJ, de Koff EM, Lusarreta Parga P, Balcazar Lopez CE, Koppensteiner L, Clerc M, Hasrat R, Arp K, Chu MLJN, de Groot PCM, Sanders EAM, van Houten MA, de Steenhuijsen Piters WAA. Mother-to-infant microbiota transmission and infant microbiota development across multiple body sites. Cell Host Microbe 2023; 31:447-460.e6. [PMID: 36893737 DOI: 10.1016/j.chom.2023.01.018] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 03/11/2023]
Abstract
Early-life microbiota seeding and subsequent development is crucial to future health. Cesarean-section (CS) birth, as opposed to vaginal delivery, affects early mother-to-infant transmission of microbes. Here, we assess mother-to-infant microbiota seeding and early-life microbiota development across six maternal and four infant niches over the first 30 days of life in 120 mother-infant pairs. Across all infants, we estimate that on average 58.5% of the infant microbiota composition can be attributed to any of the maternal source communities. All maternal source communities seed multiple infant niches. We identify shared and niche-specific host/environmental factors shaping the infant microbiota. In CS-born infants, we report reduced seeding of infant fecal microbiota by maternal fecal microbes, whereas colonization with breastmilk microbiota is increased when compared with vaginally born infants. Therefore, our data suggest auxiliary routes of mother-to-infant microbial seeding, which may compensate for one another, ensuring that essential microbes/microbial functions are transferred irrespective of disrupted transmission routes.
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Affiliation(s)
- Debby Bogaert
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK.
| | - Gina J van Beveren
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Emma M de Koff
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Paula Lusarreta Parga
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Carlos E Balcazar Lopez
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Lilian Koppensteiner
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Melanie Clerc
- Centre for Inflammation Research, Queen's Medical Research Institute, University of Edinburgh, EH16 4TJ Edinburgh, UK
| | - Raiza Hasrat
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Kayleigh Arp
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Mei Ling J N Chu
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | - Pieter C M de Groot
- Department of Obstetrics and Gynaecology, Spaarne Gasthuis, 2035 RC Haarlem, the Netherlands
| | - Elisabeth A M Sanders
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands
| | | | - Wouter A A de Steenhuijsen Piters
- Department of Paediatric Immunology and Infectious Diseases, Wilhelmina Children's Hospital, University Medical Center Utrecht, 3584 EA Utrecht, the Netherlands; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, 3721 MA Bilthoven, the Netherlands.
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