1
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Miller TE, El Farran CA, Couturier CP, Chen Z, D'Antonio JP, Verga J, Villanueva MA, Gonzalez Castro LN, Tong YE, Saadi TA, Chiocca AN, Zhang Y, Fischer DS, Heiland DH, Guerriero JL, Petrecca K, Suva ML, Shalek AK, Bernstein BE. Programs, origins and immunomodulatory functions of myeloid cells in glioma. Nature 2025; 640:1072-1082. [PMID: 40011771 PMCID: PMC12018266 DOI: 10.1038/s41586-025-08633-8] [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: 10/09/2023] [Accepted: 01/10/2025] [Indexed: 02/28/2025]
Abstract
Gliomas are incurable malignancies notable for having an immunosuppressive microenvironment with abundant myeloid cells, the immunomodulatory phenotypes of which remain poorly defined1. Here we systematically investigate these phenotypes by integrating single-cell RNA sequencing, chromatin accessibility, spatial transcriptomics and glioma organoid explant systems. We discovered four immunomodulatory expression programs: microglial inflammatory and scavenger immunosuppressive programs, which are both unique to primary brain tumours, and systemic inflammatory and complement immunosuppressive programs, which are also expressed by non-brain tumours. The programs are not contingent on myeloid cell type, developmental origin or tumour mutational state, but instead are driven by microenvironmental cues, including tumour hypoxia, interleukin-1β, TGFβ and standard-of-care dexamethasone treatment. Their relative expression can predict immunotherapy response and overall survival. By associating the respective programs with mediating genomic elements, transcription factors and signalling pathways, we uncover strategies for manipulating myeloid-cell phenotypes. Our study provides a framework to understand immunomodulation by myeloid cells in glioma and a foundation for the development of more-effective immunotherapies.
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Affiliation(s)
- Tyler E Miller
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard Medical School, Boston, MA, USA
- Department of Pathology, Case Western Reserve University School of Medicine and Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Chadi A El Farran
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
- Ludwig Center at Harvard Medical School, Boston, MA, USA
| | - Charles P Couturier
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA
- Ragon Institute of MGH, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
- Brain Tumour Research Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Zeyu Chen
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
| | - Joshua P D'Antonio
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA
| | - Julia Verga
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Martin A Villanueva
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - L Nicolas Gonzalez Castro
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute and Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yuzhou Evelyn Tong
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Tariq Al Saadi
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrew N Chiocca
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
| | - Yuanyuan Zhang
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Dieter Henrik Heiland
- Microenvironment and Immunology Research Laboratory, Medical Center - University of Freiburg, Freiburg, Germany
- Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jennifer L Guerriero
- Ludwig Center at Harvard Medical School, Boston, MA, USA
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA
| | - Kevin Petrecca
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada
| | - Mario L Suva
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alex K Shalek
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Ragon Institute of MGH, Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Bradley E Bernstein
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard Medical School, Boston, MA, USA.
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2
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Chen H, Zhang X, Cheng Q, Shen X, Zeng L, Wang Y, Fan L, Jiang W. snRNA-seq of long-preserved FFPE samples from colorectal liver metastasis lesions with diverse prognoses. Sci Data 2024; 11:1434. [PMID: 39725704 DOI: 10.1038/s41597-024-04323-8] [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: 06/05/2024] [Accepted: 12/18/2024] [Indexed: 12/28/2024] Open
Abstract
Differences in prognostic outcomes are prevalent in patients with colorectal cancer liver metastases. Comparative analysis of tissue samples, particularly applying single-cell transcriptome sequencing technology, can provide a deeper understanding of potential impacting factors. However, long-term monitoring for prognosis determination necessitates extended preservation of tissue samples using formalin-fixed and paraffin-embedded (FFPE) treatments, which can cause substantial RNA degradation, presenting challenges to single-cell or single-nucleus sequencing. In this study, employing snRandom-seq, a single-nucleus RNA sequencing (snRNA-seq) technology specifically for FFPE samples, we tested multiple lesion samples from 18 distinctive colorectal cancer liver metastasis cases with diverse prognostic outcomes that have been preserved for at least three years (mostly over five years). The process yielded expression data from 82,285 cells. The high-quality snRNA-seq data demonstrate the feasibility of single-nucleus sequencing in long-term preserved FFPE samples, offering potential insights into the heterogeneity between different prognoses of colorectal cancer liver metastases, and the relationship between the heterogeneity within different lesions of the same patient and prognosis.
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Affiliation(s)
- Hongyu Chen
- School of Medicine, Hangzhou City University, Hangzhou, China
- Institute of Bioinformatics and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China
| | - Xiang Zhang
- Department of Colorectal Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- The First Clinical Medical College of Lanzhou University, Lanzhou, China
| | - Qing Cheng
- Institute of Bioinformatics and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China
| | - Xiner Shen
- Institute of Bioinformatics and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China
| | - Linghui Zeng
- School of Medicine, Hangzhou City University, Hangzhou, China
| | - Yongcheng Wang
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Longjiang Fan
- Institute of Bioinformatics and James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China
| | - Weiqin Jiang
- Department of Colorectal Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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3
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Kuemmerle LB, Luecken MD, Firsova AB, Barros de Andrade E Sousa L, Straßer L, Mekki II, Campi F, Heumos L, Shulman M, Beliaeva V, Hediyeh-Zadeh S, Schaar AC, Mahbubani KT, Sountoulidis A, Balassa T, Kovacs F, Horvath P, Piraud M, Ertürk A, Samakovlis C, Theis FJ. Probe set selection for targeted spatial transcriptomics. Nat Methods 2024; 21:2260-2270. [PMID: 39558096 PMCID: PMC11621025 DOI: 10.1038/s41592-024-02496-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/30/2024] [Indexed: 11/20/2024]
Abstract
Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.
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Affiliation(s)
- Louis B Kuemmerle
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Malte D Luecken
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Institute of Lung Health & Immunity, Helmholtz Munich, Member of the German Center for Lung Research (DZL), Munich, Germany
- German Center for Lung Research (DZL), Gießen, Germany
| | - Alexandra B Firsova
- SciLifeLab and Department of Molecular Biosciences, Stockholm University, Stockholm, Sweden
| | | | - Lena Straßer
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | | | - Francesco Campi
- Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
| | - Lukas Heumos
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
- Institute of Lung Biology and Disease and Comprehensive Pneumology Center, Helmholtz Zentrum München, German Center for Lung Research (DZL), Munich, Germany
| | - Maiia Shulman
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Valentina Beliaeva
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
| | - Soroor Hediyeh-Zadeh
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
- Munich Center for Machine Learning, Technical University of Munich, Munich, Germany
| | - Krishnaa T Mahbubani
- Department of Surgery, University of Cambridge and Cambridge NIHR Biomedical Research Centre, Cambridge, UK
| | | | - Tamás Balassa
- Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged, Hungary
| | | | - Peter Horvath
- Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged, Hungary
- Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Marie Piraud
- Helmholtz AI, Helmholtz Zentrum München, Neuherberg, Germany
| | - Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- School of Medicine, Koç University, İstanbul, Turkey
| | - Christos Samakovlis
- SciLifeLab and Department of Molecular Biosciences, Stockholm University, Stockholm, Sweden
- Cardiopulmonary Institute, Justus Liebig University, Giessen, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
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4
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Sun Y, Qiu P. Hierarchical marker genes selection in scRNA-seq analysis. PLoS Comput Biol 2024; 20:e1012643. [PMID: 39666603 PMCID: PMC11637363 DOI: 10.1371/journal.pcbi.1012643] [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: 11/21/2023] [Accepted: 11/16/2024] [Indexed: 12/14/2024] Open
Abstract
When analyzing scRNA-seq data containing heterogeneous cell populations, an important task is to select informative marker genes to distinguish various cell clusters and annotate the clusters with biologically meaningful cell types. In existing analysis methods and pipelines, marker genes are typically identified using a one-vs-all strategy, examining differential expression between one cell cluster versus the combination of all other cell clusters. However, this strategy applied to cell clusters belonging to closely related cell types often generates overlapping marker genes, which capture the common signature of closely related cell clusters but provide limited information for distinguishing them. To address the limitations of the one-vs-all strategy, we propose a hierarchical marker gene selection strategy that groups similar cell clusters and selects marker genes in a hierarchical manner. This strategy is able to improve the accuracy and interpretability of cell type identification in single-cell RNA-seq data.
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Affiliation(s)
- Yutong Sun
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Peng Qiu
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States of America
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5
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Gardner AL, Jost TA, Morgan D, Brock A. Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations. NPJ Syst Biol Appl 2024; 10:120. [PMID: 39420005 PMCID: PMC11487074 DOI: 10.1038/s41540-024-00441-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 09/17/2024] [Indexed: 10/19/2024] Open
Abstract
Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression and there is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet systems to study these processes in vitro are limited. Single-cell RNA sequencing (scRNA-seq) has revealed that some cancer cell lines include distinct subpopulations. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. With clusterCleaver, ESAM and BST2/tetherin were experimentally validated as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification of surface markers for tracking and isolating transcriptomically distinct subpopulations within cell lines. This tool paves the way for studies on coexisting cancer cell subpopulations in well-defined in vitro systems.
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Affiliation(s)
- Andrea L Gardner
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Tyler A Jost
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Daylin Morgan
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
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6
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Xie Y, Yang J, Ouyang JF, Petretto E. scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets. Brief Bioinform 2024; 25:bbae482. [PMID: 39350339 PMCID: PMC11442147 DOI: 10.1093/bib/bbae482] [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: 03/25/2024] [Revised: 08/30/2024] [Accepted: 09/12/2024] [Indexed: 10/04/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.
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Affiliation(s)
- Yi Xie
- Programme in Cardiovascular and Metabolic Disorders, Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
| | - Jianfei Yang
- The School of Mechanical and Aerospace Engineering and the School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Ave, Singapore 639798, Singapore
| | - John F Ouyang
- Programme in Cardiovascular and Metabolic Disorders, Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
| | - Enrico Petretto
- Programme in Cardiovascular and Metabolic Disorders, Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore
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7
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Fesneau O, Thevin V, Pinet V, Goldsmith C, Vieille B, M'Homa Soudja S, Lattanzio R, Hahne M, Dardalhon V, Hernandez-Vargas H, Benech N, Marie JC. An intestinal T H17 cell-derived subset can initiate cancer. Nat Immunol 2024; 25:1637-1649. [PMID: 39060651 PMCID: PMC11362008 DOI: 10.1038/s41590-024-01909-7] [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: 06/15/2023] [Accepted: 06/24/2024] [Indexed: 07/28/2024]
Abstract
Approximately 25% of cancers are preceded by chronic inflammation that occurs at the site of tumor development. However, whether this multifactorial oncogenic process, which commonly occurs in the intestines, can be initiated by a specific immune cell population is unclear. Here, we show that an intestinal T cell subset, derived from interleukin-17 (IL-17)-producing helper T (TH17) cells, induces the spontaneous transformation of the intestinal epithelium. This subset produces inflammatory cytokines, and its tumorigenic potential is not dependent on IL-17 production but on the transcription factors KLF6 and T-BET and interferon-γ. The development of this cell type is inhibited by transforming growth factor-β1 (TGFβ1) produced by intestinal epithelial cells. TGFβ signaling acts on the pretumorigenic TH17 cell subset, preventing its progression to the tumorigenic stage by inhibiting KLF6-dependent T-BET expression. This study therefore identifies an intestinal T cell subset initiating cancer.
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Affiliation(s)
- Olivier Fesneau
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
| | - Valentin Thevin
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
| | - Valérie Pinet
- Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier, CNRS, Montpellier, France
| | - Chloe Goldsmith
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
| | - Baptiste Vieille
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
| | - Saïdi M'Homa Soudja
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
| | - Rossano Lattanzio
- Department of Innovative Technologies in Medicine & Dentistry, Center for Advanced Studies and Technology (CAST), G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Michael Hahne
- Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier, CNRS, Montpellier, France
| | - Valérie Dardalhon
- Institut de Génétique Moléculaire de Montpellier (IGMM), Université de Montpellier, CNRS, Montpellier, France
| | - Hector Hernandez-Vargas
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
| | - Nicolas Benech
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France
- Hospices Civils de Lyon, Service d'Hépato-Gastroentérologie, Croix Rousse Hospital, Lyon, France
| | - Julien C Marie
- Cancer Research Center of Lyon (CRCL) INSERM U 1052, CNRS UMR 5286, Centre Léon Bérard, Claude Bernard Lyon 1 University, Lyon, France.
- Equipe Labellisée Ligue Nationale Contre le Cancer, Lyon, France.
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8
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Ji L, Wang A, Sonthalia S, Naiman DQ, Younes L, Colantuoni C, Geman D. CellCover Captures Neural Stem Cell Progression in Mammalian Neocortical Development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.06.535943. [PMID: 37383947 PMCID: PMC10299349 DOI: 10.1101/2023.04.06.535943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Definition of cell classes across the tissues of living organisms is central in the analysis of growing atlases of single-cell RNA sequencing (scRNA-seq) data across biomedicine. Marker genes for cell classes are most often defined by differential expression (DE) methods that serially assess individual genes across landscapes of diverse cells. This serial approach has been extremely useful, but is limited because it ignores possible redundancy or complementarity across genes that can only be captured by analyzing multiple genes simultaneously. We aim to identify discriminating panels of genes. To efficiently explore the vast space of possible marker panels, leverage the large number of cells often sequenced, and overcome zero-inflation in scRNA-seq data, we propose viewing gene panel selection as a variation of the "minimal set-covering problem" in combinatorial optimization. We show that this new method, CellCover, captures cell-class-specific signals in the developing mouse neocortex that are distinct from those defined by DE methods. Transfer learning experiments across mouse, primate, and human data demonstrate that CellCover identifies markers of conserved cell classes in neurogenesis, as well as temporal progression in both progenitors and neurons. Exploring markers of human outer radial glia (oRG, or basal RG) across mammals, we show that transcriptomic elements of this key cell type in the expansion of the human cortex appeared in gliogenic precursors of the rodent before the full program emerged in the primate lineage. We have assembled the public datasets we use in this report at NeMO analytics where the expression of individual genes {NeMO Individual Genes} and marker gene panels can be freely explored {NeMO: Telley 3 Sets Covering Panels}, {NeMO: Telley 12 Sets Covering Panels}, and {NeMO: Sorted Brain Cell Covering Panels}. CellCover is available in {CellCover R} and {CellCover Python}.
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9
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Jia Y, Ma P, Yao Q. CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes. Sci Rep 2024; 14:13151. [PMID: 38849445 PMCID: PMC11161599 DOI: 10.1038/s41598-024-63492-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe ( https://github.com/yao-laboratory/cellMarkerPipe ), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.
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Affiliation(s)
- Yinglu Jia
- School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA
- Department of Chemistry, University of Nebraska Lincoln, Hamilton Hall, Lincoln, NE, 68588, USA
| | - Pengchong Ma
- School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA
| | - Qiuming Yao
- School of Computing, University of Nebraska Lincoln, 256 Avery Hall, Lincoln, NE, 68588, USA.
- Nebraska Center for the Prevention of Obesity Diseases, 316C Leverton Hall, Lincoln, NE, 68583, USA.
- Nebraska Center for Virology, University of Nebraska, 4240 Fair St., Lincoln, NE, 68583, USA.
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10
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Gardner AL, Jost TA, Brock A. Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.28.596337. [PMID: 38854060 PMCID: PMC11160629 DOI: 10.1101/2024.05.28.596337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.
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Affiliation(s)
- Andrea L. Gardner
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Tyler A. Jost
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin
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11
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Su EY, Fread K, Goggin S, Zunder ER, Cahan P. Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells. Sci Data 2024; 11:559. [PMID: 38816402 PMCID: PMC11139855 DOI: 10.1038/s41597-024-03399-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/21/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.
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Affiliation(s)
- Emily Y Su
- Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kristen Fread
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Sarah Goggin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Eli R Zunder
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Patrick Cahan
- Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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12
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Lee EJ, Suh M, Choi H, Choi Y, Hwang DW, Bae S, Lee DS. Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model. BMC Genomics 2024; 25:516. [PMID: 38796425 PMCID: PMC11128132 DOI: 10.1186/s12864-024-10434-8] [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: 02/18/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.
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Affiliation(s)
- Eun Ji Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Minseok Suh
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yoori Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Cliniclal Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Do Won Hwang
- Research and Development Center, THERABEST Inc., Seocho-daero 40-gil, Seoul, 06657, Republic of Korea
| | - Sungwoo Bae
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Medical Science and Engineering, School of Convergence Science and Technology, POSTECH, Pohang, Republic of Korea.
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13
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Zhou X, Seow WY, Ha N, Cheng TH, Jiang L, Boonruangkan J, Goh JJL, Prabhakar S, Chou N, Chen KH. Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes. Nat Commun 2024; 15:2342. [PMID: 38491027 PMCID: PMC10943009 DOI: 10.1038/s41467-024-46669-y] [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: 08/11/2023] [Accepted: 03/04/2024] [Indexed: 03/18/2024] Open
Abstract
High-dimensional, spatially resolved analysis of intact tissue samples promises to transform biomedical research and diagnostics, but existing spatial omics technologies are costly and labor-intensive. We present Fluorescence In Situ Hybridization of Cellular HeterogeneIty and gene expression Programs (FISHnCHIPs) for highly sensitive in situ profiling of cell types and gene expression programs. FISHnCHIPs achieves this by simultaneously imaging ~2-35 co-expressed genes (clustered into modules) that are spatially co-localized in tissues, resulting in similar spatial information as single-gene Fluorescence In Situ Hybridization (FISH), but with ~2-20-fold higher sensitivity. Using FISHnCHIPs, we image up to 53 modules from the mouse kidney and mouse brain, and demonstrate high-speed, large field-of-view profiling of a whole tissue section. FISHnCHIPs also reveals spatially restricted localizations of cancer-associated fibroblasts in a human colorectal cancer biopsy. Overall, FISHnCHIPs enables fast, robust, and scalable cell typing of tissues with normal physiology or undergoing pathogenesis.
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Affiliation(s)
- Xinrui Zhou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Wan Yi Seow
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Norbert Ha
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Teh How Cheng
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Lingfan Jiang
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jeeranan Boonruangkan
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Jolene Jie Lin Goh
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Shyam Prabhakar
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore
| | - Nigel Chou
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
| | - Kok Hao Chen
- Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Singapore, 138672, Singapore.
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14
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Nechanitzky R, Ramachandran P, Nechanitzky D, Li WY, Wakeham AC, Haight J, Saunders ME, Epelman S, Mak TW. CaSSiDI: novel single-cell "Cluster Similarity Scoring and Distinction Index" reveals critical functions for PirB and context-dependent Cebpb repression. Cell Death Differ 2024; 31:265-279. [PMID: 38383888 PMCID: PMC10923835 DOI: 10.1038/s41418-024-01268-8] [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: 05/16/2023] [Revised: 01/15/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
PirB is an inhibitory cell surface receptor particularly prominent on myeloid cells. PirB curtails the phenotypes of activated macrophages during inflammation or tumorigenesis, but its functions in macrophage homeostasis are obscure. To elucidate PirB-related functions in macrophages at steady-state, we generated and compared single-cell RNA-sequencing (scRNAseq) datasets obtained from myeloid cell subsets of wild type (WT) and PirB-deficient knockout (PirB KO) mice. To facilitate this analysis, we developed a novel approach to clustering parameter optimization called "Cluster Similarity Scoring and Distinction Index" (CaSSiDI). We demonstrate that CaSSiDI is an adaptable computational framework that facilitates tandem analysis of two scRNAseq datasets by optimizing clustering parameters. We further show that CaSSiDI offers more advantages than a standard Seurat analysis because it allows direct comparison of two or more independently clustered datasets, thereby alleviating the need for batch-correction while identifying the most similar and different clusters. Using CaSSiDI, we found that PirB is a novel regulator of Cebpb expression that controls the generation of Ly6Clo patrolling monocytes and the expansion properties of peritoneal macrophages. PirB's effect on Cebpb is tissue-specific since it was not observed in splenic red pulp macrophages (RPMs). However, CaSSiDI revealed a segregation of the WT RPM population into a CD68loIrf8+ "neuronal-primed" subset and an CD68hiFtl1+ "iron-loaded" subset. Our results establish the utility of CaSSiDI for single-cell assay analyses and the determination of optimal clustering parameters. Our application of CaSSiDI in this study has revealed previously unknown roles for PirB in myeloid cell populations. In particular, we have discovered homeostatic functions for PirB that are related to Cebpb expression in distinct macrophage subsets.
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Affiliation(s)
- Robert Nechanitzky
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.
- Providence Therapeutics Holdings Inc., Calgary, AB, Canada.
| | - Parameswaran Ramachandran
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Duygu Nechanitzky
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Wanda Y Li
- Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Andrew C Wakeham
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Jillian Haight
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Mary E Saunders
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada
| | - Slava Epelman
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Ted Rogers Centre for Heart Research, Translational Biology and Engineering Program, Toronto, ON, Canada
- Peter Munk Cardiac Centre, UHN, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Departments of Immunology and Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Tak W Mak
- Princess Margaret Cancer Centre, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada.
- Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China.
- Department of Pathology Department of Pathology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
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15
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Minutti CM, Piot C, Pereira da Costa M, Chakravarty P, Rogers N, Huerga Encabo H, Cardoso A, Loong J, Bessou G, Mionnet C, Langhorne J, Bonnet D, Dalod M, Tomasello E, Reis e Sousa C. Distinct ontogenetic lineages dictate cDC2 heterogeneity. Nat Immunol 2024; 25:448-461. [PMID: 38351322 PMCID: PMC10907303 DOI: 10.1038/s41590-024-01745-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 01/08/2024] [Indexed: 03/03/2024]
Abstract
Conventional dendritic cells (cDCs) include functionally and phenotypically diverse populations, such as cDC1s and cDC2s. The latter population has been variously subdivided into Notch-dependent cDC2s, KLF4-dependent cDC2s, T-bet+ cDC2As and T-bet- cDC2Bs, but it is unclear how all these subtypes are interrelated and to what degree they represent cell states or cell subsets. All cDCs are derived from bone marrow progenitors called pre-cDCs, which circulate through the blood to colonize peripheral tissues. Here, we identified distinct mouse pre-cDC2 subsets biased to give rise to cDC2As or cDC2Bs. We showed that a Siglec-H+ pre-cDC2A population in the bone marrow preferentially gave rise to Siglec-H- CD8α+ pre-cDC2As in tissues, which differentiated into T-bet+ cDC2As. In contrast, a Siglec-H- fraction of pre-cDCs in the bone marrow and periphery mostly generated T-bet- cDC2Bs, a lineage marked by the expression of LysM. Our results showed that cDC2A versus cDC2B fate specification starts in the bone marrow and suggest that cDC2 subsets are ontogenetically determined lineages, rather than cell states imposed by the peripheral tissue environment.
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Affiliation(s)
- Carlos M Minutti
- Immunobiology Laboratory, The Francis Crick Institute, London, UK.
- Immunoregulation Laboratory, Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
| | - Cécile Piot
- Immunobiology Laboratory, The Francis Crick Institute, London, UK
| | | | - Probir Chakravarty
- Bioinformatics and Biostatistics, The Francis Crick Institute, London, UK
| | - Neil Rogers
- Immunobiology Laboratory, The Francis Crick Institute, London, UK
| | | | - Ana Cardoso
- Immunobiology Laboratory, The Francis Crick Institute, London, UK
| | - Jane Loong
- Retroviral Immunology Laboratory, The Francis Crick Institute, London, UK
| | - Gilles Bessou
- Aix-Marseille University, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, Marseille, France
| | - Cyrille Mionnet
- Aix-Marseille University, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, Marseille, France
| | - Jean Langhorne
- Malaria Immunology Laboratory, The Francis Crick Institute, London, UK
| | - Dominique Bonnet
- Haematopoietic Stem Cell Laboratory, The Francis Crick Institute, London, UK
| | - Marc Dalod
- Aix-Marseille University, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, Marseille, France
| | - Elena Tomasello
- Aix-Marseille University, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Centre d'Immunologie de Marseille-Luminy, Turing Center for Living Systems, Marseille, France
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16
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Pullin JM, McCarthy DJ. A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol 2024; 25:56. [PMID: 38409056 PMCID: PMC10895860 DOI: 10.1186/s13059-024-03183-0] [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: 09/13/2022] [Accepted: 02/07/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalog and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper, we benchmark 59 computational methods for selecting marker genes in scRNA-seq data. RESULTS We compare the performance of the methods using 14 real scRNA-seq datasets and over 170 additional simulated datasets. Methods are compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed, and their implementation quality. In addition, various case studies are used to scrutinize the most commonly used methods, highlighting issues and inconsistencies. CONCLUSIONS Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student's t-test, and logistic regression.
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Affiliation(s)
- Jeffrey M Pullin
- Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, 3065, VIC, Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, VIC, Australia
- Melbourne Integrative Genomics, University of Melbourne, Parkville, 3010, VIC, Australia
| | - Davis J McCarthy
- Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, 9 Princes St, Fitzroy, 3065, VIC, Australia.
- School of Mathematics and Statistics, University of Melbourne, Parkville, 3010, VIC, Australia.
- Melbourne Integrative Genomics, University of Melbourne, Parkville, 3010, VIC, Australia.
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17
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Gregory W, Sarwar N, Kevrekidis G, Villar S, Dumitrascu B. MarkerMap: nonlinear marker selection for single-cell studies. NPJ Syst Biol Appl 2024; 10:17. [PMID: 38351188 PMCID: PMC10864304 DOI: 10.1038/s41540-024-00339-3] [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: 04/26/2023] [Accepted: 01/17/2024] [Indexed: 02/16/2024] Open
Abstract
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intractable. Here we introduce MarkerMap, a generative model for selecting minimal gene sets which are maximally informative of cell type origin and enable whole transcriptome reconstruction. MarkerMap provides a scalable framework for both supervised marker selection, aimed at identifying specific cell type populations, and unsupervised marker selection, aimed at gene expression imputation and reconstruction. We benchmark MarkerMap's competitive performance against previously published approaches on real single cell gene expression data sets. MarkerMap is available as a pip installable package, as a community resource aimed at developing explainable machine learning techniques for enhancing interpretability in single-cell studies.
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Affiliation(s)
- Wilson Gregory
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Nabeel Sarwar
- Center for Data Science, New York University, New York, NY, 10012, USA
| | - George Kevrekidis
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Soledad Villar
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
| | - Bianca Dumitrascu
- Department of Statistics, Columbia University, New York, NY, 10027, USA.
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, 10027, USA.
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18
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Dawson A, Zarou MM, Prasad B, Bittencourt-Silvestre J, Zerbst D, Himonas E, Hsieh YC, van Loon I, Blanco GR, Ianniciello A, Kerekes Z, Krishnan V, Agarwal P, Almasoudi H, McCluskey L, Hopcroft LEM, Scott MT, Baquero P, Dunn K, Vetrie D, Copland M, Bhatia R, Coffelt SB, Tiong OS, Wheadon H, Zanivan S, Kirschner K, Helgason GV. Leukaemia exposure alters the transcriptional profile and function of BCR::ABL1 negative macrophages in the bone marrow niche. Nat Commun 2024; 15:1090. [PMID: 38316788 PMCID: PMC10844594 DOI: 10.1038/s41467-024-45471-0] [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: 04/29/2022] [Accepted: 01/25/2024] [Indexed: 02/07/2024] Open
Abstract
Macrophages are fundamental cells of the innate immune system that support normal haematopoiesis and play roles in both anti-cancer immunity and tumour progression. Here we use a chimeric mouse model of chronic myeloid leukaemia (CML) and human bone marrow (BM) derived macrophages to study the impact of the dysregulated BM microenvironment on bystander macrophages. Utilising single-cell RNA sequencing (scRNA-seq) of Philadelphia chromosome (Ph) negative macrophages we reveal unique subpopulations of immature macrophages residing in the CML BM microenvironment. CML exposed macrophages separate from their normal counterparts by reduced expression of the surface marker CD36, which significantly reduces clearance of apoptotic cells. We uncover aberrant production of CML-secreted factors, including the immune modulatory protein lactotransferrin (LTF), that suppresses efferocytosis, phagocytosis, and CD36 surface expression in BM macrophages, indicating that the elevated secretion of LTF is, at least partially responsible for the supressed clearance function of Ph- macrophages.
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Affiliation(s)
- Amy Dawson
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Martha M Zarou
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Bodhayan Prasad
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Joana Bittencourt-Silvestre
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G12 0ZD, UK
| | - Désirée Zerbst
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Ekaterini Himonas
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Ya-Ching Hsieh
- Cancer Research UK Scotland Institute, Glasgow, G61 1BD, UK
| | - Isabel van Loon
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | | | - Angela Ianniciello
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Zsombor Kerekes
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Vaidehi Krishnan
- Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Puneet Agarwal
- Division of Hematology and Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hassan Almasoudi
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, 61441, Kingdom of Saudi Arabia
| | - Laura McCluskey
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G12 0ZD, UK
| | - Lisa E M Hopcroft
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G12 0ZD, UK
| | - Mary T Scott
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Pablo Baquero
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
- Universidad de Alcalá, Facultad de Medicina y Ciencias de la Salud, Dpto. de Biología de Sistemas, Unidad de Bioquímica y Biología Molecular, E-28805, Madrid, Spain
| | - Karen Dunn
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G12 0ZD, UK
| | - David Vetrie
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
| | - Mhairi Copland
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G12 0ZD, UK
| | - Ravi Bhatia
- Division of Hematology and Oncology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Seth B Coffelt
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
- Cancer Research UK Scotland Institute, Glasgow, G61 1BD, UK
| | - Ong Sin Tiong
- Cancer & Stem Cell Biology Signature Research Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Helen Wheadon
- Paul O'Gorman Leukaemia Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G12 0ZD, UK
| | - Sara Zanivan
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK
- Cancer Research UK Scotland Institute, Glasgow, G61 1BD, UK
| | - Kristina Kirschner
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK.
- Cancer Research UK Scotland Institute, Glasgow, G61 1BD, UK.
| | - G Vignir Helgason
- Wolfson Wohl Cancer Research Centre, School of Cancer Sciences, University of Glasgow, Glasgow, G61 1QH, UK.
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19
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Arigoni M, Ratto ML, Riccardo F, Balmas E, Calogero L, Cordero F, Beccuti M, Calogero RA, Alessandri L. A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity. Sci Data 2024; 11:159. [PMID: 38307867 PMCID: PMC10837414 DOI: 10.1038/s41597-024-03002-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/25/2024] [Indexed: 02/04/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.
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Affiliation(s)
- Maddalena Arigoni
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Maria Luisa Ratto
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Federica Riccardo
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Elisa Balmas
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Lorenzo Calogero
- Department of Electronics and Telecommunications (DET), Politecnico di Torino, Torino, Italy
| | | | - Marco Beccuti
- Department of Computer Science, University of Torino, Torino, Italy
| | - Raffaele A Calogero
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.
| | - Luca Alessandri
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
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20
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Yao Q, Jia Y, Ma P. cellMarkerPipe: Cell Marker Identification and Evaluation Pipeline in Single Cell Transcriptomes. RESEARCH SQUARE 2024:rs.3.rs-3844718. [PMID: 38313296 PMCID: PMC10836098 DOI: 10.21203/rs.3.rs-3844718/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https://github.com/yao-laboratory/cellMarkerPipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker's overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.
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21
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Cai M, Zhou J, McKennan C, Wang J. scMD facilitates cell type deconvolution using single-cell DNA methylation references. Commun Biol 2024; 7:1. [PMID: 38168620 PMCID: PMC10762261 DOI: 10.1038/s42003-023-05690-5] [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: 08/14/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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22
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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23
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Brockmann L, Tran A, Huang Y, Edwards M, Ronda C, Wang HH, Ivanov II. Intestinal microbiota-specific Th17 cells possess regulatory properties and suppress effector T cells via c-MAF and IL-10. Immunity 2023; 56:2719-2735.e7. [PMID: 38039966 PMCID: PMC10964950 DOI: 10.1016/j.immuni.2023.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 08/04/2023] [Accepted: 11/05/2023] [Indexed: 12/03/2023]
Abstract
Commensal microbes induce cytokine-producing effector tissue-resident CD4+ T cells, but the function of these T cells in mucosal homeostasis is not well understood. Here, we report that commensal-specific intestinal Th17 cells possess an anti-inflammatory phenotype marked by expression of interleukin (IL)-10 and co-inhibitory receptors. The anti-inflammatory phenotype of gut-resident commensal-specific Th17 cells was driven by the transcription factor c-MAF. IL-10-producing commensal-specific Th17 cells were heterogeneous and derived from a TCF1+ gut-resident progenitor Th17 cell population. Th17 cells acquired IL-10 expression and anti-inflammatory phenotype in the small-intestinal lamina propria. IL-10 production by CD4+ T cells and IL-10 signaling in intestinal macrophages drove IL-10 expression by commensal-specific Th17 cells. Intestinal commensal-specific Th17 cells possessed immunoregulatory functions and curbed effector T cell activity in vitro and in vivo in an IL-10-dependent and c-MAF-dependent manner. Our results suggest that tissue-resident commensal-specific Th17 cells perform regulatory functions in mucosal homeostasis.
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Affiliation(s)
- Leonie Brockmann
- Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Alexander Tran
- Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Yiming Huang
- Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY 10032, USA; Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Madeline Edwards
- Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Carlotta Ronda
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Harris H Wang
- Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Ivaylo I Ivanov
- Department of Microbiology and Immunology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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24
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Tangherloni A, Riva SG, Myers B, Buffa FM, Cazzaniga P. MAGNETO: Cell type marker panel generator from single-cell transcriptomic data. J Biomed Inform 2023; 147:104510. [PMID: 37797704 DOI: 10.1016/j.jbi.2023.104510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 09/12/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023]
Abstract
Single-cell RNA sequencing experiments produce data useful to identify different cell types, including uncharacterized and rare ones. This enables us to study the specific functional roles of these cells in different microenvironments and contexts. After identifying a (novel) cell type of interest, it is essential to build succinct marker panels, composed of a few genes referring to cell surface proteins and clusters of differentiation molecules, able to discriminate the desired cells from the other cell populations. In this work, we propose a fully-automatic framework called MAGNETO, which can help construct optimal marker panels starting from a single-cell gene expression matrix and a cell type identity for each cell. MAGNETO builds effective marker panels solving a tailored bi-objective optimization problem, where the first objective regards the identification of the genes able to isolate a specific cell type, while the second conflicting objective concerns the minimization of the total number of genes included in the panel. Our results on three public datasets show that MAGNETO can identify marker panels that identify the cell populations of interest better than state-of-the-art approaches. Finally, by fine-tuning MAGNETO, our results demonstrate that it is possible to obtain marker panels with different specificity levels.
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Affiliation(s)
- Andrea Tangherloni
- Department of Computing Sciences, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Bocconi Institute for Data Science and Analytics, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Department of Human and Social Sciences, University of Bergamo, Piazzale S. Agostino 2, Bergamo, 24129, Italy.
| | - Simone G Riva
- Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS, United Kingdom
| | - Brynelle Myers
- Wellcome Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, United Kingdom
| | - Francesca M Buffa
- Department of Computing Sciences, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Bocconi Institute for Data Science and Analytics, Bocconi University, Via Guglielmo Röntgen 1, Milan, 20136, Italy; Department of Oncology, University of Oxford, Old Road Campus Research Building, Oxford, OX3 7DQ, United Kingdom
| | - Paolo Cazzaniga
- Department of Human and Social Sciences, University of Bergamo, Piazzale S. Agostino 2, Bergamo, 24129, Italy; Bicocca Bioinformatics, Biostatistics, and Bioimaging Centre - B4, Via Follereau 3, Vedano al Lambro, 20854, Italy
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25
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Miller TE, El Farran CA, Couturier CP, Chen Z, D’Antonio JP, Verga J, Villanueva MA, Castro LNG, Tong YE, Saadi TA, Chiocca AN, Fischer DS, Heiland DH, Guerriero JL, Petrecca K, Suva ML, Shalek AK, Bernstein BE. Programs, Origins, and Niches of Immunomodulatory Myeloid Cells in Gliomas. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.24.563466. [PMID: 37961527 PMCID: PMC10634776 DOI: 10.1101/2023.10.24.563466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Gliomas are incurable malignancies notable for an immunosuppressive microenvironment with abundant myeloid cells whose immunomodulatory properties remain poorly defined. Here, utilizing scRNA-seq data for 183,062 myeloid cells from 85 human tumors, we discover that nearly all glioma-associated myeloid cells express at least one of four immunomodulatory activity programs: Scavenger Immunosuppressive, C1Q Immunosuppressive, CXCR4 Inflammatory, and IL1B Inflammatory. All four programs are present in IDH1 mutant and wild-type gliomas and are expressed in macrophages, monocytes, and microglia whether of blood or resident myeloid cell origins. Integrating our scRNA-seq data with mitochondrial DNA-based lineage tracing, spatial transcriptomics, and organoid explant systems that model peripheral monocyte infiltration, we show that these programs are driven by microenvironmental cues and therapies rather than myeloid cell type, origin, or mutation status. The C1Q Immunosuppressive program is driven by routinely administered dexamethasone. The Scavenger Immunosuppressive program includes ligands with established roles in T-cell suppression, is induced in hypoxic regions, and is associated with immunotherapy resistance. Both immunosuppressive programs are less prevalent in lower-grade gliomas, which are instead enriched for the CXCR4 Inflammatory program. Our study provides a framework to understand immunomodulatory myeloid cells in glioma, and a foundation to develop more effective immunotherapies.
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Affiliation(s)
- Tyler E. Miller
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology and Pathology, Harvard Medical School, Boston, MA 02215, USA
- Ludwig Center at Harvard Medical School, Boston, MA, USA
| | - Chadi A. El Farran
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology and Pathology, Harvard Medical School, Boston, MA 02215, USA
- Ludwig Center at Harvard Medical School, Boston, MA, USA
| | - Charles P. Couturier
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Neurosurgery, Brigham and Women’s Hospital, Boston, MA 02115 USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Zeyu Chen
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology and Pathology, Harvard Medical School, Boston, MA 02215, USA
| | - Joshua P. D’Antonio
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology and Pathology, Harvard Medical School, Boston, MA 02215, USA
| | - Julia Verga
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | - Martin A. Villanueva
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - L. Nicolas Gonzalez Castro
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Center for Neuro-Oncology, Dana-Farber Cancer Institute; Department of Neurology, Brigham and Women’s Hospital, Boston, MA 02115 USA
| | - Yuzhou Evelyn Tong
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Tariq Al Saadi
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrew N. Chiocca
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
| | | | - Dieter Henrik Heiland
- Microenvironment and Immunology Research Laboratory, Medical Center - University of Freiburg, Freiburg, Germany. Department of Neurological Surgery, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Jennifer L. Guerriero
- Ludwig Center at Harvard Medical School, Boston, MA, USA
- Breast Oncology Program, Dana-Farber Cancer Institute; Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA, USA
| | - Kevin Petrecca
- Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital, McGill University, Montreal, Quebec, Canada
| | - Mario L. Suva
- Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Alex K. Shalek
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Institute for Medical Engineering and Sciences and Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Ragon Institute of MGH, MIT and Harvard, Cambridge, MA 02139, USA
| | - Bradley E. Bernstein
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Cancer Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology and Pathology, Harvard Medical School, Boston, MA 02215, USA
- Ludwig Center at Harvard Medical School, Boston, MA, USA
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26
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Ng GYL, Tan SC, Ong CS. On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data. PLoS One 2023; 18:e0292961. [PMID: 37856458 PMCID: PMC10586655 DOI: 10.1371/journal.pone.0292961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/03/2023] [Indexed: 10/21/2023] Open
Abstract
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.
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Affiliation(s)
- Grace Yee Lin Ng
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
| | - Shing Chiang Tan
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
| | - Chia Sui Ong
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
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27
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Tansey M, Boles J, Uriarte Huarte O. Microfluidics-free single-cell genomics reveals complex central-peripheral immune crosstalk in the mouse brain during peripheral inflammation. RESEARCH SQUARE 2023:rs.3.rs-3428910. [PMID: 37886510 PMCID: PMC10602178 DOI: 10.21203/rs.3.rs-3428910/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Inflammation is a realized detriment to brain health in a growing number of neurological diseases, but querying neuroinflammation in its cellular complexity remains a challenge. This manuscript aims to provide a reliable and accessible strategy for examining the brain's immune system. We compare the efficacy of cell isolation methods in producing ample and pure immune samples from mouse brains. Then, with the high-input single-cell genomics platform PIPseq, we generate a rich neuroimmune dataset containing microglia and many peripheral immune populations. To demonstrate this strategy's utility, we interrogate the well-established model of LPS-induced neuroinflammation with single-cell resolution. We demonstrate the activation of crosstalk between microglia and peripheral phagocytes and highlight the unique contributions of microglia and peripheral immune cells to neuroinflammation. Our approach enables the high-depth evaluation of inflammation in longstanding rodent models of neurological disease to reveal novel insight into the contributions of the immune system to brain health.
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28
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Ouyang JF, Mishra K, Xie Y, Park H, Huang KY, Petretto E, Behmoaras J. Systems level identification of a matrisome-associated macrophage polarisation state in multi-organ fibrosis. eLife 2023; 12:e85530. [PMID: 37706477 PMCID: PMC10547479 DOI: 10.7554/elife.85530] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 09/13/2023] [Indexed: 09/15/2023] Open
Abstract
Tissue fibrosis affects multiple organs and involves a master-regulatory role of macrophages which respond to an initial inflammatory insult common in all forms of fibrosis. The recently unravelled multi-organ heterogeneity of macrophages in healthy and fibrotic human disease suggests that macrophages expressing osteopontin (SPP1) associate with lung and liver fibrosis. However, the conservation of this SPP1+ macrophage population across different tissues and its specificity to fibrotic diseases with different etiologies remain unclear. Integrating 15 single-cell RNA-sequencing datasets to profile 235,930 tissue macrophages from healthy and fibrotic heart, lung, liver, kidney, skin, and endometrium, we extended the association of SPP1+ macrophages with fibrosis to all these tissues. We also identified a subpopulation expressing matrisome-associated genes (e.g., matrix metalloproteinases and their tissue inhibitors), functionally enriched for ECM remodelling and cell metabolism, representative of a matrisome-associated macrophage (MAM) polarisation state within SPP1+ macrophages. Importantly, the MAM polarisation state follows a differentiation trajectory from SPP1+ macrophages and is associated with a core set of regulon activity. SPP1+ macrophages without the MAM polarisation state (SPP1+MAM-) show a positive association with ageing lung in mice and humans. These results suggest an advanced and conserved polarisation state of SPP1+ macrophages in fibrotic tissues resulting from prolonged inflammatory cues within each tissue microenvironment.
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Affiliation(s)
- John F Ouyang
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
| | - Kunal Mishra
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
| | - Yi Xie
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
| | - Harry Park
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
| | - Kevin Y Huang
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
| | - Enrico Petretto
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University (CPU)NanjingChina
| | - Jacques Behmoaras
- Centre for Computational Biology, Duke-NUS Medical SchoolSingaporeSingapore
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical SchoolSingaporeSingapore
- Department of Immunology and Inflammation, Centre for Inflammatory Disease, Imperial College LondonLondonUnited Kingdom
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29
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Zilbauer M, James KR, Kaur M, Pott S, Li Z, Burger A, Thiagarajah JR, Burclaff J, Jahnsen FL, Perrone F, Ross AD, Matteoli G, Stakenborg N, Sujino T, Moor A, Bartolome-Casado R, Bækkevold ES, Zhou R, Xie B, Lau KS, Din S, Magness ST, Yao Q, Beyaz S, Arends M, Denadai-Souza A, Coburn LA, Gaublomme JT, Baldock R, Papatheodorou I, Ordovas-Montanes J, Boeckxstaens G, Hupalowska A, Teichmann SA, Regev A, Xavier RJ, Simmons A, Snyder MP, Wilson KT. A Roadmap for the Human Gut Cell Atlas. Nat Rev Gastroenterol Hepatol 2023; 20:597-614. [PMID: 37258747 PMCID: PMC10527367 DOI: 10.1038/s41575-023-00784-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/14/2023] [Indexed: 06/02/2023]
Abstract
The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.
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Affiliation(s)
- Matthias Zilbauer
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- University Department of Paediatrics, University of Cambridge, Cambridge, UK.
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals, Cambridge, UK.
| | - Kylie R James
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Mandeep Kaur
- School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa
| | - Sebastian Pott
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Zhixin Li
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Albert Burger
- Department of Computer Science, Heriot-watt University, Edinburgh, UK
| | - Jay R Thiagarajah
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Burclaff
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Frode L Jahnsen
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Francesca Perrone
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Alexander D Ross
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
- University Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Gianluca Matteoli
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Nathalie Stakenborg
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Tomohisa Sujino
- Center for the Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan
| | - Andreas Moor
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Raquel Bartolome-Casado
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Wellcome Sanger Institute, Hinxton, UK
| | - Espen S Bækkevold
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ran Zhou
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Bingqing Xie
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shahida Din
- Edinburgh IBD Unit, Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Scott T Magness
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qiuming Yao
- Department of Computer Science and Engineering, University of Nebraska Lincoln, Lincoln, NE, USA
| | - Semir Beyaz
- Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, NY, USA
| | - Mark Arends
- Division of Pathology, Centre for Comparative Pathology, Cancer Research UK Edinburgh Centre, Institute of Cancer and Genetics, University of Edinburgh, Edinburgh, UK
| | - Alexandre Denadai-Souza
- Laboratory of Mucosal Biology, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Lori A Coburn
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
| | | | | | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Guy Boeckxstaens
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | | | - Sarah A Teichmann
- Wellcome Sanger Institute, Hinxton, UK
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK
| | - Aviv Regev
- Genentech, San Francisco, CA, USA
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ramnik J Xavier
- Broad Institute and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alison Simmons
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | | | - Keith T Wilson
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
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30
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Tang Y, Li X, Shi M. LIDER: cell embedding based deep neural network classifier for supervised cell type identification. PeerJ 2023; 11:e15862. [PMID: 37601262 PMCID: PMC10439717 DOI: 10.7717/peerj.15862] [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: 04/14/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Background Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. Results LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.
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Affiliation(s)
- Yachen Tang
- Hefei University of Technology, Hefei, China
| | - Xuefeng Li
- Hefei University of Technology, Hefei, China
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31
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Cai M, Zhou J, McKennan C, Wang J. scMD: cell type deconvolution using single-cell DNA methylation references. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551733. [PMID: 37577715 PMCID: PMC10418231 DOI: 10.1101/2023.08.03.551733] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
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32
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Hong Y, Song K, Zhang Z, Deng Y, Zhang X, Zhao J, Jiang J, Zhang Q, Guo C, Peng C. The spatiotemporal dynamics of spatially variable genes in developing mouse brain revealed by a novel computational scheme. Cell Death Discov 2023; 9:264. [PMID: 37500639 PMCID: PMC10374563 DOI: 10.1038/s41420-023-01569-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: 04/15/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/29/2023] Open
Abstract
To understand how brain regions form and work, it is important to explore the spatially variable genes (SVGs) enriched in specific brain regions during development. Spatial transcriptomics techniques provide opportunity to select SVGs in the high-throughput way. However, previous methods neglected the ranking order and combinatorial effect of SVGs, making them difficult to automatically select the high-priority SVGs from spatial transcriptomics data. Here, we proposed a novel computational pipeline, called SVGbit, to rank the individual and combinatorial SVGs for marker selection in various brain regions, which was tested in different kinds of public datasets for both human and mouse brains. We then generated the spatial transcriptomics and immunohistochemistry data from mouse brain at critical embryonic and neonatal stages. The results show that our ranking and clustering scheme captures the key SVGs which coincide with known anatomic regions in the developing mouse brain. More importantly, SVGbit can facilitate the identification of multiple gene combination sets in different brain regions. We identified three dynamical sub-regions which can be segregated by the staining of Sox2 and Calb2 in thalamus, and we also found that Nr4a2 expression gradually segregates the neocortex and hippocampus during the development. In summary, our work not only reveals the spatiotemporal dynamics of individual and combinatorial SVGs in developing mouse brain, but also provides a novel computational pipeline to facilitate the selection of marker genes from spatial transcriptomics data.
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Affiliation(s)
- Yingzhou Hong
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Kai Song
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Zongbo Zhang
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Yuxia Deng
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Xue Zhang
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Jinqian Zhao
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Jun Jiang
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Qing Zhang
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China
| | - Chunming Guo
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China.
| | - Cheng Peng
- Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, 650500, China.
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33
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Bod L, Kye YC, Shi J, Torlai Triglia E, Schnell A, Fessler J, Ostrowski SM, Von-Franque MY, Kuchroo JR, Barilla RM, Zaghouani S, Christian E, Delorey TM, Mohib K, Xiao S, Slingerland N, Giuliano CJ, Ashenberg O, Li Z, Rothstein DM, Fisher DE, Rozenblatt-Rosen O, Sharpe AH, Quintana FJ, Apetoh L, Regev A, Kuchroo VK. B-cell-specific checkpoint molecules that regulate anti-tumour immunity. Nature 2023; 619:348-356. [PMID: 37344597 PMCID: PMC10795478 DOI: 10.1038/s41586-023-06231-0] [Citation(s) in RCA: 82] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 05/17/2023] [Indexed: 06/23/2023]
Abstract
The role of B cells in anti-tumour immunity is still debated and, accordingly, immunotherapies have focused on targeting T and natural killer cells to inhibit tumour growth1,2. Here, using high-throughput flow cytometry as well as bulk and single-cell RNA-sequencing and B-cell-receptor-sequencing analysis of B cells temporally during B16F10 melanoma growth, we identified a subset of B cells that expands specifically in the draining lymph node over time in tumour-bearing mice. The expanding B cell subset expresses the cell surface molecule T cell immunoglobulin and mucin domain 1 (TIM-1, encoded by Havcr1) and a unique transcriptional signature, including multiple co-inhibitory molecules such as PD-1, TIM-3, TIGIT and LAG-3. Although conditional deletion of these co-inhibitory molecules on B cells had little or no effect on tumour burden, selective deletion of Havcr1 in B cells both substantially inhibited tumour growth and enhanced effector T cell responses. Loss of TIM-1 enhanced the type 1 interferon response in B cells, which augmented B cell activation and increased antigen presentation and co-stimulation, resulting in increased expansion of tumour-specific effector T cells. Our results demonstrate that manipulation of TIM-1-expressing B cells enables engagement of the second arm of adaptive immunity to promote anti-tumour immunity and inhibit tumour growth.
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Affiliation(s)
- Lloyd Bod
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yoon-Chul Kye
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jingwen Shi
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- BeiGene, Beijing, China
| | - Elena Torlai Triglia
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alexandra Schnell
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Johannes Fessler
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Division of Immunology and Pathophysiology, Medical University of Graz, Graz, Austria
| | | | - Max Y Von-Franque
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Juhi R Kuchroo
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Rocky M Barilla
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Zaghouani
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Elena Christian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Toni Marie Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kanishka Mohib
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sheng Xiao
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Nadine Slingerland
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhaorong Li
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David M Rothstein
- Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - David E Fisher
- Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Arlene H Sharpe
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA
| | - Francisco J Quintana
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lionel Apetoh
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- INSERM, Tours, France
- Faculté de Médecine, Université de Tours, Tours, France
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, San Francisco, CA, USA.
| | - Vijay K Kuchroo
- Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets. ARXIV 2023:arXiv:2305.06501v1. [PMID: 37214135 PMCID: PMC10197733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes. However, brain tissue or immune cell populations have cell types with substantially different cell sizes, total mRNA expression, and transcriptional activity. When existing deconvolution approaches are applied to these tissues, these systematic differences in cell sizes and transcriptomic activity confound accurate cell proportion estimates and instead may quantify total mRNA content. Furthermore, there is a lack of standard reference atlases and computational approaches to facilitate integrative analyses, including not only bulk and single cell/nuclei RNA-seq data, but also new data modalities from spatial -omic or imaging approaches. New multi-assay datasets need to be collected with orthogonal data types generated from the same tissue block and the same individual, to serve as a "gold standard" for evaluating new and existing deconvolution methods. Below, we discuss these key challenges and how they can be addressed with the acquisition of new datasets and approaches to analysis.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | | | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
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35
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Baran Y, Doğan B. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies. Comput Biol Med 2023; 155:106634. [PMID: 36774895 DOI: 10.1016/j.compbiomed.2023.106634] [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: 11/05/2022] [Revised: 01/28/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.
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Affiliation(s)
- Yusuf Baran
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey
| | - Berat Doğan
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey.
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36
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Abstract
rCASC is a modular workflow providing an integrated environment for single-cell RNA-seq (scRNA-Seq) data analysis exploiting Docker containers to achieve functional and computational reproducibility. It was initially developed as an R package usable also through a Java GUI. However, the Java frontend cannot be employed when running rCASC on a remote server, a typical setup due to the significant computational resources commonly needed to analyze scRNA-Seq data.To allow the use of rCASC through a graphical user interface on the client side and to harness the many advantages provided by the Galaxy platform, we have made rCASC available as a Galaxy set of tools, also providing a dedicated public instance of Galaxy named "Galaxy-rCASC." To integrate rCASC into Galaxy, all its functions, originally implemented as a set of Docker containers to maximize reproducibility, have been extensively reworked to become independent from the R package functions that launch them in the original implementation. Furthermore, suitable Galaxy wrappers have been developed for most functions of rCASC. We provide a detailed reference document to the use of Galaxy-rCASC with insights and explanations on the platform functionalities, parameters, and output while guiding the reader through the typical rCASC analysis workflow of a scRNA-Seq dataset.
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37
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Identifying Gene Markers Associated with Cell Subpopulations. Methods Mol Biol 2022; 2584:251-268. [PMID: 36495455 DOI: 10.1007/978-1-0716-2756-3_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
An important point of the analysis of a single-cell RNA experiment is the identification of the key elements, i.e., genes, characterizing each cell subpopulation cluster. In this chapter, we describe the use of sparsely connected autoencoder, as a tool to convert single-cell clusters in pseudo-RNAseq experiments to be used as input for differential expression analysis, and the use of COMET, as a tool to depict cluster-specific gene markers.
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38
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Haensel D, Gaddam S, Li NY, Gonzalez F, Patel T, Cloutier JM, Sarin KY, Tang JY, Rieger KE, Aasi SZ, Oro AE. LY6D marks pre-existing resistant basosquamous tumor subpopulations. Nat Commun 2022; 13:7520. [PMID: 36473848 PMCID: PMC9726704 DOI: 10.1038/s41467-022-35020-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 11/15/2022] [Indexed: 12/12/2022] Open
Abstract
Improved response to canonical therapies requires a mechanistic understanding of dynamic tumor heterogeneity by identifying discrete cellular populations with enhanced cellular plasticity. We have previously demonstrated distinct resistance mechanisms in skin basal cell carcinomas, but a comprehensive understanding of the cellular states and markers associated with these populations remains poorly understood. Here we identify a pre-existing resistant cellular population in naive basal cell carcinoma tumors marked by the surface marker LY6D. LY6D+ tumor cells are spatially localized and possess basal cell carcinoma and squamous cell carcinoma-like features. Using computational tools, organoids, and spatial tools, we show that LY6D+ basosquamous cells represent a persister population lying on a central node along the skin lineage-associated spectrum of epithelial states with local environmental and applied therapies determining the kinetics of accumulation. Surprisingly, LY6D+ basosquamous populations exist in many epithelial tumors, such as pancreatic adenocarcinomas, which have poor outcomes. Overall, our results identify the resistant LY6D+ basosquamous population as an important clinical target and suggest strategies for future therapeutic approaches to target them.
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Affiliation(s)
- Daniel Haensel
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sadhana Gaddam
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Nancy Y Li
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Fernanda Gonzalez
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Tiffany Patel
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jeffrey M Cloutier
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kavita Y Sarin
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jean Y Tang
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kerri E Rieger
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sumaira Z Aasi
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
| | - Anthony E Oro
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA.
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Zamani M, Cheng YH, Charbonier F, Gupta VK, Mayer AT, Trevino AE, Quertermous T, Chaudhuri O, Cahan P, Huang NF. Single-Cell Transcriptomic Census of Endothelial Changes Induced by Matrix Stiffness and the Association with Atherosclerosis. ADVANCED FUNCTIONAL MATERIALS 2022; 32:2203069. [PMID: 36816792 PMCID: PMC9937733 DOI: 10.1002/adfm.202203069] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Indexed: 05/28/2023]
Abstract
Vascular endothelial cell (EC) plasticity plays a critical role in the progression of atherosclerosis by giving rise to mesenchymal phenotypes in the plaque lesion. Despite the evidence for arterial stiffening as a major contributor to atherosclerosis, the complex interplay among atherogenic stimuli in vivo has hindered attempts to determine the effects of extracellular matrix (ECM) stiffness on endothelial-mesenchymal transition (EndMT). To study the regulatory effects of ECM stiffness on EndMT, an in vitro model is developed in which human coronary artery ECs are cultured on physiological or pathological stiffness substrates. Leveraging single-cell RNA sequencing, cell clusters with mesenchymal transcriptional features are identified to be more prevalent on pathological substrates than physiological substrates. Trajectory inference analyses reveal a novel mesenchymal-to-endothelial reverse transition, which is blocked by pathological stiffness substrates, in addition to the expected EndMT trajectory. ECs pushed to a mesenchymal character by pathological stiffness substrates are enriched in transcriptional signatures of atherosclerotic ECs from human and murine plaques. This study characterizes at single-cell resolution the transcriptional programs that underpin EC plasticity in both physiological or pathological milieus, and thus serves as a valuable resource for more precisely defining EndMT and the transcriptional programs contributing to atherosclerosis.
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Affiliation(s)
- Maedeh Zamani
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA
| | - Yu-Hao Cheng
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Frank Charbonier
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Vivek Kumar Gupta
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | | | - Thomas Quertermous
- Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA 94305, USA
| | - Ovijit Chaudhuri
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Patrick Cahan
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Ngan F Huang
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA 94305, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA 94305, USA
- Department of Chemical Engineering, Stanford University, Stanford, CA 94305, USA
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
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40
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Ferrall-Fairbanks MC, Dhawan A, Johnson B, Newman H, Volpe V, Letson C, Ball M, Hunter AM, Balasis ME, Kruer T, Ben-Crentsil NA, Kroeger JL, Balderas R, Komrokji RS, Sallman DA, Zhang J, Bejar R, Altrock PM, Padron E. Progenitor Hierarchy of Chronic Myelomonocytic Leukemia Identifies Inflammatory Monocytic-Biased Trajectory Linked to Worse Outcomes. Blood Cancer Discov 2022; 3:536-553. [PMID: 36053528 PMCID: PMC9627238 DOI: 10.1158/2643-3230.bcd-21-0217] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 05/16/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
Myeloblast expansion is a hallmark of disease progression and comprises CD34+ hematopoietic stem and progenitor cells (HSPC). How this compartment evolves during disease progression in chronic myeloid neoplasms is unknown. Using single-cell RNA sequencing and high-parameter flow cytometry, we show that chronic myelomonocytic leukemia (CMML) CD34+ HSPC can be classified into three differentiation trajectories: monocytic, megakaryocyte-erythroid progenitor (MEP), and normal-like. Hallmarks of monocytic-biased trajectory were enrichment of CD120b+ inflammatory granulocyte-macrophage progenitor (GMP)-like cells, activated cytokine receptor signaling, phenotypic hematopoietic stem cell (HSC) depletion, and adverse outcomes. Cytokine receptor diversity was generally an adverse feature and elevated in CD120b+ GMPs. Hypomethylating agents decreased monocytic-biased cells in CMML patients. Given the enrichment of RAS pathway mutations in monocytic-biased cells, NRAS-competitive transplants and LPS-treated xenograft models recapitulated monocytic-biased CMML, suggesting that hematopoietic stress precipitates the monocytic-biased state. Deconvolution of HSPC compartments in other myeloid neoplasms and identifying therapeutic strategies to mitigate the monocytic-biased differentiation trajectory should be explored. SIGNIFICANCE Our findings establish that multiple differentiation states underlie CMML disease progression. These states are negatively augmented by inflammation and positively affected by hypomethylating agents. Furthermore, we identify HSC depletion and expansion of GMP-like cells with increased cytokine receptor diversity as a feature of myeloblast expansion in inflammatory chronic myeloid neoplasms. This article is highlighted in the In This Issue feature, p. 476.
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Affiliation(s)
- Meghan C. Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida
- University of Florida Health Cancer Center, University of Florida, Gainesville, Florida
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Abhishek Dhawan
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Brian Johnson
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Hannah Newman
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Virginia Volpe
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Christopher Letson
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Markus Ball
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Anthony M. Hunter
- Department of Hematology and Medical Oncology, Winship Cancer Institute of Emory University, Atlanta, Georgia
| | - Maria E. Balasis
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Traci Kruer
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | | | - Jodi L. Kroeger
- Flow Cytometry Core Facility, Moffitt Cancer Center, Tampa, Florida
| | | | - Rami S. Komrokji
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - David A. Sallman
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
| | - Jing Zhang
- McArdle Laboratory for Cancer Research, University of Wisconsin–Madison, Madison, Wisconsin
| | - Rafael Bejar
- Moores Cancer Center, University of California San Diego Health, La Jolla, California
| | - Philipp M. Altrock
- Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, Florida
- Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Ploen, Germany
| | - Eric Padron
- Department of Malignant Hematology, Moffitt Cancer Center, Tampa, Florida
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41
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Gómez‐Escolar C, Serrano‐Navarro A, Benguria A, Dopazo A, Sánchez‐Cabo F, Ramiro AR. Single cell clonal analysis identifies an AID-dependent pathway of plasma cell differentiation. EMBO Rep 2022; 23:e55000. [PMID: 36205653 PMCID: PMC9724673 DOI: 10.15252/embr.202255000] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/07/2022] Open
Abstract
Germinal centers (GC) are microstructures where B cells that have been activated by antigen can improve the affinity of their B cell receptors and differentiate into memory B cells (MBCs) or antibody-secreting plasma cells. Here, we have addressed the role of activation-induced deaminase (AID), which initiates somatic hypermutation and class switch recombination, in the terminal differentiation of GC B cells. By combining single cell transcriptome and immunoglobulin clonal analysis in a mouse model that traces AID-experienced cells, we have identified a novel subset of late-prePB cells (L-prePB), which shares the strongest clonal relationships with plasmablasts (PBs). Mice lacking AID have various alterations in the size and expression profiles of transcriptional clusters. We find that AID deficiency leads to a reduced proportion of L-prePB cells and severely impairs transitions between the L-prePB and the PB subsets. Thus, AID shapes the differentiation fate of GC B cells by enabling PB generation from a prePB state.
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Affiliation(s)
- Carmen Gómez‐Escolar
- B Lymphocyte Biology LabCentro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
| | - Alvaro Serrano‐Navarro
- B Lymphocyte Biology LabCentro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
| | - Alberto Benguria
- Genomics UnitCentro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
| | - Ana Dopazo
- Genomics UnitCentro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain,CIBER de Enfermedades Cardiovasculares (CIBERCV)MadridSpain
| | - Fátima Sánchez‐Cabo
- Bioinformatics UnitCentro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
| | - Almudena R Ramiro
- B Lymphocyte Biology LabCentro Nacional de Investigaciones Cardiovasculares (CNIC)MadridSpain
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Keenan BP, McCarthy EE, Ilano A, Yang H, Zhang L, Allaire K, Fan Z, Li T, Lee DS, Sun Y, Cheung A, Luong D, Chang H, Chen B, Marquez J, Sheldon B, Kelley RK, Ye CJ, Fong L. Circulating monocytes associated with anti-PD-1 resistance in human biliary cancer induce T cell paralysis. Cell Rep 2022; 40:111384. [PMID: 36130508 PMCID: PMC10060099 DOI: 10.1016/j.celrep.2022.111384] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 05/20/2022] [Accepted: 08/29/2022] [Indexed: 01/17/2023] Open
Abstract
Suppressive myeloid cells can contribute to immunotherapy resistance, but their role in response to checkpoint inhibition (CPI) in anti-PD-1 refractory cancers, such as biliary tract cancer (BTC), remains elusive. We use multiplexed single-cell transcriptomic and epitope sequencing to profile greater than 200,000 peripheral blood mononuclear cells from advanced BTC patients (n = 9) and matched healthy donors (n = 8). Following anti-PD-1 treatment, CD14+ monocytes expressing high levels of immunosuppressive cytokines and chemotactic molecules (CD14CTX) increase in the circulation of patients with BTC tumors that are CPI resistant. CD14CTX can directly suppress CD4+ T cells and induce SOCS3 expression in CD4+ T cells, rendering them functionally unresponsive. The CD14CTX gene signature associates with worse survival in patients with BTC as well as in other anti-PD-1 refractory cancers. These results demonstrate that monocytes arising after anti-PD-1 treatment can induce T cell paralysis as a distinct mode of tumor-mediated immunosuppression leading to CPI resistance.
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Affiliation(s)
- Bridget P Keenan
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Elizabeth E McCarthy
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Arielle Ilano
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA
| | - Hai Yang
- Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Li Zhang
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Kathryn Allaire
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA
| | - Zenghua Fan
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA
| | - Tony Li
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David S Lee
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Yang Sun
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Alexander Cheung
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Diamond Luong
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA
| | - Hewitt Chang
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA
| | - Brandon Chen
- Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jaqueline Marquez
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA
| | - Brenna Sheldon
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Robin K Kelley
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Chun Jimmie Ye
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Chan Zuckerberg Biohub, San Francisco, CA, USA; J. David Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
| | - Lawrence Fong
- Division of Hematology/Oncology, University of California, San Francisco, San Francisco, CA, USA; Cancer Immunotherapy Program, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA; Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA; Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
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43
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Li R, Banjanin B, Schneider RK, Costa IG. Detection of cell markers from single cell RNA-seq with sc2marker. BMC Bioinformatics 2022; 23:276. [PMID: 35831796 PMCID: PMC9281170 DOI: 10.1186/s12859-022-04817-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 06/28/2022] [Indexed: 11/12/2022] Open
Abstract
Background Single-cell RNA sequencing (scRNA-seq) allows the detection of rare cell types in complex tissues. The detection of markers for rare cell types is useful for further biological analysis of, for example, flow cytometry and imaging data sets for either physical isolation or spatial characterization of these cells. However, only a few computational approaches consider the problem of selecting specific marker genes from scRNA-seq data. Results Here, we propose sc2marker, which is based on the maximum margin index and a database of proteins with antibodies, to select markers for flow cytometry or imaging. We evaluated the performances of sc2marker and competing methods in ranking known markers in scRNA-seq data of immune and stromal cells. The results showed that sc2marker performed better than the competing methods in accuracy, while having a competitive running time. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04817-5.
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Affiliation(s)
- Ronghui Li
- Joint Research Center for Computational Biomedicine, Institute for Computational Genomics, RWTH Aachen University, Aachen, Germany
| | - Bella Banjanin
- Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Rebekka K Schneider
- Department of Cell Biology, Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Ivan G Costa
- Joint Research Center for Computational Biomedicine, Institute for Computational Genomics, RWTH Aachen University, Aachen, Germany.
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Murphy M, Jegelka S, Fraenkel E. Self-supervised learning of cell type specificity from immunohistochemical images. Bioinformatics 2022; 38:i395-i403. [PMID: 35758799 PMCID: PMC9235491 DOI: 10.1093/bioinformatics/btac263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
MOTIVATION Advances in bioimaging now permit in situ proteomic characterization of cell-cell interactions in complex tissues, with important applications across a spectrum of biological problems from development to disease. These methods depend on selection of antibodies targeting proteins that are expressed specifically in particular cell types. Candidate marker proteins are often identified from single-cell transcriptomic data, with variable rates of success, in part due to divergence between expression levels of proteins and the genes that encode them. In principle, marker identification could be improved by using existing databases of immunohistochemistry for thousands of antibodies in human tissue, such as the Human Protein Atlas. However, these data lack detailed annotations of the types of cells in each image. RESULTS We develop a method to predict cell type specificity of protein markers from unlabeled images. We train a convolutional neural network with a self-supervised objective to generate embeddings of the images. Using non-linear dimensionality reduction, we observe that the model clusters images according to cell types and anatomical regions for which the stained proteins are specific. We then use estimates of cell type specificity derived from an independent single-cell transcriptomics dataset to train an image classifier, without requiring any human labelling of images. Our scheme demonstrates superior classification of known proteomic markers in kidney compared to selection via single-cell transcriptomics. AVAILABILITY AND IMPLEMENTATION Code and trained model are available at www.github.com/murphy17/HPA-SimCLR. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Michael Murphy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stefanie Jegelka
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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45
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Chen X, Chen S, Thomson M. Minimal gene set discovery in single-cell mRNA-seq datasets with ActiveSVM. NATURE COMPUTATIONAL SCIENCE 2022; 2:387-398. [PMID: 38177588 PMCID: PMC10766518 DOI: 10.1038/s43588-022-00263-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 05/17/2022] [Indexed: 01/06/2024]
Abstract
Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here we introduce an active learning method that identifies minimal but highly informative gene sets that enable the identification of cell types, physiological states and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data by employing an active support vector machine (ActiveSVM) classifier. We demonstrate that ActiveSVM feature selection identifies gene sets that enable ~90% cell-type classification accuracy across, for example, cell atlas and disease-characterization datasets. The discovery of small but highly informative gene sets should enable reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery and genetic screens.
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Affiliation(s)
- Xiaoqiao Chen
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA
| | - Sisi Chen
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA
- Beckman Institute Single-cell Profiling and Engineering Center, Pasadena, California, USA
| | - Matt Thomson
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, USA.
- Beckman Institute Single-cell Profiling and Engineering Center, Pasadena, California, USA.
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46
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Collier A, Liu A, Torkelson J, Pattison J, Gaddam S, Zhen H, Patel T, McCarthy K, Ghanim H, Oro AE. Gibbin mesodermal regulation patterns epithelial development. Nature 2022; 606:188-196. [PMID: 35585237 PMCID: PMC9202145 DOI: 10.1038/s41586-022-04727-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 04/05/2022] [Indexed: 02/04/2023]
Abstract
Proper ectodermal patterning during human development requires previously identified transcription factors such as GATA3 and p63, as well as positional signalling from regional mesoderm1-6. However, the mechanism by which ectoderm and mesoderm factors act to stably pattern gene expression and lineage commitment remains unclear. Here we identify the protein Gibbin, encoded by the Xia-Gibbs AT-hook DNA-binding-motif-containing 1 (AHDC1) disease gene7-9, as a key regulator of early epithelial morphogenesis. We find that enhancer- or promoter-bound Gibbin interacts with dozens of sequence-specific zinc-finger transcription factors and methyl-CpG-binding proteins to regulate the expression of mesoderm genes. The loss of Gibbin causes an increase in DNA methylation at GATA3-dependent mesodermal genes, resulting in a loss of signalling between developing dermal and epidermal cell types. Notably, Gibbin-mutant human embryonic stem-cell-derived skin organoids lack dermal maturation, resulting in p63-expressing basal cells that possess defective keratinocyte stratification. In vivo chimeric CRISPR mouse mutants reveal a spectrum of Gibbin-dependent developmental patterning defects affecting craniofacial structure, abdominal wall closure and epidermal stratification that mirror patient phenotypes. Our results indicate that the patterning phenotypes seen in Xia-Gibbs and related syndromes derive from abnormal mesoderm maturation as a result of gene-specific DNA methylation decisions.
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Affiliation(s)
- Ann Collier
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Angela Liu
- Stem Cell Biology and Regenerative Medicine Program, Stanford University, Stanford, CA, USA
| | - Jessica Torkelson
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Jillian Pattison
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Sadhana Gaddam
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Hanson Zhen
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Tiffany Patel
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Kelly McCarthy
- Program in Epithelial Biology, Stanford University, Stanford, CA, USA
| | - Hana Ghanim
- Stem Cell Biology and Regenerative Medicine Program, Stanford University, Stanford, CA, USA
| | - Anthony E Oro
- Stem Cell Biology and Regenerative Medicine Program, Stanford University, Stanford, CA, USA.
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47
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Lu Y, Wu CT, Parker SJ, Cheng Z, Saylor G, Van Eyk JE, Yu G, Clarke R, Herrington DM, Wang Y. COT: an efficient and accurate method for detecting marker genes among many subtypes. BIOINFORMATICS ADVANCES 2022; 2:vbac037. [PMID: 35673616 PMCID: PMC9163574 DOI: 10.1093/bioadv/vbac037] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 04/10/2022] [Accepted: 05/16/2022] [Indexed: 01/27/2023]
Abstract
Motivation Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others-so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG. Results We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods. Availability and implementation The Python COT software with a detailed user's manual and a vignette are freely available at https://github.com/MintaYLu/COT. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Yingzhou Lu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Chiung-Ting Wu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Sarah J Parker
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Zuolin Cheng
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Georgia Saylor
- Department of Internal Medicine, Wake Forest University, Winston-Salem, NC 27157, USA
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Guoqiang Yu
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA
| | - Robert Clarke
- The Hormel Institute, University of Minnesota, Austin, MN 55912, USA
| | - David M Herrington
- Department of Internal Medicine, Wake Forest University, Winston-Salem, NC 27157, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA,To whom correspondence should be addressed.
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Ahmadi S, Sukprasert P, Vegesna R, Sinha S, Schischlik F, Artzi N, Khuller S, Schäffer AA, Ruppin E. The landscape of receptor-mediated precision cancer combination therapy via a single-cell perspective. Nat Commun 2022; 13:1613. [PMID: 35338126 PMCID: PMC8956718 DOI: 10.1038/s41467-022-29154-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 02/22/2022] [Indexed: 02/08/2023] Open
Abstract
Mining a large cohort of single-cell transcriptomics data, here we employ combinatorial optimization techniques to chart the landscape of optimal combination therapies in cancer. We assume that each individual therapy can target any one of 1269 genes encoding cell surface receptors, which may be targets of CAR-T, conjugated antibodies or coated nanoparticle therapies. We find that in most cancer types, personalized combinations composed of at most four targets are then sufficient for killing at least 80% of tumor cells while sparing at least 90% of nontumor cells in the tumor microenvironment. However, as more stringent and selective killing is required, the number of targets needed rises rapidly. Emerging individual targets include PTPRZ1 for brain and head and neck cancers and EGFR in multiple tumor types. In sum, this study provides a computational estimate of the identity and number of targets needed in combination to target cancers selectively and precisely.
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Affiliation(s)
- Saba Ahmadi
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Department of Computer Science, Northwestern University, Evanston, IL, 60208, USA
- Toyota Technological Institute at Chicago, Chicago, IL, 60637, USA
| | - Pattara Sukprasert
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Department of Computer Science, Northwestern University, Evanston, IL, 60208, USA
| | - Rahulsimham Vegesna
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Sanju Sinha
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Fiorella Schischlik
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Natalie Artzi
- Department of Medicine, Engineering in Medicine Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02139, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, 02139, USA
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, 02139, USA
| | - Samir Khuller
- Department of Computer Science, University of Maryland, College Park, MD, 20742, USA
- Department of Computer Science, Northwestern University, Evanston, IL, 60208, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, 20892, USA.
| | - Eytan Ruppin
- Cancer Data Science Laboratory, National Cancer Institute, Bethesda, MD, 20892, USA.
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Pauken KE, Lagattuta KA, Lu BY, Lucca LE, Daud AI, Hafler DA, Kluger HM, Raychaudhuri S, Sharpe AH. TCR-sequencing in cancer and autoimmunity: barcodes and beyond. Trends Immunol 2022; 43:180-194. [PMID: 35090787 PMCID: PMC8882139 DOI: 10.1016/j.it.2022.01.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/04/2022] [Accepted: 01/04/2022] [Indexed: 01/21/2023]
Abstract
The T cell receptor (TCR) endows T cells with antigen specificity and is central to nearly all aspects of T cell function. Each naïve T cell has a unique TCR sequence that is stably maintained during cell division. In this way, the TCR serves as a molecular barcode that tracks processes such as migration, differentiation, and proliferation of T cells. Recent technological advances have enabled sequencing of the TCR from single cells alongside deep molecular phenotypes on an unprecedented scale. In this review, we discuss strengths and limitations of TCR sequences as molecular barcodes and their application to study immune responses following Programmed Death-1 (PD-1) blockade in cancer. Additionally, we consider applications of TCR data beyond use as a barcode.
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Affiliation(s)
- Kristen E Pauken
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA.
| | - Kaitlyn A Lagattuta
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Benjamin Y Lu
- Department of Neurology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA; Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Liliana E Lucca
- Department of Neurology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | - Adil I Daud
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - David A Hafler
- Department of Neurology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Harriet M Kluger
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Boston, MA, USA; Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA; Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PL, UK
| | - Arlene H Sharpe
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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A Sight of the Diagnostic Value of Aberrant Cell-Free DNA Methylation in Lung Cancer. DISEASE MARKERS 2022; 2022:9619357. [PMID: 35126793 PMCID: PMC8814721 DOI: 10.1155/2022/9619357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 12/12/2021] [Accepted: 12/24/2021] [Indexed: 11/18/2022]
Abstract
Background Lung cancer is one of the most commonly diagnosed cancer worldwide. As one of the liquid biopsy analytes, alternations in cell-free DNA (cfDNA) methylation could function as promising biomarkers for lung cancer detection. Methods In this study, differential methylation analysis was performed to identify candidate markers, and lasso regression with 10-fold cross-validation (CV) was used to establish the diagnostic marker panel. The performance of the binary classifier was evaluated using the receiver operating characteristic (ROC) curve and the precision-recall (PR) curve. Results We identified 4072 differentially methylated regions (DMRs) based on cfDNA methylation data, and then a 10-DMR marker panel was established. The panel achieved an area under the ROC curve (AUROC) of 0.922 and an area under the PR curve (AUPR) of 0.899 in a cfDNA cohort containing 29 lung cancer and 74 normal samples, showing outstanding performance. Besides, the cfDNA-derived markers also performed well in primary tissue datasets, which were more robust than the tissue-derived markers. Conclusion Our study suggested that the 10-DMR marker panel attained high accuracy and robustness and may function as a novel and promising target for lung cancer detection.
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