1
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Sun SJ, Aguirre-Gamboa R, de Bree LCJ, Sanz J, Dumaine A, van der Velden WJFM, Joosten LAB, Khader S, Divangahi M, Netea MG, Barreiro LB. BCG vaccination alters the epigenetic landscape of progenitor cells in human bone marrow to influence innate immune responses. Immunity 2024:S1074-7613(24)00370-4. [PMID: 39153479 DOI: 10.1016/j.immuni.2024.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 04/20/2024] [Accepted: 07/22/2024] [Indexed: 08/19/2024]
Abstract
Although the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo. We showed that BCG alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occurred primarily within uncommitted stem cells. By contrast, changes in chromatin accessibility were most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF) and early growth response (EGR) transcription factors and were highly correlated (r > 0.8) with the interleukin (IL)-1β secretion capacity of paired peripheral blood mononuclear cells (PBMCs). Our findings shed light on BCG vaccination's profound and lasting effects on HSPCs and its influence on innate immune responses and trained immunity.
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Affiliation(s)
- Sarah J Sun
- Committee on Immunology, University of Chicago, Chicago, IL, USA; Medical Scientist Training Program, University of Chicago, Chicago, IL, USA
| | - Raúl Aguirre-Gamboa
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - L Charlotte J de Bree
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Joaquin Sanz
- Institute for Biocomputation and Physics of Complex Systems (BIFI) and Department of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Anne Dumaine
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Leo A B Joosten
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Shabaana Khader
- Department of Microbiology, University of Chicago, Chicago, IL, USA
| | - Maziar Divangahi
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Medicine, Meakins-Christie Laboratories, Research Institute McGill University Health Centre, McGill University, Montreal, QC, Canada
| | - Mihai G Netea
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands; Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Luis B Barreiro
- Committee on Immunology, University of Chicago, Chicago, IL, USA; Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA; Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.
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2
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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3
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Xu S, Hu E, Cai Y, Xie Z, Luo X, Zhan L, Tang W, Wang Q, Liu B, Wang R, Xie W, Wu T, Xie L, Yu G. Using clusterProfiler to characterize multiomics data. Nat Protoc 2024:10.1038/s41596-024-01020-z. [PMID: 39019974 DOI: 10.1038/s41596-024-01020-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 05/13/2024] [Indexed: 07/19/2024]
Abstract
With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .
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Affiliation(s)
- Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yantong Cai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Zijing Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Xiao Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bingdong Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Rui Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Wenqin Xie
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Liwei Xie
- State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Guangdong Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
- Division of Laboratory Medicine, Microbiome Center, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
- Dermatology Hospital, Southern Medical University, Guangzhou, China.
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4
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Jin C, Chen Y, Wang Y, Li J, Liang J, Zheng S, Zhang L, Li Q, Wang Y, Ling F, Li Y, Zheng Y, Nie Q, Feng Q, Wang J, Yang H. Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis. Sci Rep 2024; 14:15778. [PMID: 38982264 PMCID: PMC11233624 DOI: 10.1038/s41598-024-66947-5] [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: 03/30/2024] [Accepted: 07/05/2024] [Indexed: 07/11/2024] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is the most predominant type of idiopathic interstitial pneumonia and has an increasing incidence, poor prognosis, and unclear pathogenesis. In order to investigate the molecular mechanisms underlying IPF further, we performed single-cell RNA sequencing analysis on three healthy controls and five IPF lung tissue samples. The results revealed a significant shift in epithelial cells (ECs) phenotypes in IPF, which may be attributed to the differentiation of alveolar type 2 cells to basal cells. In addition, several previously unrecognized basal cell subtypes were preliminarily identified, including extracellular matrix basal cells, which were increased in the IPF group. We identified a special population of fibroblasts that highly expressed extracellular matrix-related genes, POSTN, CTHRC1, COL3A1, COL5A2, and COL12A1. We propose that the close interaction between ECs and fibroblasts through ligand-receptor pairs may have a critical function in IPF development. Collectively, these outcomes provide innovative perspectives on the complexity and diversity of basal cells and fibroblasts in IPF and contribute to the understanding of possible mechanisms in pathological lung fibrosis.
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Affiliation(s)
- Chengji Jin
- Department of Respiratory Medicine, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Yahong Chen
- Department of Respiratory Medicine, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Yujie Wang
- Department of Respiratory Medicine, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Jia Li
- The Second Affiliated Clinical College, Hainan Medical University, Haikou, 570100, China
| | - Jin Liang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Shaomao Zheng
- Department of Respiratory Medicine, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Lipeng Zhang
- The Second Affiliated Clinical College, Hainan Medical University, Haikou, 570100, China
| | - Qiaoyu Li
- The Second Affiliated Clinical College, Hainan Medical University, Haikou, 570100, China
| | - Yongchao Wang
- Singleron Biotechnologies, Yaogu Avenue 11, Nanjing, 211800, China
| | - Fayu Ling
- Department of Thoracic Surgery, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Yongjie Li
- Department of Thoracic Surgery, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Yu Zheng
- The Second Affiliated Clinical College, Hainan Medical University, Haikou, 570100, China
| | - Qiuli Nie
- The Second Affiliated Clinical College, Hainan Medical University, Haikou, 570100, China
| | - Qiong Feng
- Department of Respiratory Medicine, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China
| | - Jing Wang
- Department of Respiratory Medicine, The Second Affiliated Hospital, Hainan Medical University, Haikou, 570100, China.
- NHC Key Laboratory of Tropical Disease Control, Hainan Medical University, Haikou, 571199, China.
| | - Huiling Yang
- School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China.
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5
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You Q, Chen L, Li S, Liu M, Tian M, Cheng Y, Xia L, Li W, Yao Y, Li Y, Zhou Y, Ma Y, Lv D, Zhao L, Wang H, Wu Z, Hu J, Ju J, Jia C, Xu N, Luo J, Zhang S. Topical JAK inhibition ameliorates EGFR inhibitor-induced rash in rodents and humans. Sci Transl Med 2024; 16:eabq7074. [PMID: 38896602 DOI: 10.1126/scitranslmed.abq7074] [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: 04/26/2022] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
Epidermal growth factor receptor inhibitors (EGFRis) are used to treat many cancers, but their use is complicated by the development of a skin rash that may be severe, limiting their use and adversely affecting patient quality of life. Most studies of EGFRi-induced rash have focused on the fully developed stage of this skin disorder, and early pathological changes remain unclear. We analyzed high-throughput transcriptome sequencing of skin samples from rats exposed to the EGFRi afatinib and identified that keratinocyte activation is an early pathological alteration in EGFRi-induced rash. Mechanistically, the induction of S100 calcium-binding protein A9 (S100A9) occurred before skin barrier disruption and led to keratinocyte activation, resulting in expression of specific cytokines, chemokines, and surface molecules such as interleukin 6 (Il6) and C-C motif chemokine ligand 2 (CCL2) to recruit and activate monocytes through activation of the Janus kinase (JAK)-signal transducers and activators of transcription (STAT) pathway, further recruiting more immune cells. Topical JAK inhibition suppressed the recruitment of immune cells and ameliorated the severity of skin rash in afatinib-treated rats and mice with epidermal deletion of EGFR, while having no effect on EGFRi efficacy in tumor-bearing mice. In a pilot clinical trial (NCT05120362), 11 patients with EGFRi-induced rash were treated with delgocitinib ointment, resulting in improvement in rash severity by at least one grade in 10 of them according to the MASCC EGFR inhibitor skin toxicity tool (MESTT) criteria. These findings provide a better understanding of the early pathophysiology of EGFRi-induced rash and suggest a strategy to manage this condition.
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Affiliation(s)
- Qing You
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Leying Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shuaihu Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Min Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Meng Tian
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuan Cheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liangyong Xia
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Wenxi Li
- OnQuality Pharmaceuticals LLC., Shanghai 201112, China
| | - Yang Yao
- OnQuality Pharmaceuticals LLC., Shanghai 201112, China
| | - Yinan Li
- OnQuality Pharmaceuticals LLC., Shanghai 201112, China
| | - Ying Zhou
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yurui Ma
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dazhao Lv
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Longfei Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hejie Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zhaoyu Wu
- OnQuality Pharmaceuticals LLC., Shanghai 201112, China
| | - Jiajun Hu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Juegang Ju
- OnQuality Pharmaceuticals LLC., Shanghai 201112, China
| | - Chuanlong Jia
- Department of Dermatology, Shanghai East Hospital, Tongji University, 150 Jimo Road, Shanghai 200120, China
| | - Nan Xu
- Department of Dermatology, Shanghai East Hospital, Tongji University, 150 Jimo Road, Shanghai 200120, China
| | - Jie Luo
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shiyi Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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6
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Chen H, Ryu J, Vinyard ME, Lerer A, Pinello L. SIMBA: single-cell embedding along with features. Nat Methods 2024; 21:1003-1013. [PMID: 37248389 PMCID: PMC11166568 DOI: 10.1038/s41592-023-01899-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).
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Affiliation(s)
- Huidong Chen
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael E Vinyard
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Adam Lerer
- Facebook AI Research, New York, NY, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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7
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Farber G, Dong Y, Wang Q, Rathod M, Wang H, Dixit M, Keepers B, Xie Y, Butz K, Polacheck WJ, Liu J, Qian L. Direct conversion of cardiac fibroblasts into endothelial-like cells using Sox17 and Erg. Nat Commun 2024; 15:4170. [PMID: 38755186 PMCID: PMC11098819 DOI: 10.1038/s41467-024-48354-6] [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: 12/12/2022] [Accepted: 04/25/2024] [Indexed: 05/18/2024] Open
Abstract
Endothelial cells are a heterogeneous population with various organ-specific and conserved functions that are critical to organ development, function, and regeneration. Here we report a Sox17-Erg direct reprogramming approach that uses cardiac fibroblasts to create differentiated endothelial cells that demonstrate endothelial-like molecular and physiological functions in vitro and in vivo. Injection of these induced endothelial cells into myocardial infarct sites after injury results in improved vascular perfusion of the scar region. Furthermore, we use genomic analyses to illustrate that Sox17-Erg reprogramming instructs cardiac fibroblasts toward an arterial-like identity. This results in a more efficient direct conversion of fibroblasts into endothelial-like cells when compared to traditional Etv2-based reprogramming. Overall, this Sox17-Erg direct reprogramming strategy offers a robust tool to generate endothelial cells both in vitro and in vivo, and has the potential to be used in repairing injured tissue.
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Grants
- R01 HL139880 NHLBI NIH HHS
- R01HL139880 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- P30 CA016086 NCI NIH HHS
- R35HL155656 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- R01HL139976 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- AHA20EIA35320128 American Heart Association (American Heart Association, Inc.)
- R01 HL139976 NHLBI NIH HHS
- P30 ES010126 NIEHS NIH HHS
- AHA20EIA35310348 American Heart Association (American Heart Association, Inc.)
- F30 HL154659 NHLBI NIH HHS
- R35 HL155656 NHLBI NIH HHS
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
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Affiliation(s)
- Gregory Farber
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yanhan Dong
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Qiaozi Wang
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Mitesh Rathod
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill and Raleigh, NC, USA
- University of North Carolina Kidney Center, Chapel Hill, NC, USA
| | - Haofei Wang
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Michelle Dixit
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Benjamin Keepers
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Yifang Xie
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kendall Butz
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - William J Polacheck
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill and Raleigh, NC, USA
| | - Jiandong Liu
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Li Qian
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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8
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Popp JM, Rhodes K, Jangi R, Li M, Barr K, Tayeb K, Battle A, Gilad Y. Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592174. [PMID: 38746382 PMCID: PMC11092595 DOI: 10.1101/2024.05.02.592174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.
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9
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Ji G, Yang Q, Wang S, Yan X, Ou Q, Gong L, Zhao J, Zhou Y, Tian F, Lei J, Mu X, Wang J, Wang T, Wang X, Sun J, Zhang J, Jia C, Jiang T, Zhao MG, Lu Q. Single-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma. Genome Med 2024; 16:49. [PMID: 38566201 PMCID: PMC10985969 DOI: 10.1186/s13073-024-01320-9] [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: 06/16/2023] [Accepted: 03/21/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND The efficacy of neoadjuvant chemo-immunotherapy (NAT) in esophageal squamous cell carcinoma (ESCC) is challenged by the intricate interplay within the tumor microenvironment (TME). Unveiling the immune landscape of ESCC in the context of NAT could shed light on heterogeneity and optimize therapeutic strategies for patients. METHODS We analyzed single cells from 22 baseline and 24 post-NAT treatment samples of stage II/III ESCC patients to explore the association between the immune landscape and pathological response to neoadjuvant anti-PD-1 combination therapy, including pathological complete response (pCR), major pathological response (MPR), and incomplete pathological response (IPR). RESULTS Single-cell profiling identified 14 major cell subsets of cancer, immune, and stromal cells. Trajectory analysis unveiled an interesting link between cancer cell differentiation and pathological response to NAT. ESCC tumors enriched with less differentiated cancer cells exhibited a potentially favorable pathological response to NAT, while tumors enriched with clusters of more differentiated cancer cells may resist treatment. Deconvolution of transcriptomes in pre-treatment tumors identified gene signatures in response to NAT contributed by specific immune cell populations. Upregulated genes associated with better pathological responses in CD8 + effector T cells primarily involved interferon-gamma (IFNγ) signaling, neutrophil degranulation, and negative regulation of the T cell apoptotic process, whereas downregulated genes were dominated by those in the immune response-activating cell surface receptor signaling pathway. Natural killer cells in pre-treatment tumors from pCR patients showed a similar upregulation of gene expression in response to IFNγ but a downregulation of genes in the neutrophil-mediated immunity pathways. A decreased cellular contexture of regulatory T cells in ESCC TME indicated a potentially favorable pathological response to NAT. Cell-cell communication analysis revealed extensive interactions between CCL5 and its receptor CCR5 in various immune cells of baseline pCR tumors. Immune checkpoint interaction pairs, including CTLA4-CD86, TIGIT-PVR, LGALS9-HAVCR2, and TNFSF4-TNFRSF4, might serve as additional therapeutic targets for ICI therapy in ESCC. CONCLUSIONS This pioneering study unveiled an intriguing association between cancer cell differentiation and pathological response in esophageal cancer patients, revealing distinct subgroups of tumors for which neoadjuvant chemo-immunotherapy might be effective. We also delineated the immune landscape of ESCC tumors in the context of clinical response to NAT, which provides clinical insights for better understanding how patients respond to the treatment and further identifying novel therapeutic targets for ESCC patients in the future.
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Affiliation(s)
- Gang Ji
- Department of Digestive Surgery, Xijing Hospital, Air Force Medical University, No. 169 Changle West Road, Xi'an, 710032, China
| | - Qi Yang
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Song Wang
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc, Nanjing, 210000, Jiangsu, China
| | - Xiaolong Yan
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Qiuxiang Ou
- Geneseeq Research Institute, Nanjing Geneseeq Technology Inc, Nanjing, 210000, Jiangsu, China
| | - Li Gong
- Department of Pathology, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jinbo Zhao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Yongan Zhou
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Feng Tian
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jie Lei
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Xiaorong Mu
- Department of Pathology, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jian Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Tao Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Xiaoping Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jianyong Sun
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Jipeng Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China
| | - Chenghui Jia
- Department of Thoracic Surgery, The First Affiliated Hospital of Xi'an Medical College, Xi'an, 710000, China
| | - Tao Jiang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China.
| | - Ming-Gao Zhao
- Precision Pharmacy & Drug Development Center, Department of Pharmacy, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China.
| | - Qiang Lu
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, No. 569 Xinsi Road, Xi'an, 710038, China.
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10
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Huang X, Liu R, Yang S, Chen X, Li H. scAnnoX: an R package integrating multiple public tools for single-cell annotation. PeerJ 2024; 12:e17184. [PMID: 38560451 PMCID: PMC10981883 DOI: 10.7717/peerj.17184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Background Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking. Methods This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms. Results The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.
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Affiliation(s)
- Xiaoqian Huang
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Ruiqi Liu
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Shiwei Yang
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Xiaozhou Chen
- School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, Yunnan Province, China
| | - Huamei Li
- Department of Hepatobiliary Surgery, the Affiliated Drum Tower Hospital, Medical School, Nanjing University, Nanjing, Jiangsu Province, China
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11
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Fan D, Cong Y, Liu J, Zhang H, Du Z. Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference. Cell Rep 2024; 43:113928. [PMID: 38461413 DOI: 10.1016/j.celrep.2024.113928] [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: 08/08/2023] [Revised: 01/31/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.
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Affiliation(s)
- Duchangjiang Fan
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Yulin Cong
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Jinyi Liu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Haoye Zhang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Zhuo Du
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
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12
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Saha O, Melo de Farias AR, Pelletier A, Siedlecki-Wullich D, Landeira BS, Gadaut J, Carrier A, Vreulx AC, Guyot K, Shen Y, Bonnefond A, Amouyel P, Tcw J, Kilinc D, Queiroz CM, Delahaye F, Lambert JC, Costa MR. The Alzheimer's disease risk gene BIN1 regulates activity-dependent gene expression in human-induced glutamatergic neurons. Mol Psychiatry 2024:10.1038/s41380-024-02502-y. [PMID: 38514804 DOI: 10.1038/s41380-024-02502-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 02/22/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
Bridging Integrator 1 (BIN1) is the second most important Alzheimer's disease (AD) risk gene, but its physiological roles in neurons and its contribution to brain pathology remain largely elusive. In this work, we show that BIN1 plays a critical role in the regulation of calcium homeostasis, electrical activity, and gene expression of glutamatergic neurons. Using single-cell RNA-sequencing on cerebral organoids generated from isogenic BIN1 wild type (WT), heterozygous (HET) and homozygous knockout (KO) human-induced pluripotent stem cells (hiPSCs), we show that BIN1 is mainly expressed by oligodendrocytes and glutamatergic neurons, like in the human brain. Both BIN1 HET and KO cerebral organoids show specific transcriptional alterations, mainly associated with ion transport and synapses in glutamatergic neurons. We then demonstrate that BIN1 cell-autonomously regulates gene expression in glutamatergic neurons by using a novel protocol to generate pure culture of hiPSC-derived induced neurons (hiNs). Using this system, we also show that BIN1 plays a key role in the regulation of neuronal calcium transients and electrical activity via its interaction with the L-type voltage-gated calcium channel Cav1.2. BIN1 KO hiNs show reduced activity-dependent internalization and higher Cav1.2 expression compared to WT hiNs. Pharmacological blocking of this channel with clinically relevant doses of nifedipine, a calcium channel blocker, partly rescues electrical and gene expression alterations in BIN1 KO glutamatergic neurons. Further, we show that transcriptional alterations in BIN1 KO hiNs that affect biological processes related to calcium homeostasis are also present in glutamatergic neurons of the human brain at late stages of AD pathology. Together, these findings suggest that BIN1-dependent alterations in neuronal properties could contribute to AD pathophysiology and that treatment with low doses of clinically approved calcium blockers should be considered as an option to slow disease-onset and progression.
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Affiliation(s)
- Orthis Saha
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Ana Raquel Melo de Farias
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
- Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho, 3000, Campus Universitário, Lagoa, Nova, 59078-970, Natal, Brazil
| | - Alexandre Pelletier
- Univ. Lille, Inserm, CNRS, CHU Lille, Institut Pasteur de Lille, U1283-UMR 8199 EGID, Pôle Recherche, 1 Place de Verdun, 59045, Lille, Cedex, France
- Department of Pharmacology, Physiology & Biophysics, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Dolores Siedlecki-Wullich
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Bruna Soares Landeira
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Johanna Gadaut
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Arnaud Carrier
- Univ. Lille, Inserm, CNRS, CHU Lille, Institut Pasteur de Lille, U1283-UMR 8199 EGID, Pôle Recherche, 1 Place de Verdun, 59045, Lille, Cedex, France
| | - Anaïs-Camille Vreulx
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Karine Guyot
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Yun Shen
- Department of Pharmacology, Physiology & Biophysics, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Amelie Bonnefond
- Univ. Lille, Inserm, CNRS, CHU Lille, Institut Pasteur de Lille, U1283-UMR 8199 EGID, Pôle Recherche, 1 Place de Verdun, 59045, Lille, Cedex, France
| | - Philippe Amouyel
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Julia Tcw
- Department of Pharmacology, Physiology & Biophysics, Boston University, Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
- Bioinformatics Program, Faculty of Computing & Data Sciences, Boston University, Boston, MA, 02115, USA
| | - Devrim Kilinc
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Claudio Marcos Queiroz
- Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho, 3000, Campus Universitário, Lagoa, Nova, 59078-970, Natal, Brazil
| | - Fabien Delahaye
- Univ. Lille, Inserm, CNRS, CHU Lille, Institut Pasteur de Lille, U1283-UMR 8199 EGID, Pôle Recherche, 1 Place de Verdun, 59045, Lille, Cedex, France
| | - Jean-Charles Lambert
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France
| | - Marcos R Costa
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France.
- Brain Institute, Federal University of Rio Grande do Norte, Av. Senador Salgado Filho, 3000, Campus Universitário, Lagoa, Nova, 59078-970, Natal, Brazil.
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13
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Costa MR. Switch of innate to adaptative immune responses in the brain of patients with Alzheimer's disease correlates with tauopathy progression. NPJ AGING 2024; 10:19. [PMID: 38499592 PMCID: PMC10948755 DOI: 10.1038/s41514-024-00145-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 03/05/2024] [Indexed: 03/20/2024]
Abstract
Neuroinflammation is a key feature of Alzheimer's disease (AD). In this work, analysis of single- cell RNA-sequencing (scRNA-seq) data obtained from the brain of patients with AD provides evidence supporting a switch from an innate to an adaptative immune response during tauopathy progression, with both disease-associated microglia (DAM) and CD8+ T cells becoming more frequent at advanced Braak stages.
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Affiliation(s)
- Marcos R Costa
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE facteurs de risqué et déterminants moléculaires des maladies liées au vieillissement, DISTALZ, 1 rue du Professeur Calmette, 59019, Lille, France.
- Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil.
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14
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Wilk AJ, Shalek AK, Holmes S, Blish CA. Comparative analysis of cell-cell communication at single-cell resolution. Nat Biotechnol 2024; 42:470-483. [PMID: 37169965 PMCID: PMC10638471 DOI: 10.1038/s41587-023-01782-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/05/2023] [Indexed: 05/13/2023]
Abstract
Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.
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Affiliation(s)
- Aaron J Wilk
- Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA.
| | - Alex K Shalek
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- 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, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA, USA
| | - Catherine A Blish
- Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
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15
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Wilk AJ, Marceau JO, Kazer SW, Fleming I, Miao VN, Galvez-Reyes J, Kimata JT, Shalek AK, Holmes S, Overbaugh J, Blish CA. Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection. Genome Med 2024; 16:24. [PMID: 38317183 PMCID: PMC10840164 DOI: 10.1186/s13073-024-01290-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: 04/17/2023] [Accepted: 01/19/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND The Lentivirus human immunodeficiency virus (HIV) causes chronic inflammation and AIDS in humans, with variable rates of disease progression between individuals driven by both host and viral factors. Similarly, simian lentiviruses vary in their pathogenicity based on characteristics of both the host species and the virus strain, yet the immune underpinnings that drive differential Lentivirus pathogenicity remain incompletely understood. METHODS We profile immune responses in a unique model of differential lentiviral pathogenicity where pig-tailed macaques are infected with highly genetically similar variants of SIV that differ in virulence. We apply longitudinal single-cell transcriptomics to this cohort, along with single-cell resolution cell-cell communication techniques, to understand the immune mechanisms underlying lentiviral pathogenicity. RESULTS Compared to a minimally pathogenic lentiviral variant, infection with a highly pathogenic variant results in a more delayed, broad, and sustained activation of inflammatory pathways, including an extensive global interferon signature. Conversely, individual cells infected with highly pathogenic Lentivirus upregulated fewer interferon-stimulated genes at a lower magnitude, indicating that highly pathogenic Lentivirus has evolved to partially escape from interferon responses. Further, we identify CXCL10 and CXCL16 as important molecular drivers of inflammatory pathways specifically in response to highly pathogenic Lentivirus infection. Immune responses to highly pathogenic Lentivirus infection are characterized by amplifying regulatory circuits of pro-inflammatory cytokines with dense longitudinal connectivity. CONCLUSIONS Our work presents a model of lentiviral pathogenicity where failures in early viral control mechanisms lead to delayed, sustained, and amplifying pro-inflammatory circuits, which in turn drives disease progression.
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Affiliation(s)
- Aaron J Wilk
- Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Joshua O Marceau
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Samuel W Kazer
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- 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
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Ira Fleming
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- 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
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Vincent N Miao
- Institute for Medical Engineering & Science, 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
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Program in Health Sciences & Technology, Harvard Medical School & MIT, Boston, MA, 02115, USA
| | - Jennyfer Galvez-Reyes
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- 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
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jason T Kimata
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Alex K Shalek
- Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- 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
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Susan Holmes
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA
| | - Julie Overbaugh
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
| | - Catherine A Blish
- Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Medical Scientist Training Program, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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16
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Zhou S, Li Y, Wu W, Li L. scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data. Brief Bioinform 2024; 25:bbad523. [PMID: 38300515 PMCID: PMC10833085 DOI: 10.1093/bib/bbad523] [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: 09/04/2023] [Revised: 11/27/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024] Open
Abstract
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.
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Affiliation(s)
- Songqi Zhou
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Yang Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
| | - Wenyuan Wu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Li Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
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17
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Harripaul R, Morini E, Salani M, Logan E, Kirchner E, Bolduc J, Chekuri A, Currall B, Yadav R, Erdin S, Talkowski ME, Gao D, Slaugenhaupt S. Transcriptome analysis in a humanized mouse model of familial dysautonomia reveals tissue-specific gene expression disruption in the peripheral nervous system. Sci Rep 2024; 14:570. [PMID: 38177237 PMCID: PMC10766950 DOI: 10.1038/s41598-023-51137-6] [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: 10/31/2023] [Accepted: 12/31/2023] [Indexed: 01/06/2024] Open
Abstract
Familial dysautonomia (FD) is a rare recessive neurodevelopmental disease caused by a splice mutation in the Elongator acetyltransferase complex subunit 1 (ELP1) gene. This mutation results in a tissue-specific reduction of ELP1 protein, with the lowest levels in the central and peripheral nervous systems (CNS and PNS, respectively). FD patients exhibit complex neurological phenotypes due to the loss of sensory and autonomic neurons. Disease symptoms include decreased pain and temperature perception, impaired or absent myotatic reflexes, proprioceptive ataxia, and progressive retinal degeneration. While the involvement of the PNS in FD pathogenesis has been clearly recognized, the underlying mechanisms responsible for the preferential neuronal loss remain unknown. In this study, we aimed to elucidate the molecular mechanisms underlying FD by conducting a comprehensive transcriptome analysis of neuronal tissues from the phenotypic mouse model TgFD9; Elp1Δ20/flox. This mouse recapitulates the same tissue-specific ELP1 mis-splicing observed in patients while modeling many of the disease manifestations. Comparison of FD and control transcriptomes from dorsal root ganglion (DRG), trigeminal ganglion (TG), medulla (MED), cortex, and spinal cord (SC) showed significantly more differentially expressed genes (DEGs) in the PNS than the CNS. We then identified genes that were tightly co-expressed and functionally dependent on the level of full-length ELP1 transcript. These genes, defined as ELP1 dose-responsive genes, were combined with the DEGs to generate tissue-specific dysregulated FD signature genes and networks. Within the PNS networks, we observed direct connections between Elp1 and genes involved in tRNA synthesis and genes related to amine metabolism and synaptic signaling. Importantly, transcriptomic dysregulation in PNS tissues exhibited enrichment for neuronal subtype markers associated with peptidergic nociceptors and myelinated sensory neurons, which are known to be affected in FD. In summary, this study has identified critical tissue-specific gene networks underlying the etiology of FD and provides new insights into the molecular basis of the disease.
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Affiliation(s)
- Ricardo Harripaul
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Elisabetta Morini
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Monica Salani
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Emily Logan
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Emily Kirchner
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Jessica Bolduc
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
| | - Anil Chekuri
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin Currall
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Rachita Yadav
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Serkan Erdin
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dadi Gao
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Susan Slaugenhaupt
- Center for Genomic Medicine, Massachusetts General Hospital Research Institute, Boston, MA, USA.
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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18
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Liang Q, Huang Y, He S, Chen K. Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity. Nat Commun 2023; 14:8416. [PMID: 38110427 PMCID: PMC10728201 DOI: 10.1038/s41467-023-44206-x] [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: 06/26/2023] [Accepted: 12/04/2023] [Indexed: 12/20/2023] Open
Abstract
Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.
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Affiliation(s)
- Qingnan Liang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Yuefan Huang
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Shan He
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, UT MD Anderson Cancer Center, Houston, TX, USA.
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19
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Rienstra J, Hernández-García J, Weijers D. To bind or not to bind: how AUXIN RESPONSE FACTORs select their target genes. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:6922-6932. [PMID: 37431145 PMCID: PMC10690724 DOI: 10.1093/jxb/erad259] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/05/2023] [Indexed: 07/12/2023]
Abstract
Most plant growth and development processes are regulated in one way or another by auxin. The best-studied mechanism by which auxin exerts its regulatory effects is through the nuclear auxin pathway (NAP). In this pathway, Auxin Response Factors (ARFs) are the transcription factors that ultimately determine which genes become auxin regulated by binding to specific DNA sequences. ARFs have primarily been studied in Arabidopsis thaliana, but recent studies in other species have revealed family-wide DNA binding specificities for different ARFs and the minimal functional system of the NAP system, consisting of a duo of competing ARFs of the A and B classes. In this review, we provide an overview of key aspects of ARF DNA binding such as auxin response elements (TGTCNN) and tandem repeat motifs, and consider how structural biology and in vitro studies help us understand ARF DNA preferences. We also highlight some recent aspects related to the regulation of ARF levels inside a cell, which may alter the DNA binding profile of ARFs in different tissues. We finally emphasize the need to study minimal NAP systems to understand fundamental aspects of ARF function, the need to characterize algal ARFs to understand how ARFs evolved, how cutting-edge techniques can increase our understanding of ARFs, and which remaining questions can only be answered by structural biology.
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Affiliation(s)
- Juriaan Rienstra
- Laboratory of Biochemistry, Wageningen University, Stippeneng 4, 6708WE Wageningen, The Netherlands
| | - Jorge Hernández-García
- Laboratory of Biochemistry, Wageningen University, Stippeneng 4, 6708WE Wageningen, The Netherlands
| | - Dolf Weijers
- Laboratory of Biochemistry, Wageningen University, Stippeneng 4, 6708WE Wageningen, The Netherlands
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20
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Ghaddar B, De S. Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census. Bioinformatics 2023; 39:btad714. [PMID: 38011649 PMCID: PMC10713118 DOI: 10.1093/bioinformatics/btad714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 10/26/2023] [Accepted: 11/25/2023] [Indexed: 11/29/2023] Open
Abstract
MOTIVATION Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. RESULTS We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. AVAILABILITY AND IMPLEMENTATION Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.
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Affiliation(s)
- Bassel Ghaddar
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
| | - Subhajyoti De
- Center for Systems and Computational Biology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 08901, United States
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21
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Sun SJ, Aguirre-Gamboa R, de Bree LCJ, Sanz J, Dumaine A, Joosten LA, Divangahi M, Netea MG, Barreiro LB. BCG vaccination impacts the epigenetic landscape of progenitor cells in human bone marrow. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.28.569076. [PMID: 38077046 PMCID: PMC10705418 DOI: 10.1101/2023.11.28.569076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
While the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo administration. We show that BCG vaccination significantly alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occur primarily on the most uncommitted stem cells and are reflective of a persistent myeloid bias. In contrast, BCG-induced changes in chromatin accessibility are most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF)/SP and EGR transcription factors (TFs). These TFs are also activated in the most uncommitted stem cells, indicating that activated TFs, which drive persistent changes in HSC gene expression, likely also drive chromatin dynamics appearing within downstream progenitor cells. This perspective contests the prevailing notion that epigenetic modifications linked to innate immune memory transfer directly from stem cells to their differentiated derivatives. Finally, we show that alterations in gene expression and chromatin accessibility in HSPCs due to BCG vaccination were highly correlated (r>0.8) with the IL-1β secretion capacity of paired PBMCs upon secondary immune challenge. Overall, our findings shed light on BCG vaccination's profound and lasting effects on HSPCs and its influence on innate immune responses.
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Affiliation(s)
- Sarah J. Sun
- Committee on Immunology, University of Chicago, Chicago, IL, USA
- Medical Scientist Training program, University of Chicago, Chicago, IL, USA
| | - Raúl Aguirre-Gamboa
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - L. Charlotte J. de Bree
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Joaquin Sanz
- Institute for Biocomputation and Physics of Complex Systems (BIFI) and Dept. of Theoretical Physics, University of Zaragoza, Zaragoza, Spain
| | - Anne Dumaine
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Leo A.B. Joosten
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Medical Genetics, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Maziar Divangahi
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Medicine, Meakins-Christie Laboratories, Research Institute McGill University Health Centre, McGill University, Montreal, Canada
| | - Mihai G. Netea
- Department of Internal Medicine and Radbound Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Luis B. Barreiro
- Committee on Immunology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA
- Lead contact
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22
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Harripaul R, Morini E, Salani M, Logan E, Kirchner E, Bolduc J, Chekuri A, Currall B, Yadav R, Erdin S, Talkowski ME, Gao D, Slaugenhaupt S. Transcriptome analysis in a humanized mouse model of familial dysautonomia reveals tissue-specific gene expression disruption in the peripheral nervous system. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.28.559870. [PMID: 37808686 PMCID: PMC10557663 DOI: 10.1101/2023.09.28.559870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Familial dysautonomia (FD) is a rare recessive neurodevelopmental disease caused by a splice mutation in the Elongator acetyltransferase complex subunit 1 ( ELP1 ) gene. This mutation results in a tissue-specific reduction of ELP1 protein, with the lowest levels in the central and peripheral nervous systems (CNS and PNS, respectively). FD patients exhibit complex neurological phenotypes due to the loss of sensory and autonomic neurons. Disease symptoms include decreased pain and temperature perception, impaired or absent myotatic reflexes, proprioceptive ataxia, and progressive retinal degeneration. While the involvement of the PNS in FD pathogenesis has been clearly recognized, the underlying mechanisms responsible for the preferential neuronal loss remain unknown. In this study, we aimed to elucidate the molecular mechanisms underlying FD by conducting a comprehensive transcriptome analysis of neuronal tissues from the phenotypic mouse model TgFD9 ; Elp1 Δ 20/flox . This mouse recapitulates the same tissue-specific ELP1 mis-splicing observed in patients while modeling many of the disease manifestations. Comparison of FD and control transcriptomes from dorsal root ganglion (DRG), trigeminal ganglion (TG), medulla (MED), cortex, and spinal cord (SC) showed significantly more differentially expressed genes (DEGs) in the PNS than the CNS. We then identified genes that were tightly co-expressed and functionally dependent on the level of full-length ELP1 transcript. These genes, defined as ELP1 dose-responsive genes, were combined with the DEGs to generate tissue-specific dysregulated FD signature genes and networks. Within the PNS networks, we observed direct connections between Elp1 and genes involved in tRNA synthesis and genes related to amine metabolism and synaptic signaling. Importantly, transcriptomic dysregulation in PNS tissues exhibited enrichment for neuronal subtype markers associated with peptidergic nociceptors and myelinated sensory neurons, which are known to be affected in FD. In summary, this study has identified critical tissue-specific gene networks underlying the etiology of FD and provides new insights into the molecular basis of the disease.
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23
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Li G, Liu L, Lu T, Sui Y, Zhang C, Wang Y, Zhang T, Xie Y, Xiao P, Zhao Z, Cheng C, Hu J, Chen H, Xue D, Chen H, Wang G, Kong R, Tan H, Bai X, Li Z, McAllister F, Li L, Sun B. Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis. Nat Commun 2023; 14:6179. [PMID: 37794047 PMCID: PMC10550972 DOI: 10.1038/s41467-023-41950-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 09/21/2023] [Indexed: 10/06/2023] Open
Abstract
Hypertriglyceridemic pancreatitis (HTGP) is featured by higher incidence of complications and poor clinical outcomes. Gut microbiota dysbiosis is associated with pancreatic injury in HTGP and the mechanism remains unclear. Here, we observe lower diversity of gut microbiota and absence of beneficial bacteria in HTGP patients. In a fecal microbiota transplantation mouse model, the colonization of gut microbiota from HTGP patients recruits neutrophils and increases neutrophil extracellular traps (NETs) formation that exacerbates pancreatic injury and systemic inflammation. We find that decreased abundance of Bacteroides uniformis in gut microbiota impairs taurine production and increases IL-17 release in colon that triggers NETs formation. Moreover, Bacteroides uniformis or taurine inhibits the activation of NF-κB and IL-17 signaling pathways in neutrophils which harness NETs and alleviate pancreatic injury. Our findings establish roles of endogenous Bacteroides uniformis-derived metabolic and inflammatory products on suppressing NETs release, which provides potential insights of ameliorating HTGP through gut microbiota modulation.
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Affiliation(s)
- Guanqun Li
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Liwei Liu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Tianqi Lu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Yuhang Sui
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Can Zhang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Yongwei Wang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Tao Zhang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Yu Xie
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Peng Xiao
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Zhongjie Zhao
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Chundong Cheng
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Jisheng Hu
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Hongze Chen
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Dongbo Xue
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Hua Chen
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Gang Wang
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Rui Kong
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China
| | - Hongtao Tan
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Xuewei Bai
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Zhibo Li
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Florencia McAllister
- Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Clinical Cancer Genetics Program, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Le Li
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China.
| | - Bei Sun
- Department of Pancreatic and Biliary Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
- Key Laboratory of Hepatosplenic Surgery, Ministry of Education, Harbin, 150001, China.
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24
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Ren P, Shi X, Yu Z, Dong X, Ding X, Wang J, Sun L, Yan Y, Hu J, Zhang P, Chen Q, Zhang J, Li T, Wang C. Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references. CELL REPORTS METHODS 2023; 3:100577. [PMID: 37751689 PMCID: PMC10545911 DOI: 10.1016/j.crmeth.2023.100577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 06/11/2023] [Accepted: 08/09/2023] [Indexed: 09/28/2023]
Abstract
The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.
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Affiliation(s)
- Pengfei Ren
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China
| | - Xiaoying Shi
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Zhiguang Yu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Guangxi 530004, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xuanxin Ding
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jin Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Liangdong Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Yilv Yan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Junjie Hu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China
| | - Qianming Chen
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Research Unit of Oral Carcinogenesis and Management, Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Research Center for Translational Medicine, Shanghai East Hospital, School of Life Science and Technology, Tongji University, Shanghai, China.
| | - Taiwen Li
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Research Unit of Oral Carcinogenesis and Management, Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211166, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Science and Technology, Tongji University, Shanghai 200092, China; Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.
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25
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Yang Q, Xu Z, Zhou W, Wang P, Jiang Q, Juan L. An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation. Brief Bioinform 2023; 24:bbad199. [PMID: 37225419 PMCID: PMC10359080 DOI: 10.1093/bib/bbad199] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) detects whole transcriptome signals for large amounts of individual cells and is powerful for determining cell-to-cell differences and investigating the functional characteristics of various cell types. scRNA-seq datasets are usually sparse and highly noisy. Many steps in the scRNA-seq analysis workflow, including reasonable gene selection, cell clustering and annotation, as well as discovering the underlying biological mechanisms from such datasets, are difficult. In this study, we proposed an scRNA-seq analysis method based on the latent Dirichlet allocation (LDA) model. The LDA model estimates a series of latent variables, i.e. putative functions (PFs), from the input raw cell-gene data. Thus, we incorporated the 'cell-function-gene' three-layer framework into scRNA-seq analysis, as this framework is capable of discovering latent and complex gene expression patterns via a built-in model approach and obtaining biologically meaningful results through a data-driven functional interpretation process. We compared our method with four classic methods on seven benchmark scRNA-seq datasets. The LDA-based method performed best in the cell clustering test in terms of both accuracy and purity. By analysing three complex public datasets, we demonstrated that our method could distinguish cell types with multiple levels of functional specialization, and precisely reconstruct cell development trajectories. Moreover, the LDA-based method accurately identified the representative PFs and the representative genes for the cell types/cell stages, enabling data-driven cell cluster annotation and functional interpretation. According to the literature, most of the previously reported marker/functionally relevant genes were recognized.
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Affiliation(s)
- Qi Yang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Zhaochun Xu
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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26
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Tao S, Yu H, You T, Kong X, Wei X, Zheng Z, Zheng L, Feng Z, Huang B, Zhang X, Chen F, Chen X, Song H, Li J, Chen B, Chen J, Yao Q, Zhao F. A Dual-Targeted Metal-Organic Framework Based Nanoplatform for the Treatment of Rheumatoid Arthritis by Restoring the Macrophage Niche. ACS NANO 2023. [PMID: 37429012 DOI: 10.1021/acsnano.3c03828] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/12/2023]
Abstract
Inflammatory infiltration and bone destruction are important pathological features of rheumatoid arthritis (RA), which originate from the disturbed niche of macrophages. Here, we identified a niche-disrupting process in RA: due to overactivation of complement, the barrier function of VSIg4+ lining macrophages is disrupted and mediates inflammatory infiltration within the joint, thereby activating excessive osteoclastogenesis and bone resorption. However, complement antagonists have poor biological applications due to superphysiologic dose requirements and inadequate effects on bone resorption. Therefore, we developed a dual-targeted therapeutic nanoplatform based on the MOF framework to achieve bone-targeted delivery of the complement inhibitor CRIg-CD59 and pH-responsive sustained release. The surface-mineralized zoledronic acid (ZA) of ZIF8@CRIg-CD59@HA@ZA targets the skeletal acidic microenvironment in RA, and the sustained release of CRIg-CD59 can recognize and prevent the complement membrane attack complex (MAC) from forming on the surface of healthy cells. Importantly, ZA can inhibit osteoclast-mediated bone resorption, and CRIg-CD59 can promote the repair of the VSIg4+ lining macrophage barrier to achieve sequential niche remodeling. This combination therapy is expected to treat RA by reversing the core pathological process, circumventing the pitfalls of traditional therapy.
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Affiliation(s)
- Siyue Tao
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Hao Yu
- National Engineering Research Center of Ophthalmology and Optometry, School of Ophthalmology & Optometry, Eye Hospital, Wenzhou Medical University, 270 Xueyuan Xi Road, Wenzhou, 325027 Zhejiang, China
| | - Tao You
- The First Affiliated Hospital of USTC, Division of Life Science and Medicine, and CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, 230026 Anhui, China
| | - Xiangxi Kong
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Xiaoan Wei
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Zeyu Zheng
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Lin Zheng
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Zhenhua Feng
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Bao Huang
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Xuyang Zhang
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Feng Chen
- The First Affiliated Hospital of USTC, Division of Life Science and Medicine, and CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, 230026 Anhui, China
| | - Xiao Chen
- The First Affiliated Hospital of USTC, Division of Life Science and Medicine, and CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, 230026 Anhui, China
| | - Haixin Song
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
| | - Jie Li
- Department of Orthopaedic Surgery, Ningbo Medical Center Li Huili Hospital, Ningbo, 315100 Zhejiang, China
| | - Binhui Chen
- Department of Orthopaedic Surgery, Ningbo Medical Center Li Huili Hospital, Ningbo, 315100 Zhejiang, China
| | - Jian Chen
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
- Department of Orthopedic Surgery, Wenzhou Medical University First Affiliated Hospital, Wenzhou, 325000 Zhejiang, China
| | - Qingqing Yao
- National Engineering Research Center of Ophthalmology and Optometry, School of Ophthalmology & Optometry, Eye Hospital, Wenzhou Medical University, 270 Xueyuan Xi Road, Wenzhou, 325027 Zhejiang, China
| | - Fengdong Zhao
- Department of Orthopedic Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016 Zhejiang, China
- Key Laboratory of Musculoskeletal System Degeneration and Regeneration Translational Research of Zhejiang Province, Hangzhou, 310016 Zhejiang, China
- Department of Orthopedic Surgery, Wenzhou Medical University First Affiliated Hospital, Wenzhou, 325000 Zhejiang, China
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27
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Quatredeniers M, Serafin AS, Benmerah A, Rausell A, Saunier S, Viau A. Meta-analysis of single-cell and single-nucleus transcriptomics reveals kidney cell type consensus signatures. Sci Data 2023; 10:361. [PMID: 37280226 DOI: 10.1038/s41597-023-02209-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 05/05/2023] [Indexed: 06/08/2023] Open
Abstract
While the amount of studies involving single-cell or single-nucleus RNA-sequencing technologies grows exponentially within the biomedical research area, the kidney field requires reference transcriptomic signatures to allocate each cluster its matching cell type. The present meta-analysis of 39 previously published datasets, from 7 independent studies, involving healthy human adult kidney samples, offers a set of 24 distinct consensus kidney cell type signatures. The use of these signatures may help to assure the reliability of cell type identification in future studies involving single-cell and single-nucleus transcriptomics while improving the reproducibility in cell type allocation.
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Affiliation(s)
- Marceau Quatredeniers
- Université de Paris Cité, Imagine Institute, Laboratory of Hereditary Kidney Diseases, Paris, INSERM UMR 1163, F-75015, France.
| | - Alice S Serafin
- Université de Paris Cité, Imagine Institute, Laboratory of Hereditary Kidney Diseases, Paris, INSERM UMR 1163, F-75015, France
| | - Alexandre Benmerah
- Université de Paris Cité, Imagine Institute, Laboratory of Hereditary Kidney Diseases, Paris, INSERM UMR 1163, F-75015, France
| | - Antonio Rausell
- Université de Paris Cité, Imagine Institute, Laboratory of Clinical Bioinformatics, Paris, INSERM UMR 1163, F-75015, France
| | - Sophie Saunier
- Université de Paris Cité, Imagine Institute, Laboratory of Hereditary Kidney Diseases, Paris, INSERM UMR 1163, F-75015, France
| | - Amandine Viau
- Université de Paris Cité, Imagine Institute, Laboratory of Hereditary Kidney Diseases, Paris, INSERM UMR 1163, F-75015, France
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28
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Caliskan A, Caliskan D, Rasbach L, Yu W, Dandekar T, Breitenbach T. Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning. Comput Struct Biotechnol J 2023; 21:3293-3314. [PMID: 37333862 PMCID: PMC10276237 DOI: 10.1016/j.csbj.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 06/20/2023] Open
Abstract
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
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29
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Van de Sande B, Lee JS, Mutasa-Gottgens E, Naughton B, Bacon W, Manning J, Wang Y, Pollard J, Mendez M, Hill J, Kumar N, Cao X, Chen X, Khaladkar M, Wen J, Leach A, Ferran E. Applications of single-cell RNA sequencing in drug discovery and development. Nat Rev Drug Discov 2023; 22:496-520. [PMID: 37117846 PMCID: PMC10141847 DOI: 10.1038/s41573-023-00688-4] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/10/2023] [Indexed: 04/30/2023]
Abstract
Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.
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Affiliation(s)
| | | | | | - Bart Naughton
- Computational Neurobiology, Eisai, Cambridge, MA, USA
| | - Wendi Bacon
- EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
- The Open University, Milton Keynes, UK
| | | | - Yong Wang
- Precision Bioinformatics, Prometheus Biosciences, San Diego, CA, USA
| | | | - Melissa Mendez
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | - Jon Hill
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals Inc., Ridgefield, CT, USA
| | - Namit Kumar
- Informatics & Predictive Sciences, Bristol Myers Squibb, San Diego, CA, USA
| | - Xiaohong Cao
- Genomic Research Center, AbbVie Inc., Cambridge, MA, USA
| | - Xiao Chen
- Magnet Biomedicine, Cambridge, MA, USA
| | - Mugdha Khaladkar
- Human Genetics and Computational Biology, GlaxoSmithKline, Collegeville, PA, USA
| | - Ji Wen
- Oncology Research and Development Unit, Pfizer, La Jolla, CA, USA
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30
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Mölbert C, Haghverdi L. Adjustments to the reference dataset design improve cell type label transfer. FRONTIERS IN BIOINFORMATICS 2023; 3:1150099. [PMID: 37091908 PMCID: PMC10114588 DOI: 10.3389/fbinf.2023.1150099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 03/21/2023] [Indexed: 04/07/2023] Open
Abstract
The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets.
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Affiliation(s)
- Carla Mölbert
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
- Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany
- *Correspondence: Carla Mölbert,
| | - Laleh Haghverdi
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
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31
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Einav T, Khoo Y, Singer A. Quantitatively Visualizing Bipartite Datasets. PHYSICAL REVIEW. X 2023; 13:021002. [PMID: 38831998 PMCID: PMC11146982 DOI: 10.1103/physrevx.13.021002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily interpretable form. Often, each measurement conveys only the relationship between a pair of entries, and it is difficult to integrate these local interactions across a dataset to form a cohesive global picture. The classic localization problem tackles this question, transforming local measurements into a global map that reveals the underlying structure of a system. Here, we examine the more challenging bipartite localization problem, where pairwise distances are available only for bipartite data comprising two classes of entries (such as antibody-virus interactions, drug-cell potency, or user-rating profiles). We modify previous algorithms to solve bipartite localization and examine how each method behaves in the presence of noise, outliers, and partially observed data. As a proof of concept, we apply these algorithms to antibody-virus neutralization measurements to create a basis set of antibody behaviors, formalize how potently inhibiting some viruses necessitates weakly inhibiting other viruses, and quantify how often combinations of antibodies exhibit degenerate behavior.
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Affiliation(s)
- Tal Einav
- Divisions of Computational Biology and Basic Sciences, Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA
| | - Yuehaw Khoo
- Department of Statistics, University of Chicago, Chicago, Illinois 60637, USA
| | - Amit Singer
- Department of Mathematics and PACM, Princeton University, Princeton, New Jersey 08540, USA
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32
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Landais Y, Vallot C. Multi-modal quantification of pathway activity with MAYA. Nat Commun 2023; 14:1668. [PMID: 36966153 PMCID: PMC10039856 DOI: 10.1038/s41467-023-37410-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/16/2023] [Indexed: 03/27/2023] Open
Abstract
Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.
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Affiliation(s)
| | - Céline Vallot
- CNRS UMR3244, Institut Curie, PSL University, Paris, France.
- Translational Research Department, Institut Curie, PSL University, Paris, France.
- Single Cell Initiative, Institut Curie, PSL University, Paris, France.
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33
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Sobrino S, Magnani A, Semeraro M, Martignetti L, Cortal A, Denis A, Couzin C, Picard C, Bustamante J, Magrin E, Joseph L, Roudaut C, Gabrion A, Soheili T, Cordier C, Lortholary O, Lefrere F, Rieux-Laucat F, Casanova JL, Bodard S, Boddaert N, Thrasher AJ, Touzot F, Taque S, Suarez F, Marcais A, Guilloux A, Lagresle-Peyrou C, Galy A, Rausell A, Blanche S, Cavazzana M, Six E. Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy. Cell Rep Med 2023; 4:100919. [PMID: 36706754 PMCID: PMC9975109 DOI: 10.1016/j.xcrm.2023.100919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/20/2022] [Accepted: 01/06/2023] [Indexed: 01/27/2023]
Abstract
X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPβ expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.
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Affiliation(s)
- Steicy Sobrino
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Alessandra Magnani
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Michaela Semeraro
- Clinical Investigation Center CIC 1419, Necker-Enfants Malades Hospital, GH Paris Centre, Université Paris Cité, AP-HP, Paris, France
| | - Loredana Martignetti
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Akira Cortal
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Adeline Denis
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Chloé Couzin
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Capucine Picard
- Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Lymphocyte Activation and Susceptibility to EBV Infection Laboratory, INSERM UMR 1163, Imagine Institute, Paris, France; Centre de Références des Déficits Immunitaires Héréditaires (CEREDIH), Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Jacinta Bustamante
- Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Human Genetics of Infectious Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
| | - Elisa Magrin
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Laure Joseph
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Cécile Roudaut
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Aurélie Gabrion
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Tayebeh Soheili
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Corinne Cordier
- Plateforme de Cytométrie en Flux, Structure Fédérative de Recherche Necker, INSERM US24-CNRS UAR3633, Paris, France
| | - Olivier Lortholary
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France
| | - François Lefrere
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Department of Adult Hematology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Frédéric Rieux-Laucat
- Immunogenetics of Pediatric Autoimmune Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Jean-Laurent Casanova
- Human Genetics of Infectious Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
| | - Sylvain Bodard
- Department of Adult Radiology, Necker Enfants-Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Laboratoire d'Imagerie Biomédicale, LIB, Sorbonne Université, CNRS, INSERM, Paris, France
| | - Nathalie Boddaert
- Département de Radiologie Pédiatrique, INSERM UMR 1163 and UMR 1299, Imagine Institute, AP-HP, Necker-Enfants Malades Hospital, Paris, France
| | - Adrian J Thrasher
- UCL Great Ormond Street Institute of Child Health, London, UK; Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Fabien Touzot
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Sophie Taque
- CHU de Rennes, Département de Pédiatrie, Rennes, France
| | - Felipe Suarez
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France; Imagine Institute, Université Paris Cité, Paris, France
| | - Ambroise Marcais
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France
| | - Agathe Guilloux
- Mathematics and Modelization Laboratory, CNRS, Université Paris-Saclay, Université d'Evry, Evry, France
| | - Chantal Lagresle-Peyrou
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Anne Galy
- Genethon, Evry-Courcouronnes, France; Université Paris-Saclay, University Evry, Inserm, Genethon (UMR_S951), Evry-Courcouronnes, France
| | - Antonio Rausell
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; Service de Médecine Génomique des Maladies Rares, AP-HP, Necker-Enfants Malades Hospital, Paris, France
| | - Stephane Blanche
- Department of Pediatric Immunology, Hematology, and Rheumatology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Marina Cavazzana
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France; Imagine Institute, Université Paris Cité, Paris, France.
| | - Emmanuelle Six
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
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Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data. Nat Commun 2023; 14:679. [PMID: 36755047 PMCID: PMC9908983 DOI: 10.1038/s41467-023-36383-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.
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35
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Juan-Mateu J, Bajew S, Miret-Cuesta M, Íñiguez LP, Lopez-Pascual A, Bonnal S, Atla G, Bonàs-Guarch S, Ferrer J, Valcárcel J, Irimia M. Pancreatic microexons regulate islet function and glucose homeostasis. Nat Metab 2023; 5:219-236. [PMID: 36759540 DOI: 10.1038/s42255-022-00734-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 12/21/2022] [Indexed: 02/11/2023]
Abstract
Pancreatic islets control glucose homeostasis by the balanced secretion of insulin and other hormones, and their abnormal function causes diabetes or hypoglycaemia. Here we uncover a conserved programme of alternative microexons included in mRNAs of islet cells, particularly in genes involved in vesicle transport and exocytosis. Islet microexons (IsletMICs) are regulated by the RNA binding protein SRRM3 and represent a subset of the larger neural programme that are particularly sensitive to SRRM3 levels. Both SRRM3 and IsletMICs are induced by elevated glucose levels, and depletion of SRRM3 in human and rat beta cell lines and mouse islets, or repression of particular IsletMICs using antisense oligonucleotides, leads to inappropriate insulin secretion. Consistently, mice harbouring mutations in Srrm3 display defects in islet cell identity and function, leading to hyperinsulinaemic hypoglycaemia. Importantly, human genetic variants that influence SRRM3 expression and IsletMIC inclusion in islets are associated with fasting glucose variation and type 2 diabetes risk. Taken together, our data identify a conserved microexon programme that regulates glucose homeostasis.
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Affiliation(s)
- Jonàs Juan-Mateu
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
| | - Simon Bajew
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Marta Miret-Cuesta
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Luis P Íñiguez
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Amaya Lopez-Pascual
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Sophie Bonnal
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Goutham Atla
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Sílvia Bonàs-Guarch
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Jorge Ferrer
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Juan Valcárcel
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
- ICREA, Barcelona, Spain.
| | - Manuel Irimia
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
- ICREA, Barcelona, Spain.
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36
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Pernet E, Sun S, Sarden N, Gona S, Nguyen A, Khan N, Mawhinney M, Tran KA, Chronopoulos J, Amberkar D, Sadeghi M, Grant A, Wali S, Prevel R, Ding J, Martin JG, Thanabalasuriar A, Yipp BG, Barreiro LB, Divangahi M. Neonatal imprinting of alveolar macrophages via neutrophil-derived 12-HETE. Nature 2023; 614:530-538. [PMID: 36599368 PMCID: PMC9945843 DOI: 10.1038/s41586-022-05660-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/14/2022] [Indexed: 01/06/2023]
Abstract
Resident-tissue macrophages (RTMs) arise from embryonic precursors1,2, yet the developmental signals that shape their longevity remain largely unknown. Here we demonstrate in mice genetically deficient in 12-lipoxygenase and 15-lipoxygenase (Alox15-/- mice) that neonatal neutrophil-derived 12-HETE is required for self-renewal and maintenance of alveolar macrophages (AMs) during lung development. Although the seeding and differentiation of AM progenitors remained intact, the absence of 12-HETE led to a significant reduction in AMs in adult lungs and enhanced senescence owing to increased prostaglandin E2 production. A compromised AM compartment resulted in increased susceptibility to acute lung injury induced by lipopolysaccharide and to pulmonary infections with influenza A virus or SARS-CoV-2. Our results highlight the complexity of prenatal RTM programming and reveal their dependency on in trans eicosanoid production by neutrophils for lifelong self-renewal.
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Affiliation(s)
- Erwan Pernet
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada.
| | - Sarah Sun
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Nicole Sarden
- Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases and Department of Critical Care, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Saideep Gona
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Angela Nguyen
- Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases and Department of Critical Care, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Nargis Khan
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Martin Mawhinney
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Kim A Tran
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Julia Chronopoulos
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Dnyandeo Amberkar
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Mina Sadeghi
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| | - Alexandre Grant
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada
| | - Shradha Wali
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Renaud Prevel
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Jun Ding
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - James G Martin
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada
| | - Ajitha Thanabalasuriar
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Quebec, Canada
| | - Bryan G Yipp
- Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases and Department of Critical Care, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Luis B Barreiro
- Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
- Department of Genetics, CHU Sainte-Justine Research Center, Montreal, Quebec, Canada
| | - Maziar Divangahi
- McGill University Health Centre, Meakins-Christie Laboratories, McGill University, Montreal, Quebec, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada.
- Department of Pathology, McGill University, Montreal, Quebec, Canada.
- McGill International TB Centre, McGill University, Montreal, Quebec, Canada.
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37
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Joung J, Ma S, Tay T, Geiger-Schuller KR, Kirchgatterer PC, Verdine VK, Guo B, Arias-Garcia MA, Allen WE, Singh A, Kuksenko O, Abudayyeh OO, Gootenberg JS, Fu Z, Macrae RK, Buenrostro JD, Regev A, Zhang F. A transcription factor atlas of directed differentiation. Cell 2023; 186:209-229.e26. [PMID: 36608654 PMCID: PMC10344468 DOI: 10.1016/j.cell.2022.11.026] [Citation(s) in RCA: 53] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 08/04/2022] [Accepted: 11/23/2022] [Indexed: 01/07/2023]
Abstract
Transcription factors (TFs) regulate gene programs, thereby controlling diverse cellular processes and cell states. To comprehensively understand TFs and the programs they control, we created a barcoded library of all annotated human TF splice isoforms (>3,500) and applied it to build a TF Atlas charting expression profiles of human embryonic stem cells (hESCs) overexpressing each TF at single-cell resolution. We mapped TF-induced expression profiles to reference cell types and validated candidate TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. Targeted screens with subsets of the library allowed us to create a tailored cellular disease model and integrate mRNA expression and chromatin accessibility data to identify downstream regulators. Finally, we characterized the effects of combinatorial TF overexpression by developing and validating a strategy for predicting combinations of TFs that produce target expression profiles matching reference cell types to accelerate cellular engineering efforts.
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Affiliation(s)
- Julia Joung
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Sai Ma
- Department of Biology, MIT, Cambridge, MA 02139, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tristan Tay
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kathryn R Geiger-Schuller
- Department of Biology, MIT, Cambridge, MA 02139, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Paul C Kirchgatterer
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Vanessa K Verdine
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Baolin Guo
- McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mario A Arias-Garcia
- McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - William E Allen
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA; Society of Fellows, Harvard University, Cambridge, MA, USA
| | - Ankita Singh
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Olena Kuksenko
- Department of Biology, MIT, Cambridge, MA 02139, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Omar O Abudayyeh
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Jonathan S Gootenberg
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Zhanyan Fu
- McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Rhiannon K Macrae
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA; Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aviv Regev
- Department of Biology, MIT, Cambridge, MA 02139, USA; Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Feng Zhang
- Department of Biological Engineering, MIT, Cambridge, MA 02139, USA; Department of Brain and Cognitive Science, MIT, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; McGovern Institute for Brain Research at MIT, Cambridge, MA 02139, USA; Howard Hughes Medical Institute, MIT, Cambridge, MA 02139, USA.
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38
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Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding. Nat Commun 2022; 13:7640. [PMID: 36496406 PMCID: PMC9741613 DOI: 10.1038/s41467-022-35288-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.
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39
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Deciphering Transcriptional Networks during Human Cardiac Development. Cells 2022; 11:cells11233915. [PMID: 36497174 PMCID: PMC9739390 DOI: 10.3390/cells11233915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 11/28/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
Human heart development is governed by transcription factor (TF) networks controlling dynamic and temporal gene expression alterations. Therefore, to comprehensively characterize these transcriptional regulations, day-to-day transcriptomic profiles were generated throughout the directed cardiac differentiation, starting from three distinct human- induced pluripotent stem cell lines from healthy donors (32 days). We applied an expression-based correlation score to the chronological expression profiles of the TF genes, and clustered them into 12 sequential gene expression waves. We then identified a regulatory network of more than 23,000 activation and inhibition links between 216 TFs. Within this network, we observed previously unknown inferred transcriptional activations linking IRX3 and IRX5 TFs to three master cardiac TFs: GATA4, NKX2-5 and TBX5. Luciferase and co-immunoprecipitation assays demonstrated that these five TFs could (1) activate each other's expression; (2) interact physically as multiprotein complexes; and (3) together, finely regulate the expression of SCN5A, encoding the major cardiac sodium channel. Altogether, these results unveiled thousands of interactions between TFs, generating multiple robust hypotheses governing human cardiac development.
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40
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Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics. Exp Mol Med 2022; 54:2060-2076. [PMID: 36434043 PMCID: PMC9722784 DOI: 10.1038/s12276-022-00896-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/18/2022] [Accepted: 10/05/2022] [Indexed: 11/27/2022] Open
Abstract
The cell ecology and spatial niche implicated in the dynamic and sequential process of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) have not yet been elucidated. Here, we performed an integrative analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to characterize the cell atlas of the invasion trajectory of LUAD. We found that the UBE2C + cancer cell subpopulation constantly increased during the invasive process of LUAD with remarkable elevation in IAC, and its spatial distribution was in the peripheral cancer region of the IAC, representing a more malignant phenotype. Furthermore, analysis of the TME cell type subpopulation showed a constant decrease in mast cells, monocytes, and lymphatic endothelial cells, which were implicated in the whole process of invasive LUAD, accompanied by an increase in NK cells and MALT B cells from AIS to MIA and an increase in Tregs and secretory B cells from MIA to IAC. Notably, for AIS, cancer cells, NK cells, and mast cells were colocalized in the cancer region; however, for IAC, Tregs colocalized with cancer cells. Finally, communication and interaction between cancer cells and TME cell-induced constitutive activation of TGF-β signaling were involved in the invasion of IAC. Therefore, our results reveal the specific cellular information and spatial architecture of cancer cells and TME subpopulations, as well as the cellular interaction between them, which will facilitate the identification and development of precision medicine in the invasive process of LUAD from AIS to IAC.
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41
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Lin Q, Fang Z, Sun J, Chen F, Ren Y, Fu Z, Yang S, Feng L, Wang F, Song Z, Chen W, Yu W, Wang C, Shi Y, Liang Y, Zhang H, Qu H, Fang X, Xi Q. Single-cell transcriptomic analysis of the tumor ecosystem of adenoid cystic carcinoma. Front Oncol 2022; 12:1063477. [DOI: 10.3389/fonc.2022.1063477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 10/27/2022] [Indexed: 11/18/2022] Open
Abstract
Adenoid cystic carcinoma (ACC) is a malignant tumor that originates from exocrine gland epithelial cells. We profiled the transcriptomes of 49,948 cells from paracarcinoma and carcinoma tissues of three patients using single-cell RNA sequencing. Three main types of the epithelial cells were identified into myoepithelial-like cells, intercalated duct-like cells, and duct-like cells by marker genes. And part of intercalated duct-like cells with special copy number variations which altered with MYB family gene and EN1 transcriptomes were identified as premalignant cells. Developmental pseudo-time analysis showed that the premalignant cells eventually transformed into malignant cells. Furthermore, MYB and MYBL1 were found to belong to two different gene modules and were expressed in a mutually exclusive manner. The two gene modules drove ACC progression into different directions. Our findings provide novel evidence to explain the high recurrence rate of ACC and its characteristic biological behavior.
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42
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Lang C, Roy S, Wang Y, Graves D, Xu Y, Serezani CH, Korrer M, Kim YJ. Efferocytosis drives myeloid NLRP3 dependent inflammasome signaling secretion of IL-1β to promote tumor growth. Front Immunol 2022; 13:993771. [PMID: 36439171 PMCID: PMC9681818 DOI: 10.3389/fimmu.2022.993771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 10/19/2022] [Indexed: 11/10/2022] Open
Abstract
Caspase-1 signaling in myeloid suppressor cells can promote T-cell independent cancer progression, but the regulation of inflammasome signaling within the highly heterogeneous myeloid population in the tumor milieu remains elusive. To resolve this complexity, single cell transcriptomic profile of Head and Neck Squamous Cell Carcinoma (HNSCC) identified distinct inflammasome-associated genes within specific clusters of tumor-infiltrating myeloid cells. Among these myeloid cells, the sensor protein, NLRP3, and downstream effector IL-1β transcripts were enriched in discreet monocytic and macrophage subtypes in the TME. We showed that deletion of NLRP3, but not AIM2, phenocopied caspase-1/IL-1β dependent tumor progression in vivo. Paradoxically, we found myeloid-intrinsic caspase-1 signaling increased myeloid survival contrary to what would be predicted from the canonical pyroptotic function of caspase-1. This myeloid NLRP3/IL-1β signaling axis promotion of tumor growth was found to be gasdermin D independent. Mechanistically, we found that phagocyte-mediated efferocytosis of dying tumor cells in the TME directly activated NLRP3-dependent inflammasome signaling to drive IL-1β secretion. Subsequently we showed that NLRP3-mediated IL-1β production drives tumor growth in vivo. Dynamic RNA velocity analysis showed a robust directional flow from efferocytosis gene-set high macrophages to an inflammasome gene-set high macrophage population. We provide a novel efferocytosis-dependent inflammasome signaling pathway which mediates homeostatic tumor cell apoptosis that characterizes chronic inflammation-induced malignancy.
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Affiliation(s)
- Cara Lang
- Department of Pathology, Microbiology & Immunology, Vanderbilt University, Nashville, TN, United States
| | - Sohini Roy
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yu Wang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Diana Graves
- Department of Pathology, Microbiology & Immunology, Vanderbilt University, Nashville, TN, United States
| | - Yaomin Xu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - C. Henrique Serezani
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Korrer
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Young J. Kim
- Department of Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, United States
- *Correspondence: Young J. Kim,
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43
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Wang X, Cheng W, Zeng X, Dou X, Zhou Z, Pei Q. EPSTI1 as an immune biomarker predicts the prognosis of patients with stage III colon cancer. Front Immunol 2022; 13:987394. [PMID: 36330510 PMCID: PMC9623419 DOI: 10.3389/fimmu.2022.987394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
Abstract
Objective The poor prognosis and heterogeneity of stage III colon cancer (CC) suggest the need for more prognostic biomarkers. The tumor microenvironment (TME) plays a crucial role in tumor progression. We aimed to explore novel immune infiltration-associated molecules that serve as potential prognostic and therapeutic targets. Methods TME immune scores were calculated using “TMEscore” algorithm. Differentially expressed genes between the high and low TME immune score groups were identified and further investigated through a protein-protein interaction network and the Molecular Complex Detection algorithm. Cox regression, meta-analysis and immunohistochemistry were applied to identify genes significantly correlated with relapse-free survival (RFS). We estimated immune infiltration using three different algorithms (TIMER 2.0, CIBERSORTx, and TIDE). Single-cell sequencing data were processed by Seurat software. Results Poor RFS was observed in the low TME immune score groups (log-rank P < 0.05). EPSTI1 was demonstrated to be significantly correlated with RFS (P < 0.05) in stage III CC. Meta-analysis comprising 547 patients revealed that EPSTI1 was a protective factor (HR = 0.79, 95% CI, 0.65-0. 96; P < 0.05)). More immune infiltrates were observed in the high EPSTI1 group, especially M1 macrophage and myeloid dendritic cell infiltration (P < 0.05). Conclusion The TME immune score is positively associated with better survival outcomes. EPSTI1 could serve as a novel immune prognostic biomarker for stage III CC.
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Affiliation(s)
- Xitao Wang
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
- Key Laboratory of Molecular Radiation Oncology Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
| | - Wei Cheng
- Department of Hepatobiliary Surgery, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Xingzhi Zeng
- Department of General Surgery, The First Affiliated Hospital of Shaoyang University, Shaoyang, China
| | - Xiaolin Dou
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Zhongyi Zhou
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
| | - Qian Pei
- Key Laboratory of Molecular Radiation Oncology Hunan Province, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China
- *Correspondence: Qian Pei,
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Wang H, Keepers B, Qian Y, Xie Y, Colon M, Liu J, Qian L. Cross-lineage potential of Ascl1 uncovered by comparing diverse reprogramming regulatomes. Cell Stem Cell 2022; 29:1491-1504.e9. [PMID: 36206732 PMCID: PMC9557912 DOI: 10.1016/j.stem.2022.09.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 08/10/2022] [Accepted: 09/15/2022] [Indexed: 11/22/2022]
Abstract
Direct reprogramming has revolutionized the fields of stem cell biology and regenerative medicine. However, the common mechanisms governing how reprogramming cells undergo transcriptome and epigenome remodeling (i.e., regulatome remodeling) have not been investigated. Here, by characterizing early changes in the regulatome of three different types of direct reprogramming, we identify lineage-specific features as well as common regulatory transcription factors. Of particular interest, we discover that the neuronal factor Ascl1 possesses cross-lineage potential; together with Mef2c, it drives efficient cardiac reprogramming toward a mature and induced cardiomyocyte phenotype. Through ChIP-seq and RNA-seq, we find that MEF2C drives the shift in ASCL1 binding away from neuronal genes toward cardiac genes, guiding their co-operative epigenetic and transcription activities. Together, these findings demonstrate the existence of common regulators of different direct reprogramming and argue against the premise that transcription factors possess only lineage-specific capabilities for altering cell fate - the basic premise used to develop direct reprogramming approaches.
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Affiliation(s)
- Haofei Wang
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Benjamin Keepers
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yunzhe Qian
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yifang Xie
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Marazzano Colon
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiandong Liu
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Qian
- The McAllister Heart Institute, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Ke Y, Jian-yuan H, Ping Z, Yue W, Na X, Jian Y, Kai-xuan L, Yi-fan S, Han-bin L, Rong L. The progressive application of single-cell RNA sequencing technology in cardiovascular diseases. Biomed Pharmacother 2022; 154:113604. [DOI: 10.1016/j.biopha.2022.113604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/20/2022] [Accepted: 08/23/2022] [Indexed: 11/02/2022] Open
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Yang F, Wang W, Wang F, Fang Y, Tang D, Huang J, Lu H, Yao J. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00534-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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47
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Pandey RS, Krebs MP, Bolisetty MT, Charette JR, Naggert JK, Robson P, Nishina PM, Carter GW. Single-Cell RNA Sequencing Reveals Molecular Features of Heterogeneity in the Murine Retinal Pigment Epithelium. Int J Mol Sci 2022; 23:10419. [PMID: 36142331 PMCID: PMC9499471 DOI: 10.3390/ijms231810419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/09/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022] Open
Abstract
Transcriptomic analysis of the mammalian retinal pigment epithelium (RPE) aims to identify cellular networks that influence ocular development, maintenance, function, and disease. However, available evidence points to RPE cell heterogeneity within native tissue, which adds complexity to global transcriptomic analysis. Here, to assess cell heterogeneity, we performed single-cell RNA sequencing of RPE cells from two young adult male C57BL/6J mice. Following quality control to ensure robust transcript identification limited to cell singlets, we detected 13,858 transcripts among 2667 and 2846 RPE cells. Dimensional reduction by principal component analysis and uniform manifold approximation and projection revealed six distinct cell populations. All clusters expressed transcripts typical of RPE cells; the smallest (C1, containing 1-2% of total cells) exhibited the hallmarks of stem and/or progenitor (SP) cells. Placing C1-6 along a pseudotime axis suggested a relative decrease in melanogenesis and SP gene expression and a corresponding increase in visual cycle gene expression upon RPE maturation. K-means clustering of all detected transcripts identified additional expression patterns that may advance the understanding of RPE SP cell maintenance and the evolution of cellular metabolic networks during development. This work provides new insights into the transcriptome of the mouse RPE and a baseline for identifying experimentally induced transcriptional changes in future studies of this tissue.
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Affiliation(s)
- Ravi S. Pandey
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT 06032, USA
| | - Mark P. Krebs
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Mohan T. Bolisetty
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT 06032, USA
| | | | | | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr., Farmington, CT 06032, USA
| | - Patsy M. Nishina
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
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Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00518-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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You Y, Wen D, Zeng L, Lu J, Xiao X, Chen Y, Song H, Liu Z. ALKBH5/MAP3K8 axis regulates PD-L1+ macrophage infiltration and promotes hepatocellular carcinoma progression. Int J Biol Sci 2022; 18:5001-5018. [PMID: 35982895 PMCID: PMC9379398 DOI: 10.7150/ijbs.70149] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma is one of the most common malignant tumors.M6A is a novel epigenetic modification that have been emerged as vital regulators for the progression of HCC. However, the regulatory role, clinical significance and the details of the modification, such as the impact on the local tumor environment, remain largely unclear. Our study showed that ALKBH5 was highly expressed in HCC and high ALKBH5 expression predicted a worse prognosis of HCC patients. Prediction of ALKBH5 function by tissue samples and single cell sequencing Gene Set Variation Analysis. Primary CD3 + T lymphocytes and bone marrow-derived macrophages were used to evaluate the effect of ALKBH5 on immune microenvironment. The results indicated that ALKBH5 promote HCC cell proliferation, metastasis and PD-L1+macrophage recruitment. Mechanistically the results showed that ALKBH5 regulates MAP3K8 expression in a m6A dependent manner which mediates the proliferation and metastasis of HCC cells. ALKBH5 also promotes the activation of JNK and ERK pathways through upregulating MAP3K8, thus regulating the expression of IL-8 and promoting macrophage recruitment. Taken together, these data show that ALKBH5 promotes HCC growth, metastasis and macrophage recruitment through ALKBH5/MAP3K8 axis and it may serve as a potential diagnostic marker and target for treatment of HCC patients.
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Affiliation(s)
- Yu You
- Hepatobiliary Surgery Department, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Diguang Wen
- Hepatobiliary Surgery Department, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Lu Zeng
- Department of Gastroenterology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Jiao Lu
- Hepatobiliary Surgery Department, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Xiao Xiao
- Department of Gastroenterology, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing 400010, China
| | - Yucheng Chen
- Hepatobiliary Surgery Department, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Hua Song
- Hepatobiliary Surgery Department, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
| | - Zuojin Liu
- Hepatobiliary Surgery Department, Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China
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Wilk AJ, Shalek AK, Holmes S, Blish CA. Comparative analysis of cell-cell communication at single-cell resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.02.04.479209. [PMID: 35169794 PMCID: PMC8845414 DOI: 10.1101/2022.02.04.479209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. Here we present Scriabin â€" a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data, genetic perturbation screens, and direct experimental manipulation of receptor-ligand interactions to validate that the recovered edges are biologically meaningful. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche and revealing species-specific communication pathways. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease.
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