1
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Schiebout C, Frost HR. CAraCAl: CAMML with the integration of chromatin accessibility. BMC Bioinformatics 2024; 25:212. [PMID: 38872103 DOI: 10.1186/s12859-024-05833-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression. RESULTS To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity. CONCLUSIONS We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.
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Affiliation(s)
- Courtney Schiebout
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA.
| | - H Robert Frost
- Department of Biomedical Data Science, Dartmouth College, Hanover, NH, 03766, USA
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2
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Lee CY, Clatworthy MR, Withers DR. Decoding changes in tumor-infiltrating leukocytes through dynamic experimental models and single-cell technologies. Immunol Cell Biol 2024. [PMID: 38853634 DOI: 10.1111/imcb.12787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/13/2024] [Accepted: 05/13/2024] [Indexed: 06/11/2024]
Abstract
The ability to characterize immune cells and explore the molecular interactions that govern their functions has never been greater, fueled in recent years by the revolutionary advance of single-cell analysis platforms. However, precisely how immune cells respond to different stimuli and where differentiation processes and effector functions operate remain incompletely understood. Inferring cellular fate within single-cell transcriptomic analyses is now omnipresent, despite the assumptions typically required in such analyses. Recently developed experimental models support dynamic analyses of the immune response, providing insights into the temporal changes that occur within cells and the tissues in which such transitions occur. Here we will review these approaches and discuss how these can be combined with single-cell technologies to develop a deeper understanding of the immune responses that should support the development of better therapeutic options for patients.
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Affiliation(s)
- Colin Yc Lee
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - Menna R Clatworthy
- Cambridge Institute of Therapeutic Immunology and Infection Disease, University of Cambridge, Cambridge, UK
| | - David R Withers
- Institute of Immunology and Immunotherapy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
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3
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Hua H, Wang Y, Wang X, Wang S, Zhou Y, Liu Y, Liang Z, Ren H, Lu S, Wu S, Jiang Y, Pu Y, Zheng X, Tang C, Shen Z, Li C, Du Y, Deng H. Remodeling ceramide homeostasis promotes functional maturation of human pluripotent stem cell-derived β cells. Cell Stem Cell 2024; 31:850-865.e10. [PMID: 38697109 DOI: 10.1016/j.stem.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 03/21/2024] [Accepted: 04/12/2024] [Indexed: 05/04/2024]
Abstract
Human pluripotent stem cell-derived β cells (hPSC-β cells) show the potential to restore euglycemia. However, the immature functionality of hPSC-β cells has limited their efficacy in application. Here, by deciphering the continuous maturation process of hPSC-β cells post transplantation via single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we show that functional maturation of hPSC-β cells is an orderly multistep process during which cells sequentially undergo metabolic adaption, removal of negative regulators of cell function, and establishment of a more specialized transcriptome and epigenome. Importantly, remodeling lipid metabolism, especially downregulating the metabolic activity of ceramides, the central hub of sphingolipid metabolism, is critical for β cell maturation. Limiting intracellular accumulation of ceramides in hPSC-β cells remarkably enhanced their function, as indicated by improvements in insulin processing and glucose-stimulated insulin secretion. In summary, our findings provide insights into the maturation of human pancreatic β cells and highlight the importance of ceramide homeostasis in function acquisition.
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Affiliation(s)
- Huijuan Hua
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yaqi Wang
- School of Life Sciences, Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China
| | | | - Shusen Wang
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Yunlu Zhou
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yinan Liu
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhen Liang
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Huixia Ren
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Sufang Lu
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | | | - Yong Jiang
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | - Yue Pu
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | - Xiang Zheng
- Hangzhou Repugene Technology, Hangzhou, China
| | - Chao Tang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhongyang Shen
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Cheng Li
- School of Life Sciences, Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China.
| | - Yuanyuan Du
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
| | - Hongkui Deng
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; Changping Laboratory, Beijing, China.
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4
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Takeuchi F, Kato N. Ploidy inference from single-cell data: application to human and mouse cell atlases. Genetics 2024; 227:iyae061. [PMID: 38651869 DOI: 10.1093/genetics/iyae061] [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: 03/18/2024] [Revised: 03/18/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN.
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Affiliation(s)
- Fumihiko Takeuchi
- Baker Department of Cardiometabolic Health, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
- Systems Genomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
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Liu Z, Hu Y, Xie H, Chen K, Wen L, Fu W, Zhou X, Tang F. Single-Cell Chromatin Accessibility Analysis Reveals the Epigenetic Basis and Signature Transcription Factors for the Molecular Subtypes of Colorectal Cancers. Cancer Discov 2024; 14:1082-1105. [PMID: 38445965 DOI: 10.1158/2159-8290.cd-23-1445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/06/2024] [Accepted: 03/04/2024] [Indexed: 03/07/2024]
Abstract
Colorectal cancer is a highly heterogeneous disease, with well-characterized subtypes based on genome, DNA methylome, and transcriptome signatures. To chart the epigenetic landscape of colorectal cancers, we generated a high-quality single-cell chromatin accessibility atlas of epithelial cells for 29 patients. Abnormal chromatin states acquired in adenomas were largely retained in colorectal cancers, which were tightly accompanied by opposite changes of DNA methylation. Unsupervised analysis on malignant cells revealed two epigenetic subtypes, exactly matching the iCMS classification, and key iCMS-specific transcription factors (TFs) were identified, including HNF4A and PPARA for iCMS2 tumors and FOXA3 and MAFK for iCMS3 tumors. Notably, subtype-specific TFs bind to distinct target gene sets and contribute to both interpatient similarities and diversities for both chromatin accessibilities and RNA expressions. Moreover, we identified CpG-island methylator phenotypes and pinpointed chromatin state signatures and TF regulators for the CIMP-high subtype. Our work systematically revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers. SIGNIFICANCE Our work revealed the epigenetic basis of the well-known iCMS and CIMP classifications of colorectal cancers. Moreover, interpatient minor similarities and major diversities of chromatin accessibility signatures of TF target genes can faithfully explain the corresponding interpatient minor similarities and major diversities of RNA expression signatures of colorectal cancers, respectively. This article is featured in Selected Articles from This Issue, p. 897.
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Affiliation(s)
- Zhenyu Liu
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
| | - Yuqiong Hu
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Haoling Xie
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Kexuan Chen
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
| | - Lu Wen
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
| | - Wei Fu
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Peking University Third Hospital Cancer Center, Beijing, China
| | - Xin Zhou
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Peking University Third Hospital Cancer Center, Beijing, China
| | - Fuchou Tang
- School of Life Sciences, Biomedical Pioneering Innovation Center, Department of General Surgery, Third Hospital, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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6
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Sun H, Qiu J, Qiu J. Epigenetic regulation of innate lymphoid cells. Eur J Immunol 2024:e2350379. [PMID: 38824666 DOI: 10.1002/eji.202350379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 05/17/2024] [Accepted: 05/21/2024] [Indexed: 06/04/2024]
Abstract
Innate lymphoid cells (ILCs) lack antigen-specific receptors and are considered the innate arm of the immune system, phenotypically and functionally mirroring CD4+ helper T cells. ILCs are categorized into groups 1, 2, and 3 based on transcription factors and cytokine expression. ILCs predominantly reside in mucosal tissues and play important roles in regional immune responses. The development and function of ILC subsets are controlled by both transcriptional and epigenetic mechanisms, which have been extensively studied in recent years. Epigenetic regulation refers to inheritable changes in gene expression that occur without affecting DNA sequences. This mainly includes chromatin status, histone modifications, and DNA methylation. In this review, we summarize recent discoveries on epigenetic mechanisms regulating ILC development and function, and how these regulations affect disease progression under pathological conditions. Although the ablation of specific epigenetic regulators can cause global changes in corresponding epigenetic modifications to the chromatin, only partial genes with altered epigenetic modifications change their mRNA expression, resulting in specific outcomes in cell differentiation and function. Therefore, elucidating epigenetic mechanisms underlying the regulation of ILCs will provide potential targets for the diagnosis and treatment of inflammatory diseases.
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Affiliation(s)
- Hanxiao Sun
- Department of Laboratory Medicine, Department of Blood Transfusion, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jinxin Qiu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Ju Qiu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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7
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Rivero-Garcia I, Torres M, Sánchez-Cabo F. Deep generative models in single-cell omics. Comput Biol Med 2024; 176:108561. [PMID: 38749321 DOI: 10.1016/j.compbiomed.2024.108561] [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: 01/26/2024] [Revised: 04/30/2024] [Accepted: 05/05/2024] [Indexed: 05/31/2024]
Abstract
Deep Generative Models (DGMs) are becoming instrumental for inferring probability distributions inherent to complex processes, such as most questions in biomedical research. For many years, there was a lack of mathematical methods that would allow this inference in the scarce data scenario of biomedical research. The advent of single-cell omics has finally made square the so-called "skinny matrix", allowing to apply mathematical methods already extensively used in other areas. Moreover, it is now possible to integrate data at different molecular levels in thousands or even millions of samples, thanks to the number of single-cell atlases being collaboratively generated. Additionally, DGMs have proven useful in other frequent tasks in single-cell analysis pipelines, from dimensionality reduction, cell type annotation to RNA velocity inference. In spite of its promise, DGMs need to be used with caution in biomedical research, paying special attention to its use to answer the right questions and the definition of appropriate error metrics and validation check points that confirm not only its correct use but also its relevance. All in all, DGMs provide an exciting tool that opens a bright future for the integrative analysis of single-cell -omics to understand health and disease.
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Affiliation(s)
- Inés Rivero-Garcia
- Universidad Politécnica de Madrid, Madrid, 28040, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain
| | - Miguel Torres
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain
| | - Fátima Sánchez-Cabo
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, 28029, Spain.
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8
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Fan L, Liu J, Hu W, Chen Z, Lan J, Zhang T, Zhang Y, Wu X, Zhong Z, Zhang D, Zhang J, Qin R, Chen H, Zong Y, Zhang J, Chen B, Jiang J, Cheng J, Zhou J, Gao Z, Liu Z, Chai Y, Fan J, Wu P, Chen Y, Zhu Y, Wang K, Yuan Y, Huang P, Zhang Y, Feng H, Song K, Zeng X, Zhu W, Hu X, Yin W, Chen W, Wang J. Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans. Cell Res 2024; 34:407-427. [PMID: 38491170 PMCID: PMC11143203 DOI: 10.1038/s41422-024-00945-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/24/2024] [Indexed: 03/18/2024] Open
Abstract
Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1+ T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.
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Affiliation(s)
- Lin Fan
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Junwei Liu
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
- Guangzhou National Laboratory, Guangzhou, Guangdong, China
| | - Wei Hu
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zexin Chen
- Center of Clinical Epidemiology and Biostatistics and Department of Scientific Research, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jie Lan
- National Laboratory of Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Beijing, China
- Department of Bioinformatics, The Basic Medical School of Chongqing Medical University, Chongqing, China
| | - Tongtong Zhang
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Hepatobiliary and Pancreatic Surgery, The Center for Integrated Oncology and Precision Medicine, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xianpeng Wu
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zhiwei Zhong
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Danyang Zhang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jinlong Zhang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Rui Qin
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
- The MOE Frontier Science Center for Brain Science & Brain-machine Integration, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hui Chen
- National Laboratory of Biomacromolecules, Institute of Biophysics, University of Chinese Academy of Sciences, Beijing, China
| | - Yunfeng Zong
- National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianmin Zhang
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Bing Chen
- Department of Vascular Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jun Jiang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Jifang Cheng
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingyi Zhou
- Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhiwei Gao
- Department of Vascular Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zhenjie Liu
- Department of Vascular Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Chai
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Junqiang Fan
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Pin Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinxuan Chen
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuefeng Zhu
- Department of Vascular Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kai Wang
- Department of Respiratory, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Yuan
- Department of Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Pintong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Zhang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Huiqin Feng
- Department of Clinical Research Center, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaichen Song
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xun Zeng
- National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wei Zhu
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xinyang Hu
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China.
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China.
| | - Weiwei Yin
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wei Chen
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China.
- Department of Cell Biology, Zhejiang University School of Medicine, and Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China.
- Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
- The MOE Frontier Science Center for Brain Science & Brain-machine Integration, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Jian'an Wang
- Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Cardiovascular Key Laboratory of Zhejiang Province, Hangzhou, Zhejiang, China.
- Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China.
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9
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Tayyebi Z, Pine AR, Leslie CS. Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace. Nat Methods 2024; 21:1014-1022. [PMID: 38724693 PMCID: PMC11166566 DOI: 10.1038/s41592-024-02274-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: 05/02/2022] [Accepted: 04/11/2024] [Indexed: 06/13/2024]
Abstract
Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.
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Affiliation(s)
- Zakieh Tayyebi
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Allison R Pine
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Christina S Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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10
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024:10.1038/s44320-024-00045-6. [PMID: 38811801 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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11
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Bower G, Hollingsworth EW, Jacinto S, Clock B, Cao K, Liu M, Dziulko A, Alcaina-Caro A, Xu Q, Skowronska-Krawczyk D, Lopez-Rios J, Dickel DE, Bardet AF, Pennacchio LA, Visel A, Kvon EZ. Conserved Cis-Acting Range Extender Element Mediates Extreme Long-Range Enhancer Activity in Mammals. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.26.595809. [PMID: 38826394 PMCID: PMC11142232 DOI: 10.1101/2024.05.26.595809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
While most mammalian enhancers regulate their cognate promoters over moderate distances of tens of kilobases (kb), some enhancers act over distances in the megabase range. The sequence features enabling such extreme-distance enhancer-promoter interactions remain elusive. Here, we used in vivo enhancer replacement experiments in mice to show that short- and medium-range enhancers cannot initiate gene expression at extreme-distance range. We uncover a novel conserved cis-acting element, Range EXtender (REX), that confers extreme-distance regulatory activity and is located next to a long-range enhancer of Sall1. The REX element itself has no endogenous enhancer activity. However, addition of the REX to other short- and mid-range enhancers substantially increases their genomic interaction range. In the most extreme example observed, addition of the REX increased the range of an enhancer by an order of magnitude, from its native 71kb to 840kb. The REX element contains highly conserved [C/T]AATTA homeodomain motifs. These motifs are enriched around long-range limb enhancers genome-wide, including the ZRS, a benchmark long-range limb enhancer of Shh. Mutating the [C/T]AATTA motifs within the ZRS does not affect its limb-specific enhancer activity at short range, but selectively abolishes its long-range activity, resulting in severe limb reduction in knock-in mice. In summary, we identify a sequence signature globally associated with long-range enhancer-promoter interactions and describe a prototypical REX element that is necessary and sufficient to confer extreme-distance gene activation by remote enhancers.
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Affiliation(s)
- Grace Bower
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Ethan W. Hollingsworth
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
- Medical Scientist Training Program, University of California, Irvine, CA 92967, USA
| | - Sandra Jacinto
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Benjamin Clock
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Kaitlyn Cao
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Mandy Liu
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
| | - Adam Dziulko
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ana Alcaina-Caro
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, 41013, Spain
| | - Qianlan Xu
- Department of Physiology and Biophysics, Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, University of California, Irvine, CA, USA
| | - Dorota Skowronska-Krawczyk
- Department of Physiology and Biophysics, Department of Ophthalmology, Center for Translational Vision Research, School of Medicine, University of California, Irvine, CA, USA
| | - Javier Lopez-Rios
- Centro Andaluz de Biología del Desarrollo (CABD), CSIC-Universidad Pablo de Olavide-Junta de Andalucía, Seville, 41013, Spain
| | - Diane E. Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Anaïs F. Bardet
- Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg, CNRS UMR7104, INSERM U1258, 67400 Illkirch, France
| | - Len A. Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
- Comparative Biochemistry Program, University of California, Berkeley, CA 94720, USA
| | - Axel Visel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA
- School of Natural Sciences, University of California, Merced, CA 95343, USA
| | - Evgeny Z. Kvon
- Department of Developmental and Cell Biology, University of California, Irvine, CA 92967, USA
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12
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Barlow GL, Schürch CM, Bhate SS, Phillips D, Young A, Dong S, Martinez HA, Kaber G, Nagy N, Ramachandran S, Meng J, Korpos E, Bluestone JA, Nolan GP, Bollyky PL. The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.03.15.23287145. [PMID: 36993739 PMCID: PMC10055577 DOI: 10.1101/2023.03.15.23287145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In autoimmune Type 1 diabetes (T1D), immune cells infiltrate and destroy the islets of Langerhans - islands of endocrine tissue dispersed throughout the pancreas. However, the contribution of cellular programs outside islets to insulitis is unclear. Here, using CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples, we simultaneously examine islet and extra-islet inflammation in human T1D. We identify four sub-states of inflamed islets characterized by the activation profiles of CD8 + T cells enriched in islets relative to the surrounding tissue. We further find that the extra-islet space of lobules with extensive islet-infiltration differs from the extra-islet space of less infiltrated areas within the same tissue section. Finally, we identify lymphoid structures away from islets enriched in CD45RA + T cells - a population also enriched in one of the inflamed islet sub-states. Together, these data help define the coordination between islets and the extra-islet pancreas in the pathogenesis of human T1D.
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13
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Kotliar M, Kartashov A, Barski A. Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.28.582604. [PMID: 38464095 PMCID: PMC10925325 DOI: 10.1101/2024.02.28.582604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.
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Affiliation(s)
- Michael Kotliar
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | | | - Artem Barski
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Datirium, LLC, Cincinnati, OH, USA
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14
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Smith AL, Skupa SA, Eiken AP, Reznicek TE, Schmitz E, Williams N, Moore DY, D’Angelo CR, Kallam A, Lunning MA, Bociek RG, Vose JM, Mohamed E, Mahr AR, Denton PW, Powell B, Bollag G, Rowley MJ, El-Gamal D. BET inhibition reforms the immune microenvironment and alleviates T cell dysfunction in chronic lymphocytic leukemia. JCI Insight 2024; 9:e177054. [PMID: 38775157 PMCID: PMC11141939 DOI: 10.1172/jci.insight.177054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 04/12/2024] [Indexed: 06/02/2024] Open
Abstract
Redundant tumor microenvironment (TME) immunosuppressive mechanisms and epigenetic maintenance of terminal T cell exhaustion greatly hinder functional antitumor immune responses in chronic lymphocytic leukemia (CLL). Bromodomain and extraterminal (BET) proteins regulate key pathways contributing to CLL pathogenesis and TME interactions, including T cell function and differentiation. Herein, we report that blocking BET protein function alleviates immunosuppressive networks in the CLL TME and repairs inherent CLL T cell defects. The pan-BET inhibitor OPN-51107 reduced exhaustion-associated cell signatures resulting in improved T cell proliferation and effector function in the Eμ-TCL1 splenic TME. Following BET inhibition (BET-i), TME T cells coexpressed significantly fewer inhibitory receptors (IRs) (e.g., PD-1, CD160, CD244, LAG3, VISTA). Complementary results were witnessed in primary CLL cultures, wherein OPN-51107 exerted proinflammatory effects on T cells, regardless of leukemic cell burden. BET-i additionally promotes a progenitor T cell phenotype through reduced expression of transcription factors that maintain terminal differentiation and increased expression of TCF-1, at least in part through altered chromatin accessibility. Moreover, direct T cell effects of BET-i were unmatched by common targeted therapies in CLL. This study demonstrates the immunomodulatory action of BET-i on CLL T cells and supports the inclusion of BET inhibitors in the management of CLL to alleviate terminal T cell dysfunction and potentially enhance tumoricidal T cell activity.
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Affiliation(s)
| | | | | | | | | | - Nolan Williams
- Eppley Institute for Research in Cancer and Allied Diseases
| | - Dalia Y. Moore
- Eppley Institute for Research in Cancer and Allied Diseases
| | - Christopher R. D’Angelo
- Division of Hematology and Oncology, Department of Internal Medicine, and
- Fred & Pamela Buffett Cancer Center (FPBCC), University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
| | - Avyakta Kallam
- Division of Hematology and Oncology, Department of Internal Medicine, and
- Fred & Pamela Buffett Cancer Center (FPBCC), University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
| | - Matthew A. Lunning
- Division of Hematology and Oncology, Department of Internal Medicine, and
- Fred & Pamela Buffett Cancer Center (FPBCC), University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
| | - R. Gregory Bociek
- Division of Hematology and Oncology, Department of Internal Medicine, and
- Fred & Pamela Buffett Cancer Center (FPBCC), University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
| | - Julie M. Vose
- Division of Hematology and Oncology, Department of Internal Medicine, and
- Fred & Pamela Buffett Cancer Center (FPBCC), University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
| | - Eslam Mohamed
- College of Medicine and College of Graduate Studies, California Northstate University, Elk Grove, California, USA
| | - Anna R. Mahr
- Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Paul W. Denton
- Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, USA
| | - Ben Powell
- Plexxikon Inc., South San Francisco, California, USA
| | | | | | - Dalia El-Gamal
- Eppley Institute for Research in Cancer and Allied Diseases
- Fred & Pamela Buffett Cancer Center (FPBCC), University of Nebraska Medical Center (UNMC), Omaha, Nebraska, USA
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15
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Wang C, Qiu J, Liu M, Wang Y, Yu Y, Liu H, Zhang Y, Han L. Microfluidic Biochips for Single-Cell Isolation and Single-Cell Analysis of Multiomics and Exosomes. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2401263. [PMID: 38767182 DOI: 10.1002/advs.202401263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/26/2024] [Indexed: 05/22/2024]
Abstract
Single-cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in-depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single-cell isolation, which is crucial for single-cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high-throughput single-cell isolation and real-time single-cell analysis on- or off-chip. Therefore, most current single-cell isolation and analysis technologies are based on the single-cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single-cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single-cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single-cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single-cell isolation and multiomic and exosome analyses.
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Affiliation(s)
- Chao Wang
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
| | - Jiaoyan Qiu
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
| | - Mengqi Liu
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
| | - Yihe Wang
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
| | - Yang Yu
- Department of Periodontology, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, 250100, China
| | - Hong Liu
- State Key Laboratory of Crystal Materials, Shandong University, Jinan, 250100, China
| | - Yu Zhang
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
| | - Lin Han
- Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China
- Shandong Engineering Research Center of Biomarker and Artificial Intelligence Application, Jinan, 250100, China
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16
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Pacalin NM, Steinhart Z, Shi Q, Belk JA, Dorovskyi D, Kraft K, Parker KR, Shy BR, Marson A, Chang HY. Bidirectional epigenetic editing reveals hierarchies in gene regulation. Nat Biotechnol 2024:10.1038/s41587-024-02213-3. [PMID: 38760566 DOI: 10.1038/s41587-024-02213-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/19/2024] [Indexed: 05/19/2024]
Abstract
CRISPR perturbation methods are limited in their ability to study non-coding elements and genetic interactions. In this study, we developed a system for bidirectional epigenetic editing, called CRISPRai, in which we apply activating (CRISPRa) and repressive (CRISPRi) perturbations to two loci simultaneously in the same cell. We developed CRISPRai Perturb-seq by coupling dual perturbation gRNA detection with single-cell RNA sequencing, enabling study of pooled perturbations in a mixed single-cell population. We applied this platform to study the genetic interaction between two hematopoietic lineage transcription factors, SPI1 and GATA1, and discovered novel characteristics of their co-regulation on downstream target genes, including differences in SPI1 and GATA1 occupancy at genes that are regulated through different modes. We also studied the regulatory landscape of IL2 (interleukin-2) in Jurkat T cells, primary T cells and chimeric antigen receptor (CAR) T cells and elucidated mechanisms of enhancer-mediated IL2 gene regulation. CRISPRai facilitates investigation of context-specific genetic interactions, provides new insights into gene regulation and will enable exploration of non-coding disease-associated variants.
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Affiliation(s)
- Naomi M Pacalin
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Zachary Steinhart
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Quanming Shi
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Julia A Belk
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Dmytro Dorovskyi
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Katerina Kraft
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Kevin R Parker
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Program in Epithelial Biology, Stanford University School of Medicine, Stanford, CA, USA
- Cartography Biosciences, Inc., South San Francisco, CA, USA
| | - Brian R Shy
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Alexander Marson
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Diabetes Center, University of California, San Francisco, San Francisco, CA, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
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17
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Rupp BT, Cook CD, Purcell EA, Pop M, Radomski AE, Mesyngier N, Bailey RC, Nagrath S. CellMag-CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications. Adv Biol (Weinh) 2024:e2400066. [PMID: 38741244 DOI: 10.1002/adbi.202400066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Indexed: 05/16/2024]
Abstract
The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.
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Affiliation(s)
- Brittany T Rupp
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Claire D Cook
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Emma A Purcell
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Matei Pop
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Abigail E Radomski
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Nicolas Mesyngier
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ryan C Bailey
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Sunitha Nagrath
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA
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18
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Li J, Zhang X, Wang X, Wang Z, Li X, Zheng J, Li J, Xu G, Sun C, Yi G, Yang N. Single-nucleus transcriptional and chromatin accessible profiles reveal critical cell types and molecular architecture underlying chicken sex determination. J Adv Res 2024:S2090-1232(24)00185-1. [PMID: 38734369 DOI: 10.1016/j.jare.2024.05.007] [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: 09/09/2023] [Revised: 01/23/2024] [Accepted: 05/06/2024] [Indexed: 05/13/2024] Open
Abstract
INTRODUCTION Understanding the sex determination mechanisms in birds has great significance for the biological sciences and production in the poultry industry. Sex determination in chickens is a complex process that involves fate decisions of supporting cells such as granulosa or Sertoli cells. However, a systematic understanding of the genetic regulation and cell commitment process underlying sex determination in chickens is still lacking. OBJECTIVES We aimed to dissect the molecular characteristics associated with sex determination in the gonads of chicken embryos. METHODS Single-nucleus RNA-seq (snRNA-seq) and ATAC-seq (snATAC-seq) analysis were conducted on the gonads of female and male chickens at embryonic day 3.5 (E3.5), E4.5, and E5.5. RESULTS Here, we provided a time-course transcriptional and chromatin accessible profiling of gonads during chicken sex determination at single-cell resolution. We uncovered differences in cell composition and developmental trajectories between female and male gonads and found that the divergence of transcription and accessibility in gonadal cells first emerged at E5.5. Furthermore, we revealed key cell-type-specific transcription factors (TFs) and regulatory networks that drive lineage commitment. Sex determination signaling pathways, dominated by BMP signaling, are preferentially activated in males during gonadal development. Further pseudotime analysis of the supporting cells indicated that granulosa cells were regulated mainly by the TEAD gene family and that Sertoli cells were driven by the DMRT1 regulons. Cross-species analysis suggested high conservation of both cell types and cell-lineage-specific TFs across the six vertebrates. CONCLUSIONS Overall, our study will contribute to accelerating the development of sex manipulation technology in the poultry industry and the application of chickens as a unique model for studying cell fate decisions.
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Affiliation(s)
- Jianbo Li
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Xiuan Zhang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Xiqiong Wang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Zhen Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Xingzheng Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Jiangxia Zheng
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Junying Li
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Guiyun Xu
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China
| | - Congjiao Sun
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China.
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Kunpeng Institute of Modern Agriculture at Foshan, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
| | - Ning Yang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; National Engineering Laboratory for Animal Breeding and Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100193, China.
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19
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Shu C, Street K, Breton CV, Bastain TM, Wilson ML. A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 2024:1-20. [PMID: 38709139 DOI: 10.1080/17501911.2024.2343276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.
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Affiliation(s)
- Chang Shu
- Center for Genetic Epidemiology, Division of Epidemiology & Genetics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Kelly Street
- Division of Biostatistics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Carrie V Breton
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Theresa M Bastain
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Melissa L Wilson
- Division of Disease Prevention, Policy, & Global Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles,CA USA
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20
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Ma J, Wu Y, Ma L, Yang X, Zhang T, Song G, Li T, Gao K, Shen X, Lin J, Chen Y, Liu X, Fu Y, Gu X, Chen Z, Jiang S, Rao D, Pan J, Zhang S, Zhou J, Huang C, Shi S, Fan J, Guo G, Zhang X, Gao Q. A blueprint for tumor-infiltrating B cells across human cancers. Science 2024; 384:eadj4857. [PMID: 38696569 DOI: 10.1126/science.adj4857] [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: 07/02/2023] [Accepted: 03/06/2024] [Indexed: 05/04/2024]
Abstract
B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell-receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell-driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell-targeting immunotherapy.
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Affiliation(s)
- Jiaqiang Ma
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yingcheng Wu
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Lifeng Ma
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, and Stem Cell Institute, Zhejiang University, Hangzhou 310058, China
| | - Xupeng Yang
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Tiancheng Zhang
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Guohe Song
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Teng Li
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ke Gao
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Xia Shen
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jian Lin
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Yamin Chen
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoshan Liu
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuting Fu
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, and Stem Cell Institute, Zhejiang University, Hangzhou 310058, China
| | - Xixi Gu
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zechuan Chen
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shan Jiang
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Dongning Rao
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jiaomeng Pan
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Shu Zhang
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Chen Huang
- Department of Gastrointestinal Surgery, Shanghai General Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200080, China
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Guoji Guo
- Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, and Stem Cell Institute, Zhejiang University, Hangzhou 310058, China
| | - Xiaoming Zhang
- The Center for Microbes, Development and Health, Key Laboratory of Immune Response and Immunotherapy, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qiang Gao
- Department of Liver Surgery and Transplantation, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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21
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Mannens CCA, Hu L, Lönnerberg P, Schipper M, Reagor CC, Li X, He X, Barker RA, Sundström E, Posthuma D, Linnarsson S. Chromatin accessibility during human first-trimester neurodevelopment. Nature 2024:10.1038/s41586-024-07234-1. [PMID: 38693260 DOI: 10.1038/s41586-024-07234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/02/2024] [Indexed: 05/03/2024]
Abstract
The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.
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Affiliation(s)
- Camiel C A Mannens
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Caleb C Reagor
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY, USA
| | - Xiaofei Li
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Erik Sundström
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden.
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22
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Chen C, Zhang Z, Tang P, Liu X, Huang B. Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis. Comput Biol Med 2024; 174:108449. [PMID: 38626512 DOI: 10.1016/j.compbiomed.2024.108449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/27/2024] [Accepted: 04/07/2024] [Indexed: 04/18/2024]
Abstract
Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.
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Affiliation(s)
- Cui Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Zuping Zhang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
| | - Panrui Tang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Xin Liu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Bo Huang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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23
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Doan AE, Mueller KP, Chen AY, Rouin GT, Chen Y, Daniel B, Lattin J, Markovska M, Mozarsky B, Arias-Umana J, Hapke R, Jung IY, Wang A, Xu P, Klysz D, Zuern G, Bashti M, Quinn PJ, Miao Z, Sandor K, Zhang W, Chen GM, Ryu F, Logun M, Hall J, Tan K, Grupp SA, McClory SE, Lareau CA, Fraietta JA, Sotillo E, Satpathy AT, Mackall CL, Weber EW. FOXO1 is a master regulator of memory programming in CAR T cells. Nature 2024; 629:211-218. [PMID: 38600391 PMCID: PMC11062920 DOI: 10.1038/s41586-024-07300-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 03/12/2024] [Indexed: 04/12/2024]
Abstract
A major limitation of chimeric antigen receptor (CAR) T cell therapies is the poor persistence of these cells in vivo1. The expression of memory-associated genes in CAR T cells is linked to their long-term persistence in patients and clinical efficacy2-6, suggesting that memory programs may underpin durable CAR T cell function. Here we show that the transcription factor FOXO1 is responsible for promoting memory and restraining exhaustion in human CAR T cells. Pharmacological inhibition or gene editing of endogenous FOXO1 diminished the expression of memory-associated genes, promoted an exhaustion-like phenotype and impaired the antitumour activity of CAR T cells. Overexpression of FOXO1 induced a gene-expression program consistent with T cell memory and increased chromatin accessibility at FOXO1-binding motifs. CAR T cells that overexpressed FOXO1 retained their function, memory potential and metabolic fitness in settings of chronic stimulation, and exhibited enhanced persistence and tumour control in vivo. By contrast, overexpression of TCF1 (encoded by TCF7) did not enforce canonical memory programs or enhance the potency of CAR T cells. Notably, FOXO1 activity correlated with positive clinical outcomes of patients treated with CAR T cells or tumour-infiltrating lymphocytes, underscoring the clinical relevance of FOXO1 in cancer immunotherapy. Our results show that overexpressing FOXO1 can increase the antitumour activity of human CAR T cells, and highlight memory reprogramming as a broadly applicable approach for optimizing therapeutic T cell states.
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Affiliation(s)
- Alexander E Doan
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine P Mueller
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andy Y Chen
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Geoffrey T Rouin
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yingshi Chen
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bence Daniel
- Department of Pathology, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Genentech, South San Francisco, CA, USA
| | - John Lattin
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Martina Markovska
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brett Mozarsky
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jose Arias-Umana
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert Hapke
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - In-Young Jung
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alice Wang
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Peng Xu
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Dorota Klysz
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Gabrielle Zuern
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Malek Bashti
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick J Quinn
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Zhuang Miao
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Katalin Sandor
- Department of Pathology, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Wenxi Zhang
- Department of Pathology, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
| | - Gregory M Chen
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Faith Ryu
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Meghan Logun
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Junior Hall
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kai Tan
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephan A Grupp
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan E McClory
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Caleb A Lareau
- Department of Pathology, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Joseph A Fraietta
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elena Sotillo
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Ansuman T Satpathy
- Department of Pathology, Stanford University, Stanford, CA, USA
- Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
| | - Crystal L Mackall
- Center for Cancer Cell Therapy, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
- Department of Pediatrics, Stanford University, Stanford, CA, USA.
- Department of Medicine, Stanford University, Stanford, CA, USA.
| | - Evan W Weber
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA.
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24
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Augustin RC, Cai WL, Luke JJ, Bao R. Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies. Clin Cancer Res 2024; 30:1724-1732. [PMID: 38236069 PMCID: PMC11062841 DOI: 10.1158/1078-0432.ccr-22-2241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/28/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.
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Affiliation(s)
- Ryan C. Augustin
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
- Mayo Clinic, Department of Medical Oncology, Rochester, MN
| | - Wesley L. Cai
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
| | - Jason J. Luke
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
| | - Riyue Bao
- UPMC Hillman Cancer Center, Pittsburgh, PA
- University of Pittsburgh, Department of Medicine, Pittsburgh, PA
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25
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Cui X, Chen X, Li Z, Gao Z, Chen S, Jiang R. Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity. NATURE COMPUTATIONAL SCIENCE 2024; 4:346-359. [PMID: 38730185 DOI: 10.1038/s43588-024-00625-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/05/2024] [Indexed: 05/12/2024]
Abstract
Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.
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Affiliation(s)
- Xuejian Cui
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Zijing Gao
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China.
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26
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Izzo F, Myers RM, Ganesan S, Mekerishvili L, Kottapalli S, Prieto T, Eton EO, Botella T, Dunbar AJ, Bowman RL, Sotelo J, Potenski C, Mimitou EP, Stahl M, El Ghaity-Beckley S, Arandela J, Raviram R, Choi DC, Hoffman R, Chaligné R, Abdel-Wahab O, Smibert P, Ghobrial IM, Scandura JM, Marcellino B, Levine RL, Landau DA. Mapping genotypes to chromatin accessibility profiles in single cells. Nature 2024; 629:1149-1157. [PMID: 38720070 PMCID: PMC11139586 DOI: 10.1038/s41586-024-07388-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 04/04/2024] [Indexed: 05/19/2024]
Abstract
In somatic tissue differentiation, chromatin accessibility changes govern priming and precursor commitment towards cellular fates1-3. Therefore, somatic mutations are likely to alter chromatin accessibility patterns, as they disrupt differentiation topologies leading to abnormal clonal outgrowth. However, defining the impact of somatic mutations on the epigenome in human samples is challenging due to admixed mutated and wild-type cells. Here, to chart how somatic mutations disrupt epigenetic landscapes in human clonal outgrowths, we developed genotyping of targeted loci with single-cell chromatin accessibility (GoT-ChA). This high-throughput platform links genotypes to chromatin accessibility at single-cell resolution across thousands of cells within a single assay. We applied GoT-ChA to CD34+ cells from patients with myeloproliferative neoplasms with JAK2V617F-mutated haematopoiesis. Differential accessibility analysis between wild-type and JAK2V617F-mutant progenitors revealed both cell-intrinsic and cell-state-specific shifts within mutant haematopoietic precursors, including cell-intrinsic pro-inflammatory signatures in haematopoietic stem cells, and a distinct profibrotic inflammatory chromatin landscape in megakaryocytic progenitors. Integration of mitochondrial genome profiling and cell-surface protein expression measurement allowed expansion of genotyping onto DOGMA-seq through imputation, enabling single-cell capture of genotypes, chromatin accessibility, RNA expression and cell-surface protein expression. Collectively, we show that the JAK2V617F mutation leads to epigenetic rewiring in a cell-intrinsic and cell type-specific manner, influencing inflammation states and differentiation trajectories. We envision that GoT-ChA will empower broad future investigations of the critical link between somatic mutations and epigenetic alterations across clonal populations in malignant and non-malignant contexts.
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Affiliation(s)
- Franco Izzo
- New York Genome Center, New York, NY, USA.
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
- Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Robert M Myers
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional MD-PhD Program, Weill Cornell Medicine, Rockefeller University, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Saravanan Ganesan
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Levan Mekerishvili
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Physiology, Biophysics and Systems Biology Graduate Program, Weill Cornell Medicine, New York, NY, USA
| | - Sanjay Kottapalli
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Tamara Prieto
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Elliot O Eton
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- Tri-Institutional MD-PhD Program, Weill Cornell Medicine, Rockefeller University, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Theo Botella
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Andrew J Dunbar
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Robert L Bowman
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jesus Sotelo
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Catherine Potenski
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Eleni P Mimitou
- New York Genome Center, New York, NY, USA
- Immunai, New York, NY, USA
| | - Maximilian Stahl
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medical Oncology, Division of Leukemia, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sebastian El Ghaity-Beckley
- Division of Hematology/Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - JoAnn Arandela
- Division of Hematology/Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ramya Raviram
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Daniel C Choi
- Laboratory of Molecular Hematopoiesis, Hematology and Oncology, Weill Cornell Medicine, New York, NY, USA
- Richard T. Silver MD Myeloproliferative Neoplasm Center, Weill Cornell Medicine, New York, NY, USA
- Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ronald Hoffman
- Division of Hematology/Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ronan Chaligné
- New York Genome Center, New York, NY, USA
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
- SAIL: Single-cell Analytics Innovation Lab, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Omar Abdel-Wahab
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter Smibert
- New York Genome Center, New York, NY, USA
- 10x Genomics, Pleasanton, CA, USA
| | - Irene M Ghobrial
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joseph M Scandura
- Laboratory of Molecular Hematopoiesis, Hematology and Oncology, Weill Cornell Medicine, New York, NY, USA
- Richard T. Silver MD Myeloproliferative Neoplasm Center, Weill Cornell Medicine, New York, NY, USA
- Regenerative Medicine, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Bridget Marcellino
- Division of Hematology/Medical Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ross L Levine
- Molecular Pharmacology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dan A Landau
- New York Genome Center, New York, NY, USA.
- Division of Hematology and Medical Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
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27
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Ver Heul AM, Mack M, Zamidar L, Tamari M, Yang TL, Trier AM, Kim DH, Janzen-Meza H, Van Dyken SJ, Hsieh CS, Karo JM, Sun JC, Kim BS. RAG suppresses group 2 innate lymphoid cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590767. [PMID: 38712036 PMCID: PMC11071423 DOI: 10.1101/2024.04.23.590767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Antigen specificity is the central trait distinguishing adaptive from innate immune function. Assembly of antigen-specific T cell and B cell receptors occurs through V(D)J recombination mediated by the Recombinase Activating Gene endonucleases RAG1 and RAG2 (collectively called RAG). In the absence of RAG, mature T and B cells do not develop and thus RAG is critically associated with adaptive immune function. In addition to adaptive T helper 2 (Th2) cells, group 2 innate lymphoid cells (ILC2s) contribute to type 2 immune responses by producing cytokines like Interleukin-5 (IL-5) and IL-13. Although it has been reported that RAG expression modulates the function of innate natural killer (NK) cells, whether other innate immune cells such as ILC2s are affected by RAG remains unclear. We find that in RAG-deficient mice, ILC2 populations expand and produce increased IL-5 and IL-13 at steady state and contribute to increased inflammation in atopic dermatitis (AD)-like disease. Further, we show that RAG modulates ILC2 function in a cell-intrinsic manner independent of the absence or presence of adaptive T and B lymphocytes. Lastly, employing multiomic single cell analyses of RAG1 lineage-traced cells, we identify key transcriptional and epigenomic ILC2 functional programs that are suppressed by a history of RAG expression. Collectively, our data reveal a novel role for RAG in modulating innate type 2 immunity through suppression of ILC2s.
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Affiliation(s)
- Aaron M. Ver Heul
- Division of Allergy and Immunology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Madison Mack
- Immunology & Inflammation Research Therapeutic Area, Sanofi, Cambridge, MA 02141, USA
| | - Lydia Zamidar
- Kimberly and Eric J. Waldman Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mark Lebwohl Center for Neuroinflammation and Sensation, Icahn School of Medicine at Mount Sinai, New York, NY 10019, USA
- Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Masato Tamari
- Kimberly and Eric J. Waldman Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mark Lebwohl Center for Neuroinflammation and Sensation, Icahn School of Medicine at Mount Sinai, New York, NY 10019, USA
- Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ting-Lin Yang
- Division of Dermatology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Anna M. Trier
- Division of Dermatology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Do-Hyun Kim
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA
- Department of Life Science, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea
| | - Hannah Janzen-Meza
- Division of Allergy and Immunology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Steven J. Van Dyken
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA
| | - Chyi-Song Hsieh
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jenny M. Karo
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Graduate School of Medical Sciences, Weill Cornell Medical College, New York, NY 10065, USA
| | - Joseph C. Sun
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Immunology and Microbial Pathogenesis Program, Graduate School of Medical Sciences, Weill Cornell Medical College, New York, NY 10065, USA
| | - Brian S. Kim
- Kimberly and Eric J. Waldman Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mark Lebwohl Center for Neuroinflammation and Sensation, Icahn School of Medicine at Mount Sinai, New York, NY 10019, USA
- Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Allen Discovery Center for Neuroimmune Interactions, Icahn School of Medicine at Mount Sinai 10019
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28
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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29
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Bagheri M, Mohamed GA, Mohamed Saleem MA, Ognjenovic NB, Lu H, Kolling FW, Wilkins OM, Das S, LaCroix IS, Nagaraj SH, Muller KE, Gerber SA, Miller TW, Pattabiraman DR. Pharmacological induction of chromatin remodeling drives chemosensitization in triple-negative breast cancer. Cell Rep Med 2024; 5:101504. [PMID: 38593809 PMCID: PMC11031425 DOI: 10.1016/j.xcrm.2024.101504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/11/2023] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
Targeted therapies have improved outcomes for certain cancer subtypes, but cytotoxic chemotherapy remains a mainstay for triple-negative breast cancer (TNBC). The epithelial-to-mesenchymal transition (EMT) is a developmental program co-opted by cancer cells that promotes metastasis and chemoresistance. There are no therapeutic strategies specifically targeting mesenchymal-like cancer cells. We report that the US Food and Drug Administration (FDA)-approved chemotherapeutic eribulin induces ZEB1-SWI/SNF-directed chromatin remodeling to reverse EMT that curtails the metastatic propensity of TNBC preclinical models. Eribulin induces mesenchymal-to-epithelial transition (MET) in primary TNBC in patients, but conventional chemotherapy does not. In the treatment-naive setting, but not after acquired resistance to other agents, eribulin sensitizes TNBC cells to subsequent treatment with other chemotherapeutics. These findings provide an epigenetic mechanism of action of eribulin, supporting its use early in the disease process for MET induction to prevent metastatic progression and chemoresistance. These findings warrant prospective clinical evaluation of the chemosensitizing effects of eribulin in the treatment-naive setting.
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Affiliation(s)
- Meisam Bagheri
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Gadisti Aisha Mohamed
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | | | - Nevena B Ognjenovic
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Hanxu Lu
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Fred W Kolling
- Center for Quantitative Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Owen M Wilkins
- Center for Quantitative Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | | | - Ian S LaCroix
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia; Translational Research Institute, Brisbane, QLD 4102, Australia
| | - Kristen E Muller
- Department of Pathology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Scott A Gerber
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Todd W Miller
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA; Department of Pharmacology & Toxicology, Medical College of Wisconsin, Milwaukee, WI, USA; Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Diwakar R Pattabiraman
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
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30
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de Smith AJ, Wahlster L, Jeon S, Kachuri L, Black S, Langie J, Cato LD, Nakatsuka N, Chan TF, Xia G, Mazumder S, Yang W, Gazal S, Eng C, Hu D, Burchard EG, Ziv E, Metayer C, Mancuso N, Yang JJ, Ma X, Wiemels JL, Yu F, Chiang CWK, Sankaran VG. A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children. CELL GENOMICS 2024; 4:100526. [PMID: 38537633 PMCID: PMC11019360 DOI: 10.1016/j.xgen.2024.100526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/11/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
Hispanic/Latino children have the highest risk of acute lymphoblastic leukemia (ALL) in the US compared to other racial/ethnic groups, yet the basis of this remains incompletely understood. Through genetic fine-mapping analyses, we identified a new independent childhood ALL risk signal near IKZF1 in self-reported Hispanic/Latino individuals, but not in non-Hispanic White individuals, with an effect size of ∼1.44 (95% confidence interval = 1.33-1.55) and a risk allele frequency of ∼18% in Hispanic/Latino populations and <0.5% in European populations. This risk allele was positively associated with Indigenous American ancestry, showed evidence of selection in human history, and was associated with reduced IKZF1 expression. We identified a putative causal variant in a downstream enhancer that is most active in pro-B cells and interacts with the IKZF1 promoter. This variant disrupts IKZF1 autoregulation at this enhancer and results in reduced enhancer activity in B cell progenitors. Our study reveals a genetic basis for the increased ALL risk in Hispanic/Latino children.
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Affiliation(s)
- Adam J de Smith
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA.
| | - Lara Wahlster
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Soyoung Jeon
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Susan Black
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jalen Langie
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Liam D Cato
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Tsz-Fung Chan
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Guangze Xia
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Soumyaa Mazumder
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wenjian Yang
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Celeste Eng
- Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Donglei Hu
- Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Esteban González Burchard
- Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Bioengineering and Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Elad Ziv
- Department of Medicine, Institute for Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Catherine Metayer
- School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Jun J Yang
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Xiaomei Ma
- Yale School of Public Health, New Haven, CT 06520, USA
| | - Joseph L Wiemels
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Fulong Yu
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University, Guangzhou, China
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, University of Southern California Keck School of Medicine, Los Angeles, CA 90033, USA; USC Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA 90033, USA
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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31
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Roehrig A, Hirsch TZ, Pire A, Morcrette G, Gupta B, Marcaillou C, Imbeaud S, Chardot C, Gonzales E, Jacquemin E, Sekiguchi M, Takita J, Nagae G, Hiyama E, Guérin F, Fabre M, Aerts I, Taque S, Laithier V, Branchereau S, Guettier C, Brugières L, Fresneau B, Zucman-Rossi J, Letouzé E. Single-cell multiomics reveals the interplay of clonal evolution and cellular plasticity in hepatoblastoma. Nat Commun 2024; 15:3031. [PMID: 38589411 PMCID: PMC11001886 DOI: 10.1038/s41467-024-47280-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: 04/06/2023] [Accepted: 03/21/2024] [Indexed: 04/10/2024] Open
Abstract
Hepatoblastomas (HB) display heterogeneous cellular phenotypes that influence the clinical outcome, but the underlying mechanisms are poorly understood. Here, we use a single-cell multiomic strategy to unravel the molecular determinants of this plasticity. We identify a continuum of HB cell states between hepatocytic (scH), liver progenitor (scLP) and mesenchymal (scM) differentiation poles, with an intermediate scH/LP population bordering scLP and scH areas in spatial transcriptomics. Chromatin accessibility landscapes reveal the gene regulatory networks of each differentiation pole, and the sequence of transcription factor activations underlying cell state transitions. Single-cell mapping of somatic alterations reveals the clonal architecture of each tumor, showing that each genetic subclone displays its own range of cellular plasticity across differentiation states. The most scLP subclones, overexpressing stem cell and DNA repair genes, proliferate faster after neo-adjuvant chemotherapy. These results highlight how the interplay of clonal evolution and epigenetic plasticity shapes the potential of HB subclones to respond to chemotherapy.
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Affiliation(s)
- Amélie Roehrig
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Theo Z Hirsch
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Aurore Pire
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | - Guillaume Morcrette
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
- Department of Pathology, Robert Debré and Necker-Enfants Malades Hospitals, APHP, Paris, France
| | - Barkha Gupta
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | | | - Sandrine Imbeaud
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France
| | | | - Emmanuel Gonzales
- Pediatric Hepatology and Liver Transplantation Unit, National Reference Centre for Rare Pediatric Liver Diseases, FILFOIE, ERN RARE LIVER, APHP, Bicêtre University Hospital, University of Paris-Saclay, Le Kremlin Bicêtre, and INSERM UMR_S 1193, Hepatinov, University of Paris-Saclay, Orsay, France
| | - Emmanuel Jacquemin
- Pediatric Hepatology and Liver Transplantation Unit, National Reference Centre for Rare Pediatric Liver Diseases, FILFOIE, ERN RARE LIVER, APHP, Bicêtre University Hospital, University of Paris-Saclay, Le Kremlin Bicêtre, and INSERM UMR_S 1193, Hepatinov, University of Paris-Saclay, Orsay, France
| | - Masahiro Sekiguchi
- Department of Pediatrics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Junko Takita
- Department of Pediatrics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Genta Nagae
- Genome Science Laboratory, Research Center for Advanced Science and Technology (RCAST), the University of Tokyo, Tokyo, Japan
| | - Eiso Hiyama
- Department of Pediatric Surgery, Hiroshima University Hospital, Hiroshima, Japan
- Department of Biomedical Science, Natural Science Center for Basic Research and Development, Hiroshima University, Hiroshima, Japan
| | - Florent Guérin
- Department of Pediatric Surgery, Bicêtre Hospital, APHP, Paris-Saclay University, Orsay, France
| | - Monique Fabre
- Department of Pathology, Hôpital Universitaire Necker-Enfants malades, AP-HP, Paris, France
| | - Isabelle Aerts
- Oncology Center SIREDO, Institut Curie, PSL Research University, Paris, France
| | - Sophie Taque
- Département de Pédiatrie, CHU Fontenoy, Rennes, France
| | - Véronique Laithier
- Department of Children Oncology, Centre Hospitalier Universitaire Besançon, Besançon, France
| | - Sophie Branchereau
- Department of Pediatric Surgery, Bicêtre Hospital, APHP, Paris-Saclay University, Orsay, France
| | - Catherine Guettier
- Department of Pathology Hôpital Bicêtre-AP-HP, INSERM U1193, Paris-Saclay University, Orsay, France
| | - Laurence Brugières
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Brice Fresneau
- Gustave Roussy, Université Paris-Saclay, Department of Children and Adolescents Oncology, Villejuif, France
| | - Jessica Zucman-Rossi
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France.
- Hôpital Européen Georges Pompidou, Assistance Publique Hôpitaux de Paris, Paris, France.
| | - Eric Letouzé
- Centre de Recherche des Cordeliers, Université Paris Cité, Sorbonne Université, INSERM, Paris, France.
- CRCI2NA, Nantes Université, INSERM, CNRS, Nantes, France.
- University Hospital Hôtel-Dieu, Nantes, France.
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32
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Zhang X, Marand AP, Yan H, Schmitz RJ. scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. Genome Biol 2024; 25:90. [PMID: 38589969 PMCID: PMC11003106 DOI: 10.1186/s13059-024-03235-5] [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: 09/28/2023] [Accepted: 04/01/2024] [Indexed: 04/10/2024] Open
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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33
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Annotating cell types in single-cell ATAC data via the guidance of the underlying DNA sequences. NATURE COMPUTATIONAL SCIENCE 2024; 4:261-262. [PMID: 38671305 DOI: 10.1038/s43588-024-00626-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
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34
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Zeng Y, Luo M, Shangguan N, Shi P, Feng J, Xu J, Chen K, Lu Y, Yu W, Yang Y. Deciphering cell types by integrating scATAC-seq data with genome sequences. NATURE COMPUTATIONAL SCIENCE 2024; 4:285-298. [PMID: 38600256 DOI: 10.1038/s43588-024-00622-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/18/2024] [Indexed: 04/12/2024]
Abstract
The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.
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Affiliation(s)
- Yuansong Zeng
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mai Luo
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Ningyuan Shangguan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Junxi Feng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Ken Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Weijiang Yu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Guangzhou, China.
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35
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Zhang W, Cui Y, Liu B, Loza M, Park SJ, Nakai K. HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data. Brief Bioinform 2024; 25:bbae152. [PMID: 38581422 PMCID: PMC10998639 DOI: 10.1093/bib/bbae152] [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: 12/19/2023] [Revised: 02/19/2024] [Accepted: 03/10/2024] [Indexed: 04/08/2024] Open
Abstract
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.
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Affiliation(s)
- Weihang Zhang
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Yang Cui
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Bowen Liu
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Martin Loza
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Sung-Joon Park
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
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36
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Ye F, Zhang S, Fu Y, Yang L, Zhang G, Wu Y, Pan J, Chen H, Wang X, Ma L, Niu H, Jiang M, Zhang T, Jia D, Wang J, Wang Y, Han X, Guo G. Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3. Cell Discov 2024; 10:33. [PMID: 38531851 DOI: 10.1038/s41421-023-00642-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: 04/03/2023] [Accepted: 12/21/2023] [Indexed: 03/28/2024] Open
Abstract
Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yijun Wu
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jun Pan
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haide Chen
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinru Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haofu Niu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingyue Zhang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Danmei Jia
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yongcheng Wang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang, China.
- Institute of Hematology, Zhejiang University, Hangzhou, Zhejiang, China.
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37
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Zhang G, Fu Y, Yang L, Ye F, Zhang P, Zhang S, Ma L, Li J, Wu H, Han X, Wang J, Guo G. Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq. Dev Cell 2024; 59:793-811.e8. [PMID: 38330939 DOI: 10.1016/j.devcel.2024.01.015] [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: 06/01/2023] [Revised: 11/03/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Despite recent advances in single-cell genomics, the lack of maps for single-cell candidate cis-regulatory elements (cCREs) in non-mammal species has limited our exploration of conserved regulatory programs across vertebrates and invertebrates. Here, we developed a combinatorial-hybridization-based method for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) named CH-ATAC-seq, enabling the construction of single-cell accessible chromatin landscapes for zebrafish, Drosophila, and earthworms (Eisenia andrei). By integrating scATAC censuses of humans, monkeys, and mice, we systematically identified 152 distinct main cell types and around 0.8 million cell-type-specific cCREs. Our analysis provided insights into the conservation of neural, muscle, and immune lineages across species, while epithelial cells exhibited a higher organ-origin heterogeneity. Additionally, a large-scale gene regulatory network (GRN) was constructed in four vertebrates by integrating scRNA-seq censuses. Overall, our study provides a valuable resource for comparative epigenomics, identifying the evolutionary conservation and divergence of gene regulation across different species.
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Affiliation(s)
- Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China
| | - Peijing Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Hanyu Wu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou 310058, China.
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China.
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou 310058, China; Institute of Hematology, Zhejiang University, Hangzhou, China.
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38
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Miao Z, Wang J, Park K, Kuang D, Kim J. PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.30.551108. [PMID: 37577623 PMCID: PMC10418058 DOI: 10.1101/2023.07.30.551108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data.
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Affiliation(s)
- Zhen Miao
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jianqiao Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kernyu Park
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Da Kuang
- Deptartment Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhyong Kim
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
- Deptartment Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
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39
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Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, Ledford JG, Adey AC, Cusanovich DA. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol 2024; 25:78. [PMID: 38519979 PMCID: PMC10958877 DOI: 10.1186/s13059-023-03150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.
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Affiliation(s)
- Hao Zhang
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Ryan M Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Iannuzo
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
| | - Dominique O Farrera
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Francesca Polverino
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Arizona, Tucson, AZ, USA
- Banner - University Medicine North, Pulmonary - Clinic F, Tucson, AZ, USA
| | - James J Galligan
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Julie G Ledford
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA.
- Oregon Health & Science University, Knight Cancer Institute, Portland, OR, USA.
- Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
| | - Darren A Cusanovich
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
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Feng AC, Thomas BJ, Purbey PK, de Melo FM, Liu X, Daly AE, Sun F, Lo JHH, Cheng L, Carey MF, Scumpia PO, Smale ST. The transcription factor NF-κB orchestrates nucleosome remodeling during the primary response to Toll-like receptor 4 signaling. Immunity 2024; 57:462-477.e9. [PMID: 38430908 PMCID: PMC10984581 DOI: 10.1016/j.immuni.2024.02.004] [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: 06/25/2023] [Revised: 11/26/2023] [Accepted: 02/07/2024] [Indexed: 03/05/2024]
Abstract
Inducible nucleosome remodeling at hundreds of latent enhancers and several promoters shapes the transcriptional response to Toll-like receptor 4 (TLR4) signaling in macrophages. We aimed to define the identities of the transcription factors that promote TLR-induced remodeling. An analysis strategy based on ATAC-seq and single-cell ATAC-seq that enriched for genomic regions most likely to undergo remodeling revealed that the transcription factor nuclear factor κB (NF-κB) bound to all high-confidence peaks marking remodeling during the primary response to the TLR4 ligand, lipid A. Deletion of NF-κB subunits RelA and c-Rel resulted in the loss of remodeling at high-confidence ATAC-seq peaks, and CRISPR-Cas9 mutagenesis of NF-κB-binding motifs impaired remodeling. Remodeling selectivity at defined regions was conferred by collaboration with other inducible factors, including IRF3- and MAP-kinase-induced factors. Thus, NF-κB is unique among TLR4-activated transcription factors in its broad contribution to inducible nucleosome remodeling, alongside its ability to activate poised enhancers and promoters assembled into open chromatin.
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Affiliation(s)
- An-Chieh Feng
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Brandon J Thomas
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA 98195, USA
| | - Prabhat K Purbey
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Filipe Menegatti de Melo
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xin Liu
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Allison E Daly
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Fei Sun
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jerry Hung-Hao Lo
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Lijing Cheng
- Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael F Carey
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Philip O Scumpia
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Stephen T Smale
- Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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Peidli S, Green TD, Shen C, Gross T, Min J, Garda S, Yuan B, Schumacher LJ, Taylor-King JP, Marks DS, Luna A, Blüthgen N, Sander C. scPerturb: harmonized single-cell perturbation data. Nat Methods 2024; 21:531-540. [PMID: 38279009 DOI: 10.1038/s41592-023-02144-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 12/04/2023] [Indexed: 01/28/2024]
Abstract
Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation-response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.
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Affiliation(s)
- Stefan Peidli
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Tessa D Green
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Ciyue Shen
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | | | - Joseph Min
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Samuele Garda
- Institute of Biology, Humboldt-Universität, Berlin, Germany
- Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bo Yuan
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Linus J Schumacher
- Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Augustin Luna
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA.
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany.
- Institute of Biology, Humboldt-Universität, Berlin, Germany.
| | - Chris Sander
- Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA.
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
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42
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Kwok AJ, Lu J, Huang J, Ip BY, Mok VCT, Lai HM, Ko H. High-resolution omics of vascular ageing and inflammatory pathways in neurodegeneration. Semin Cell Dev Biol 2024; 155:30-49. [PMID: 37380595 DOI: 10.1016/j.semcdb.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 06/07/2023] [Indexed: 06/30/2023]
Abstract
High-resolution omics, particularly single-cell and spatial transcriptomic profiling, are rapidly enhancing our comprehension of the normal molecular diversity of gliovascular cells, as well as their age-related changes that contribute to neurodegeneration. With more omic profiling studies being conducted, it is becoming increasingly essential to synthesise valuable information from the rapidly accumulating findings. In this review, we present an overview of the molecular features of neurovascular and glial cells that have been recently discovered through omic profiling, with a focus on those that have potentially significant functional implications and/or show cross-species differences between human and mouse, and that are linked to vascular deficits and inflammatory pathways in ageing and neurodegenerative disorders. Additionally, we highlight the translational applications of omic profiling, and discuss omic-based strategies to accelerate biomarker discovery and facilitate disease course-modifying therapeutics development for neurodegenerative conditions.
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Affiliation(s)
- Andrew J Kwok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Jianning Lu
- Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Junzhe Huang
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Bonaventure Y Ip
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Hei Ming Lai
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Ho Ko
- Division of Neurology, Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Margaret K. L. Cheung Research Centre for Management of Parkinsonism, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Chinese, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China; Gerald Choa Neuroscience Institute, The Chinese University of Hong Kong, Hong Kong SAR, China.
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43
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Ghaffari S, Saleh M, Akbari B, Ramezani F, Mirzaei HR. Applications of single-cell omics for chimeric antigen receptor T cell therapy. Immunology 2024; 171:339-364. [PMID: 38009707 DOI: 10.1111/imm.13720] [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: 07/02/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023] Open
Abstract
Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single-cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single-cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single-cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single-cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi-omics of CAR T cell therapy.
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Affiliation(s)
- Sasan Ghaffari
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Mahshid Saleh
- Wisconsin National Primate Research Center, University of Wisconsin Graduate School, Madison, Wisconsin, USA
| | - Behnia Akbari
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Faezeh Ramezani
- Department of Medical Biotechnology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Medical Laboratory Sciences, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamid Reza Mirzaei
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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44
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Wang J, Zhang Y, Zhang T, Tan WT, Lambert F, Darmawan J, Huber R, Wan Y. RNA structure profiling at single-cell resolution reveals new determinants of cell identity. Nat Methods 2024; 21:411-422. [PMID: 38177506 PMCID: PMC10927541 DOI: 10.1038/s41592-023-02128-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: 02/08/2023] [Accepted: 11/10/2023] [Indexed: 01/06/2024]
Abstract
RNA structure is critical for multiple steps in gene regulation. However, how the structures of transcripts differ both within and between individual cells is unknown. Here we develop a SHAPE-inspired method called single-cell structure probing of RNA transcripts that enables simultaneous determination of transcript secondary structure and abundance at single-cell resolution. We apply single-cell structure probing of RNA transcripts to human embryonic stem cells and differentiating neurons. Remarkably, RNA structure is more homogeneous in human embryonic stem cells compared with neurons, with the greatest homogeneity found in coding regions. More extensive heterogeneity is found within 3' untranslated regions and is determined by specific RNA-binding proteins. Overall RNA structure profiles better discriminate cell type identity and differentiation stage than gene expression profiles alone. We further discover a cell-type variable region of 18S ribosomal RNA that is associated with cell cycle and translation control. Our method opens the door to the systematic characterization of RNA structure-function relationships at single-cell resolution.
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Affiliation(s)
- Jiaxu Wang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
| | - Yu Zhang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Tong Zhang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Wen Ting Tan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Finnlay Lambert
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
- Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Jefferson Darmawan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Roland Huber
- Bioinformatics Institute, A*STAR, Singapore, Singapore
| | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, Singapore.
- Department of Biochemistry, National University of Singapore, Singapore, Singapore.
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45
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Zhou G, Li T, Du J, Wu M, Lin D, Pu W, Zhang J, Gu Z. Harnessing HetHydrogel: A Universal Platform to Dropletize Single-Cell Multiomics. SMALL METHODS 2024:e2301631. [PMID: 38419597 DOI: 10.1002/smtd.202301631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/12/2024] [Indexed: 03/02/2024]
Abstract
A universal platform is developed for dropletizing single cell plate-based multiomic assays, consisting of three main pillars: a miniaturized open Heterogeneous Hydrogel reactor (abbreviated HetHydrogel) for multi-step biochemistry, its tunable permeability that allows Tn5 tagmentation, and single cell droplet barcoding. Through optimizing the HetHydrogel manufacturing procedure, the chemical composition, and cell permeation conditions, simultaneous high-throughput mitochondrial DNA genotyping and chromatin profiling at the single-cell level are demonstrated using a mixed-species experiment. This platform offers a powerful way to investigate the genotype-phenotype relationships of various mtDNA mutations in biological processes. The HetHydrogel platform is believed to have the potential to democratize droplet technologies, upgrading a whole range of plate-based single cell assays to high throughput format.
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Affiliation(s)
- Guoqiang Zhou
- Center for Mitochondrial Genetics and Health, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458, China
| | - Ting Li
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, 200438, China
| | - Jingjing Du
- Center for Mitochondrial Genetics and Health, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458, China
| | - Mengying Wu
- Center for Mitochondrial Genetics and Health, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458, China
| | - Deng Lin
- Center for Mitochondrial Genetics and Health, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458, China
| | - Weilin Pu
- Center for Mitochondrial Genetics and Health, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458, China
| | - Jingwei Zhang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, 200438, China
- Zhejiang Lab, Hangzhou, 310000, China
| | - Zhenglong Gu
- Center for Mitochondrial Genetics and Health, Greater Bay Area Institute of Precision Medicine (Guangzhou), Fudan University, Guangzhou, 511458, China
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46
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Zhang X, Marand AP, Yan H, Schmitz RJ. Massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.17.558155. [PMID: 37786710 PMCID: PMC10541611 DOI: 10.1101/2023.09.17.558155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called single-cell combinatorial fluidic indexing ATAC-sequencing ("scifi-ATAC-seq"), which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using a widely commercialized microfluidics platform (10X Genomics). With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples in a single emulsion reaction can be indexed, representing a ~20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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Yan H, Mendieta JP, Zhang X, Marand AP, Liang Y, Luo Z, Minow MAA, Roulé T, Wagner D, Tu X, Wang Y, Zhong S, Wessler SR, Schmitz RJ. Evolution of plant cell-type-specific cis -regulatory elements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.574753. [PMID: 38260561 PMCID: PMC10802394 DOI: 10.1101/2024.01.08.574753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Cis -regulatory elements (CREs) are critical in regulating gene expression, and yet our understanding of CRE evolution remains a challenge. Here, we constructed a comprehensive single-cell atlas of chromatin accessibility in Oryza sativa , integrating data from 104,029 nuclei representing 128 discrete cell states across nine distinct organs. We used comparative genomics to compare cell-type resolved chromatin accessibility between O. sativa and 57,552 nuclei from four additional grass species ( Zea mays, Sorghum bicolor, Panicum miliaceum , and Urochloa fusca ). Accessible chromatin regions (ACRs) had different levels of conservation depending on the degree of cell-type specificity. We found a complex relationship between ACRs with conserved noncoding sequences, cell-type specificity, conservation, and tissue-specific switching. Additionally, we found that epidermal ACRs were less conserved compared to other cell types, potentially indicating that more rapid regulatory evolution has occurred in the L1 epidermal layer of these species. Finally, we identified and characterized a conserved subset of ACRs that overlapped the repressive histone modification H3K27me3, implicating them as potentially critical silencer CREs maintained by evolution. Collectively, this comparative genomics approach highlights the dynamics of cell-type-specific CRE evolution in plants.
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48
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Wayman JA, Yang Z, Angerman E, Bonkowski E, Jurickova I, Chen X, Bejjani AT, Parks L, Parameswaran S, Miethke AG, VanDussen KL, Dhaliwal J, Weirauch MT, Kottyan LC, Denson LA, Miraldi ER. Accessible chromatin maps of inflammatory bowel disease intestine nominate cell-type mediators of genetic disease risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579678. [PMID: 38405748 PMCID: PMC10888857 DOI: 10.1101/2024.02.09.579678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Inflammatory Bowel Disease ( IBD ) is a chronic and often debilitating autoinflammatory condition, with an increasing incidence in children. Standard-of-care therapies lead to sustained transmural healing and clinical remission in fewer than one-third of patients. For children, TNFα inhibition remains the only FDA-approved biologic therapy, providing an even greater urgency to understanding mechanisms of response. Genome-wide association studies ( GWAS ) have identified 418 independent genetic risk loci contributing to IBD, yet the majority are noncoding and their mechanisms of action are difficult to decipher. If causal, they likely alter transcription factor ( TF ) binding and downstream gene expression in particular cell types and contexts. To bridge this knowledge gap, we built a novel resource: multiome-seq (tandem single-nuclei ( sn )RNA-seq and chromatin accessibility ( snATAC )-seq) of intestinal tissue from pediatric IBD patients, where anti-TNF response was defined by endoscopic healing. From the snATAC-seq data, we generated a first-time atlas of chromatin accessibility (putative regulatory elements) for diverse intestinal cell types in the context of IBD. For cell types/contexts mediating genetic risk, we reasoned that accessible chromatin will co-localize with genetic disease risk loci. We systematically tested for significant co-localization of our chromatin accessibility maps and risk variants for 758 GWAS traits. Globally, genetic risk variants for IBD, autoimmune and inflammatory diseases are enriched in accessible chromatin of immune populations, while other traits (e.g., colorectal cancer, metabolic) are enriched in epithelial and stromal populations. This resource opens new avenues to uncover the complex molecular and cellular mechanisms mediating genetic disease risk.
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49
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Taskiran II, Spanier KI, Dickmänken H, Kempynck N, Pančíková A, Ekşi EC, Hulselmans G, Ismail JN, Theunis K, Vandepoel R, Christiaens V, Mauduit D, Aerts S. Cell-type-directed design of synthetic enhancers. Nature 2024; 626:212-220. [PMID: 38086419 PMCID: PMC10830415 DOI: 10.1038/s41586-023-06936-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/05/2023] [Indexed: 01/19/2024]
Abstract
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.
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Affiliation(s)
- Ibrahim I Taskiran
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Katina I Spanier
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Hannah Dickmänken
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Niklas Kempynck
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Alexandra Pančíková
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB-KULeuven Center for Cancer Biology, Leuven, Belgium
| | - Eren Can Ekşi
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Joy N Ismail
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- UK Dementia Research Institute at Imperial College London, London, UK
| | - Koen Theunis
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Roel Vandepoel
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Valerie Christiaens
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - David Mauduit
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Stein Aerts
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium.
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
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Lu C, Wei Y, Abbas M, Agula H, Wang E, Meng Z, Zhang R. Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects. Int J Mol Sci 2024; 25:1479. [PMID: 38338756 PMCID: PMC10855595 DOI: 10.3390/ijms25031479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.
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Affiliation(s)
- Chao Lu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
- Key Laboratory of Herbage & Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Hasi Agula
- Key Laboratory of Herbage & Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Edwin Wang
- Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
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