1
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Yang X, Mann KK, Wu H, Ding J. scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration. Genome Biol 2024; 25:198. [PMID: 39075536 PMCID: PMC11285326 DOI: 10.1186/s13059-024-03338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.
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Affiliation(s)
- Xiuhui Yang
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Koren K Mann
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Hao Wu
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China.
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada.
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada.
- Mila-Quebec AI Institute, Montreal, QC, H2S 3H1, Canada.
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2
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Rhaman MS, Ali M, Ye W, Li B. Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae026. [PMID: 38996445 DOI: 10.1093/gpbjnl/qzae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 07/14/2024]
Abstract
Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.
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Affiliation(s)
- Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Muhammad Ali
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Wenxiu Ye
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Bosheng Li
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
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3
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Sun K, Liu X, Xu R, Liu C, Meng A, Lan X. Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq. Nat Cell Biol 2024; 26:1187-1199. [PMID: 38977847 DOI: 10.1038/s41556-024-01449-0] [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: 12/15/2022] [Accepted: 05/30/2024] [Indexed: 07/10/2024]
Abstract
Currently, the dynamic accessible elements that determine regulatory programs responsible for the unique identity and function of each cell type during zebrafish embryogenesis lack detailed study. Here we present SPATAC-seq: a split-pool ligation-based assay for transposase-accessible chromatin using sequencing. Using SPATAC-seq, we profiled chromatin accessibility in more than 800,000 individual nuclei across 20 developmental stages spanning the sphere stage to the early larval protruding mouth stage. Using this chromatin accessibility map, we identified 604 cell states and inferred their developmental relationships. We also identified 959,040 candidate cis-regulatory elements (cCREs) and delineated development-specific cCREs, as well as transcription factors defining diverse cell identities. Importantly, enhancer reporter assays confirmed that the majority of tested cCREs exhibited robust enhanced green fluorescent protein expression in restricted cell types or tissues. Finally, we explored gene regulatory programs that drive pigment and notochord cell differentiation. Our work provides a valuable open resource for exploring driver regulators of cell fate decisions in zebrafish embryogenesis.
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Affiliation(s)
- Keyong Sun
- School of Medicine, Tsinghua University, Beijing, China
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Tsinghua University, Beijing, China
| | - Xin Liu
- School of Life Sciences, Tsinghua University, Beijing, China
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China
| | - Runda Xu
- School of Medicine, Tsinghua University, Beijing, China
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China
| | - Chang Liu
- School of Medicine, Tsinghua University, Beijing, China
| | - Anming Meng
- School of Life Sciences, Tsinghua University, Beijing, China.
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China.
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing, China.
- Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, Tsinghua University, Beijing, China.
- Tsinghua University-Peking University Center for Life Sciences, Beijing, China.
- Ministry of Education Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
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4
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Gupta P, O’Neill H, Wolvetang E, Chatterjee A, Gupta I. Advances in single-cell long-read sequencing technologies. NAR Genom Bioinform 2024; 6:lqae047. [PMID: 38774511 PMCID: PMC11106032 DOI: 10.1093/nargab/lqae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/18/2024] [Accepted: 04/29/2024] [Indexed: 05/24/2024] Open
Abstract
With an increase in accuracy and throughput of long-read sequencing technologies, they are rapidly being assimilated into the single-cell sequencing pipelines. For transcriptome sequencing, these techniques provide RNA isoform-level information in addition to the gene expression profiles. Long-read sequencing technologies not only help in uncovering complex patterns of cell-type specific splicing, but also offer unprecedented insights into the origin of cellular complexity and thus potentially new avenues for drug development. Additionally, single-cell long-read DNA sequencing enables high-quality assemblies, structural variant detection, haplotype phasing, resolving high-complexity regions, and characterization of epigenetic modifications. Given that significant progress has primarily occurred in single-cell RNA isoform sequencing (scRiso-seq), this review will delve into these advancements in depth and highlight the practical considerations and operational challenges, particularly pertaining to downstream analysis. We also aim to offer a concise introduction to complementary technologies for single-cell sequencing of the genome, epigenome and epitranscriptome. We conclude by identifying certain key areas of innovation that may drive these technologies further and foster more widespread application in biomedical science.
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Affiliation(s)
- Pallavi Gupta
- University of Queensland – IIT Delhi Research Academy, Hauz Khas, New Delhi 110016, India
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD 4072, Australia
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Hannah O’Neill
- Department of Pathology, Dunedin School of Medicine, University of Otago, 58 Hanover Street, Dunedin 9054, New Zealand
| | - Ernst J Wolvetang
- Australian Institute of Bioengineering and Nanotechnology (AIBN), The University of Queensland, St Lucia, QLD 4072, Australia
| | - Aniruddha Chatterjee
- Department of Pathology, Dunedin School of Medicine, University of Otago, 58 Hanover Street, Dunedin 9054, New Zealand
| | - Ishaan Gupta
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
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5
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Su Y, He S, Chen Q, Zhang H, Huang C, Zhao Q, Pu Y, He X, Jiang L, Ma Y, Zhao Q. Integrative ATAC-seq and RNA-seq analysis of myogenic differentiation of ovine skeletal muscle satellite cell. Genomics 2024; 116:110851. [PMID: 38692440 DOI: 10.1016/j.ygeno.2024.110851] [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: 01/29/2024] [Revised: 04/01/2024] [Accepted: 04/28/2024] [Indexed: 05/03/2024]
Abstract
Skeletal muscle satellite cells (SMSCs) play an important role in regulating muscle growth and regeneration. Chromatin accessibility allows physical interactions that synergistically regulate gene expression through enhancers, promoters, insulators, and chromatin binding factors. However, the chromatin accessibility altas and its regulatory role in ovine myoblast differentiation is still unclear. Therefore, ATAC-seq and RNA-seq analysis were performed on ovine SMSCs at the proliferation stage (SCG) and differentiation stage (SCD). 17,460 DARs (differential accessibility regions) and 3732 DEGs (differentially expressed genes) were identified. Based on joint analysis of ATAC-seq and RNA-seq, we revealed that PI3K-Akt, TGF-β and other signaling pathways regulated SMSCs differentiation. We identified two novel candidate genes, FZD5 and MAP2K6, which may affect the proliferation and differentiation of SMSCs. Our data identify potential cis regulatory elements of ovine SMSCs. This study can provide a reference for exploring the mechanisms of the differentiation and regeneration of SMSCs in the future.
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Affiliation(s)
- Yingxiao Su
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China
| | - Siqi He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China; College of Animal Science, Shanxi Agricultural University, Taigu 030801, China
| | - Qian Chen
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China; College of Animal Science, Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Hechun Zhang
- Chaoyang Chaomu Breeding Farm Co., LTD, Chaoyang, Liaoning 122629, China
| | - Chang Huang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China; College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Qian Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China; College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Yabin Pu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China
| | - Xiaohong He
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China
| | - Lin Jiang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China
| | - Yuehui Ma
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China
| | - Qianjun Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193,China.
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6
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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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7
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Xu Q, Zhang Y, Xu W, Liu D, Jin W, Chen X, Hong N. The chromatin accessibility dynamics during cell fate specifications in zebrafish early embryogenesis. Nucleic Acids Res 2024; 52:3106-3120. [PMID: 38364856 PMCID: PMC11014328 DOI: 10.1093/nar/gkae095] [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: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/30/2024] [Indexed: 02/18/2024] Open
Abstract
Chromatin accessibility plays a critical role in the regulation of cell fate decisions. Although gene expression changes have been extensively profiled at the single-cell level during early embryogenesis, the dynamics of chromatin accessibility at cis-regulatory elements remain poorly studied. Here, we used a plate-based single-cell ATAC-seq method to profile the chromatin accessibility dynamics of over 10 000 nuclei from zebrafish embryos. We investigated several important time points immediately after zygotic genome activation (ZGA), covering key developmental stages up to dome. The results revealed key chromatin signatures in the first cell fate specifications when cells start to differentiate into enveloping layer (EVL) and yolk syncytial layer (YSL) cells. Finally, we uncovered many potential cell-type specific enhancers and transcription factor motifs that are important for the cell fate specifications.
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Affiliation(s)
- Qiushi Xu
- Harbin Institute of Technology, Harbin, China
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055 Guangdong, China
| | - Yunlong Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055 Guangdong, China
| | - Wei Xu
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangdong, China
| | - Dong Liu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055 Guangdong, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055 Guangdong, China
| | - Xi Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055 Guangdong, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055 Guangdong, China
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8
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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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9
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Goshisht MK. Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges. ACS OMEGA 2024; 9:9921-9945. [PMID: 38463314 PMCID: PMC10918679 DOI: 10.1021/acsomega.3c05913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 03/12/2024]
Abstract
Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.
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Affiliation(s)
- Manoj Kumar Goshisht
- Department of Chemistry, Natural and
Applied Sciences, University of Wisconsin—Green
Bay, Green
Bay, Wisconsin 54311-7001, United States
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10
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Li K, Wang B, Hu H. Research progress of SWI/SNF complex in breast cancer. Epigenetics Chromatin 2024; 17:4. [PMID: 38365747 PMCID: PMC10873968 DOI: 10.1186/s13072-024-00531-z] [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: 11/15/2023] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
In the past decade, numerous epigenetic mechanisms have been discovered to be associated with cancer. The mammalian SWI/SNF complex is an ATP-dependent chromatin remodeling complex whose mutations are associated with various malignancies including breast cancer. As the SWI/SNF complex has become one of the most commonly mutated complexes in cancer, targeting epigenetic mutations acquired during breast cancer progress is a potential means of improving clinical efficacy in treatment strategies. This article reviews the composition of the SWI/SNF complex, its main roles and research progress in breast cancer, and links these findings to the latest discoveries in cancer epigenomics to discuss the potential mechanisms and therapeutic potential of SWI/SNF in breast cancer.
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Affiliation(s)
- Kexuan Li
- School of Medicine, Southeast University, Nanjing, 210009, Jiangsu, China
| | - Baocai Wang
- Department of Surgery, TUM School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Haolin Hu
- Breast Center, Zhongda Hospital, School of Medicine, Southeast University, 87 Dingjiaqiao Road, Nanjing, 210009, Jiangsu, China.
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11
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Wang X, Wu X, Hong N, Jin W. Progress in single-cell multimodal sequencing and multi-omics data integration. Biophys Rev 2024; 16:13-28. [PMID: 38495443 PMCID: PMC10937857 DOI: 10.1007/s12551-023-01092-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.
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Affiliation(s)
- Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xinchao Wu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
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12
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Mihai IS, Chafle S, Henriksson J. Representing and extracting knowledge from single-cell data. Biophys Rev 2024; 16:29-56. [PMID: 38495441 PMCID: PMC10937862 DOI: 10.1007/s12551-023-01091-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 06/28/2023] [Indexed: 03/19/2024] Open
Abstract
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
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Affiliation(s)
- Ionut Sebastian Mihai
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
- Industrial Doctoral School, Umeå University, Umeå, Sweden
| | - Sarang Chafle
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Johan Henriksson
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
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13
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Chen G, Xu W, Long Z, Chong Y, Lin B, Jie Y. Single-cell Technologies Provide Novel Insights into Liver Physiology and Pathology. J Clin Transl Hepatol 2024; 12:79-90. [PMID: 38250462 PMCID: PMC10794276 DOI: 10.14218/jcth.2023.00224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 06/25/2023] [Accepted: 07/12/2023] [Indexed: 01/23/2024] Open
Abstract
The liver is the largest glandular organ in the body and has a unique distribution of cells and biomolecules. However, the treatment outcome of end-stage liver disease is extremely poor. Single-cell sequencing is a new advanced and powerful technique for identifying rare cell populations and biomolecules by analyzing the characteristics of gene expression between individual cells. These cells and biomolecules might be used as potential targets for immunotherapy of liver diseases and contribute to the development of precise individualized treatment. Compared to whole-tissue RNA sequencing, single-cell RNA sequencing (scRNA-seq) or other single-cell histological techniques have solved the problem of cell population heterogeneity and characterize molecular changes associated with liver diseases with higher accuracy and resolution. In this review, we comprehensively summarized single-cell approaches including transcriptomic, spatial transcriptomic, immunomic, proteomic, epigenomic, and multiomic technologies, and described their application in liver physiology and pathology. We also discussed advanced techniques and recent studies in the field of single-cell; our review might provide new insights into the pathophysiological mechanisms of the liver to achieve precise and individualized treatment of liver diseases.
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Affiliation(s)
| | | | - Zhicong Long
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yutian Chong
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Bingliang Lin
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yusheng Jie
- Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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14
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Meng Q, Wei L, Ma K, Shi M, Lin X, Ho JWK, Li Y, Zhang X. scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq. Brief Bioinform 2024; 25:bbae011. [PMID: 38324621 PMCID: PMC10849189 DOI: 10.1093/bib/bbae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/18/2023] [Accepted: 01/05/2024] [Indexed: 02/09/2024] Open
Abstract
Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.
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Affiliation(s)
- Qiuchen Meng
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Kun Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Ming Shi
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xinyi Lin
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Yinqing Li
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
- IDG-McGovern Institute for Brain Research, Center for Synthetic and Systems Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- MOE Key Lab of Bioinformatics & Bioinformatics Division BRNIST, Department of Automation, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China
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15
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Guo ZH, Wang YB, Wang S, Zhang Q, Huang DS. scCorrector: a robust method for integrating multi-study single-cell data. Brief Bioinform 2024; 25:bbad525. [PMID: 38271483 PMCID: PMC10810333 DOI: 10.1093/bib/bbad525] [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: 09/15/2023] [Revised: 11/12/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.
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Affiliation(s)
- Zhen-Hao Guo
- College of Electronics and Information Engineering, Tongji University, Shanghai 200000, China
| | - Yan-Bin Wang
- College of Computer Science and Technology, Zhejiang University 310027, China
| | - Siguo Wang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| | - Qinhu Zhang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
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16
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Yekelchyk M, Li X, Guenther S, Braun T. Single-Nucleus ATAC-seq for Mapping Chromatin Accessibility in Individual Cells of Murine Hearts. Methods Mol Biol 2024; 2752:245-257. [PMID: 38194039 DOI: 10.1007/978-1-0716-3621-3_16] [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] [Indexed: 01/10/2024]
Abstract
During the last decade a wide range of single-cell and single-nucleus next-generation sequencing techniques have been developed, which revolutionized detection of rare cell populations, enabling creation of comprehensive cell atlases of complex organs and tissues. State-of-the-art methods do not only allow classical transcriptomics of individual cells but also comprise a number of epigenetic approaches, including assessment of chromatin accessibility by single-nucleus Assay for Transposase Accessible Chromatin ATAC-seq (snATAC-seq). The snATAC-seq assay detects "open chromatin," a term for low nucleosome occupancy of genomic regions, which is a prerequisite for effective transcription factor binding. Information about open chromatin at the single-nucleus level helps to recognize epigenetic changes, sometimes before transcription of respective genes occurs. snATAC-seq detects cellular heterogeneity in otherwise still transcriptionally and/or morphologically homogeneous cell populations. Chromatin accessibility assays may be used to detect epigenetic changes in cardiac lineages during heart development, chromatin landscape changes during aging, and epigenetic alterations in heart diseases. Here, we provide an optimized protocol for snATAC-seq of murine hearts. We describe isolation of single nuclei from snap-frozen hearts, provide hints for preparation of libraries suitable for snATAC-seq next-generation sequencing (NGS) using the Chromium 10× platform, and give general recommendations for downstream analysis using conventional bioinformatic pipelines and packages. The protocol should serve as a beginner's guide to generate high-quality snATAC-seq datasets and to perform chromatin accessibility analysis of individual heart-derived cell nuclei.
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Affiliation(s)
- Michail Yekelchyk
- Department of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Xiang Li
- Department of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Stefan Guenther
- Department of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Thomas Braun
- Department of Cardiac Development and Remodelling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany.
- German Centre for Cardiovascular Research (DZHK), Partner site Rhein-Main, Frankfurt am Main, Germany.
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17
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Turkalj S, Jakobsen NA, Groom A, Radtke FA, Vyas P. A protocol for simultaneous high-sensitivity genotyping and chromatin accessibility profiling in single cells. STAR Protoc 2023; 4:102641. [PMID: 37897733 PMCID: PMC10641301 DOI: 10.1016/j.xpro.2023.102641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/19/2023] [Accepted: 09/22/2023] [Indexed: 10/30/2023] Open
Abstract
Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) resolves the heterogeneity of epigenetic states across cells but does not typically capture exonic mutations, which limits our knowledge of how somatic mutations alter chromatin landscapes. Here, we present a plate-based approach coupling high-sensitivity genotyping of genomic loci with high-content scATAC-seq libraries from the same single cells. We first describe steps for optimization of genotyping primers, followed by detailed guidance on the preparation of both scATAC-seq and single-cell genotyping libraries, fully automated on high-throughput liquid handling platforms. For complete details on the use and execution of this protocol, please refer to Turkalj, Jakobsen et al.1.
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Affiliation(s)
- Sven Turkalj
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Niels Asger Jakobsen
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Angus Groom
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Felix A Radtke
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Paresh Vyas
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
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18
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Smits JGA, Cunha DL, Amini M, Bertolin M, Laberthonnière C, Qu J, Owen N, Latta L, Seitz B, Roux LN, Stachon T, Ferrari S, Moosajee M, Aberdam D, Szentmary N, van Heeringen SJ, Zhou H. Identification of the regulatory circuit governing corneal epithelial fate determination and disease. PLoS Biol 2023; 21:e3002336. [PMID: 37856539 PMCID: PMC10586658 DOI: 10.1371/journal.pbio.3002336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 09/14/2023] [Indexed: 10/21/2023] Open
Abstract
The transparent corneal epithelium in the eye is maintained through the homeostasis regulated by limbal stem cells (LSCs), while the nontransparent epidermis relies on epidermal keratinocytes for renewal. Despite their cellular similarities, the precise cell fates of these two types of epithelial stem cells, which give rise to functionally distinct epithelia, remain unknown. We performed a multi-omics analysis of human LSCs from the cornea and keratinocytes from the epidermis and characterized their molecular signatures, highlighting their similarities and differences. Through gene regulatory network analyses, we identified shared and cell type-specific transcription factors (TFs) that define specific cell fates and established their regulatory hierarchy. Single-cell RNA-seq (scRNA-seq) analyses of the cornea and the epidermis confirmed these shared and cell type-specific TFs. Notably, the shared and LSC-specific TFs can cooperatively target genes associated with corneal opacity. Importantly, we discovered that FOSL2, a direct PAX6 target gene, is a novel candidate associated with corneal opacity, and it regulates genes implicated in corneal diseases. By characterizing molecular signatures, our study unveils the regulatory circuitry governing the LSC fate and its association with corneal opacity.
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Affiliation(s)
- Jos G. A. Smits
- Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
| | - Dulce Lima Cunha
- Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
| | - Maryam Amini
- Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research, Saarland University, Homburg/Saar, Germany
| | | | - Camille Laberthonnière
- Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
| | - Jieqiong Qu
- Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
- Department of Medical Microbiology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands
| | - Nicholas Owen
- Development, Ageing and Disease, UCL Institute of Ophthalmology, London, United Kingdom
| | - Lorenz Latta
- Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research, Saarland University, Homburg/Saar, Germany
- Department of Ophthalmology, Saarland University Medical Center, UKS, Homburg, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center, UKS, Homburg, Germany
| | | | - Tanja Stachon
- Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research, Saarland University, Homburg/Saar, Germany
| | | | - Mariya Moosajee
- Development, Ageing and Disease, UCL Institute of Ophthalmology, London, United Kingdom
- Department of Genetics, Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Daniel Aberdam
- INSERM U976, Paris, France
- Université de Paris, INSERM U1138, Centre des Cordeliers, Paris, France
| | - Nora Szentmary
- Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research, Saarland University, Homburg/Saar, Germany
| | - Simon J. van Heeringen
- Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
| | - Huiqing Zhou
- Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
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19
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Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24:695-713. [PMID: 37280296 PMCID: PMC10242609 DOI: 10.1038/s41580-023-00615-w] [Citation(s) in RCA: 114] [Impact Index Per Article: 114.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/08/2023]
Abstract
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
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Affiliation(s)
- Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
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20
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Li H, Zhang Z, Squires M, Chen X, Zhang X. scMultiSim: simulation of single cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions. RESEARCH SQUARE 2023:rs.3.rs-3301625. [PMID: 37790516 PMCID: PMC10543280 DOI: 10.21203/rs.3.rs-3301625/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, hile also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data, many of them were not benchmarked before due to the lack of proper tools. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.
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Affiliation(s)
- Hechen Li
- Georgia Institute of Technology, Atlanta, USA
| | - Ziqi Zhang
- Georgia Institute of Technology, Atlanta, USA
| | | | - Xi Chen
- Southern University of Science and Technology, Shenzhen, China
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21
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Feng W, Liu S, Deng Q, Fu S, Yang Y, Dai X, Wang S, Wang Y, Liu Y, Lin X, Pan X, Hao S, Yuan Y, Gu Y, Zhang X, Li H, Liu L, Liu C, Fei JF, Wei X. A scATAC-seq atlas of chromatin accessibility in axolotl brain regions. Sci Data 2023; 10:627. [PMID: 37709774 PMCID: PMC10502032 DOI: 10.1038/s41597-023-02533-0] [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: 05/18/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
Axolotl (Ambystoma mexicanum) is an excellent model for investigating regeneration, the interaction between regenerative and developmental processes, comparative genomics, and evolution. The brain, which serves as the material basis of consciousness, learning, memory, and behavior, is the most complex and advanced organ in axolotl. The modulation of transcription factors is a crucial aspect in determining the function of diverse regions within the brain. There is, however, no comprehensive understanding of the gene regulatory network of axolotl brain regions. Here, we utilized single-cell ATAC sequencing to generate the chromatin accessibility landscapes of 81,199 cells from the olfactory bulb, telencephalon, diencephalon and mesencephalon, hypothalamus and pituitary, and the rhombencephalon. Based on these data, we identified key transcription factors specific to distinct cell types and compared cell type functions across brain regions. Our results provide a foundation for comprehensive analysis of gene regulatory programs, which are valuable for future studies of axolotl brain development, regeneration, and evolution, as well as on the mechanisms underlying cell-type diversity in vertebrate brains.
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Affiliation(s)
- Weimin Feng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Shuai Liu
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
- BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450000, China
| | - Qiuting Deng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Sulei Fu
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510080, China
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education; Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Yunzhi Yang
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
- BGI College & Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450000, China
| | - Xi Dai
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Shuai Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Yijin Wang
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Yang Liu
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Xiumei Lin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Xiangyu Pan
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovsacular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Shijie Hao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Yue Yuan
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | - Ying Gu
- BGI-Shenzhen, Shenzhen, 518103, China
| | | | - Hanbo Li
- BGI-Shenzhen, Shenzhen, 518103, China
- BGI-Qingdao, Qingdao, 266555, China
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Qingdao, Qingdao, 266555, China
| | - Longqi Liu
- BGI-Hangzhou, Hangzhou, 310012, China
- BGI-Shenzhen, Shenzhen, 518103, China
| | | | - Ji-Feng Fei
- Department of Pathology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, 510080, China.
- School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
- School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China.
| | - Xiaoyu Wei
- BGI-Hangzhou, Hangzhou, 310012, China.
- BGI-Shenzhen, Shenzhen, 518103, China.
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22
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Mendieta JP, Sangra A, Yan H, Minow MAA, Schmitz RJ. Exploring plant cis-regulatory elements at single-cell resolution: overcoming biological and computational challenges to advance plant research. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:1486-1499. [PMID: 37309871 PMCID: PMC10598807 DOI: 10.1111/tpj.16351] [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: 04/20/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/14/2023]
Abstract
Cis-regulatory elements (CREs) are important sequences for gene expression and for plant biological processes such as development, evolution, domestication, and stress response. However, studying CREs in plant genomes has been challenging. The totipotent nature of plant cells, coupled with the inability to maintain plant cell types in culture and the inherent technical challenges posed by the cell wall has limited our understanding of how plant cell types acquire and maintain their identities and respond to the environment via CRE usage. Advances in single-cell epigenomics have revolutionized the field of identifying cell-type-specific CREs. These new technologies have the potential to significantly advance our understanding of plant CRE biology, and shed light on how the regulatory genome gives rise to diverse plant phenomena. However, there are significant biological and computational challenges associated with analyzing single-cell epigenomic datasets. In this review, we discuss the historical and foundational underpinnings of plant single-cell research, challenges, and common pitfalls in the analysis of plant single-cell epigenomic data, and highlight biological challenges unique to plants. Additionally, we discuss how the application of single-cell epigenomic data in various contexts stands to transform our understanding of the importance of CREs in plant genomes.
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Affiliation(s)
| | - Ankush Sangra
- Department of Genetics, University of Georgia, Athens, 30602, Georgia, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, 30602, Georgia, USA
| | - Mark A A Minow
- Department of Genetics, University of Georgia, Athens, 30602, Georgia, USA
| | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, 30602, Georgia, USA
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23
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CellBender removes technical artifacts from single-cell RNA sequencing data. Nat Methods 2023; 20:1285-1286. [PMID: 37592183 DOI: 10.1038/s41592-023-01946-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
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24
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Yang F, Nourse C, Helgason GV, Kirschner K. Unraveling Heterogeneity in the Aging Hematopoietic Stem Cell Compartment: An Insight From Single-cell Approaches. Hemasphere 2023; 7:e895. [PMID: 37304939 PMCID: PMC10256339 DOI: 10.1097/hs9.0000000000000895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 04/18/2023] [Indexed: 06/13/2023] Open
Abstract
Specific cell types and, therefore, organs respond differently during aging. This is also true for the hematopoietic system, where it has been demonstrated that hematopoietic stem cells alter a variety of features, such as their metabolism, and accumulate DNA damage, which can lead to clonal outgrowth over time. In addition, profound changes in the bone marrow microenvironment upon aging lead to senescence in certain cell types such as mesenchymal stem cells and result in increased inflammation. This heterogeneity makes it difficult to pinpoint the molecular drivers of organismal aging gained from bulk approaches, such as RNA sequencing. A better understanding of the heterogeneity underlying the aging process in the hematopoietic compartment is, therefore, needed. With the advances of single-cell technologies in recent years, it is now possible to address fundamental questions of aging. In this review, we discuss how single-cell approaches can and indeed are already being used to understand changes observed during aging in the hematopoietic compartment. We will touch on established and novel methods for flow cytometric detection, single-cell culture approaches, and single-cell omics.
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Affiliation(s)
- Fei Yang
- School of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom
- Cancer Research UK Beatson Institute, Glasgow, United Kingdom
| | - Craig Nourse
- School of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom
- Cancer Research UK Beatson Institute, Glasgow, United Kingdom
| | - G. Vignir Helgason
- School of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom
| | - Kristina Kirschner
- School of Cancer Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, United Kingdom
- Cancer Research UK Beatson Institute, Glasgow, United Kingdom
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25
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Meng Y, Carrelha J, Drissen R, Ren X, Zhang B, Gambardella A, Valletta S, Thongjuea S, Jacobsen SE, Nerlov C. Epigenetic programming defines haematopoietic stem cell fate restriction. Nat Cell Biol 2023; 25:812-822. [PMID: 37127714 DOI: 10.1038/s41556-023-01137-5] [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: 01/19/2022] [Accepted: 03/22/2023] [Indexed: 05/03/2023]
Abstract
Haematopoietic stem cells (HSCs) are multipotent, but individual HSCs can show restricted lineage output in vivo. Currently, the molecular mechanisms and physiological role of HSC fate restriction remain unknown. Here we show that lymphoid fate is epigenetically but not transcriptionally primed in HSCs. In multi-lineage HSCs that produce lymphocytes, lymphoid-specific upstream regulatory elements (LymUREs) but not promoters are preferentially accessible compared with platelet-biased HSCs that do not produce lymphoid cell types, providing transcriptionally silent lymphoid lineage priming. Runx3 is preferentially expressed in multi-lineage HSCs, and reinstating Runx3 expression increases LymURE accessibility and lymphoid-primed multipotent progenitor 4 (MPP4) output in old, platelet-biased HSCs. In contrast, platelet-biased HSCs show elevated levels of epigenetic platelet-lineage priming and give rise to MPP2 progenitors with molecular platelet bias. These MPP2 progenitors generate platelets with faster kinetics and through a more direct cellular pathway compared with MPP2s derived from multi-lineage HSCs. Epigenetic programming therefore predicts both fate restriction and differentiation kinetics in HSCs.
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Affiliation(s)
- Yiran Meng
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Joana Carrelha
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Haematopoietic Stem Cell Laboratory, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Roy Drissen
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Xiying Ren
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Bowen Zhang
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Adriana Gambardella
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Simona Valletta
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Supat Thongjuea
- MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Sten Eirik Jacobsen
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Haematopoietic Stem Cell Laboratory, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Cell and Molecular Biology, Wallenberg Institute for Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Stockholm, Sweden
| | - Claus Nerlov
- MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, UK.
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26
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Chen M, Jiang J, Hou J. Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications. Biomark Res 2023; 11:55. [PMID: 37259170 DOI: 10.1186/s40364-023-00502-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 05/12/2023] [Indexed: 06/02/2023] Open
Abstract
Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.
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Affiliation(s)
- Mengping Chen
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jinxing Jiang
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jian Hou
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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27
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Turkalj S, Jakobsen NA, Groom A, Metzner M, Riva SG, Gür ER, Usukhbayar B, Salazar MA, Hentges LD, Mickute G, Clark K, Sopp P, Davies JOJ, Hughes JR, Vyas P. GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells. Cell Stem Cell 2023; 30:722-740.e11. [PMID: 37146586 DOI: 10.1016/j.stem.2023.04.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 02/23/2023] [Accepted: 04/12/2023] [Indexed: 05/07/2023]
Abstract
Understanding clonal evolution and cancer development requires experimental approaches for characterizing the consequences of somatic mutations on gene regulation. However, no methods currently exist that efficiently link high-content chromatin accessibility with high-confidence genotyping in single cells. To address this, we developed Genotyping with the Assay for Transposase-Accessible Chromatin (GTAC), enabling accurate mutation detection at multiple amplified loci, coupled with robust chromatin accessibility readout. We applied GTAC to primary acute myeloid leukemia, obtaining high-quality chromatin accessibility profiles and clonal identities for multiple mutations in 88% of cells. We traced chromatin variation throughout clonal evolution, showing the restriction of different clones to distinct differentiation stages. Furthermore, we identified switches in transcription factor motif accessibility associated with a specific combination of driver mutations, which biased transformed progenitors toward a leukemia stem cell-like chromatin state. GTAC is a powerful tool to study clonal heterogeneity across a wide spectrum of pre-malignant and neoplastic conditions.
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Affiliation(s)
- Sven Turkalj
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Niels Asger Jakobsen
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Angus Groom
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Marlen Metzner
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Simone G Riva
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - E Ravza Gür
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Batchimeg Usukhbayar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Mirian Angulo Salazar
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Lance D Hentges
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Gerda Mickute
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Kevin Clark
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Paul Sopp
- Flow Cytometry Facility, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - James O J Davies
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Jim R Hughes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Paresh Vyas
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK; Oxford Centre for Haematology, NIHR Oxford Biomedical Research Centre, Oxford, UK; Department of Haematology, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
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28
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Ge Y, Chen X, Nan N, Bard J, Wu F, Yergeau D, Liu T, Wang J, Mu X. Key transcription factors influence the epigenetic landscape to regulate retinal cell differentiation. Nucleic Acids Res 2023; 51:2151-2176. [PMID: 36715342 PMCID: PMC10018358 DOI: 10.1093/nar/gkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/31/2023] Open
Abstract
How the diverse neural cell types emerge from multipotent neural progenitor cells during central nervous system development remains poorly understood. Recent scRNA-seq studies have delineated the developmental trajectories of individual neural cell types in many neural systems including the neural retina. Further understanding of the formation of neural cell diversity requires knowledge about how the epigenetic landscape shifts along individual cell lineages and how key transcription factors regulate these changes. In this study, we dissect the changes in the epigenetic landscape during early retinal cell differentiation by scATAC-seq and identify globally the enhancers, enriched motifs, and potential interacting transcription factors underlying the cell state/type specific gene expression in individual lineages. Using CUT&Tag, we further identify the enhancers bound directly by four key transcription factors, Otx2, Atoh7, Pou4f2 and Isl1, including those dependent on Atoh7, and uncover the sequential and combinatorial interactions of these factors with the epigenetic landscape to control gene expression along individual retinal cell lineages such as retinal ganglion cells (RGCs). Our results reveal a general paradigm in which transcription factors collaborate and compete to regulate the emergence of distinct retinal cell types such as RGCs from multipotent retinal progenitor cells (RPCs).
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Affiliation(s)
- Yichen Ge
- Department of Ophthalmology/Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Xushen Chen
- Department of Ophthalmology/Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Nan Nan
- Department of Ophthalmology/Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
- Department of Biostatistics, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
| | - Jonathan Bard
- New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY, USA
| | - Fuguo Wu
- Department of Ophthalmology/Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA
| | - Donald Yergeau
- New York State Center of Excellence in Bioinformatics and Life Sciences, University at Buffalo, Buffalo, NY, USA
| | - Tao Liu
- Department of Biostatistics & Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA
| | - Jie Wang
- Correspondence may also be addressed to Jie Wang.
| | - Xiuqian Mu
- To whom correspondence should be addressed. Tel: +1 716 881 7463; Fax: +1 716 887 2991;
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29
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Li H, Zhang Z, Squires M, Chen X, Zhang X. scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks. RESEARCH SQUARE 2023:rs.3.rs-2675530. [PMID: 36993284 PMCID: PMC10055660 DOI: 10.21203/rs.3.rs-2675530/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.
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Affiliation(s)
- Hechen Li
- Georgia Institute of Technology, Atlanta, USA
| | - Ziqi Zhang
- Georgia Institute of Technology, Atlanta, USA
| | | | - Xi Chen
- Southern University of Science and Technology, China
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30
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Riegel D, Romero-Fernández E, Simon M, Adenugba AR, Singer K, Mayr R, Weber F, Kleemann M, Imbusch CD, Kreutz M, Brors B, Ugele I, Werner JM, Siska PJ, Schmidl C. Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8 + T cells. Mol Cell 2023; 83:622-636.e10. [PMID: 36657444 DOI: 10.1016/j.molcel.2022.12.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/22/2022] [Accepted: 12/23/2022] [Indexed: 01/19/2023]
Abstract
Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8+ TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.
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Affiliation(s)
- Dania Riegel
- Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany
| | | | - Malte Simon
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany; Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | | | - Katrin Singer
- Department of Internal Medicine III, University Hospital Regensburg, 93053 Regensburg, Germany; Department of Otorhinolaryngology, University Hospital Regensburg, 93053 Regensburg, Germany
| | - Roman Mayr
- Department of Urology, Caritas St. Josef Medical Centre, University of Regensburg, 93053 Regensburg, Germany
| | - Florian Weber
- Institute of Pathology, University of Regensburg, 93053 Regensburg, Germany
| | - Mark Kleemann
- Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany
| | - Charles D Imbusch
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Marina Kreutz
- Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany; Department of Internal Medicine III, University Hospital Regensburg, 93053 Regensburg, Germany
| | - Benedikt Brors
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Ines Ugele
- Department of Otorhinolaryngology, University Hospital Regensburg, 93053 Regensburg, Germany
| | - Jens M Werner
- Department of Surgery, University Hospital Regensburg, 93053 Regensburg, Germany
| | - Peter J Siska
- Department of Internal Medicine III, University Hospital Regensburg, 93053 Regensburg, Germany
| | - Christian Schmidl
- Leibniz Institute for Immunotherapy (LIT), 93053 Regensburg, Germany.
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31
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Wang M, Li Q, Liu L. Factors and Methods for the Detection of Gene Expression Regulation. Biomolecules 2023; 13:biom13020304. [PMID: 36830673 PMCID: PMC9953580 DOI: 10.3390/biom13020304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/01/2023] [Accepted: 02/03/2023] [Indexed: 02/10/2023] Open
Abstract
Gene-expression regulation involves multiple processes and a range of regulatory factors. In this review, we describe the key factors that regulate gene expression, including transcription factors (TFs), chromatin accessibility, histone modifications, DNA methylation, and RNA modifications. In addition, we also describe methods that can be used to detect these regulatory factors.
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32
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Mishra S, Pandey N, Chawla S, Sharma M, Chandra O, Jha IP, SenGupta D, Natarajan KN, Kumar V. Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis. Genome Res 2023; 33:218-231. [PMID: 36653120 PMCID: PMC10069468 DOI: 10.1101/gr.277015.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 01/09/2023] [Indexed: 01/19/2023]
Abstract
The true benefits of large single-cell transcriptome and epigenome data sets can be realized only with the development of new approaches and search tools for annotating individual cells. Matching a single-cell epigenome profile to a large pool of reference cells remains a major challenge. Here, we present scEpiSearch, which enables searching, comparison, and independent classification of single-cell open-chromatin profiles against a large reference of single-cell expression and open-chromatin data sets. Across performance benchmarks, scEpiSearch outperformed multiple methods in accuracy of search and low-dimensional coembedding of single-cell profiles, irrespective of platforms and species. Here we also demonstrate the unconventional utilities of scEpiSearch by applying it on single-cell epigenome profiles of K562 cells and samples from patients with acute leukaemia to reveal different aspects of their heterogeneity, multipotent behavior, and dedifferentiated states. Applying scEpiSearch on our single-cell open-chromatin profiles from embryonic stem cells (ESCs), we identified ESC subpopulations with more activity and poising for endoplasmic reticulum stress and unfolded protein response. Thus, scEpiSearch solves the nontrivial problem of amalgamating information from a large pool of single cells to identify and study the regulatory states of cells using their single-cell epigenomes.
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Affiliation(s)
- Shreya Mishra
- Department for Computational Biology, IIIT Delhi 110020, India
| | - Neetesh Pandey
- Department for Computational Biology, IIIT Delhi 110020, India
| | - Smriti Chawla
- Department for Computational Biology, IIIT Delhi 110020, India
| | - Madhu Sharma
- Department for Computational Biology, IIIT Delhi 110020, India
| | - Omkar Chandra
- Department for Computational Biology, IIIT Delhi 110020, India
| | | | - Debarka SenGupta
- Department for Computational Biology, IIIT Delhi 110020, India.,Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane 4001, Australia
| | - Kedar Nath Natarajan
- DTU Bioengineering, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Vibhor Kumar
- Department for Computational Biology, IIIT Delhi 110020, India;
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33
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O'Connell BL, Nichols RV, Pokholok D, Thomas J, Acharya SN, Nishida A, Thornton CA, Co M, Fields AJ, Steemers FJ, Adey AC. Atlas-scale single-cell chromatin accessibility using nanowell-based combinatorial indexing. Genome Res 2023; 33:208-217. [PMID: 36792372 PMCID: PMC10069466 DOI: 10.1101/gr.276655.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 12/08/2022] [Indexed: 02/17/2023]
Abstract
Here we present advancements in single-cell combinatorial indexed Assay for Transposase Accessible Chromatin (sciATAC) to measure chromatin accessibility that leverage nanowell chips to achieve atlas-scale cell throughput (>105 cells) at low cost. The platform leverages the core of the sciATAC workflow where multiple indexed tagmentation reactions are performed, followed by pooling and distribution to a second set of reaction wells for polymerase chain reaction (PCR)-based indexing. In this work, we instead leverage a chip containing 5184 nanowells at the PCR stage of indexing, enabling a 52-fold improvement in scale and reduction in per-cell preparation costs. We detail three variants that balance cell throughput and depth of coverage, and apply these methods to banked mouse brain tissue, producing maps of cell types as well as neuronal subtypes that include integration with existing single-cell Assay for Transposase Accessible Chromatin (scATAC) and scRNA-seq data sets. Our optimized workflow achieves a high fraction of reads that fall within called peaks (>80%) and low cell doublet rates. The high cell coverage technique produces high unique reads per cell, while retaining high enrichment for open chromatin regions, enabling the assessment of >70,000 unique accessible loci on average for each cell profiled. When compared to current methods in the field, our technique provides similar or superior per-cell information with very low levels of cell-to-cell cross talk, and achieves this at a cost point much lower than existing assays.
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Affiliation(s)
- Brendan L O'Connell
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | - Ruth V Nichols
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | | | | | - Sonia N Acharya
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | - Andrew Nishida
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | - Casey A Thornton
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | - Marissa Co
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | - Andrew J Fields
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA
| | | | - Andrew C Adey
- Oregon Health & Science University, Department of Molecular and Medical Genetics, Portland, Oregon 97239, USA; .,Oregon Health & Science University, Knight Cancer Institute, Portland, Oregon 97239, USA.,Oregon Health & Science University, Cancer Early Detection Advanced Research Center, Portland, Oregon 97239, USA.,Oregon Health & Science University, Knight Cardiovascular Institute, Portland, Oregon 97239, USA
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34
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Cai S, Hu B, Wang X, Liu T, Lin Z, Tong X, Xu R, Chen M, Duo T, Zhu Q, Liang Z, Li E, Chen Y, Li J, Liu X, Mo D. Integrative single-cell RNA-seq and ATAC-seq analysis of myogenic differentiation in pig. BMC Biol 2023; 21:19. [PMID: 36726129 PMCID: PMC9893630 DOI: 10.1186/s12915-023-01519-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/18/2023] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Skeletal muscle development is a multistep process whose understanding is central in a broad range of fields and applications, from the potential medical value to human society, to its economic value associated with improvement of agricultural animals. Skeletal muscle initiates in the somites, with muscle precursor cells generated in the dermomyotome and dermomyotome-derived myotome before muscle differentiation ensues, a developmentally regulated process that is well characterized in model organisms. However, the regulation of skeletal muscle ontogeny during embryonic development remains poorly defined in farm animals, for instance in pig. Here, we profiled gene expression and chromatin accessibility in developing pig somites and myotomes at single-cell resolution. RESULTS We identified myogenic cells and other cell types and constructed a differentiation trajectory of pig skeletal muscle ontogeny. Along this trajectory, the dynamic changes in gene expression and chromatin accessibility coincided with the activities of distinct cell type-specific transcription factors. Some novel genes upregulated along the differentiation trajectory showed higher expression levels in muscular dystrophy mice than that in healthy mice, suggesting their involvement in myogenesis. Integrative analysis of chromatin accessibility, gene expression data, and in vitro experiments identified EGR1 and RHOB as critical regulators of pig embryonic myogenesis. CONCLUSIONS Collectively, our results enhance our understanding of the molecular and cellular dynamics in pig embryonic myogenesis and offer a high-quality resource for the further study of pig skeletal muscle development and human muscle disease.
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Affiliation(s)
- Shufang Cai
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
- Guangdong Key Laboratory of Animal Breeding and Nutrition, State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640 Guangdong China
| | - Bin Hu
- Guangdong Key Laboratory of Animal Breeding and Nutrition, State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640 Guangdong China
| | - Xiaoyu Wang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Tongni Liu
- Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4 Canada
| | - Zhuhu Lin
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Xian Tong
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Rong Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Meilin Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Tianqi Duo
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Qi Zhu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Ziyun Liang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Enru Li
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Jianhao Li
- Guangdong Key Laboratory of Animal Breeding and Nutrition, State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640 Guangdong China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
| | - Delin Mo
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510006 Guangdong China
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Yu Q, Liu X, Fang J, Wu H, Guo C, Zhang W, Liu N, Jiang C, Sha Q, Yuan X, Wang Z, Qu K. Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution. SCIENCE ADVANCES 2023; 9:eadd2175. [PMID: 36696508 PMCID: PMC9876548 DOI: 10.1126/sciadv.add2175] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Although mitotic chromosomes are highly compacted and transcriptionally inert, some active chromatin features are retained during mitosis to ensure the proper postmitotic reestablishment of maternal transcriptional programs, a phenomenon termed "mitotic bookmarking." However, the dynamics and regulation of mitotic bookmarking have not been systemically surveyed. Using single-cell transposase-accessible chromatin sequencing (scATAC-seq), we examined 6538 mitotic L02 human liver cells of variable stages and found that chromatin accessibility remained changing throughout cell division, with a constant decrease until metaphase and a gradual increase as chromosomes segregated. In particular, a subset of chromatin regions were identified to remain open throughout mitosis, and genes associated with these bookmarked regions are primarily linked to rapid reactivation upon mitotic exit. We also demonstrated that nuclear transcription factor Y subunit α (NF-YA) preferentially occupied bookmarked regions and contributed to transcriptional reactivation after mitosis. Our study uncovers the dynamic and regulatory blueprint of mitotic bookmarking.
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Affiliation(s)
- Qiaoni Yu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230021, China
| | - Xu Liu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
| | - Jingwen Fang
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- HanGene Biotech, Xiaoshan Innovation Polis, Hangzhou, Zhejiang 311200, China
| | - Huihui Wu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
| | - Chuang Guo
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Wen Zhang
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Nianping Liu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Chen Jiang
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Qing Sha
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Xiao Yuan
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
| | - Zhikai Wang
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Kun Qu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230021, China
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
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Zhang Z, Sun H, Mariappan R, Chen X, Chen X, Jain MS, Efremova M, Teichmann SA, Rajan V, Zhang X. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nat Commun 2023; 14:384. [PMID: 36693837 PMCID: PMC9873790 DOI: 10.1038/s41467-023-36066-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023] Open
Abstract
Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.
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Affiliation(s)
- Ziqi Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haoran Sun
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ragunathan Mariappan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
| | - Xi Chen
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Xinyu Chen
- Bioengineering Program, Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | | | - Vaibhav Rajan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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Li Z, Gao E, Zhou J, Han W, Xu X, Gao X. Applications of deep learning in understanding gene regulation. CELL REPORTS METHODS 2023; 3:100384. [PMID: 36814848 PMCID: PMC9939384 DOI: 10.1016/j.crmeth.2022.100384] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.
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Affiliation(s)
- Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Elva Gao
- The KAUST School, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
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Kong S, Li R, Tian Y, Zhang Y, Lu Y, Ou Q, Gao P, Li K, Zhang Y. Single-cell omics: A new direction for functional genetic research in human diseases and animal models. Front Genet 2023; 13:1100016. [PMID: 36685871 PMCID: PMC9846559 DOI: 10.3389/fgene.2022.1100016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023] Open
Abstract
Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.
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Affiliation(s)
- Siyuan Kong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
| | - Rongrong Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yunhan Tian
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Yaqiu Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yuhui Lu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qiaoer Ou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Peiwen Gao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yubo Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,College of Life Science and Engineering, Foshan University, Foshan, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
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Liu C, Wang L, Liu Z. Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss. BMC Bioinformatics 2023; 24:5. [PMID: 36600199 PMCID: PMC9812356 DOI: 10.1186/s12859-022-05126-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. RESULTS By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. CONCLUSIONS MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.
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Affiliation(s)
- Chaozhong Liu
- grid.39382.330000 0001 2160 926XGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA
| | - Linhua Wang
- grid.39382.330000 0001 2160 926XGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA
| | - Zhandong Liu
- grid.416975.80000 0001 2200 2638Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, USA ,grid.39382.330000 0001 2160 926XDepartment of Pediatrics, Baylor College of Medicine, Houston, USA
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Yekelchyk M, Guenther S, Braun T. Assay for Transposase-Accessible Chromatin Using Sequencing of Freshly Isolated Muscle Stem Cells. Methods Mol Biol 2023; 2640:397-412. [PMID: 36995609 DOI: 10.1007/978-1-0716-3036-5_27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
Actively transcribed genes harbor cis-regulatory modules with comparatively low nucleosome occupancy and few high-order structures (="open chromatin"), whereas non-transcribed genes are characterized by high nucleosome density and extensive interactions between nucleosomes (="closed chromatin"), preventing transcription factor binding. Knowledge about chromatin accessibility is crucial to understand gene regulatory networks determining cellular decisions. Several techniques are available to map chromatin accessibility, among which the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is one of the most popular. ATAC-seq is based on a straightforward and robust protocol but requires adjustments for different cell types. Here, we describe an optimized protocol for ATAC-seq of freshly isolated murine muscle stem cells. We provide details for the isolation of MuSC, tagmentation, library amplification, double-sided SPRI bead cleanup, and library quality assessment and give recommendations for sequencing parameters and downstream analysis. The protocol should facilitate generation of high-quality data sets of chromatin accessibility in MuSCs, even for newcomers to the field.
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Affiliation(s)
- Michail Yekelchyk
- Department of Cardiac Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Rhein-Main, Frankfurt am Main, Germany
| | - Stefan Guenther
- Department of Cardiac Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
- German Centre for Cardiovascular Research (DZHK), Partner site Rhein-Main, Frankfurt am Main, Germany
| | - Thomas Braun
- Department of Cardiac Development and Remodeling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany.
- German Centre for Cardiovascular Research (DZHK), Partner site Rhein-Main, Frankfurt am Main, Germany.
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Preissl S, Gaulton KJ, Ren B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat Rev Genet 2023; 24:21-43. [PMID: 35840754 PMCID: PMC9771884 DOI: 10.1038/s41576-022-00509-1] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2022] [Indexed: 12/24/2022]
Abstract
Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.
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Affiliation(s)
- Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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Abstract
Single-cell studies are enabling our understanding of the molecular processes of normal cell development and the onset of several pathologies. For instance, single-cell RNA sequencing (scRNA-Seq) measures the transcriptome-wide gene expression at a single-cell resolution, allowing for studying the heterogeneity among the cells of the same population and revealing complex and rare cell populations. On the other hand, single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-Seq) can be used to define transcriptional and epigenetic changes by analyzing the chromatin accessibility at the single-cell level. However, the integration of multi-omics data still remains one of the most difficult tasks in bioinformatics. In this chapter, we focus on the combination of scRNA-Seq and scATACSeq data to perform an integrative analysis of the single-cell transcriptome and chromatin accessibility of human fetal progenitors.
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Cao K, Gong Q, Hong Y, Wan L. A unified computational framework for single-cell data integration with optimal transport. Nat Commun 2022; 13:7419. [PMID: 36456571 PMCID: PMC9715710 DOI: 10.1038/s41467-022-35094-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 11/18/2022] [Indexed: 12/05/2022] Open
Abstract
Single-cell data integration can provide a comprehensive molecular view of cells. However, how to integrate heterogeneous single-cell multi-omics as well as spatially resolved transcriptomic data remains a major challenge. Here we introduce uniPort, a unified single-cell data integration framework that combines a coupled variational autoencoder (coupled-VAE) and minibatch unbalanced optimal transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration to handle the heterogeneity across datasets, and it is scalable to large-scale datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for gene imputation across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute heterogeneous spatial transcriptomic data using an optimal transport plan, instead of embedding latent space. We demonstrate the capability of uniPort by applying it to integrate a variety of datasets, including single-cell transcriptomics, chromatin accessibility, and spatially resolved transcriptomic data.
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Affiliation(s)
- Kai Cao
- grid.484479.2LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China ,grid.410726.60000 0004 1797 8419School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Qiyu Gong
- grid.16821.3c0000 0004 0368 8293Shanghai Institute of Immunology, Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yiguang Hong
- grid.24516.340000000123704535Department of Control Science and Engineering, Tongji University, Shanghai, China
| | - Lin Wan
- grid.484479.2LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China ,grid.410726.60000 0004 1797 8419School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
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Duan H, Li F, Shang J, Liu J, Li Y, Liu X. scVAEBGM: Clustering Analysis of Single-Cell ATAC-seq Data Using a Deep Generative Model. Interdiscip Sci 2022; 14:917-928. [PMID: 35939233 DOI: 10.1007/s12539-022-00536-w] [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: 04/13/2022] [Revised: 07/15/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
A surge in research has occurred because of current developments in single-cell technologies. Above all, single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) is a popular approach of analyzing chromatin accessibility differences at the level of single cell, either within or between groups. As a result, it is critical to examine cell heterogeneity at a previously unseen level and to identify both recognized and unknown cell types. However, with the ever-increasing number of cells engendered by technological development and the characteristics of the data, such as high noise, sparsity and dimension, challenges in distinguishing cell types have emerged. We propose scVAEBGM, which integrates a Variational Autoencoder (VAE) with a Bayesian Gaussian-mixture model (BGM) to process and analyze scATAC-seq data. This method combines and takes benefits of a Bayesian Gaussian mixture model to estimate the number of cell types without determining the cluster number in a beforehand. In other words, the size of the clusters is inferred from the data, thus avoiding biases introduced by subjective assessments when manually determining the size of the clusters. Additionally, the method is more robust to noise and can better represent single-cell data in lower dimensions. We also create a further clustering strategy. It is indicated by experiments that further clustering based on the already completed clustering can improve the clustering accuracy again. We test on six public datasets, and scVAEBGM outperforms various dimension reduction baselines. In downstream applications, scVAEBGM can reveal biological cell types.
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Affiliation(s)
- Hongyu Duan
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China
| | - Jinxing Liu
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China
| | - Yan Li
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan, 250031, Shandong, China
| | - Xikui Liu
- Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan, 250031, Shandong, China
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45
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PPARγ lipodystrophy mutants reveal intermolecular interactions required for enhancer activation. Nat Commun 2022; 13:7090. [PMID: 36402763 PMCID: PMC9675755 DOI: 10.1038/s41467-022-34766-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 11/07/2022] [Indexed: 11/21/2022] Open
Abstract
Peroxisome proliferator-activated receptor γ (PPARγ) is the master regulator of adipocyte differentiation, and mutations that interfere with its function cause lipodystrophy. PPARγ is a highly modular protein, and structural studies indicate that PPARγ domains engage in several intra- and inter-molecular interactions. How these interactions modulate PPARγ's ability to activate target genes in a cellular context is currently poorly understood. Here we take advantage of two previously uncharacterized lipodystrophy mutations, R212Q and E379K, that are predicted to interfere with the interaction of the hinge of PPARγ with DNA and with the interaction of PPARγ ligand binding domain (LBD) with the DNA-binding domain (DBD) of the retinoid X receptor, respectively. Using biochemical and genome-wide approaches we show that these mutations impair PPARγ function on an overlapping subset of target enhancers. The hinge region-DNA interaction appears mostly important for binding and remodelling of target enhancers in inaccessible chromatin, whereas the PPARγ-LBD:RXR-DBD interface stabilizes the PPARγ:RXR:DNA ternary complex. Our data demonstrate how in-depth analyses of lipodystrophy mutants can unravel molecular mechanisms of PPARγ function.
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Highly sensitive single-cell chromatin accessibility assay and transcriptome coassay with METATAC. Proc Natl Acad Sci U S A 2022; 119:e2206450119. [PMID: 36161934 PMCID: PMC9546615 DOI: 10.1073/pnas.2206450119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The thriving field of single-cell genomics allows researchers to dissect the complexity and heterogeneity of tissues at single-cell resolution at large scale, involving transcriptome and epigenome. However, single-cell chromatin accessibility profiling methods exhibit low sensitivity. Here, we increased accessible chromatin detection sensitivity in single cells with METATAC, a single-cell ATAC-seq technique, with the help of META amplification strategy and other biochemical modifications. METATAC reached the highest accessible chromatin region detection efficiency compared with existing techniques, allowing more accurate cis-regulatory element coaccessibility measurement and allele-specific chromatin accessibility analysis in complex tissue samples. In combination with a high-resolution single-cell RNA sequencing assay, we further developed a high-sensitivity joint single-cell ATAC–RNA strategy, which helps us to better resolve gene regulatory programs. Recent advances in single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) and its coassays have transformed the field of single-cell epigenomics and transcriptomics. However, the low detection efficiency of current methods has limited our understanding of the true complexity of chromatin accessibility and its relationship with gene expression in single cells. Here, we report a high-sensitivity scATAC-seq method, termed multiplexed end-tagging amplification of transposase accessible chromatin (METATAC), which detects a large number of accessible sites per cell and is compatible with automation. Our high detectability and statistical framework allowed precise linking of enhancers to promoters without merging single cells. We systematically investigated allele-specific accessibility in the mouse cerebral cortex, revealing allele-specific accessibility of promotors of certain imprinted genes but biallelic accessibility of their enhancers. Finally, we combined METATAC with our high-sensitivity single-cell RNA sequencing (scRNA-seq) method, multiple annealing and looping based amplification cycles for digital transcriptomics (MALBAC-DT), to develop a joint ATAC–RNA assay, termed METATAC and MALBAC-DT coassay by sequencing (M2C-seq). M2C-seq achieved significant improvements for both ATAC and RNA compared with previous methods, with consistent performance across cell lines and early mouse embryos.
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Xu W, Yang W, Zhang Y, Chen Y, Hong N, Zhang Q, Wang X, Hu Y, Song K, Jin W, Chen X. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods 2022; 19:1243-1249. [PMID: 36109677 DOI: 10.1038/s41592-022-01601-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/01/2022] [Indexed: 11/09/2022]
Abstract
Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA-DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.
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Affiliation(s)
- Wei Xu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Weilong Yang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yunlong Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yawen Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Qian Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yukun Hu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Kun Song
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
| | - Xi Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
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Im NG, Guillaumet-Adkins A, Wal M, Rogers AJ, Frede J, Havig CC, Yang J, Anand P, Stegmann SK, Waldschmidt JM, Sotudeh N, Niu L, Voisine J, Schweiger MR, Grassberger C, Lohr JG, Knoechel B. Regulatory Programs of B-cell Activation and Germinal Center Reaction Allow B-ALL Escape from CD19 CAR T-cell Therapy. Cancer Immunol Res 2022; 10:1055-1068. [PMID: 35759797 PMCID: PMC9444959 DOI: 10.1158/2326-6066.cir-21-0626] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 04/13/2022] [Accepted: 06/23/2022] [Indexed: 11/16/2022]
Abstract
Chimeric antigen receptor (CAR) T-cell therapy has led to tremendous successes in the treatment of B-cell malignancies. However, a large fraction of treated patients relapse, often with disease expressing reduced levels of the target antigen. Here, we report that exposing CD19+ B-cell acute lymphoblastic leukemia (B-ALL) cells to CD19 CAR T cells reduced CD19 expression within hours. Initially, CD19 CAR T cells caused clustering of CD19 at the T cell-leukemia cell interface followed by CD19 internalization and decreased CD19 surface expression on the B-ALL cells. CD19 expression was then repressed by transcriptional rewiring. Using single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing, we demonstrated that a subset of refractory CD19low cells sustained decreased CD19 expression through transcriptional programs of physiologic B-cell activation and germinal center reaction. Inhibiting B-cell activation programs with the Bruton's tyrosine kinase inhibitor ibrutinib increased the cytotoxicity of CD19 CAR T cells without affecting CAR T-cell viability. These results demonstrate transcriptional plasticity as an underlying mechanism of escape from CAR T cells and highlight the importance of combining CAR T-cell therapy with targeted therapies that aim to overcome this plasticity. See related Spotlight by Zhao and Melenhorst, p. 1040.
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Affiliation(s)
- Nam Gyu Im
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Institute for Translational Epigenetics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Amy Guillaumet-Adkins
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Megha Wal
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anna J. Rogers
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Julia Frede
- Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Claire C. Havig
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jing Yang
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Praveen Anand
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Johannes M. Waldschmidt
- Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Noori Sotudeh
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leili Niu
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jordan Voisine
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Michal R. Schweiger
- Institute for Translational Epigenetics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Clemens Grassberger
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jens G. Lohr
- Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Corresponding Authors: Birgit Knoechel, MD, PhD, Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2072; ., Jens G. Lohr, MD, PhD, Division of Hematologic Neoplasia, Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2069;
| | - Birgit Knoechel
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Division of Hematology/Oncology, Department of Medicine, Boston Children’s Hospital, MA, USA.,Corresponding Authors: Birgit Knoechel, MD, PhD, Department of Pediatric Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2072; ., Jens G. Lohr, MD, PhD, Division of Hematologic Neoplasia, Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA; Phone: 617-632-2069;
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Shi P, Nie Y, Yang J, Zhang W, Tang Z, Xu J. Fundamental and practical approaches for single-cell ATAC-seq analysis. ABIOTECH 2022; 3:212-223. [PMID: 36313930 PMCID: PMC9590475 DOI: 10.1007/s42994-022-00082-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/07/2022] [Indexed: 11/28/2022]
Abstract
Assays for transposase-accessible chromatin through high-throughput sequencing (ATAC-seq) are effective tools in the study of genome-wide chromatin accessibility landscapes. With the rapid development of single-cell technology, open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches. The application of scATAC-seq has become as popular as that of scRNA-seq. However, owing to the nature of scATAC-seq data, which are sparse and noisy, processing the data requires different methodologies and empirical experience. This review presents a practical guide for processing scATAC-seq data, from quality evaluation to downstream analysis, for various applications. In addition to the epigenomic profiling from scATAC-seq, we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage. We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.
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Affiliation(s)
- Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Yage Nie
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jiawen Yang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Weixing Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Zhongjie Tang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
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50
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Meneri M, Bonato S, Gagliardi D, Comi GP, Corti S. New Insights into Cerebral Vessel Disease Landscapes at Single-Cell Resolution: Pathogenetic and Therapeutic Perspectives. Biomedicines 2022; 10:biomedicines10071693. [PMID: 35884997 PMCID: PMC9313091 DOI: 10.3390/biomedicines10071693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 07/03/2022] [Accepted: 07/11/2022] [Indexed: 11/19/2022] Open
Abstract
Cerebrovascular diseases are a leading cause of death and disability globally. The development of new therapeutic targets for cerebrovascular diseases (e.g., ischemic, and hemorrhagic stroke, vascular dementia) is limited by a lack of knowledge of the cellular and molecular biology of health and disease conditions and the factors that cause injury to cerebrovascular structures. Here, we describe the role of advances in omics technology, particularly RNA sequencing, in studying high-dimensional, multifaceted profiles of thousands of individual blood and vessel cells at single-cell resolution. This analysis enables the dissection of the heterogeneity of diseased cerebral vessels and their atherosclerotic plaques, including the microenvironment, cell evolutionary trajectory, and immune response pathway. In animal models, RNA sequencing permits the tracking of individual cells (including immunological, endothelial, and vascular smooth muscle cells) that compose atherosclerotic plaques and their alteration under experimental settings such as phenotypic transition. We describe how single-cell RNA transcriptomics in humans allows mapping to the molecular and cellular levels of atherosclerotic plaques in cerebral arteries, tracking individual lymphocytes and macrophages, and how these data can aid in identifying novel immune mechanisms that could be exploited as therapeutic targets for cerebrovascular diseases. Single-cell multi-omics approaches will likely provide the unprecedented resolution and depth of data needed to generate clinically relevant cellular and molecular signatures for the precise treatment of cerebrovascular diseases.
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Affiliation(s)
- Megi Meneri
- Dino Ferrari Centre, Department of Pathophysiology and Transplantation (DEPT), University of Milan, 20122 Milan, Italy; (M.M.); (D.G.); (G.P.C.)
- Neurology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Sara Bonato
- Stroke Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy;
| | - Delia Gagliardi
- Dino Ferrari Centre, Department of Pathophysiology and Transplantation (DEPT), University of Milan, 20122 Milan, Italy; (M.M.); (D.G.); (G.P.C.)
- Neurology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Giacomo P. Comi
- Dino Ferrari Centre, Department of Pathophysiology and Transplantation (DEPT), University of Milan, 20122 Milan, Italy; (M.M.); (D.G.); (G.P.C.)
- Neuromuscular and Rare Diseases Unit, Department of Neuroscience, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Stefania Corti
- Dino Ferrari Centre, Department of Pathophysiology and Transplantation (DEPT), University of Milan, 20122 Milan, Italy; (M.M.); (D.G.); (G.P.C.)
- Neurology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, 20122 Milan, Italy
- Correspondence:
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