51
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Wang X, Lian Q, Dong H, Xu S, Su Y, Wu X. Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae014. [PMID: 39049508 PMCID: PMC11423854 DOI: 10.1093/gpbjnl/qzae014] [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: 05/01/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 07/27/2024]
Abstract
Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.
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Affiliation(s)
- Xi Wang
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Qiwei Lian
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Haoyu Dong
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
| | - Shuo Xu
- Department of Automation, Xiamen University, Xiamen 361005, China
| | - Yaru Su
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
| | - Xiaohui Wu
- Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China
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52
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Shwab EK, Gingerich DC, Man Z, Gamache J, Garrett ME, Crawford GE, Ashley-Koch AE, Serrano GE, Beach TG, Lutz MW, Chiba-Falek O. Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks. Acta Neuropathol Commun 2024; 12:111. [PMID: 38956662 PMCID: PMC11218415 DOI: 10.1186/s40478-024-01803-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 05/27/2024] [Indexed: 07/04/2024] Open
Abstract
The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.
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Affiliation(s)
- E Keats Shwab
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Daniel C Gingerich
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Zhaohui Man
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Julia Gamache
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA
- Division of Medical Genetics, Department of Pediatrics, Duke University Medical Center, Durham, NC, 27708, USA
- Center for Advanced Genomic Technologies, Duke University Medical Center, Durham, NC, 27708, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, 27701, USA
- Department of Medicine, Duke University Medical Center, Durham, NC, 27708, USA
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Michael W Lutz
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA
| | - Ornit Chiba-Falek
- Division of Translational Brain Sciences, Department of Neurology, Duke University School of Medicine, Duke University Medical Center, Durham, NC, 27710, USA.
- Center for Genomic and Computational Biology, Duke University Medical Center, Durham, NC, 27708, USA.
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53
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Sun K, Liu X, Lan X. A single-cell atlas of chromatin accessibility in mouse organogenesis. Nat Cell Biol 2024; 26:1200-1211. [PMID: 38977846 DOI: 10.1038/s41556-024-01435-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/29/2024] [Indexed: 07/10/2024]
Abstract
Organogenesis is a highly complex and precisely regulated process. Here we profiled the chromatin accessibility in >350,000 cells derived from 13 mouse embryos at four developmental stages from embryonic day (E) 10.5 to E13.5 by SPATAC-seq in a single experiment. The resulting atlas revealed the status of 830,873 candidate cis-regulatory elements in 43 major cell types. By integrating the chromatin accessibility atlas with the previous transcriptomic dataset, we characterized cis-regulatory sequences and transcription factors associated with cell fate commitment, such as Nr5a2 in the development of gastrointestinal tract, which was preliminarily supported by the in vivo experiment in zebrafish. Finally, we integrated this atlas with the previous single-cell chromatin accessibility dataset from 13 adult mouse tissues to delineate the developmental stage-specific gene regulatory programmes within and across different cell types and identify potential molecular switches throughout lineage development. This comprehensive dataset provides a foundation for exploring transcriptional regulation in organogenesis.
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Affiliation(s)
- Keyong Sun
- School of Medicine, Tsinghua University, Beijing, China
- Peking-Tsinghua-NIBS Joint Graduate Program, Tsinghua University, Beijing, China
- Tsinghua-Peking Center for Life Sciences, Beijing, China
| | - Xin Liu
- Tsinghua-Peking Center for Life Sciences, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing, China.
- Peking-Tsinghua-NIBS Joint Graduate Program, Tsinghua University, Beijing, China.
- Tsinghua-Peking Center for Life Sciences, Beijing, China.
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China.
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54
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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55
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Kim GD, Shin SI, Jung SW, An H, Choi SY, Eun M, Jun CD, Lee S, Park J. Cell Type- and Age-Specific Expression of lncRNAs across Kidney Cell Types. J Am Soc Nephrol 2024; 35:870-885. [PMID: 38621182 PMCID: PMC11230714 DOI: 10.1681/asn.0000000000000354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
Key Points
We constructed a single-cell long noncoding RNA atlas of various tissues, including normal and aged kidneys.We identified age- and cell type–specific expression changes of long noncoding RNAs in kidney cells.
Background
Accumulated evidence demonstrates that long noncoding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood because of the lack of a reference map of lncRNA transcriptome in various cell types.
Methods
In this study, we used a targeted single-cell RNA sequencing method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys.
Results
Through tissue-specific clustering analysis, we identified cell type–specific lncRNAs that showed a high correlation with known cell-type marker genes. Furthermore, we constructed gene regulatory networks to explore the functional roles of differentially expressed lncRNAs in each cell type. In the kidney, we observed dynamic expression changes of lncRNAs during aging, with specific changes in glomerular cells. These cell type– and age-specific expression patterns of lncRNAs suggest that lncRNAs may have a potential role in regulating cellular processes, such as immune response and energy metabolism, during kidney aging.
Conclusions
Our study sheds light on the comprehensive landscape of lncRNA expression and function and provides a valuable resource for future analysis of lncRNAs (https://gist-fgl.github.io/sc-lncrna-atlas/).
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Affiliation(s)
- Gyeong Dae Kim
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - So-I Shin
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Su Woong Jung
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
- Division of Nephrology, Department of Internal Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Hyunsu An
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Sin Young Choi
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Minho Eun
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Chang-Duk Jun
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
| | - Sangho Lee
- Division of Nephrology, Department of Internal Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, Republic of Korea
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56
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Ma Y, Guo S, Chen Y, Peng Y, Su X, Jiang H, Lin X, Zhang J. Single-nucleus chromatin landscape dataset of mouse brain development and aging. Sci Data 2024; 11:616. [PMID: 38866804 PMCID: PMC11169343 DOI: 10.1038/s41597-024-03382-1] [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: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 06/14/2024] Open
Abstract
The development and aging of the brain constitute a lifelong dynamic process, marked by structural and functional changes that entail highly coordinated cellular differentiation and epigenetic regulatory mechanisms. Chromatin accessibility serves as the foundational basis for genetic activity. However, the holistic and dynamic chromatin landscape that spans various brain regions throughout development and ageing remains predominantly unexplored. In this study, we employed single-nucleus ATAC-seq to generate comprehensive chromatin accessibility maps, incorporating data from 69,178 cells obtained from four distinct brain regions - namely, the olfactory bulb (OB), cerebellum (CB), prefrontal cortex (PFC), and hippocampus (HP) - across key developmental time points at 7 P, 3 M, 12 M, and 18 M. We delineated the distribution of cell types across different age stages and brain regions, providing insight into chromatin accessible regions and key transcription factors specific to different cell types. Our data contribute to understanding the epigenetic basis of the formation of different brain regions, providing a dynamic landscape and comprehensive resource for revealing gene regulatory programs during brain development and aging.
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Affiliation(s)
- Yuting Ma
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China
- BGI Genomics, Shenzhen, 518083, China
| | - Sicheng Guo
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China
- BGI Genomics, Shenzhen, 518083, China
| | - Yixi Chen
- BGI Research, Shenzhen, 518083, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | | | - Xi Su
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China
- BGI Genomics, Shenzhen, 518083, China
| | - Hui Jiang
- BGI Genomics, Shenzhen, 518083, China
| | - Xiumei Lin
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
- BGI Research, Shenzhen, 518083, China.
| | - Jianguo Zhang
- Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Clin Lab, BGI Genomics, Shijiazhuang, 050035, China.
- BGI Genomics, Shenzhen, 518083, China.
- BGI Research, Shenzhen, 518083, China.
- School of Public Health, Hebei Medical University, Shijiazhuang, 050017, China.
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57
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Takeuchi F, Kato N. Ploidy inference from single-cell data: application to human and mouse cell atlases. Genetics 2024; 227:iyae061. [PMID: 38651869 PMCID: PMC11151930 DOI: 10.1093/genetics/iyae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 03/18/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN.
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Affiliation(s)
- Fumihiko Takeuchi
- Baker Department of Cardiometabolic Health, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
- Systems Genomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
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58
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De Rop FV, Hulselmans G, Flerin C, Soler-Vila P, Rafels A, Christiaens V, González-Blas CB, Marchese D, Caratù G, Poovathingal S, Rozenblatt-Rosen O, Slyper M, Luo W, Muus C, Duarte F, Shrestha R, Bagdatli ST, Corces MR, Mamanova L, Knights A, Meyer KB, Mulqueen R, Taherinasab A, Maschmeyer P, Pezoldt J, Lambert CLG, Iglesias M, Najle SR, Dossani ZY, Martelotto LG, Burkett Z, Lebofsky R, Martin-Subero JI, Pillai S, Sebé-Pedrós A, Deplancke B, Teichmann SA, Ludwig LS, Braun TP, Adey AC, Greenleaf WJ, Buenrostro JD, Regev A, Aerts S, Heyn H. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol 2024; 42:916-926. [PMID: 37537502 PMCID: PMC11180611 DOI: 10.1038/s41587-023-01881-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/22/2023] [Indexed: 08/05/2023]
Abstract
Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.
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Grants
- R01 DA047237 NIDA NIH HHS
- R00 AG059918 NIA NIH HHS
- U19 AI057266 NIAID NIH HHS
- G0B5619N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- RF1 MH128842 NIMH NIH HHS
- UM1 HG009436 NHGRI NIH HHS
- 1S80920N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- UM1 HG012076 NHGRI NIH HHS
- RM1 HG007735 NHGRI NIH HHS
- G094121N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- R35 GM124704 NIGMS NIH HHS
- UM1 HG009442 NHGRI NIH HHS
- Wellcome Trust
- H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).
- M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.
- K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.
- This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)
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Affiliation(s)
- Florian V De Rop
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Chris Flerin
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Paula Soler-Vila
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Albert Rafels
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Valerie Christiaens
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Domenica Marchese
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ginevra Caratù
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | | | | | - Wendy Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Fabiana Duarte
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rojesh Shrestha
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
| | | | | | | | - Ryan Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Akram Taherinasab
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Patrick Maschmeyer
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Jörn Pezoldt
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Camille Lucie Germaine Lambert
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Marta Iglesias
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sebastián R Najle
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Zain Y Dossani
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Luciano G Martelotto
- Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- University of Melbourne Centre for Cancer Research, Victoria Comprehensive Cancer Centre, Melbourne, Victoria, Australia
| | - Zach Burkett
- Digital Biology Group, Bio-Rad, Pleasanton, CA, USA
| | | | - José Ignacio Martin-Subero
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Departament de Fonaments Clínics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Satish Pillai
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Arnau Sebé-Pedrós
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics/Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Theodore P Braun
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute of Integrative Cancer Research, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Stein Aerts
- VIB Center for Brain and Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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59
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Kotliar M, Kartashov A, Barski A. Accelerating Single-Cell Sequencing Data Analysis with SciDAP: A User-Friendly Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.28.582604. [PMID: 38464095 PMCID: PMC10925325 DOI: 10.1101/2024.02.28.582604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Single-cell (sc) RNA, ATAC and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform (https://SciDAP.com) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.
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Affiliation(s)
- Michael Kotliar
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | | | - Artem Barski
- Division of Allergy and Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
- University of Cincinnati College of Medicine, Cincinnati, OH, 45267, USA
- Datirium, LLC, Cincinnati, OH, USA
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60
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Mendieta JP, Tu X, Jiang D, Yan H, Zhang X, Marand AP, Zhong S, Schmitz RJ. Investigating the cis-Regulatory Basis of C 3 and C 4 Photosynthesis in Grasses at Single-Cell Resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574340. [PMID: 38405933 PMCID: PMC10888913 DOI: 10.1101/2024.01.05.574340] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
While considerable knowledge exists about the enzymes pivotal for C4 photosynthesis, much less is known about the cis-regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C4 enzymes for five different grass species. This study spans four C4 species, covering three distinct photosynthetic subtypes: Zea mays and Sorghum bicolor (NADP-ME), Panicum miliaceum (NAD-ME), Urochloa fusca (PEPCK), along with the C3 outgroup Oryza sativa. We studied the cis-regulatory landscape of enzymes essential across all C4 species and those unique to C4 subtypes, measuring cell-type-specific biases for C4 enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C4 evolution. Besides promoter proximal ACRs, we found that, on average, C4 genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C4 evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution of cis-regulation at these C4 loci. This study illuminates the dynamic and complex nature of CRE evolution in C4 photosynthesis, particularly highlighting the intricate cis-regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C3 crop performance under changing climatic conditions.
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Affiliation(s)
| | - Xiaoyu Tu
- Joint Center for Single Cell Biology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Daiquan Jiang
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
| | - Haidong Yan
- Department of Genetics, University of Georgia
| | - Xuan Zhang
- Department of Genetics, University of Georgia
| | - Alexandre P Marand
- Department of Genetics, University of Georgia
- Department of Molecular, Cellular, and Development Biology, University of Michigan
| | - Silin Zhong
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
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61
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Singh R, Wu AP, Mudide A, Berger B. Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors. Cell Syst 2024; 15:462-474.e5. [PMID: 38754366 DOI: 10.1016/j.cels.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/25/2023] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.
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Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
| | - Alexander P Wu
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Anish Mudide
- Phillips Exeter Academy, Exeter, NH 03883, USA; Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, MIT, Cambridge, MA 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, MIT, Cambridge, MA 02139, USA.
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62
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Chen S, Liang B, Xu J. Unveiling heterogeneity in MSCs: exploring marker-based strategies for defining MSC subpopulations. J Transl Med 2024; 22:459. [PMID: 38750573 PMCID: PMC11094970 DOI: 10.1186/s12967-024-05294-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 05/11/2024] [Indexed: 05/19/2024] Open
Abstract
Mesenchymal stem/stromal cells (MSCs) represent a heterogeneous cell population distributed throughout various tissues, demonstrating remarkable adaptability to microenvironmental cues and holding immense promise for disease treatment. However, the inherent diversity within MSCs often leads to variability in therapeutic outcomes, posing challenges for clinical applications. To address this heterogeneity, purification of MSC subpopulations through marker-based isolation has emerged as a promising approach to ensure consistent therapeutic efficacy. In this review, we discussed the reported markers of MSCs, encompassing those developed through candidate marker strategies and high-throughput approaches, with the aim of explore viable strategies for addressing the heterogeneity of MSCs and illuminate prospective research directions in this field.
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Affiliation(s)
- Si Chen
- Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, People's Republic of China
| | - Bowei Liang
- Shenzhen University Medical School, Shenzhen University, Shenzhen, 518000, People's Republic of China
| | - Jianyong Xu
- Shenzhen Key Laboratory of Reproductive Immunology for Peri-Implantation, Guangdong Engineering Technology Research Center of Reproductive Immunology for Peri-Implantation, Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital), Fuqiang Avenue 1001, Shenzhen, 518060, Guangdong, People's Republic of China.
- Guangdong Engineering Technology Research Center of Reproductive Immunology for Peri-Implantation, Shenzhen, 518000, People's Republic of China.
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63
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Suen HC, Ou F, Miu KK, Wang Z, Chan WY, Liao J. The single-cell chromatin landscape in gonadal cell lineage specification. BMC Genomics 2024; 25:464. [PMID: 38741085 DOI: 10.1186/s12864-024-10376-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 05/03/2024] [Indexed: 05/16/2024] Open
Abstract
Gonad development includes sex determination and divergent maturation of the testes and ovaries. Recent advances in measuring gene expression in single cells are providing new insights into this complex process. However, the underlying epigenetic regulatory mechanisms remain unclear. Here, we profiled chromatin accessibility in mouse gonadal cells of both sexes from embryonic day 11.5 to 14.5 using single-cell assay for transposase accessible chromatin by sequencing (scATAC-seq). Our results showed that individual cell types can be inferred by the chromatin landscape, and that cells can be temporally ordered along developmental trajectories. Integrative analysis of transcriptomic and chromatin-accessibility maps identified multiple putative regulatory elements proximal to key gonadal genes Nr5a1, Sox9 and Wt1. We also uncover cell type-specific regulatory factors underlying cell type specification. Overall, our results provide a better understanding of the epigenetic landscape associated with the progressive restriction of cell fates in the gonad.
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Affiliation(s)
- Hoi Ching Suen
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Fanghong Ou
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Kai-Kei Miu
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Zhangting Wang
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Wai-Yee Chan
- Developmental and Regenerative Biology Program, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
| | - Jinyue Liao
- Department of Chemical Pathology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
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64
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Shu C, Street K, Breton CV, Bastain TM, Wilson ML. A review of single-cell transcriptomics and epigenomics studies in maternal and child health. Epigenomics 2024; 16:775-793. [PMID: 38709139 PMCID: PMC11318716 DOI: 10.1080/17501911.2024.2343276] [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: 12/18/2023] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Single-cell sequencing technologies enhance our understanding of cellular dynamics throughout pregnancy. We outlined the workflow of single-cell sequencing techniques and reviewed single-cell studies in maternal and child health. We conducted a literature review of single cell studies on maternal and child health using PubMed. We summarized the findings from 16 single-cell atlases of the human and mammalian placenta across gestational stages and 31 single-cell studies on maternal exposures and complications including infection, obesity, diet, gestational diabetes, pre-eclampsia, environmental exposure and preterm birth. Single-cell studies provides insights on novel cell types in placenta and cell type-specific marks associated with maternal exposures and complications.
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Affiliation(s)
- Chang Shu
- Center for Genetic Epidemiology, Division of Epidemiology & Genetics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Kelly Street
- Division of Biostatistics, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Carrie V Breton
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Theresa M Bastain
- Division of Environmental Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Melissa L Wilson
- Division of Disease Prevention, Policy, & Global Health, Department of Population & Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles,CA USA
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65
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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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66
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Hu P, Rychik J, Zhao J, Bai H, Bauer A, Yu W, Rand EB, Dodds KM, Goldberg DJ, Tan K, Wilkins BJ, Pei L. Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease. Sci Transl Med 2024; 16:eadk6213. [PMID: 38657025 PMCID: PMC11103255 DOI: 10.1126/scitranslmed.adk6213] [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: 08/31/2023] [Accepted: 04/02/2024] [Indexed: 04/26/2024]
Abstract
The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type-specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets.
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Affiliation(s)
- Po Hu
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Jack Rychik
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Juanjuan Zhao
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Huajun Bai
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Aidan Bauer
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Wenbao Yu
- Center for Childhood Cancer Research, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Elizabeth B. Rand
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Kathryn M. Dodds
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- School of Nursing, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - David J. Goldberg
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Kai Tan
- Center for Childhood Cancer Research, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Benjamin J. Wilkins
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Liming Pei
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
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67
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Chen K, Han Y, Wang Y, Zhou D, Wu F, Cai W, Zheng S, Xiao Q, Zhang H, Li W. scMoresDB: A comprehensive database of single-cell multi-omics data for human respiratory system. iScience 2024; 27:109567. [PMID: 38617561 PMCID: PMC11015448 DOI: 10.1016/j.isci.2024.109567] [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: 09/07/2023] [Revised: 11/26/2023] [Accepted: 03/22/2024] [Indexed: 04/16/2024] Open
Abstract
The human respiratory system is a complex and important system that can suffer a variety of diseases. Single-cell sequencing technologies, applied in many respiratory disease studies, have enhanced our ability in characterizing molecular and phenotypic features at a single-cell resolution. The exponentially increasing data from these studies have consequently led to difficulties in data sharing and analysis. Here, we present scMoresDB, a single-cell multi-omics database platform with extensive omics types tailored for human respiratory diseases. scMoresDB re-analyzes single-cell multi-omics datasets, providing a user-friendly interface with cross-omics search capabilities, interactive visualizations, and analytical tools for comprehensive data sharing and integrative analysis. Our example applications highlight the potential significance of BSG receptor in SARS-CoV-2 infection as well as the involvement of HHIP and TGFB2 in the development and progression of chronic obstructive pulmonary disease. scMoresDB significantly increases accessibility and utility of single-cell data relevant to human respiratory system and associated diseases.
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Affiliation(s)
- Kang Chen
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Yutong Han
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Yanni Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Dingli Zhou
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Fanjie Wu
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Wenhao Cai
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Shikang Zheng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Qinyuan Xiao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
- Key Laboratory of Tropical Disease Control of Ministry of Education, Sun Yat-Sen University, Guangzhou 510080, Guangdong Province, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China
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68
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Gao Z, Jiang R, Chen S. OpenAnnotateApi: Python and R packages to efficiently annotate and analyze chromatin accessibility of genomic regions. BIOINFORMATICS ADVANCES 2024; 4:vbae055. [PMID: 38645715 PMCID: PMC11031356 DOI: 10.1093/bioadv/vbae055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/20/2024] [Accepted: 04/08/2024] [Indexed: 04/23/2024]
Abstract
Summary Chromatin accessibility serves as a critical measurement of physical contact between nuclear macromolecules and DNA sequence, providing valuable insights into the comprehensive landscape of regulatory mechanisms, thus we previously developed the OpenAnnotate web server. However, as an increasing number of epigenomic analysis software tools emerged, web-based annotation often faced limitations and inconveniences when integrated into these software pipelines. To address these issues, we here develop two software packages named OpenAnnotatePy and OpenAnnotateR. In addition to web-based functionalities, these packages encompass supplementary features, including the capability for simultaneous annotation across multiple cell types, advanced searching of systems, tissues and cell types, and converting the result to the data structure of mainstream tools. Moreover, we applied the packages to various scenarios, including cell type revealing, regulatory element prediction, and integration into mainstream single-cell ATAC-seq analysis pipelines including EpiScanpy, Signac, and ArchR. We anticipate that OpenAnnotateApi will significantly facilitate the deciphering of gene regulatory mechanisms, and offer crucial assistance in the field of epigenomic studies. Availability and implementation OpenAnnotateApi for R is available at https://github.com/ZjGaothu/OpenAnnotateR and for Python is available at https://github.com/ZjGaothu/OpenAnnotatePy.
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Affiliation(s)
- 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 100084, 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 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
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69
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Zhang X, Marand AP, Yan H, Schmitz RJ. scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. Genome Biol 2024; 25:90. [PMID: 38589969 PMCID: PMC11003106 DOI: 10.1186/s13059-024-03235-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/01/2024] [Indexed: 04/10/2024] Open
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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70
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Fang X, Zhang Y, Miao R, Zhang Y, Yin R, Guan H, Huang X, Tian J. Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes. Biomed Pharmacother 2024; 173:116292. [PMID: 38394848 DOI: 10.1016/j.biopha.2024.116292] [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: 12/07/2023] [Revised: 02/03/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.
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Affiliation(s)
- Xinyi Fang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Ruiyang Yin
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Jilin 130117, China
| | - Xinyue Huang
- First Clinical Medical College, Changzhi Medical College, Shanxi 046013, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
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71
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Yang C, Jin Y, Yin Y. Integration of single-cell transcriptome and chromatin accessibility and its application on tumor investigation. LIFE MEDICINE 2024; 3:lnae015. [PMID: 39872661 PMCID: PMC11749461 DOI: 10.1093/lifemedi/lnae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/25/2024] [Indexed: 01/30/2025]
Abstract
The advent of single-cell sequencing techniques has not only revolutionized the investigation of biological processes but also significantly contributed to unraveling cellular heterogeneity at unprecedented levels. Among the various methods, single-cell transcriptome sequencing stands out as the best established, and has been employed in exploring many physiological and pathological activities. The recently developed single-cell epigenetic sequencing techniques, especially chromatin accessibility sequencing, have further deepened our understanding of gene regulatory networks. In this review, we summarize the recent breakthroughs in single-cell transcriptome and chromatin accessibility sequencing methodologies. Additionally, we describe current bioinformatic strategies to integrate data obtained through these single-cell sequencing methods and highlight the application of this analysis strategy on a deeper understanding of tumorigenesis and tumor progression. Finally, we also discuss the challenges and anticipated developments in this field.
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Affiliation(s)
- Chunyuan Yang
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences Peking University, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Yan Jin
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences Peking University, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Yuxin Yin
- Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences Peking University, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China
- Institute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen 518036, China
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72
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Zeng Y, Luo M, Shangguan N, Shi P, Feng J, Xu J, Chen K, Lu Y, Yu W, Yang Y. Deciphering cell types by integrating scATAC-seq data with genome sequences. NATURE COMPUTATIONAL SCIENCE 2024; 4:285-298. [PMID: 38600256 DOI: 10.1038/s43588-024-00622-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 03/18/2024] [Indexed: 04/12/2024]
Abstract
The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.
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Affiliation(s)
- Yuansong Zeng
- School of Big Data and Software Engineering, Chongqing University, Chongqing, China
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Mai Luo
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Ningyuan Shangguan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Junxi Feng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Ken Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yutong Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Weijiang Yu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
- Key Laboratory of Machine Intelligence and Advanced Computing (MOE), Guangzhou, China.
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73
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Malekpour SA, Haghverdi L, Sadeghi M. Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms. Brief Bioinform 2024; 25:bbae180. [PMID: 38653489 PMCID: PMC11036345 DOI: 10.1093/bib/bbae180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
There is a growing interest in inferring context specific gene regulatory networks from single-cell RNA sequencing (scRNA-seq) data. This involves identifying the regulatory relationships between transcription factors (TFs) and genes in individual cells, and then characterizing these relationships at the level of specific cell types or cell states. In this study, we introduce scGATE (single-cell gene regulatory gate) as a novel computational tool for inferring TF-gene interaction networks and reconstructing Boolean logic gates involving regulatory TFs using scRNA-seq data. In contrast to current Boolean models, scGATE eliminates the need for individual formulations and likelihood calculations for each Boolean rule (e.g. AND, OR, XOR). By employing a Bayesian framework, scGATE infers the Boolean rule after fitting the model to the data, resulting in significant reductions in time-complexities for logic-based studies. We have applied assay for transposase-accessible chromatin with sequencing (scATAC-seq) data and TF DNA binding motifs to filter out non-relevant TFs in gene regulations. By integrating single-cell clustering with these external cues, scGATE is able to infer context specific networks. The performance of scGATE is evaluated using synthetic and real single-cell multi-omics data from mouse tissues and human blood, demonstrating its superiority over existing tools for reconstructing TF-gene networks. Additionally, scGATE provides a flexible framework for understanding the complex combinatorial and cooperative relationships among TFs regulating target genes by inferring Boolean logic gates among them.
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Affiliation(s)
- Seyed Amir Malekpour
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), 19395-5746, Tehran, Iran
| | - Laleh Haghverdi
- Berlin Institute for Medical Systems Biology, Max Delbrück Center (BIMSB-MDC) in the Helmholtz Association, Berlin, Germany
| | - Mehdi Sadeghi
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology, 1497716316, Tehran, Iran
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74
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Zhang W, Cui Y, Liu B, Loza M, Park SJ, Nakai K. HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data. Brief Bioinform 2024; 25:bbae152. [PMID: 38581422 PMCID: PMC10998639 DOI: 10.1093/bib/bbae152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/19/2024] [Accepted: 03/10/2024] [Indexed: 04/08/2024] Open
Abstract
Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.
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Affiliation(s)
- Weihang Zhang
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Yang Cui
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Bowen Liu
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Martin Loza
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Sung-Joon Park
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Kenta Nakai
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, University of Tokyo, Tokyo, Japan
- Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
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75
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Caldwell BA, Wu Y, Wang J, Li L. Altered DNA methylation underlies monocyte dysregulation and immune exhaustion memory in sepsis. Cell Rep 2024; 43:113894. [PMID: 38442017 PMCID: PMC11654472 DOI: 10.1016/j.celrep.2024.113894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/12/2024] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
Monocytes can develop an exhausted memory state characterized by reduced differentiation, pathogenic inflammation, and immune suppression that drives immune dysregulation during sepsis. Chromatin alterations, notably via histone modifications, underlie innate immune memory, but the contribution of DNA methylation remains poorly understood. Using an ex vivo sepsis model, we show altered DNA methylation throughout the genome of exhausted monocytes, including genes implicated in immune dysregulation during sepsis and COVID-19 infection (e.g., Plac8). These changes are recapitulated in septic mice induced by cecal slurry injection. Methylation profiles developed in septic mice are maintained during ex vivo culture, supporting the involvement of DNA methylation in stable monocyte exhaustion memory. Methylome reprogramming is driven in part by Wnt signaling inhibition in exhausted monocytes and can be reversed with DNA methyltransferase inhibitors, Wnt agonists, or immune training molecules. Our study demonstrates the significance of altered DNA methylation in the maintenance of stable monocyte exhaustion memory.
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Affiliation(s)
- Blake A Caldwell
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061-0910, USA
| | - Yajun Wu
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061-0910, USA
| | - Jing Wang
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061-0910, USA
| | - Liwu Li
- Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061-0910, USA.
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76
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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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77
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Miao Z, Wang J, Park K, Kuang D, Kim J. PACS allows comprehensive dissection of multiple factors governing chromatin accessibility from snATAC-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.30.551108. [PMID: 37577623 PMCID: PMC10418058 DOI: 10.1101/2023.07.30.551108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including genotype, cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, Probability model of Accessible Chromatin of Single cells (PACS), that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data.
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Affiliation(s)
- Zhen Miao
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jianqiao Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kernyu Park
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Da Kuang
- Deptartment Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhyong Kim
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
- Deptartment Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
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78
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Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, Ledford JG, Adey AC, Cusanovich DA. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol 2024; 25:78. [PMID: 38519979 PMCID: PMC10958877 DOI: 10.1186/s13059-023-03150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.
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Affiliation(s)
- Hao Zhang
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Ryan M Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Iannuzo
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
| | - Dominique O Farrera
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Francesca Polverino
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Arizona, Tucson, AZ, USA
- Banner - University Medicine North, Pulmonary - Clinic F, Tucson, AZ, USA
| | - James J Galligan
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Julie G Ledford
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA.
- Oregon Health & Science University, Knight Cancer Institute, Portland, OR, USA.
- Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
| | - Darren A Cusanovich
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
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79
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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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80
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Tang S, Cui X, Wang R, Li S, Li S, Huang X, Chen S. scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data. Nat Commun 2024; 15:1629. [PMID: 38388573 PMCID: PMC10884038 DOI: 10.1038/s41467-024-46045-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/12/2024] [Indexed: 02/24/2024] Open
Abstract
Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement performance.
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Affiliation(s)
- Songming Tang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Xuejian Cui
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Rongxiang Wang
- Department of Computer Science, University of Virginia, Charlottesville, VA, 22903, USA
| | - Sijie Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Siyu Li
- School of Statistics and Data Science, Nankai University, Tianjin, 300071, China
| | - Xin Huang
- Beijing Key Laboratory for Radiobiology, Department of Radiation Biology, Beijing Institute of Radiation Medicine, 100850, Beijing, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
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81
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Zhang X, Marand AP, Yan H, Schmitz RJ. Massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.17.558155. [PMID: 37786710 PMCID: PMC10541611 DOI: 10.1101/2023.09.17.558155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called single-cell combinatorial fluidic indexing ATAC-sequencing ("scifi-ATAC-seq"), which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using a widely commercialized microfluidics platform (10X Genomics). With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples in a single emulsion reaction can be indexed, representing a ~20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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82
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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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83
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Chew V, Chuang CH, Hsu C. Translational research on drug development and biomarker discovery for hepatocellular carcinoma. J Biomed Sci 2024; 31:22. [PMID: 38368324 PMCID: PMC10874078 DOI: 10.1186/s12929-024-01011-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 02/10/2024] [Indexed: 02/19/2024] Open
Abstract
Translational research plays a key role in drug development and biomarker discovery for hepatocellular carcinoma (HCC). However, unique challenges exist in this field because of the limited availability of human tumor samples from surgery, the lack of homogenous oncogenic driver mutations, and the paucity of adequate experimental models. In this review, we provide insights into these challenges and review recent advancements, with a particular focus on the two main agents currently used as mainstream therapies for HCC: anti-angiogenic agents and immunotherapy. First, we examine the pre-clinical and clinical studies to highlight the challenges of determining the optimal therapeutic combinations with biologically effective dosage for HCC. Second, we discuss biomarker studies focusing on anti-PD1/anti-PD-L1-based combination therapy. Finally, we discuss the progress made in our collective understanding of tumor immunology and in multi-omics analysis technology, which enhance our understanding of the mechanisms underlying immunotherapy, characterize different patient subgroups, and facilitate the development of novel combination approaches to improve treatment efficacy. In summary, this review provides a comprehensive overview of efforts in translational research aiming at advancing our understanding of and improving the treatment of HCC.
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Affiliation(s)
- Valerie Chew
- Translational Immunology Institute, SingHealth-DukeNUS Academic Medical Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Chien-Huai Chuang
- Department of Medical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
| | - Chiun Hsu
- Department of Medical Oncology, National Taiwan University Cancer Center, Taipei, Taiwan.
- Graduate Institute of Oncology, National Taiwan University College of Medicine, Taipei, Taiwan.
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan.
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84
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Sun H, Qu H, Duan K, Du W. scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data. Int J Mol Sci 2024; 25:2234. [PMID: 38396909 PMCID: PMC10889820 DOI: 10.3390/ijms25042234] [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: 12/06/2023] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations is challenging and requires stable and interpretable methods. However, the current cell type identification methods have limited performance, mainly due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, a multi-view graph convolutional network model (scMGCN) that integrates multiple graph structures from raw scRNA-seq data and applies graph convolutional networks with attention mechanisms to learn cell embeddings and predict cell labels. We evaluate our model on single-dataset, cross-species, and cross-platform experiments and compare it with other state-of-the-art methods. Our results show that scMGCN outperforms the other methods regarding stability, accuracy, and robustness to batch effects. Our main contributions are as follows: Firstly, we introduce multi-view learning and multiple graph construction methods to capture comprehensive cellular information from scRNA-seq data. Secondly, we construct a scMGCN that combines graph convolutional networks with attention mechanisms to extract shared, high-order information from cells. Finally, we demonstrate the effectiveness and superiority of the scMGCN on various datasets.
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Affiliation(s)
| | | | | | - Wei Du
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (H.S.); (H.Q.); (K.D.)
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85
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Abadie K, Clark EC, Valanparambil RM, Ukogu O, Yang W, Daza RM, Ng KKH, Fathima J, Wang AL, Lee J, Nasti TH, Bhandoola A, Nourmohammad A, Ahmed R, Shendure J, Cao J, Kueh HY. Reversible, tunable epigenetic silencing of TCF1 generates flexibility in the T cell memory decision. Immunity 2024; 57:271-286.e13. [PMID: 38301652 PMCID: PMC10922671 DOI: 10.1016/j.immuni.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 10/09/2023] [Accepted: 12/07/2023] [Indexed: 02/03/2024]
Abstract
The immune system encodes information about the severity of a pathogenic threat in the quantity and type of memory cells it forms. This encoding emerges from lymphocyte decisions to maintain or lose self-renewal and memory potential during a challenge. By tracking CD8+ T cells at the single-cell and clonal lineage level using time-resolved transcriptomics, quantitative live imaging, and an acute infection model, we find that T cells will maintain or lose memory potential early after antigen recognition. However, following pathogen clearance, T cells may regain memory potential if initially lost. Mechanistically, this flexibility is implemented by a stochastic cis-epigenetic switch that tunably and reversibly silences the memory regulator, TCF1, in response to stimulation. Mathematical modeling shows how this flexibility allows memory T cell numbers to scale robustly with pathogen virulence and immune response magnitudes. We propose that flexibility and stochasticity in cellular decisions ensure optimal immune responses against diverse threats.
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Affiliation(s)
- Kathleen Abadie
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Elisa C Clark
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Rajesh M Valanparambil
- Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Obinna Ukogu
- Department of Applied Mathematics, University of Washington, Seattle, WA 98105, USA
| | - Wei Yang
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Riza M Daza
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Kenneth K H Ng
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Jumana Fathima
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Allan L Wang
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Judong Lee
- Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Tahseen H Nasti
- Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Avinash Bhandoola
- T-Cell Biology and Development Unit, Laboratory of Genome Integrity, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD 20892, USA
| | - Armita Nourmohammad
- Department of Applied Mathematics, University of Washington, Seattle, WA 98105, USA; Department of Physics, University of Washington, Seattle, WA 98105, USA; Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Rafi Ahmed
- Emory Vaccine Center and Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA; Allen Discovery Center for Cell Lineage Tracing, Seattle, WA 98109, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA.
| | - Junyue Cao
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Laboratory of Single-Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY 10065, USA.
| | - Hao Yuan Kueh
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA.
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86
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Taskiran II, Spanier KI, Dickmänken H, Kempynck N, Pančíková A, Ekşi EC, Hulselmans G, Ismail JN, Theunis K, Vandepoel R, Christiaens V, Mauduit D, Aerts S. Cell-type-directed design of synthetic enhancers. Nature 2024; 626:212-220. [PMID: 38086419 PMCID: PMC10830415 DOI: 10.1038/s41586-023-06936-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/05/2023] [Indexed: 01/19/2024]
Abstract
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes1. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Here we show that deep learning models2-6, can be used to efficiently design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution. We evaluate the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We further exploit enhancer design to create 'dual-code' enhancers that target two cell types and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the state space searches towards local optima, we characterize enhancer codes through the strength, combination and arrangement of transcription factor activator and transcription factor repressor motifs. Finally, we apply the same strategies to successfully design human enhancers, which adhere to enhancer rules similar to those of Drosophila enhancers. Enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.
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Affiliation(s)
- Ibrahim I Taskiran
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Katina I Spanier
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Hannah Dickmänken
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Niklas Kempynck
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Alexandra Pančíková
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB-KULeuven Center for Cancer Biology, Leuven, Belgium
| | - Eren Can Ekşi
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Joy N Ismail
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- UK Dementia Research Institute at Imperial College London, London, UK
| | - Koen Theunis
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Roel Vandepoel
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Valerie Christiaens
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - David Mauduit
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Stein Aerts
- Laboratory of Computational Biology, VIB Center for AI & Computational Biology (VIB.AI), Leuven, Belgium.
- VIB-KULeuven Center for Brain & Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
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87
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [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: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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88
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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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89
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Moore JR, Nemera MT, D’Souza RD, Hamagami N, Clemens AW, Beard DC, Urman A, Mendoza VR, Gabel HW. Non-CG DNA methylation and MeCP2 stabilize repeated tuning of long genes that distinguish closely related neuron types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.30.577861. [PMID: 38352532 PMCID: PMC10862856 DOI: 10.1101/2024.01.30.577861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
The extraordinary diversity of neuron types in the mammalian brain is delineated at the highest resolution by subtle gene expression differences that may require specialized molecular mechanisms to be maintained. Neurons uniquely express the longest genes in the genome and utilize neuron-enriched non-CG DNA methylation (mCA) together with the Rett syndrome protein, MeCP2, to control gene expression, but the function of these unique gene structures and machinery in regulating finely resolved neuron type-specific gene programs has not been explored. Here, we employ epigenomic and spatial transcriptomic analyses to discover a major role for mCA and MeCP2 in maintaining neuron type-specific gene programs at the finest scale of cellular resolution. We uncover differential susceptibility to MeCP2 loss in neuronal populations depending on global mCA levels and dissect methylation patterns and intragenic enhancer repression that drive overlapping and distinct gene regulation between neuron types. Strikingly, we show that mCA and MeCP2 regulate genes that are repeatedly tuned to differentiate neuron types at the highest cellular resolution, including spatially resolved, vision-dependent gene programs in the visual cortex. These repeatedly tuned genes display genomic characteristics, including long length, numerous intragenic enhancers, and enrichment for mCA, that predispose them to regulation by MeCP2. Thus, long gene regulation by the MeCP2 pathway maintains differential gene expression between closely-related neurons to facilitate the exceptional cellular diversity in the complex mammalian brain.
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Affiliation(s)
- J. Russell Moore
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Mati T. Nemera
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Rinaldo D. D’Souza
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Nicole Hamagami
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Adam W. Clemens
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Diana C. Beard
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Alaina Urman
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Victoria Rodriguez Mendoza
- Opportunities in Genomic Research Program, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
| | - Harrison W. Gabel
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110-1093, USA
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90
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Yan Y, Liu H, Abedini A, Sheng X, Palmer M, Li H, Susztak K. Unraveling the epigenetic code: human kidney DNA methylation and chromatin dynamics in renal disease development. Nat Commun 2024; 15:873. [PMID: 38287030 PMCID: PMC10824731 DOI: 10.1038/s41467-024-45295-y] [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: 06/11/2023] [Accepted: 01/19/2024] [Indexed: 01/31/2024] Open
Abstract
Epigenetic changes may fill a critical gap in our understanding of kidney disease development, as they not only reflect metabolic changes but are also preserved and transmitted during cell division. We conducted a genome-wide cytosine methylation analysis of 399 human kidney samples, along with single-nuclear open chromatin analysis on over 60,000 cells from 14 subjects, including controls, and diabetes and hypertension attributed chronic kidney disease (CKD) patients. We identified and validated differentially methylated positions associated with disease states, and discovered that nearly 30% of these alterations were influenced by underlying genetic variations, including variants known to be associated with kidney disease in genome-wide association studies. We also identified regions showing both methylation and open chromatin changes. These changes in methylation and open chromatin significantly associated gene expression changes, most notably those playing role in metabolism and expressed in proximal tubules. Our study further demonstrated that methylation risk scores (MRS) can improve disease state annotation and prediction of kidney disease development. Collectively, our results suggest a causal relationship between epigenetic changes and kidney disease pathogenesis, thereby providing potential pathways for the development of novel risk stratification methods.
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Affiliation(s)
- Yu Yan
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Xin Sheng
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Matthew Palmer
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Epidemiology and Biostatistics, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Hongzhe Li
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA
- Department of Pathology, Perelman School of Medicine, Philadelphia, PA, 19014, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
- Institute for Diabetes, Obesity, and Metabolism, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
- Kidney Innovation Center, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19014, USA.
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91
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Yan F, Suzuki A, Iwaya C, Pei G, Chen X, Yoshioka H, Yu M, Simon LM, Iwata J, Zhao Z. Single-cell multiomics decodes regulatory programs for mouse secondary palate development. Nat Commun 2024; 15:821. [PMID: 38280850 PMCID: PMC10821874 DOI: 10.1038/s41467-024-45199-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 01/17/2024] [Indexed: 01/29/2024] Open
Abstract
Perturbations in gene regulation during palatogenesis can lead to cleft palate, which is among the most common congenital birth defects. Here, we perform single-cell multiome sequencing and profile chromatin accessibility and gene expression simultaneously within the same cells (n = 36,154) isolated from mouse secondary palate across embryonic days (E) 12.5, E13.5, E14.0, and E14.5. We construct five trajectories representing continuous differentiation of cranial neural crest-derived multipotent cells into distinct lineages. By linking open chromatin signals to gene expression changes, we characterize the underlying lineage-determining transcription factors. In silico perturbation analysis identifies transcription factors SHOX2 and MEOX2 as important regulators of the development of the anterior and posterior palate, respectively. In conclusion, our study charts epigenetic and transcriptional dynamics in palatogenesis, serving as a valuable resource for further cleft palate research.
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Affiliation(s)
- Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Akiko Suzuki
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri - Kansas City, Kansas City, Missouri, 64108, USA
| | - Chihiro Iwaya
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xian Chen
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Hiroki Yoshioka
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA
| | - Meifang Yu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Lukas M Simon
- Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, 77030, USA.
| | - Junichi Iwata
- Department of Diagnostic and Biomedical Sciences, School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- Center for Craniofacial Research, The University of Texas Health Science Center at Houston, Houston, TX, 77054, USA.
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, 77030, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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92
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Lu C, Wei Y, Abbas M, Agula H, Wang E, Meng Z, Zhang R. Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects. Int J Mol Sci 2024; 25:1479. [PMID: 38338756 PMCID: PMC10855595 DOI: 10.3390/ijms25031479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.
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Affiliation(s)
- Chao Lu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
- Key Laboratory of Herbage & Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Hasi Agula
- Key Laboratory of Herbage & Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Edwin Wang
- Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
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93
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Duan Z, Xu S, Sai Srinivasan S, Hwang A, Lee CY, Yue F, Gerstein M, Luan Y, Girgenti M, Zhang J. scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding. Brief Bioinform 2024; 25:bbae096. [PMID: 38493342 PMCID: PMC10944576 DOI: 10.1093/bib/bbae096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 03/18/2024] Open
Abstract
Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.
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Affiliation(s)
- Ziheng Duan
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | | | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | - Feng Yue
- Department of Pathology, Northwestern University, 60611 IL, USA
| | - Mark Gerstein
- Molecular Biophysics & Biochemistry, Yale, 06519 CT, USA
| | - Yu Luan
- Department of Cell Systems and Anatomy, UT Health San Antonio, 78229 TX, USA
| | - Matthew Girgenti
- Department of Psychiatry, School of Medicine, Yale, 06519 CT, USA
- Clinical Neurosciences Division, National Center for PTSD, U.S. Department of Veterans Affairs, 06477 CT, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
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94
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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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95
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Kim SS, Truong B, Jagadeesh K, Dey KK, Shen AZ, Raychaudhuri S, Kellis M, Price AL. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nat Commun 2024; 15:563. [PMID: 38233398 PMCID: PMC10794712 DOI: 10.1038/s41467-024-44742-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.
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Affiliation(s)
- Samuel S Kim
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
| | - Buu Truong
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
| | - Karthik Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber Z Shen
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK
| | - Alkes L Price
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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96
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Kousnetsov R, Bourque J, Surnov A, Fallahee I, Hawiger D. Single-cell sequencing analysis within biologically relevant dimensions. Cell Syst 2024; 15:83-103.e11. [PMID: 38198894 DOI: 10.1016/j.cels.2023.12.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/23/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024]
Abstract
The currently predominant approach to transcriptomic and epigenomic single-cell analysis depends on a rigid perspective constrained by reduced dimensions and algorithmically derived and annotated clusters. Here, we developed Seqtometry (sequencing-to-measurement), a single-cell analytical strategy based on biologically relevant dimensions enabled by advanced scoring with multiple gene sets (signatures) for examination of gene expression and accessibility across various organ systems. By utilizing information only in the form of specific signatures, Seqtometry bypasses unsupervised clustering and individual annotations of clusters. Instead, Seqtometry combines qualitative and quantitative cell-type identification with specific characterization of diverse biological processes under experimental or disease conditions. Comprehensive analysis by Seqtometry of various immune cells as well as other cells from different organs and disease-induced states, including multiple myeloma and Alzheimer's disease, surpasses corresponding cluster-based analytical output. We propose Seqtometry as a single-cell sequencing analysis approach applicable for both basic and clinical research.
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Affiliation(s)
- Robert Kousnetsov
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Jessica Bourque
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Alexey Surnov
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Ian Fallahee
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA
| | - Daniel Hawiger
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, USA.
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97
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Xiong H, Wang Q, Li CC, He A. Single-cell joint profiling of multiple epigenetic proteins and gene transcription. SCIENCE ADVANCES 2024; 10:eadi3664. [PMID: 38170774 PMCID: PMC10796078 DOI: 10.1126/sciadv.adi3664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
Sculpting the epigenome with a combination of histone modifications and transcription factor occupancy determines gene transcription and cell fate specification. Here, we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultrahigh-throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) and RUNX1 engagement priming for H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multimodal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.
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Affiliation(s)
- Haiqing Xiong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Qianhao Wang
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Chen C. Li
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Aibin He
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
- Key laboratory of Carcinogenesis and Translational Research of Ministry of Education of China, Peking University Cancer Hospital & Institute, Peking University, Beijing 100142, China
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98
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Qian FC, Zhou LW, Zhu YB, Li YY, Yu ZM, Feng CC, Fang QL, Zhao Y, Cai FH, Wang QY, Tang HF, Li CQ. scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species. Nucleic Acids Res 2024; 52:D285-D292. [PMID: 37897340 PMCID: PMC10767920 DOI: 10.1093/nar/gkad924] [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: 08/15/2023] [Revised: 09/14/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.
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Affiliation(s)
- Feng-Cui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Li-Wei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Yan-Bing Zhu
- Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yan-Yu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Zheng-Min Yu
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Chen-Chen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Qiao-Li Fang
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Zhao
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Fu-Hong Cai
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qiu-Yu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hui-Fang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chun-Quan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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99
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Abedini A, Sánchez-Navaro A, Wu J, Klötzer KA, Ma Z, Poudel B, Doke T, Balzer MS, Frederick J, Cernecka H, Liu H, Liang X, Vitale S, Kolkhof P, Susztak K. Single-cell transcriptomics and chromatin accessibility profiling elucidate the kidney-protective mechanism of mineralocorticoid receptor antagonists. J Clin Invest 2024; 134:e157165. [PMID: 37906287 PMCID: PMC10760974 DOI: 10.1172/jci157165] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
Mineralocorticoid excess commonly leads to hypertension (HTN) and kidney disease. In our study, we used single-cell expression and chromatin accessibility tools to characterize the mineralocorticoid target genes and cell types. We demonstrated that mineralocorticoid effects were established through open chromatin and target gene expression, primarily in principal and connecting tubule cells and, to a lesser extent, in segments of the distal convoluted tubule cells. We examined the kidney-protective effects of steroidal and nonsteroidal mineralocorticoid antagonists (MRAs), as well as of amiloride, an epithelial sodium channel inhibitor, in a rat model of deoxycorticosterone acetate, unilateral nephrectomy, and high-salt consumption-induced HTN and cardiorenal damage. All antihypertensive therapies protected against cardiorenal damage. However, finerenone was particularly effective in reducing albuminuria and improving gene expression changes in podocytes and proximal tubule cells, even with an equivalent reduction in blood pressure. We noted a strong correlation between the accumulation of injured/profibrotic tubule cells expressing secreted posphoprotein 1 (Spp1), Il34, and platelet-derived growth factor subunit b (Pdgfb) and the degree of fibrosis in rat kidneys. This gene signature also showed a potential for classifying human kidney samples. Our multiomics approach provides fresh insights into the possible mechanisms underlying HTN-associated kidney disease, the target cell types, the protective effects of steroidal and nonsteroidal MRAs, and amiloride.
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Affiliation(s)
- Amin Abedini
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Andrea Sánchez-Navaro
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Junnan Wu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Konstantin A. Klötzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ziyuan Ma
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Bibek Poudel
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Tomohito Doke
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Michael S. Balzer
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Julia Frederick
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Hana Cernecka
- Bayer AG, Pharmaceuticals, Research and Development, Cardiovascular Research, Wuppertal, Germany
| | - Hongbo Liu
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Xiujie Liang
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Steven Vitale
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Peter Kolkhof
- Bayer AG, Pharmaceuticals, Research and Development, Cardiovascular Research, Wuppertal, Germany
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine
- Institute for Diabetes, Obesity, and Metabolism, and
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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100
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Lee AS, Ayers LJ, Kosicki M, Chan WM, Fozo LN, Pratt BM, Collins TE, Zhao B, Rose MF, Sanchis-Juan A, Fu JM, Wong I, Zhao X, Tenney AP, Lee C, Laricchia KM, Barry BJ, Bradford VR, Lek M, MacArthur DG, Lee EA, Talkowski ME, Brand H, Pennacchio LA, Engle EC. A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.22.23300468. [PMID: 38234731 PMCID: PMC10793524 DOI: 10.1101/2023.12.22.23300468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in an in vivo transgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.
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Affiliation(s)
- Arthur S. Lee
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Lauren J. Ayers
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Michael Kosicki
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Wai-Man Chan
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Howard Hughes Medical Institute, Chevy Chase, MD
| | - Lydia N. Fozo
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Brandon M. Pratt
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Thomas E. Collins
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Boxun Zhao
- Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA
| | - Matthew F. Rose
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Pathology, Boston Children's Hospital, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Medical Genetics Training Program, Harvard Medical School, Boston, MA
| | - Alba Sanchis-Juan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Jack M. Fu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Isaac Wong
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Xuefang Zhao
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Alan P. Tenney
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Cassia Lee
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Harvard College, Cambridge, MA
| | - Kristen M. Laricchia
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Brenda J. Barry
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Howard Hughes Medical Institute, Chevy Chase, MD
| | - Victoria R. Bradford
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Monkol Lek
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Daniel G. MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Eunjung Alice Lee
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Michael E. Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Harrison Brand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
- Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, MA
| | - Len A. Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Elizabeth C. Engle
- Department of Neurology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Manton Center for Orphan Disease Research, Boston Children’s Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Howard Hughes Medical Institute, Chevy Chase, MD
- Division of Genetics and Genomics, Boston Children’s Hospital, Boston, MA
- Medical Genetics Training Program, Harvard Medical School, Boston, MA
- Department of Ophthalmology, Boston Children’s Hospital and Harvard Medical School, Boston, MA
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