101
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Herring CA, Simmons RK, Freytag S, Poppe D, Moffet JJD, Pflueger J, Buckberry S, Vargas-Landin DB, Clément O, Echeverría EG, Sutton GJ, Alvarez-Franco A, Hou R, Pflueger C, McDonald K, Polo JM, Forrest ARR, Nowak AK, Voineagu I, Martelotto L, Lister R. Human prefrontal cortex gene regulatory dynamics from gestation to adulthood at single-cell resolution. Cell 2022; 185:4428-4447.e28. [PMID: 36318921 DOI: 10.1016/j.cell.2022.09.039] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 07/19/2022] [Accepted: 09/27/2022] [Indexed: 11/05/2022]
Abstract
Human brain development is underpinned by cellular and molecular reconfigurations continuing into the third decade of life. To reveal cell dynamics orchestrating neural maturation, we profiled human prefrontal cortex gene expression and chromatin accessibility at single-cell resolution from gestation to adulthood. Integrative analyses define the dynamic trajectories of each cell type, revealing major gene expression reconfiguration at the prenatal-to-postnatal transition in all cell types followed by continuous reconfiguration into adulthood and identifying regulatory networks guiding cellular developmental programs, states, and functions. We uncover links between expression dynamics and developmental milestones, characterize the diverse timing of when cells acquire adult-like states, and identify molecular convergence from distinct developmental origins. We further reveal cellular dynamics and their regulators implicated in neurological disorders. Finally, using this reference, we benchmark cell identities and maturation states in organoid models. Together, this captures the dynamic regulatory landscape of human cortical development.
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Affiliation(s)
- Charles A Herring
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Rebecca K Simmons
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Saskia Freytag
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Daniel Poppe
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Joel J D Moffet
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Jahnvi Pflueger
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Sam Buckberry
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Dulce B Vargas-Landin
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Olivier Clément
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Enrique Goñi Echeverría
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Gavin J Sutton
- School of Biotechnology and Biomolecular Sciences, Cellular Genomics Futures Institute, and the RNA Institute, University of New South Wales, Sydney, NSW 2052, Australia
| | - Alba Alvarez-Franco
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid 28029, Spain
| | - Rui Hou
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Christian Pflueger
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Kerrie McDonald
- Lowy Cancer Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jose M Polo
- Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC 3000, Australia
| | - Alistair R R Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia
| | - Anna K Nowak
- Medical School, University of Western Australia, Perth, WA 6009, Australia
| | - Irina Voineagu
- School of Biotechnology and Biomolecular Sciences, Cellular Genomics Futures Institute, and the RNA Institute, University of New South Wales, Sydney, NSW 2052, Australia
| | - Luciano Martelotto
- Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5000, Australia; University of Melbourne Centre for Cancer Research, Victoria Comprehensive Cancer Centre, Melbourne, VIC 3000, Australia
| | - Ryan Lister
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, WA 6009, Australia; ARC Centre of Excellence in Plant Energy Biology, School of Molecular Sciences, The University of Western Australia, Perth, WA 6009, Australia.
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102
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Zhou Z, Tan C, Chau M, Jiang X, Ke Z, Chen X, Cao Y, Kwok YK, Bellgard M, Leung T, Choy K, Dong Z. TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms. Nucleic Acids Res 2022; 51:D1168-D1178. [PMID: 36350663 PMCID: PMC9825605 DOI: 10.1093/nar/gkac978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/06/2022] [Accepted: 10/15/2022] [Indexed: 11/10/2022] Open
Abstract
Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene-gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg.cuhk.edu.hk/.
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Affiliation(s)
| | | | - Matthew Hoi Kin Chau
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China,Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China,The Chinese University of Hong Kong-Baylor College of Medicine Joint Center for Medical Genetics, Hong Kong, China
| | - Xiaosen Jiang
- BGI-Shenzhen, Shenzhen 518083, China,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ziyuan Ke
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xiaoyan Chen
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China,Maternal-Fetal Medicine Institute, Shenzhen Baoan Women's and Children's Hospital, Shenzhen University, Shenzhen, China
| | - Ye Cao
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China,Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China,The Fertility Preservation Research Center, Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - Yvonne K Kwok
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - Matthew Bellgard
- eResearch Office, Queensland University of Technology, Brisbane, QLD, Australia
| | - Tak Yeung Leung
- Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China,Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen 518057, China,Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China,The Chinese University of Hong Kong-Baylor College of Medicine Joint Center for Medical Genetics, Hong Kong, China,The Fertility Preservation Research Center, Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Hong Kong, China
| | - Kwong Wai Choy
- Correspondence may also be addressed to Kwong Wai Choy. Tel: +852 3505 3099; Fax: +852 2636 0008;
| | - Zirui Dong
- To whom correspondence should be addressed. Tel: +852 3505 3099; Fax: +852 2636 0008;
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103
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Childs CJ, Eiken MK, Spence JR. Approaches to benchmark and characterize in vitro human model systems. Development 2022; 149:dev200641. [PMID: 36214410 PMCID: PMC10906492 DOI: 10.1242/dev.200641] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2024]
Abstract
In vitro human models, such as gastruloids and organoids, are complex three-dimensional (3D) structures often consist of cells from multiple germ layers that possess some attributes of a developing embryo or organ. To use these models to interrogate human development and organogenesis, these 3D models must accurately recapitulate aspects of their in vivo counterparts. Recent advances in single-cell technologies, including sequencing and spatial approaches, have enabled efforts to better understand and directly compare organoids with native tissues. For example, single-cell genomic efforts have created cell and organ atlases that enable benchmarking of in vitro models and can also be leveraged to gain novel biological insights that can be used to further improve in vitro models. This Spotlight discusses the state of current in vitro model systems, the efforts to create large publicly available atlases of the developing human and how these data are being used to improve organoids. Limitations and perspectives on future efforts are also discussed.
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Affiliation(s)
- Charlie J. Childs
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Madeline K. Eiken
- Department of Biomedical Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
| | - Jason R. Spence
- Department of Cell and Developmental Biology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
- Department of Biomedical Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
- Department of Internal Medicine, Gastroenterology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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104
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Highly sensitive single-cell chromatin accessibility assay and transcriptome coassay with METATAC. Proc Natl Acad Sci U S A 2022; 119:e2206450119. [PMID: 36161934 PMCID: PMC9546615 DOI: 10.1073/pnas.2206450119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The thriving field of single-cell genomics allows researchers to dissect the complexity and heterogeneity of tissues at single-cell resolution at large scale, involving transcriptome and epigenome. However, single-cell chromatin accessibility profiling methods exhibit low sensitivity. Here, we increased accessible chromatin detection sensitivity in single cells with METATAC, a single-cell ATAC-seq technique, with the help of META amplification strategy and other biochemical modifications. METATAC reached the highest accessible chromatin region detection efficiency compared with existing techniques, allowing more accurate cis-regulatory element coaccessibility measurement and allele-specific chromatin accessibility analysis in complex tissue samples. In combination with a high-resolution single-cell RNA sequencing assay, we further developed a high-sensitivity joint single-cell ATAC–RNA strategy, which helps us to better resolve gene regulatory programs. Recent advances in single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) and its coassays have transformed the field of single-cell epigenomics and transcriptomics. However, the low detection efficiency of current methods has limited our understanding of the true complexity of chromatin accessibility and its relationship with gene expression in single cells. Here, we report a high-sensitivity scATAC-seq method, termed multiplexed end-tagging amplification of transposase accessible chromatin (METATAC), which detects a large number of accessible sites per cell and is compatible with automation. Our high detectability and statistical framework allowed precise linking of enhancers to promoters without merging single cells. We systematically investigated allele-specific accessibility in the mouse cerebral cortex, revealing allele-specific accessibility of promotors of certain imprinted genes but biallelic accessibility of their enhancers. Finally, we combined METATAC with our high-sensitivity single-cell RNA sequencing (scRNA-seq) method, multiple annealing and looping based amplification cycles for digital transcriptomics (MALBAC-DT), to develop a joint ATAC–RNA assay, termed METATAC and MALBAC-DT coassay by sequencing (M2C-seq). M2C-seq achieved significant improvements for both ATAC and RNA compared with previous methods, with consistent performance across cell lines and early mouse embryos.
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105
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Griffin A, Mahesh A, Tiwari VK. Disruption of the gene regulatory programme in neurodevelopmental disorders. BIOCHIMICA ET BIOPHYSICA ACTA. GENE REGULATORY MECHANISMS 2022; 1865:194860. [PMID: 36007842 DOI: 10.1016/j.bbagrm.2022.194860] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
Cortical development consists of a series of synchronised events, including fate transition of cortical progenitors, neuronal migration, specification and connectivity. It is becoming clear that gene expression programs governing these events rely on the interplay between signalling molecules, transcription factors and epigenetic mechanisms. When genetic or environmental factors disrupt expression of genes involved in important brain development processes, neurodevelopmental disorders can occur. This review aims to highlight how recent advances in technologies have helped uncover and imitate the gene regulatory mechanisms commonly disrupted in neurodevelopmental disorders.
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Affiliation(s)
- Aoife Griffin
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University, Belfast BT9 7BL, United Kingdom
| | - Arun Mahesh
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University, Belfast BT9 7BL, United Kingdom
| | - Vijay K Tiwari
- Wellcome-Wolfson Institute for Experimental Medicine, School of Medicine, Dentistry & Biomedical Science, Queens University, Belfast BT9 7BL, United Kingdom.
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106
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Das AC, Foroutan A, Qian B, Hosseini Naghavi N, Shabani K, Shooshtari P. Single-Cell Chromatin Accessibility Data Combined with GWAS Improves Detection of Relevant Cell Types in 59 Complex Phenotypes. Int J Mol Sci 2022; 23:ijms231911456. [PMID: 36232752 PMCID: PMC9570273 DOI: 10.3390/ijms231911456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Several disease risk variants reside on non-coding regions of DNA, particularly in open chromatin regions of specific cell types. Identifying the cell types relevant to complex traits through the integration of chromatin accessibility data and genome-wide association studies (GWAS) data can help to elucidate the mechanisms of these traits. In this study, we created a collection of associations between the combinations of chromatin accessibility data (bulk and single-cell) with an array of 201 complex phenotypes. We integrated the GWAS data of these 201 phenotypes with bulk chromatin accessibility data from 137 cell types measured by DNase-I hypersensitive sequencing and found significant results (FDR adjusted p-value ≤ 0.05) for at least one cell type in 21 complex phenotypes, such as atopic dermatitis, Graves’ disease, and body mass index. With the integration of single-cell chromatin accessibility data measured by an assay for transposase-accessible chromatin with high-throughput sequencing (scATAC-seq), taken from 111 adult and 111 fetal cell types, the resolution of association was magnified, enabling the identification of further cell types. This resulted in the identification of significant correlations (FDR adjusted p-value ≤ 0.05) between 15 categories of single-cell subtypes and 59 phenotypes ranging from autoimmune diseases like Graves’ disease to cardiovascular traits like diastolic/systolic blood pressure.
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Affiliation(s)
- Akash Chandra Das
- Department of Pathology and Laboratory Medicine, Western University, London, ON N6A 3K7, Canada
- Children’s Health Research Institute, Lawson Research Institute, London, ON N6C 2R5, Canada
- Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Guwahati 781039, India
| | - Aidin Foroutan
- Department of Pathology and Laboratory Medicine, Western University, London, ON N6A 3K7, Canada
- Children’s Health Research Institute, Lawson Research Institute, London, ON N6C 2R5, Canada
| | - Brian Qian
- Department of Pathology and Laboratory Medicine, Western University, London, ON N6A 3K7, Canada
- Children’s Health Research Institute, Lawson Research Institute, London, ON N6C 2R5, Canada
| | - Nader Hosseini Naghavi
- Children’s Health Research Institute, Lawson Research Institute, London, ON N6C 2R5, Canada
- Department of Computer Science, Western University, London, ON N6A 5B7, Canada
| | - Kayvan Shabani
- Children’s Health Research Institute, Lawson Research Institute, London, ON N6C 2R5, Canada
- Department of Computer Science, Western University, London, ON N6A 5B7, Canada
| | - Parisa Shooshtari
- Department of Pathology and Laboratory Medicine, Western University, London, ON N6A 3K7, Canada
- Children’s Health Research Institute, Lawson Research Institute, London, ON N6C 2R5, Canada
- Department of Computer Science, Western University, London, ON N6A 5B7, Canada
- Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
- Correspondence:
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107
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Dong X, Tang K, Xu Y, Wei H, Han T, Wang C. Single-cell gene regulation network inference by large-scale data integration. Nucleic Acids Res 2022; 50:e126. [PMID: 36155797 PMCID: PMC9756951 DOI: 10.1093/nar/gkac819] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 08/11/2022] [Accepted: 09/14/2022] [Indexed: 12/24/2022] Open
Abstract
Single-cell ATAC-seq (scATAC-seq) has proven to be a state-of-art approach to investigating gene regulation at the single-cell level. However, existing methods cannot precisely uncover cell-type-specific binding of transcription regulators (TRs) and construct gene regulation networks (GRNs) in single-cell. ChIP-seq has been widely used to profile TR binding sites in the past decades. Here, we developed SCRIP, an integrative method to infer single-cell TR activity and targets based on the integration of scATAC-seq and a large-scale TR ChIP-seq reference. Our method showed improved performance in evaluating TR binding activity compared to the existing motif-based methods and reached a higher consistency with matched TR expressions. Besides, our method enables identifying TR target genes as well as building GRNs at the single-cell resolution based on a regulatory potential model. We demonstrate SCRIP's utility in accurate cell-type clustering, lineage tracing, and inferring cell-type-specific GRNs in multiple biological systems. SCRIP is freely available at https://github.com/wanglabtongji/SCRIP.
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Affiliation(s)
| | | | - Yunfan Xu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China,Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Hailin Wei
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China,Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Tong Han
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, Tongji Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China,Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Chenfei Wang
- To whom correspondence should be addressed. Tel: +86 21 65981195; Fax: +86 21 65981195;
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108
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Cao Y, Fu L, Wu J, Peng Q, Nie Q, Zhang J, Xie X. Integrated analysis of multimodal single-cell data with structural similarity. Nucleic Acids Res 2022; 50:e121. [PMID: 36130281 PMCID: PMC9757079 DOI: 10.1093/nar/gkac781] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 08/15/2022] [Accepted: 09/02/2022] [Indexed: 12/24/2022] Open
Abstract
Multimodal single-cell sequencing technologies provide unprecedented information on cellular heterogeneity from multiple layers of genomic readouts. However, joint analysis of two modalities without properly handling the noise often leads to overfitting of one modality by the other and worse clustering results than vanilla single-modality analysis. How to efficiently utilize the extra information from single cell multi-omics to delineate cell states and identify meaningful signal remains as a significant computational challenge. In this work, we propose a deep learning framework, named SAILERX, for efficient, robust, and flexible analysis of multi-modal single-cell data. SAILERX consists of a variational autoencoder with invariant representation learning to correct technical noises from sequencing process, and a multimodal data alignment mechanism to integrate information from different modalities. Instead of performing hard alignment by projecting both modalities to a shared latent space, SAILERX encourages the local structures of two modalities measured by pairwise similarities to be similar. This strategy is more robust against overfitting of noises, which facilitates various downstream analysis such as clustering, imputation, and marker gene detection. Furthermore, the invariant representation learning part enables SAILERX to perform integrative analysis on both multi- and single-modal datasets, making it an applicable and scalable tool for more general scenarios.
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Affiliation(s)
| | | | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
| | - Qinke Peng
- Systems Engineering Institute, School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shannxi 710049, China
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, CA 92697, USA,Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA,NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697, USA
| | - Jing Zhang
- To whom correspondence should be addressed. Tel: +1 949 824 9979;
| | - Xiaohui Xie
- Correspondence may also be addressed to Xiaohui Xie. Tel: +1 949 824 9289;
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109
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Pan L, Shan S, Tremmel R, Li W, Liao Z, Shi H, Chen Q, Zhang X, Li X. HTCA: a database with an in-depth characterization of the single-cell human transcriptome. Nucleic Acids Res 2022; 51:D1019-D1028. [PMID: 36130266 PMCID: PMC9825435 DOI: 10.1093/nar/gkac791] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/12/2022] [Accepted: 09/02/2022] [Indexed: 01/30/2023] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) is one of the most used single-cell omics in recent decades. The exponential growth of single-cell data has immense potential for large-scale integration and in-depth explorations that are more representative of the study population. Efforts have been made to consolidate published data, yet extensive characterization is still lacking. Many focused on raw-data database constructions while others concentrate mainly on gene expression queries. Hereby, we present HTCA (www.htcatlas.org), an interactive database constructed based on ∼2.3 million high-quality cells from ∼3000 scRNA-seq samples and comprised in-depth phenotype profiles of 19 healthy adult and matching fetal tissues. HTCA provides a one-stop interactive query to gene signatures, transcription factor (TF) activities, TF motifs, receptor-ligand interactions, enriched gene ontology (GO) terms, etc. across cell types in adult and fetal tissues. At the same time, HTCA encompasses single-cell splicing variant profiles of 16 adult and fetal tissues, spatial transcriptomics profiles of 11 adult and fetal tissues, and single-cell ATAC-sequencing (scATAC-seq) profiles of 27 adult and fetal tissues. Besides, HTCA provides online analysis tools to perform major steps in a typical scRNA-seq analysis. Altogether, HTCA allows real-time explorations of multi-omics adult and fetal phenotypic profiles and provides tools for a flexible scRNA-seq analysis.
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Affiliation(s)
| | | | - Roman Tremmel
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart 70376, Germany,University of Tuebingen, Tuebingen 72076, Germany
| | - Weiyuan Li
- School of Medicine, Yunnan University, Kunnan, Yunnan 650091, China
| | - Zehuan Liao
- Department of Microbiology, Tumor, and Cell Biology, Karolinska Institute, Solna 17177, Sweden,School of Biological Sciences, Nanyang Technological University, 637 551, Singapore
| | - Hangyu Shi
- Department of Acupuncture, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100029, China
| | - Qishuang Chen
- Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xiaolu Zhang
- Correspondence may also be addressed to Xiaolu Zhang.
| | - Xuexin Li
- To whom correspondence should be addressed. Tel: +46 0704998515;
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110
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Shi P, Nie Y, Yang J, Zhang W, Tang Z, Xu J. Fundamental and practical approaches for single-cell ATAC-seq analysis. ABIOTECH 2022; 3:212-223. [PMID: 36313930 PMCID: PMC9590475 DOI: 10.1007/s42994-022-00082-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/07/2022] [Indexed: 11/28/2022]
Abstract
Assays for transposase-accessible chromatin through high-throughput sequencing (ATAC-seq) are effective tools in the study of genome-wide chromatin accessibility landscapes. With the rapid development of single-cell technology, open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches. The application of scATAC-seq has become as popular as that of scRNA-seq. However, owing to the nature of scATAC-seq data, which are sparse and noisy, processing the data requires different methodologies and empirical experience. This review presents a practical guide for processing scATAC-seq data, from quality evaluation to downstream analysis, for various applications. In addition to the epigenomic profiling from scATAC-seq, we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage. We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.
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Affiliation(s)
- Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Yage Nie
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jiawen Yang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Weixing Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Zhongjie Tang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
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Sreenivasan VKA, Balachandran S, Spielmann M. The role of single-cell genomics in human genetics. J Med Genet 2022; 59:827-839. [PMID: 35790352 PMCID: PMC9411920 DOI: 10.1136/jmedgenet-2022-108588] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/06/2022] [Indexed: 11/16/2022]
Abstract
Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.
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Affiliation(s)
- Varun K A Sreenivasan
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck and Kiel, Germany
| | - Saranya Balachandran
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck and Kiel, Germany
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck and Kiel, Germany
- Human Molecular Genetics Group, Max Planck Institute for Molecular Genetics, Berlin, Germany
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112
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Downes DJ, Hughes JR. Natural and Experimental Rewiring of Gene Regulatory Regions. Annu Rev Genomics Hum Genet 2022; 23:73-97. [PMID: 35472292 DOI: 10.1146/annurev-genom-112921-010715] [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] [Indexed: 11/09/2022]
Abstract
The successful development and ongoing functioning of complex organisms depend on the faithful execution of the genetic code. A critical step in this process is the correct spatial and temporal expression of genes. The highly orchestrated transcription of genes is controlled primarily by cis-regulatory elements: promoters, enhancers, and insulators. The medical importance of this key biological process can be seen by the frequency with which mutations and inherited variants that alter cis-regulatory elements lead to monogenic and complex diseases and cancer. Here, we provide an overview of the methods available to characterize and perturb gene regulatory circuits. We then highlight mechanisms through which regulatory rewiring contributes to disease, and conclude with a perspective on how our understanding of gene regulation can be used to improve human health.
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Affiliation(s)
- Damien J Downes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;
| | - Jim R Hughes
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;
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113
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Song H, Bucher S, Rosenberg K, Tsui M, Burhan D, Hoffman D, Cho SJ, Rangaswami A, Breese M, Leung S, Ventura MVP, Sweet-Cordero EA, Huang FW, Nijagal A, Wang B. Single-cell analysis of hepatoblastoma identifies tumor signatures that predict chemotherapy susceptibility using patient-specific tumor spheroids. Nat Commun 2022; 13:4878. [PMID: 36008377 PMCID: PMC9411569 DOI: 10.1038/s41467-022-32473-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 08/02/2022] [Indexed: 11/22/2022] Open
Abstract
Pediatric hepatoblastoma is the most common primary liver cancer in infants and children. Studies of hepatoblastoma that focus exclusively on tumor cells demonstrate sparse somatic mutations and a common cell of origin, the hepatoblast, across patients. In contrast to the homogeneity these studies would suggest, hepatoblastoma tumors have a high degree of heterogeneity that can portend poor prognosis. In this study, we use single-cell transcriptomic techniques to analyze resected human pediatric hepatoblastoma specimens, and identify five hepatoblastoma tumor signatures that may account for the tumor heterogeneity observed in this disease. Notably, patient-derived hepatoblastoma spheroid cultures predict differential responses to treatment based on the transcriptomic signature of each tumor, suggesting a path forward for precision oncology for these tumors. In this work, we define hepatoblastoma tumor heterogeneity with single-cell resolution and demonstrate that patient-derived spheroids can be used to evaluate responses to chemotherapy.
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Affiliation(s)
- Hanbing Song
- Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, 94143, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, 94143, USA
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Simon Bucher
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Katherine Rosenberg
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA
- Division of Pediatric Surgery, Department of Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Margaret Tsui
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Deviana Burhan
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Daniel Hoffman
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA
- Division of Pediatric Surgery, Department of Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Soo-Jin Cho
- Department of Pathology, University of California, San Francisco, San Francisco, CA, 94143, USA
- The Pediatric Liver Center at UCSF Benioff Childrens' Hospitals, San Francisco, CA, 94143, USA
| | - Arun Rangaswami
- The Pediatric Liver Center at UCSF Benioff Childrens' Hospitals, San Francisco, CA, 94143, USA
- Division of Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Marcus Breese
- Division of Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Stanley Leung
- Division of Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - María V Pons Ventura
- Division of Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - E Alejandro Sweet-Cordero
- The Pediatric Liver Center at UCSF Benioff Childrens' Hospitals, San Francisco, CA, 94143, USA
- Division of Oncology, Department of Pediatrics, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Franklin W Huang
- Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Department of Medicine, San Francisco Veterans Affairs Medical Center, San Francisco, CA, 94121, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
| | - Amar Nijagal
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA.
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Division of Pediatric Surgery, Department of Surgery, University of California, San Francisco, San Francisco, CA, 94143, USA.
- The Pediatric Liver Center at UCSF Benioff Childrens' Hospitals, San Francisco, CA, 94143, USA.
- Eli and Edythe Broad Center of Regeneration Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA.
| | - Bruce Wang
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA.
- Division of Gastroenterology, Department of Medicine, University of California, San Francisco, San Francisco, CA, 94143, USA.
- The Liver Center, University of California, San Francisco, San Francisco, CA, 94143, USA.
- The Pediatric Liver Center at UCSF Benioff Childrens' Hospitals, San Francisco, CA, 94143, USA.
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Miguel-Escalada I, Maestro MÁ, Balboa D, Elek A, Bernal A, Bernardo E, Grau V, García-Hurtado J, Sebé-Pedrós A, Ferrer J. Pancreas agenesis mutations disrupt a lead enhancer controlling a developmental enhancer cluster. Dev Cell 2022; 57:1922-1936.e9. [PMID: 35998583 PMCID: PMC9426562 DOI: 10.1016/j.devcel.2022.07.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 05/30/2022] [Accepted: 07/21/2022] [Indexed: 12/04/2022]
Abstract
Sequence variants in cis-acting enhancers are important for polygenic disease, but their role in Mendelian disease is poorly understood. Redundancy between enhancers that regulate the same gene is thought to mitigate the pathogenic impact of enhancer mutations. Recent findings, however, have shown that loss-of-function mutations in a single enhancer near PTF1A cause pancreas agenesis and neonatal diabetes. Using mouse and human genetic models, we show that this enhancer activates an entire PTF1A enhancer cluster in early pancreatic multipotent progenitors. This leading role, therefore, precludes functional redundancy. We further demonstrate that transient expression of PTF1A in multipotent progenitors sets in motion an epigenetic cascade that is required for duct and endocrine differentiation. These findings shed insights into the genome regulatory mechanisms that drive pancreas differentiation. Furthermore, they reveal an enhancer that acts as a regulatory master key and is thus vulnerable to pathogenic loss-of-function mutations. The pancreas agenesis enhancer (EnhP) activates PTF1A in early pancreatic progenitors EnhP also activates other progenitor PTF1A enhancers This master key function explains why EnhP is vulnerable to loss-of-function mutations Transient PTF1A expression in progenitors controls pancreas growth and endocrinogenesis
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Affiliation(s)
- Irene Miguel-Escalada
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain.
| | - Miguel Ángel Maestro
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Diego Balboa
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Anamaria Elek
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
| | - Aina Bernal
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Edgar Bernardo
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Vanessa Grau
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Javier García-Hurtado
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Arnau Sebé-Pedrós
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain
| | - Jorge Ferrer
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain; CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain; Genetics and Genomics Section, Department of Metabolism, Digestion and Reproduction, National Institute for Health Research (NIHR) Imperial Biomedical Research Centre, Imperial College London, London W12 0NN, UK; Universitat Pompeu Fabra (UPF), Barcelona 08003, Spain.
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115
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Dhapola P, Rodhe J, Olofzon R, Bonald T, Erlandsson E, Soneji S, Karlsson G. Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data. Nat Commun 2022; 13:4616. [PMID: 35941103 PMCID: PMC9360040 DOI: 10.1038/s41467-022-32097-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 07/18/2022] [Indexed: 12/11/2022] Open
Abstract
As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf’s memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf. As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Here the authors present Scarf, a modularly designed Python package that makes the analysis workflow highly memory efficient such that even the largest existing datasets can be analyzed on an average modern laptop.
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Affiliation(s)
- Parashar Dhapola
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden.
| | - Johan Rodhe
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Rasmus Olofzon
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | | | - Eva Erlandsson
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Shamit Soneji
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Göran Karlsson
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden.
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116
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Calderon D, Blecher-Gonen R, Huang X, Secchia S, Kentro J, Daza RM, Martin B, Dulja A, Schaub C, Trapnell C, Larschan E, O’Connor-Giles KM, Furlong EEM, Shendure J. The continuum of Drosophila embryonic development at single-cell resolution. Science 2022; 377:eabn5800. [PMID: 35926038 PMCID: PMC9371440 DOI: 10.1126/science.abn5800] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Drosophila melanogaster is a powerful, long-standing model for metazoan development and gene regulation. We profiled chromatin accessibility in almost 1 million and gene expression in half a million nuclei from overlapping windows spanning the entirety of embryogenesis. Leveraging developmental asynchronicity within embryo collections, we applied deep neural networks to infer the age of each nucleus, resulting in continuous, multimodal views of molecular and cellular transitions in absolute time. We identify cell lineages; infer their developmental relationships; and link dynamic changes in enhancer usage, transcription factor (TF) expression, and the accessibility of TFs' cognate motifs. With these data, the dynamics of enhancer usage and gene expression can be explored within and across lineages at the scale of minutes, including for precise transitions like zygotic genome activation.
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Affiliation(s)
- Diego Calderon
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Ronnie Blecher-Gonen
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- The Crown Genomics Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot, Israel
| | - Xingfan Huang
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Stefano Secchia
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - James Kentro
- Molecular Biology, Cell Biology, and Biochemistry Graduate Program, Brown University, Providence, RI 02912, USA
| | - Riza M. Daza
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Beth Martin
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Alessandro Dulja
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Christoph Schaub
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA 98195, USA
| | - Erica Larschan
- Molecular Biology, Cell Biology, and Biochemistry Graduate Program, Brown University, Providence, RI 02912, USA
| | - Kate M. O’Connor-Giles
- Department of Neuroscience and Carney Institute for Brain Science, Brown University, Providence, RI 02912, USA
| | - Eileen E. M. Furlong
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA 98195, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA 98195, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
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117
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Lee IS, Takebe T. Narrative engineering of the liver. Curr Opin Genet Dev 2022; 75:101925. [PMID: 35700688 PMCID: PMC10118678 DOI: 10.1016/j.gde.2022.101925] [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: 03/13/2022] [Revised: 05/06/2022] [Accepted: 05/08/2022] [Indexed: 11/30/2022]
Abstract
Liver organoids are primary or pluripotent stem cell-derived three-dimensional structures that recapitulate regenerative or ontogenetic processes in vitro towards biomedical applications including disease modelling and diagnostics, drug safety and efficacy prediction, and therapeutic use. The cellular composition and structural organization of liver organoids may vary depending on the goal at hand, and the key challenge in general is to direct their development in a rational and controlled fashion for gaining targeted maturity, reproducibility, and scalability. Such endeavor begins with a detailed understanding of the biological processes in space and time behind hepatogenesis, followed by precise translation of these narrative processes through a bioengineering approach. Here, we discuss advancements in liver organoid technology through the lens of 'narrative engineering' in an attempt to synergize evolving understanding around molecular and cellular landscape governing hepatogenesis with engineering-inspired approaches for organoidgenesis.
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Affiliation(s)
- Inkyu S Lee
- Division of Gastroenterology, Hepatology & Nutrition, Developmental Biology, Center for Stem Cell and Organoid Medicine (CuSTOM), Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA
| | - Takanori Takebe
- Division of Gastroenterology, Hepatology & Nutrition, Developmental Biology, Center for Stem Cell and Organoid Medicine (CuSTOM), Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229-3039, USA; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA; Institute of Research, Tokyo Medical and Dental University (TMDU), Tokyo, Japan; Communication Design Center, Advanced Medical Research Center, Yokohama City University Graduate School of Medicine, Japan.
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118
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Temporally divergent regulatory mechanisms govern neuronal diversification and maturation in the mouse and marmoset neocortex. Nat Neurosci 2022; 25:1049-1058. [PMID: 35915179 PMCID: PMC9343253 DOI: 10.1038/s41593-022-01123-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/16/2022] [Indexed: 11/08/2022]
Abstract
Mammalian neocortical neurons span one of the most diverse cell type spectra of any tissue. Cortical neurons are born during embryonic development, and their maturation extends into postnatal life. The regulatory strategies underlying progressive neuronal development and maturation remain unclear. Here we present an integrated single-cell epigenomic and transcriptional analysis of individual mouse and marmoset cortical neuron classes, spanning both early postmitotic stages of identity acquisition and later stages of neuronal plasticity and circuit integration. We found that, in both species, the regulatory strategies controlling early and late stages of pan-neuronal development diverge. Early postmitotic neurons use more widely shared and evolutionarily conserved molecular regulatory programs. In contrast, programs active during later neuronal maturation are more brain- and neuron-specific and more evolutionarily divergent. Our work uncovers a temporal shift in regulatory choices during neuronal diversification and maturation in both mice and marmosets, which likely reflects unique evolutionary constraints on distinct events of neuronal development in the neocortex.
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119
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Abstract
The single-cell revolution in the field of genomics is in full bloom, with clever new molecular biology tricks appearing regularly that allow researchers to explore new modalities or scale up their projects to millions of cells and beyond. Techniques abound to measure RNA expression, DNA alterations, protein abundance, chromatin accessibility, and more, all with single-cell resolution and often in combination. Despite such a rapidly changing technology landscape, there are several fundamental principles that are applicable to the majority of experimental workflows to help users avoid pitfalls and exploit the advantages of the chosen platform. In this overview article, we describe a variety of popular single-cell genomics technologies and address some common questions pertaining to study design, sample preparation, quality control, and sequencing strategy. As the majority of relevant publications currently revolve around single-cell RNA-seq, we will prioritize this genomics modality in our discussion. © 2022 Wiley Periodicals LLC.
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Affiliation(s)
- Claire Regan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
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120
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Lin L, Zhang L. Joint analysis of scATAC-seq datasets using epiConv. BMC Bioinformatics 2022; 23:309. [PMID: 35906531 PMCID: PMC9338487 DOI: 10.1186/s12859-022-04858-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 07/20/2022] [Indexed: 11/14/2022] Open
Abstract
Background Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information. Results Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable. Conclusions In this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04858-w.
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Affiliation(s)
- Li Lin
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Liye Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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121
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Yuan Q, Duren Z. Integration of single-cell multi-omics data by regression analysis on unpaired observations. Genome Biol 2022; 23:160. [PMID: 35854350 PMCID: PMC9295346 DOI: 10.1186/s13059-022-02726-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/06/2022] [Indexed: 12/04/2022] Open
Abstract
Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.
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Affiliation(s)
- Qiuyue Yuan
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA
| | - Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA.
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122
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Tu X, Marand AP, Schmitz RJ, Zhong S. A combinatorial indexing strategy for low-cost epigenomic profiling of plant single cells. PLANT COMMUNICATIONS 2022; 3:100308. [PMID: 35605196 PMCID: PMC9284282 DOI: 10.1016/j.xplc.2022.100308] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 06/15/2023]
Abstract
Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs in animal models. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we developed a combinatorial index and dual PCR barcode strategy to profile the Arabidopsis thaliana root single-cell epigenome without any specialized equipment. We generated chromatin accessibility profiles for 13 576 root nuclei with an average of 12 784 unique Tn5 integrations per cell. Integration of the single-cell assay for transposase-accessible chromatin sequencing and RNA sequencing data sets enabled the identification of 24 cell clusters with unique transcription, chromatin, and cis-regulatory signatures. Comparison with single-cell data generated using the commercial microfluidic platform from 10X Genomics revealed that this low-cost combinatorial index method is capable of unbiased identification of cell-type-specific chromatin accessibility. We anticipate that, by removing cost, instrumentation, and other technical obstacles, this method will be a valuable tool for routine investigation of single-cell epigenomes and provide new insights into plant growth and development and plant interactions with the environment.
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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; Shanghai Collaborative Innovation Center of Agri-Seeds, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, GA 30602, USA.
| | - Silin Zhong
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Hong Kong, China.
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123
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Michaud V, Lasseaux E, Green DJ, Gerrard DT, Plaisant C, Fitzgerald T, Birney E, Arveiler B, Black GC, Sergouniotis PI. The contribution of common regulatory and protein-coding TYR variants to the genetic architecture of albinism. Nat Commun 2022; 13:3939. [PMID: 35803923 PMCID: PMC9270319 DOI: 10.1038/s41467-022-31392-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 06/15/2022] [Indexed: 12/13/2022] Open
Abstract
Genetic diseases have been historically segregated into rare Mendelian disorders and common complex conditions. Large-scale studies using genome sequencing are eroding this distinction and are gradually unmasking the underlying complexity of human traits. Here, we analysed data from the Genomics England 100,000 Genomes Project and from a cohort of 1313 individuals with albinism aiming to gain insights into the genetic architecture of this archetypal rare disorder. We investigated the contribution of protein-coding and regulatory variants both rare and common. We focused on TYR, the gene encoding tyrosinase, and found that a high-frequency promoter variant, TYR c.-301C>T [rs4547091], modulates the penetrance of a prevalent, albinism-associated missense change, TYR c.1205G>A (p.Arg402Gln) [rs1126809]. We also found that homozygosity for a haplotype formed by three common, functionally-relevant variants, TYR c.[-301C;575C>A;1205G>A], is associated with a high probability of receiving an albinism diagnosis (OR>82). This genotype is also associated with reduced visual acuity and with increased central retinal thickness in UK Biobank participants. Finally, we report how the combined analysis of rare and common variants can increase diagnostic yield and can help inform genetic counselling in families with albinism.
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Affiliation(s)
- Vincent Michaud
- Department of Medical Genetics, University Hospital of Bordeaux, Bordeaux, France
- INSERM U1211, Rare Diseases, Genetics and Metabolism, University of Bordeaux, Bordeaux, France
| | - Eulalie Lasseaux
- Department of Medical Genetics, University Hospital of Bordeaux, Bordeaux, France
| | - David J Green
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Dave T Gerrard
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Claudio Plaisant
- Department of Medical Genetics, University Hospital of Bordeaux, Bordeaux, France
| | - Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL- EBI), Wellcome Genome Campus, Cambridge, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL- EBI), Wellcome Genome Campus, Cambridge, UK
| | - Benoît Arveiler
- Department of Medical Genetics, University Hospital of Bordeaux, Bordeaux, France.
- INSERM U1211, Rare Diseases, Genetics and Metabolism, University of Bordeaux, Bordeaux, France.
| | - Graeme C Black
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
| | - Panagiotis I Sergouniotis
- Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL- EBI), Wellcome Genome Campus, Cambridge, UK.
- Manchester Centre for Genomic Medicine, Saint Mary's Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
- Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK.
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124
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Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development. Nat Genet 2022; 54:1051-1061. [PMID: 35817981 DOI: 10.1038/s41588-022-01118-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 06/01/2022] [Indexed: 12/21/2022]
Abstract
Waddington's epigenetic landscape is a metaphor frequently used to illustrate cell differentiation. Recent advances in single-cell genomics are altering our understanding of the Waddington landscape, yet the molecular mechanisms of cell-fate decisions remain poorly understood. We constructed a cell landscape of mouse lineage differentiation during development at the single-cell level and described both lineage-common and lineage-specific regulatory programs during cell-type maturation. We also found lineage-common regulatory programs that are broadly active during the development of invertebrates and vertebrates. In particular, we identified Xbp1 as an evolutionarily conserved regulator of cell-fate determinations across different species. We demonstrated that Xbp1 transcriptional regulation is important for the stabilization of the gene-regulatory networks for a wide range of mouse cell types. Our results offer genetic and molecular insights into cellular gene-regulatory programs and will serve as a basis for further advancing the understanding of cell-fate decisions.
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125
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Zhang X, Qiu H, Zhang F, Ding S. Advances in Single-Cell Multi-Omics and Application in Cardiovascular Research. Front Cell Dev Biol 2022; 10:883861. [PMID: 35733851 PMCID: PMC9207481 DOI: 10.3389/fcell.2022.883861] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/23/2022] [Indexed: 12/30/2022] Open
Abstract
With the development of ever more powerful and versatile high-throughput sequencing techniques and innovative ways to capture single cells, mapping the multicellular tissues at the single-cell level is becoming routine practice. However, it is still challenging to depict the epigenetic landscape of a single cell, especially the genome-wide chromatin accessibility, histone modifications, and DNA methylation. We summarize the most recent methodologies to profile these epigenetic marks at the single-cell level. We also discuss the development and advancement of several multi-omics sequencing technologies from individual cells. Advantages and limitations of various methods to compare and integrate datasets obtained from different sources are also included with specific practical notes. Understanding the heart tissue at single-cell resolution and multi-modal levels will help to elucidate the cell types and states involved in physiological and pathological events during heart development and disease. The rich information produced from single-cell multi-omics studies will also promote the research of heart regeneration and precision medicine on heart diseases.
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Affiliation(s)
- Xingwu Zhang
- Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China
| | - Hui Qiu
- Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Fengzhi Zhang
- First Hospital of Tsinghua University, Beijing, China
| | - Shuangyuan Ding
- Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China
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126
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Conrad T, Altmüller J. Single cell- and spatial 'Omics revolutionize physiology. Acta Physiol (Oxf) 2022; 235:e13848. [PMID: 35656634 DOI: 10.1111/apha.13848] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 04/24/2022] [Accepted: 05/27/2022] [Indexed: 11/29/2022]
Abstract
Single cell multi- 'Omics and Spatial Transcriptomics are prominent technological highlights of recent years, and both fields still witness a ceaseless firework of novel approaches for high resolution profiling of additional omics layers. As all life processes in organs and organisms are based on the functions of their fundamental building blocks, the individual cells and their interactions, these methods are of utmost worth for the study of physiology in health and disease. Recent discoveries on embryonic development, tumor immunology, the detailed cellular composition and function of complex tissues like for example the kidney or the brain, different roles of the same cell type in different organs, the oncogenic program of individual tumor entities, or the architecture of immunopathology in infected tissue are based on single cell and spatial transcriptomics experiments. In this review, we will give a broad overview of technological concepts for single cell and spatial analysis, showing both advantages and limitations, and illustrate their impact with some particularly impressive case studies.
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Affiliation(s)
- Thomas Conrad
- Genomics Technology Platform Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany
| | - Janine Altmüller
- Genomics Technology Platform Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany
- Core Facility Genomics Berlin Institute of Health at Charité ‐ Universitätsmedizin Berlin Berlin Germany
- Center for Molecular Medicine Cologne (CMMC) Cologne Germany
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127
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Turner AW, Hu SS, Mosquera JV, Ma WF, Hodonsky CJ, Wong D, Auguste G, Song Y, Sol-Church K, Farber E, Kundu S, Kundaje A, Lopez NG, Ma L, Ghosh SKB, Onengut-Gumuscu S, Ashley EA, Quertermous T, Finn AV, Leeper NJ, Kovacic JC, Björkegren JLM, Zang C, Miller CL. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat Genet 2022; 54:804-816. [PMID: 35590109 PMCID: PMC9203933 DOI: 10.1038/s41588-022-01069-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 03/31/2022] [Indexed: 12/24/2022]
Abstract
Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements. Here, we applied single-nucleus assay for transposase-accessible chromatin with sequencing to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell-type-specific elements and transcription factors, and prioritized functional CAD risk variants. We identified elements in smooth muscle cell transition states (for example, fibromyocytes) and functional variants predicted to alter smooth muscle cell- and macrophage-specific regulation of MRAS (3q22) and LIPA (10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single-nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.
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Affiliation(s)
- Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Shengen Shawn Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
| | - Wei Feng Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Medical Scientist Training Program, University of Virginia, Charlottesville, VA, USA
- Department of Pathology, University of Virginia, Charlottesville, VA, USA
| | - Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Yipei Song
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Katia Sol-Church
- Department of Pathology, University of Virginia, Charlottesville, VA, USA
- Genome Analysis & Technology Core, University of Virginia, Charlottesville, VA, USA
| | - Emily Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Genome Sciences Laboratory, University of Virginia, Charlottesville, VA, USA
| | - Soumya Kundu
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Nicolas G Lopez
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Genome Sciences Laboratory, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Euan A Ashley
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Thomas Quertermous
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | | | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- St. Vincent's Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Integrated Cardio Metabolic Centre, Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Chongzhi Zang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
- Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA.
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
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128
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Duren Z, Chang F, Naqing F, Xin J, Liu Q, Wong WH. Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG. Genome Biol 2022; 23:114. [PMID: 35578363 PMCID: PMC9109353 DOI: 10.1186/s13059-022-02682-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 04/29/2022] [Indexed: 12/12/2022] Open
Abstract
Technological development has enabled the profiling of gene expression and chromatin accessibility from the same cell. We develop scREG, a dimension reduction methodology, based on the concept of cis-regulatory potential, for single cell multiome data. This concept is further used for the construction of subpopulation-specific cis-regulatory networks. The capability of inferring useful regulatory network is demonstrated by the two-fold increment on network inference accuracy compared to the Pearson correlation-based method and the 27-fold enrichment of GWAS variants for inflammatory bowel disease in the cis-regulatory elements. The R package scREG provides comprehensive functions for single cell multiome data analysis.
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Affiliation(s)
- Zhana Duren
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA.
| | - Fengge Chang
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA
| | - Fnu Naqing
- Center for Human Genetics and Department of Genetics and Biochemistry, Clemson University, Greenwood, SC, 29646, USA
| | - Jingxue Xin
- Department of Statistics, Department of Biomedical Data Science and Bio-X Program, Stanford University, Stanford, CA, 94305, USA
| | - Qiao Liu
- Department of Statistics, Department of Biomedical Data Science and Bio-X Program, Stanford University, Stanford, CA, 94305, USA
| | - Wing Hung Wong
- Department of Statistics, Department of Biomedical Data Science and Bio-X Program, Stanford University, Stanford, CA, 94305, USA.
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129
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Vinsland E, Linnarsson S. Single-cell RNA-sequencing of mammalian brain development: insights and future directions. Development 2022; 149:275457. [DOI: 10.1242/dev.200180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
ABSTRACT
Understanding human brain development is of fundamental interest but is also very challenging. Single-cell RNA-sequencing studies in mammals have revealed that brain development is a highly dynamic process with tremendous, previously concealed, cellular heterogeneity. This Spotlight discusses key insights from these studies and their implications for experimental models. We survey published single-cell RNA-sequencing studies of mouse and human brain development, organized by anatomical regions and developmental time points. We highlight remaining gaps in the field, predominantly concerning human brain development. We propose future directions to fill the remaining gaps, and necessary complementary techniques to create an atlas integrated in space and time of human brain development.
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Affiliation(s)
- Elin Vinsland
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solnavägen 9, 171 65 Stockholm, Sweden
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solnavägen 9, 171 65 Stockholm, Sweden
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130
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Dou J, Liang S, Mohanty V, Miao Q, Huang Y, Liang Q, Cheng X, Kim S, Choi J, Li Y, Li L, Daher M, Basar R, Rezvani K, Chen R, Chen K. Bi-order multimodal integration of single-cell data. Genome Biol 2022; 23:112. [PMID: 35534898 PMCID: PMC9082907 DOI: 10.1186/s13059-022-02679-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 04/25/2022] [Indexed: 12/25/2022] Open
Abstract
Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.
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Affiliation(s)
- Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Shaoheng Liang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Qi Miao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Yuefan Huang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Qingnan Liang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Xuesen Cheng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Sangbae Kim
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Jongsu Choi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Yumei Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Li Li
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Rafet Basar
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030 USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, USA
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131
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Panara V, Monteiro R, Koltowska K. Epigenetic Regulation of Endothelial Cell Lineages During Zebrafish Development-New Insights From Technical Advances. Front Cell Dev Biol 2022; 10:891538. [PMID: 35615697 PMCID: PMC9125237 DOI: 10.3389/fcell.2022.891538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/10/2022] [Indexed: 01/09/2023] Open
Abstract
Epigenetic regulation is integral in orchestrating the spatiotemporal regulation of gene expression which underlies tissue development. The emergence of new tools to assess genome-wide epigenetic modifications has enabled significant advances in the field of vascular biology in zebrafish. Zebrafish represents a powerful model to investigate the activity of cis-regulatory elements in vivo by combining technologies such as ATAC-seq, ChIP-seq and CUT&Tag with the generation of transgenic lines and live imaging to validate the activity of these regulatory elements. Recently, this approach led to the identification and characterization of key enhancers of important vascular genes, such as gata2a, notch1b and dll4. In this review we will discuss how the latest technologies in epigenetics are being used in the zebrafish to determine chromatin states and assess the function of the cis-regulatory sequences that shape the zebrafish vascular network.
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Affiliation(s)
- Virginia Panara
- Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Rui Monteiro
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom,Birmingham Centre of Genome Biology, University of Birmingham, Birmingham, United Kingdom
| | - Katarzyna Koltowska
- Immunology Genetics and Pathology, Uppsala University, Uppsala, Sweden,*Correspondence: Katarzyna Koltowska,
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132
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Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat Biotechnol 2022; 40:1458-1466. [PMID: 35501393 PMCID: PMC9546775 DOI: 10.1038/s41587-022-01284-4] [Citation(s) in RCA: 132] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 03/15/2022] [Indexed: 12/14/2022]
Abstract
Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE. Different single-cell data modalities are integrated at atlas-scale by modeling regulatory interactions.
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133
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Li Z, Sau-Wai Ngan E. New insights empowered by single-cell sequencing: from neural crest to enteric nervous system. Comput Struct Biotechnol J 2022; 20:2464-2472. [PMID: 35664232 PMCID: PMC9133688 DOI: 10.1016/j.csbj.2022.05.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 11/03/2022] Open
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134
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Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00469-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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135
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Functional annotation of breast cancer risk loci: current progress and future directions. Br J Cancer 2022; 126:981-993. [PMID: 34741135 PMCID: PMC8980003 DOI: 10.1038/s41416-021-01612-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/20/2022] Open
Abstract
Genome-wide association studies coupled with large-scale replication and fine-scale mapping studies have identified more than 150 genomic regions that are associated with breast cancer risk. Here, we review efforts to translate these findings into a greater understanding of disease mechanism. Our review comes in the context of a recently published fine-scale mapping analysis of these regions, which reported 352 independent signals and a total of 13,367 credible causal variants. The vast majority of credible causal variants map to noncoding DNA, implicating regulation of gene expression as the mechanism by which functional variants influence risk. Accordingly, we review methods for defining candidate-regulatory sequences, methods for identifying putative target genes and methods for linking candidate-regulatory sequences to putative target genes. We provide a summary of available data resources and identify gaps in these resources. We conclude that while much work has been done, there is still much to do. There are, however, grounds for optimism; combining statistical data from fine-scale mapping with functional data that are more representative of the normal "at risk" breast, generated using new technologies, should lead to a greater understanding of the mechanisms that influence an individual woman's risk of breast cancer.
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136
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Erickson AG, Kameneva P, Adameyko I. The transcriptional portraits of the neural crest at the individual cell level. Semin Cell Dev Biol 2022; 138:68-80. [PMID: 35260294 PMCID: PMC9441473 DOI: 10.1016/j.semcdb.2022.02.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 02/04/2022] [Accepted: 02/21/2022] [Indexed: 01/15/2023]
Abstract
Since the discovery of this cell population by His in 1850, the neural crest has been under intense study for its important role during vertebrate development. Much has been learned about the function and regulation of neural crest cell differentiation, and as a result, the neural crest has become a key model system for stem cell biology in general. The experiments performed in embryology, genetics, and cell biology in the last 150 years in the neural crest field has given rise to several big questions that have been debated intensely for many years: "How does positional information impact developmental potential? Are neural crest cells individually multipotent or a mixed population of committed progenitors? What are the key factors that regulate the acquisition of stem cell identity, and how does a stem cell decide to differentiate towards one cell fate versus another?" Recently, a marriage between single cell multi-omics, statistical modeling, and developmental biology has shed a substantial amount of light on these questions, and has paved a clear path for future researchers in the field.
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Affiliation(s)
- Alek G Erickson
- Department of Physiology and Pharmacology, Karolinska Institutet, 17165 Stockholm, Sweden
| | - Polina Kameneva
- Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, 1090 Vienna, Austria
| | - Igor Adameyko
- Department of Physiology and Pharmacology, Karolinska Institutet, 17165 Stockholm, Sweden; Department of Neuroimmunology, Center for Brain Research, Medical University Vienna, 1090 Vienna, Austria.
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137
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Qiu C, Cao J, Martin BK, Li T, Welsh IC, Srivatsan S, Huang X, Calderon D, Noble WS, Disteche CM, Murray SA, Spielmann M, Moens CB, Trapnell C, Shendure J. Systematic reconstruction of cellular trajectories across mouse embryogenesis. Nat Genet 2022; 54:328-341. [PMID: 35288709 PMCID: PMC8920898 DOI: 10.1038/s41588-022-01018-x] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 01/21/2022] [Indexed: 12/12/2022]
Abstract
Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single-cell zygote gives rise to millions of cells expressing a panoply of molecular programs. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. Here, we set out to integrate several single-cell RNA-sequencing (scRNA-seq) datasets that collectively span mouse gastrulation and organogenesis, supplemented with new profiling of ~150,000 nuclei from approximately embryonic day 8.5 (E8.5) embryos staged in one-somite increments. Overall, we define cell states at each of 19 successive stages spanning E3.5 to E13.5 and heuristically connect them to their pseudoancestors and pseudodescendants. Although constructed through automated procedures, the resulting directed acyclic graph (TOME (trajectories of mammalian embryogenesis)) is largely consistent with our contemporary understanding of mammalian development. We leverage TOME to systematically nominate transcription factors (TFs) as candidate regulators of each cell type's specification, as well as 'cell-type homologs' across vertebrate evolution.
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Affiliation(s)
- Chengxiang Qiu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Junyue Cao
- The Rockefeller University, New York, NY, USA
| | - Beth K Martin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Tony Li
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Sanjay Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Xingfan Huang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Diego Calderon
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Christine M Disteche
- Department of Pathology, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Malte Spielmann
- Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, Berlin, Germany
- Institute of Human Genetics, University of Lübeck, Lübeck, Germany
| | - Cecilia B Moens
- Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
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138
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Secchia S, Forneris M, Heinen T, Stegle O, Furlong EEM. Simultaneous cellular and molecular phenotyping of embryonic mutants using single-cell regulatory trajectories. Dev Cell 2022; 57:496-511.e8. [PMID: 35176234 PMCID: PMC8893321 DOI: 10.1016/j.devcel.2022.01.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 11/04/2021] [Accepted: 01/26/2022] [Indexed: 11/09/2022]
Abstract
Developmental progression and cellular diversity are largely driven by transcription factors (TFs); yet, characterizing their loss-of-function phenotypes remains challenging and often disconnected from their underlying molecular mechanisms. Here, we combine single-cell regulatory genomics with loss-of-function mutants to jointly assess both cellular and molecular phenotypes. Performing sci-ATAC-seq at eight overlapping time points during Drosophila mesoderm development could reconstruct the developmental trajectories of all major muscle types and reveal the TFs and enhancers involved. To systematically assess mutant phenotypes, we developed a single-nucleus genotyping strategy to process embryo pools of mixed genotypes. Applying this to four TF mutants could identify and quantify their characterized phenotypes de novo and discover new ones, while simultaneously revealing their regulatory input and mode of action. Our approach is a general framework to dissect the functional input of TFs in a systematic, unbiased manner, identifying both cellular and molecular phenotypes at a scale and resolution that has not been feasible before.
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Affiliation(s)
- Stefano Secchia
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Baden-Württemberg, Germany; Collaboration for Joint PhD Degree between EMBL and Heidelberg University, Faculty of Biosciences, Baden-Württemberg, Germany
| | - Mattia Forneris
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Baden-Württemberg, Germany
| | - Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Baden-Württemberg, Germany; Heidelberg University, Faculty of Mathematics and Computer Science, 69120 Heidelberg, Baden-Württemberg, Germany
| | - Oliver Stegle
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Baden-Württemberg, Germany; Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Baden-Württemberg, Germany
| | - Eileen E M Furlong
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Baden-Württemberg, Germany.
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139
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Chen X, Chen S, Song S, Gao Z, Hou L, Zhang X, Lv H, Jiang R. Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-021-00432-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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140
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Zeng B, Bendl J, Kosoy R, Fullard JF, Hoffman GE, Roussos P. Multi-ancestry eQTL meta-analysis of human brain identifies candidate causal variants for brain-related traits. Nat Genet 2022; 54:161-169. [PMID: 35058635 PMCID: PMC8852232 DOI: 10.1038/s41588-021-00987-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 11/17/2021] [Indexed: 12/11/2022]
Abstract
While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.
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Affiliation(s)
- Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roman Kosoy
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
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141
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Yu F, Cato LD, Weng C, Liggett LA, Jeon S, Xu K, Chiang CW, Wiemels JL, Weissman JS, de Smith AJ, Sankaran VG. Variant to function mapping at single-cell resolution through network propagation.. [PMID: 35118467 PMCID: PMC8811900 DOI: 10.1101/2022.01.23.477426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
With burgeoning human disease genetic associations and single-cell genomic atlases covering a range of tissues, there are unprecedented opportunities to systematically gain insights into the mechanisms of disease-causal variation. However, sparsity and noise, particularly in the context of single-cell epigenomic data, hamper the identification of disease- or trait-relevant cell types, states, and trajectories. To overcome these challenges, we have developed the SCAVENGE method, which maps causal variants to their relevant cellular context at single-cell resolution by employing the strategy of network propagation. We demonstrate how SCAVENGE can help identify key biological mechanisms underlying human genetic variation including enrichment of blood traits at distinct stages of human hematopoiesis, defining monocyte subsets that increase the risk for severe coronavirus disease 2019 (COVID-19), and identifying intermediate lymphocyte developmental states that are critical for predisposition to acute leukemia. Our approach not only provides a framework for enabling variant-to-function insights at single-cell resolution, but also suggests a more general strategy for maximizing the inferences that can be made using single-cell genomic data.
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142
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Meng Y, Du M, Gu D, Li C, Li S, Zhang Q, Ben S, Zhu Q, Xin J, Zhang Z, Hu Z, Shen H, Jiang K, Wang M. Genome-wide association analyses identify CATSPERE as a mediator of colorectal cancer susceptibility and progression. Cancer Res 2022; 82:986-997. [DOI: 10.1158/0008-5472.can-21-2948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/12/2021] [Accepted: 01/18/2022] [Indexed: 11/16/2022]
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143
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Lin Y, Wu TY, Wan S, Yang JYH, Wong WH, Wang YXR. scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning. Nat Biotechnol 2022; 40:703-710. [DOI: 10.1038/s41587-021-01161-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 11/16/2021] [Indexed: 12/11/2022]
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144
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Cornejo-Paramo P, Roper K, Degnan S, Degnan B, Wong ES. Distal regulation, silencers, and a shared combinatorial syntax are hallmarks of animal embryogenesis. Genome Res 2022; 32:474-487. [PMID: 35045977 PMCID: PMC8896464 DOI: 10.1101/gr.275864.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 01/12/2022] [Indexed: 11/25/2022]
Abstract
The chromatin environment plays a central role in regulating developmental gene expression in metazoans. Yet, the ancestral regulatory landscape of metazoan embryogenesis is unknown. Here, we generate chromatin accessibility profiles for six embryonic, plus larval and adult stages in the sponge Amphimedon queenslandica. These profiles are reproducible within stages, reflect histone modifications, and identify transcription factor (TF) binding sequence motifs predictive of cis-regulatory elements operating during embryogenesis in other metazoans, but not the unicellular relative Capsaspora. Motif analysis of chromatin accessibility profiles across Amphimedon embryogenesis identifies three major developmental periods. As in bilaterian embryogenesis, early development in Amphimedon involves activating and repressive chromatin in regions both proximal and distal to transcription start sites. Transcriptionally repressive elements (“silencers”) are prominent during late embryogenesis. They coincide with an increase in cis-regulatory regions harboring metazoan TF binding motifs, as well as an increase in the expression of metazoan-specific genes. Changes in chromatin state and gene expression in Amphimedon suggest the conservation of distal enhancers, dynamically silenced chromatin, and TF-DNA binding specificity in animal embryogenesis.
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145
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Quan C, Ping J, Lu H, Zhou G, Lu Y. 3DSNP 2.0: update and expansion of the noncoding genomic variant annotation database. Nucleic Acids Res 2022; 50:D950-D955. [PMID: 34723317 PMCID: PMC8728236 DOI: 10.1093/nar/gkab1008] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 12/30/2022] Open
Abstract
The rapid development of single-molecule long-read sequencing (LRS) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) technologies presents both challenges and opportunities for the annotation of noncoding variants. Here, we updated 3DSNP, a comprehensive database for human noncoding variant annotation, to expand its applications to structural variation (SV) and to implement variant annotation down to single-cell resolution. The updates of 3DSNP include (i) annotation of 108 317 SVs from a full spectrum of functions, especially their potential effects on three-dimensional chromatin structures, (ii) evaluation of the accessible chromatin peaks flanking the variants across 126 cell types/subtypes in 15 human fetal tissues and 54 cell types/subtypes in 25 human adult tissues by integrating scATAC-seq data and (iii) expansion of Hi-C data to 49 human cell types. In summary, this version is a significant and comprehensive improvement over the previous version. The 3DSNP v2.0 database is freely available at https://omic.tech/3dsnpv2/.
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Affiliation(s)
- Cheng Quan
- Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, China
| | - Jie Ping
- Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, China
| | - Hao Lu
- Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, China
| | - Gangqiao Zhou
- Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, China
| | - Yiming Lu
- Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, China
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146
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Finkbeiner C, Ortuño-Lizarán I, Sridhar A, Hooper M, Petter S, Reh TA. Single-cell ATAC-seq of fetal human retina and stem-cell-derived retinal organoids shows changing chromatin landscapes during cell fate acquisition. Cell Rep 2022; 38:110294. [DOI: 10.1016/j.celrep.2021.110294] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/04/2021] [Accepted: 12/29/2021] [Indexed: 12/11/2022] Open
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147
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Abstract
The Human Genome Project marked a major milestone in the scientific community as it unravelled the ~3 billion bases that are central to crucial aspects of human life. Despite this achievement, it only scratched the surface of understanding how each nucleotide matters, both individually and as part of a larger unit. Beyond the coding genome, which comprises only ~2% of the whole genome, scientists have realized that large portions of the genome, not known to code for any protein, were crucial for regulating the coding genes. These large portions of the genome comprise the 'non-coding genome'. The history of gene regulation mediated by proteins that bind to the regulatory non-coding genome dates back many decades to the 1960s. However, the original definition of 'enhancers' was first used in the early 1980s. In this Review, we summarize benchmark studies that have mapped the role of cardiac enhancers in disease and development. We highlight instances in which enhancer-localized genetic variants explain the missing link to cardiac pathogenesis. Finally, we inspire readers to consider the next phase of exploring enhancer-based gene therapy for cardiovascular disease.
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148
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Barrozo ER, Aagaard KM. Human placental biology at single-cell resolution: a contemporaneous review. BJOG 2022; 129:208-220. [PMID: 34651399 PMCID: PMC8688323 DOI: 10.1111/1471-0528.16970] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/23/2021] [Accepted: 10/05/2021] [Indexed: 01/03/2023]
Abstract
Single-cell technologies capture cellular heterogeneity to focus on previously poorly described subpopulations of cells. Work by our laboratory and many others has metagenomically characterised a low biomass intrauterine microbial community, alongside microbial transcripts, antigens and metabolites, but the functional importance of low biomass microbial communities in placental immuno-microenvironments is still being elucidated. Given their hypothesised role in modulating inflammation and immune ontogeny to enable tolerance of beneficial microbes while warding off pathogens, there is a need for single-cell resolution. Herein, we summarise the potential for mechanistic understanding of these and other key fundamental early developmental processes by applying single-cell approaches.
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Affiliation(s)
- Enrico R. Barrozo
- Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, Baylor College of Medicine & Texas Children’s Hospital, Houston, TX, USA
| | - Kjersti M. Aagaard
- Division of Maternal-Fetal Medicine, Department of Obstetrics & Gynecology, Baylor College of Medicine & Texas Children’s Hospital, Houston, TX, USA
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149
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Kneppers J, Bergman AM, Zwart W. Prostate Cancer Epigenetic Plasticity and Enhancer Heterogeneity: Molecular Causes, Consequences and Clinical Implications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1390:255-275. [DOI: 10.1007/978-3-031-11836-4_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
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150
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Janssens J, Aibar S, Taskiran II, Ismail JN, Gomez AE, Aughey G, Spanier KI, De Rop FV, González-Blas CB, Dionne M, Grimes K, Quan XJ, Papasokrati D, Hulselmans G, Makhzami S, De Waegeneer M, Christiaens V, Southall T, Aerts S. Decoding gene regulation in the fly brain. Nature 2022; 601:630-636. [PMID: 34987221 DOI: 10.1038/s41586-021-04262-z] [Citation(s) in RCA: 71] [Impact Index Per Article: 35.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 11/17/2021] [Indexed: 12/13/2022]
Abstract
The Drosophila brain is a frequently used model in neuroscience. Single-cell transcriptome analysis1-6, three-dimensional morphological classification7 and electron microscopy mapping of the connectome8,9 have revealed an immense diversity of neuronal and glial cell types that underlie an array of functional and behavioural traits in the fly. The identities of these cell types are controlled by gene regulatory networks (GRNs), involving combinations of transcription factors that bind to genomic enhancers to regulate their target genes. Here, to characterize GRNs at the cell-type level in the fly brain, we profiled the chromatin accessibility of 240,919 single cells spanning 9 developmental timepoints and integrated these data with single-cell transcriptomes. We identify more than 95,000 regulatory regions that are used in different neuronal cell types, of which 70,000 are linked to developmental trajectories involving neurogenesis, reprogramming and maturation. For 40 cell types, uniquely accessible regions were associated with their expressed transcription factors and downstream target genes through a combination of motif discovery, network inference and deep learning, creating enhancer GRNs. The enhancer architectures revealed by DeepFlyBrain lead to a better understanding of neuronal regulatory diversity and can be used to design genetic driver lines for cell types at specific timepoints, facilitating their characterization and manipulation.
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Affiliation(s)
- Jasper Janssens
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Sara Aibar
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Ibrahim Ihsan Taskiran
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Joy N Ismail
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Gabriel Aughey
- Department of Life Sciences, Imperial College London, London, UK
| | - Katina I Spanier
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Florian V De Rop
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Marc Dionne
- Department of Life Sciences, Imperial College London, London, UK
| | - Krista Grimes
- Department of Life Sciences, Imperial College London, London, UK
| | - Xiao Jiang Quan
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Dafni Papasokrati
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Samira Makhzami
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Maxime De Waegeneer
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Valerie Christiaens
- VIB Center for Brain & Disease Research, Leuven, Belgium.,Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Tony Southall
- Department of Life Sciences, Imperial College London, London, UK
| | - Stein Aerts
- VIB Center for Brain & Disease Research, Leuven, Belgium. .,Department of Human Genetics, KU Leuven, Leuven, Belgium.
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