151
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Zhang W, Lin Z. iPoLNG-An unsupervised model for the integrative analysis of single-cell multiomics data. Front Genet 2023; 14:998504. [PMID: 36865385 PMCID: PMC9972291 DOI: 10.3389/fgene.2023.998504] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors. The low-dimensional representation of cells enables the identification of distinct cell types, and the feature by factor loading matrices help characterize cell-type specific markers and provide rich biological insights on the functional pathway enrichment analysis. iPoLNG is also able to handle the setting of partial information where certain modality of the cells is missing. Taking advantage of GPU and probabilistic programming, iPoLNG is scalable to large datasets and it takes less than 15 min to implement on datasets with 20,000 cells.
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Affiliation(s)
- Wenyu Zhang
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong, China
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152
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scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences. PLoS One 2023; 18:e0281315. [PMID: 36735690 PMCID: PMC9897517 DOI: 10.1371/journal.pone.0281315] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/19/2023] [Indexed: 02/04/2023] Open
Abstract
Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.
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153
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY (MOSCOW) 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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154
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Liu C, Wang L, Liu Z. scGREAT: Graph-based regulatory element analysis tool for single-cell multi-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.27.525916. [PMID: 36747664 PMCID: PMC9900895 DOI: 10.1101/2023.01.27.525916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Motivation With the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity. Results Here, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurement L. The graph-based correlation method assigns each cell a local L index, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson's r in ruling out confounding regulatory relationships that are not of research interests. Availability https://github.com/ChaozhongLiu/scGREAT.
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Affiliation(s)
- Chaozhong Liu
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA
| | - Linhua Wang
- Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA
| | - Zhandong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, USA
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155
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Iacobucci I, Witkowski MT, Mullighan CG. Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights. Blood 2023; 141:356-368. [PMID: 35926109 PMCID: PMC10023733 DOI: 10.1182/blood.2022016954] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/13/2022] [Accepted: 07/23/2022] [Indexed: 01/31/2023] Open
Abstract
Despite recent progress in identifying the genetic drivers of acute lymphoblastic leukemia (ALL), prognosis remains poor for those individuals who experience disease recurrence. Moreover, acute leukemias of ambiguous lineage lack a biologically informed framework to guide classification and therapy. These needs have driven the adoption of multiple complementary single-cell sequencing approaches to explore key issues in the biology of these leukemias, including cell of origin, developmental hierarchy and ontogeny, and the molecular heterogeneity driving pathogenesis, progression, and therapeutic responsiveness. There are multiple single-cell techniques for profiling a specific modality, including RNA, DNA, chromatin accessibility and methylation; and an expanding range of approaches for simultaneous analysis of multiple modalities. Single-cell sequencing approaches have also enabled characterization of cell-intrinsic and -extrinsic features of ALL biology. In this review we describe these approaches and highlight the extensive heterogeneity that underpins ALL gene expression, cellular differentiation, and clonal architecture throughout disease pathogenesis and treatment resistance. In addition, we discuss the importance of the dynamic interactions that occur between leukemia cells and the nonleukemia microenvironment. We discuss potential opportunities and limitations of single-cell sequencing for the study of ALL biology and treatment responsiveness.
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Affiliation(s)
- Ilaria Iacobucci
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN
| | - Matthew T. Witkowski
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Charles G. Mullighan
- Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN
- Hematological Malignancies Program, St Jude Children’s Research Hospital, Memphis, TN
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156
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Nagler A, Wu CJ. The end of the beginning: application of single-cell sequencing to chronic lymphocytic leukemia. Blood 2023; 141:369-379. [PMID: 36095842 PMCID: PMC9936302 DOI: 10.1182/blood.2021014669] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/12/2022] [Accepted: 07/23/2022] [Indexed: 01/31/2023] Open
Abstract
Single-cell analysis has emerged over the past decade as a transformative technology informative for the systematic analysis of complex cell populations such as in cancers and the tumor immune microenvironment. The methodologic and analytical advancements in this realm have evolved rapidly, scaling from but a few cells at its outset to the current capabilities of processing and analyzing hundreds of thousands of individual cells at a time. The types of profiling attainable at individual cell resolution now range from genetic and transcriptomic characterization and extend to epigenomic and spatial analysis. Additionally, the increasing ability to achieve multiomic integration of these data layers now yields ever richer insights into diverse molecular disease subtypes and the patterns of cellular circuitry on a per-cancer basis. Over the years, chronic lymphocytic leukemia (CLL) consistently has been at the forefront of genomic investigation, given the ready accessibility of pure leukemia cells and immune cells from circulating blood of patients with this disease. Herein, we review the recent forays into the application of single-cell analysis to CLL, which are already revealing a new understanding of the natural progression of CLL, the impact of novel therapies, and the interactions with coevolving nonmalignant immune cell populations. As we emerge from the end of the beginning of this technologic revolution, CLL stands poised to reap the benefits of single-cell analysis from the standpoints of uncovering fresh fundamental biological knowledge and of providing a path to devising regimens of personalized diagnosis, treatment, and monitoring.
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Affiliation(s)
- Adi Nagler
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
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157
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Zhang Z, Sun H, Mariappan R, Chen X, Chen X, Jain MS, Efremova M, Teichmann SA, Rajan V, Zhang X. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nat Commun 2023; 14:384. [PMID: 36693837 PMCID: PMC9873790 DOI: 10.1038/s41467-023-36066-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/13/2023] [Indexed: 01/26/2023] Open
Abstract
Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.
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Affiliation(s)
- Ziqi Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haoran Sun
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ragunathan Mariappan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
| | - Xi Chen
- Department of Biology, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Xinyu Chen
- Bioengineering Program, Georgia Institute of Technology, Atlanta, GA, USA
| | | | | | | | - Vaibhav Rajan
- Department of Information Systems and Analytics, National University of Singapore, Singapore, Singapore
| | - Xiuwei Zhang
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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158
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Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research. Int J Mol Sci 2023; 24:ijms24032271. [PMID: 36768592 PMCID: PMC9917071 DOI: 10.3390/ijms24032271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/16/2023] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
Musculoskeletal disorders, including fractures, scoliosis, heterotopic ossification, osteoporosis, osteoarthritis, disc degeneration, and muscular injury, etc., can occur at any stage of human life. Understanding the occurrence and development mechanism of musculoskeletal disorders, as well as the changes in tissues and cells during therapy, might help us find targeted treatment methods. Single-cell techniques provide excellent tools for studying alterations at the cellular level of disorders. However, the application of these techniques in research on musculoskeletal disorders is still limited. This review summarizes the current single-cell and spatial omics used in musculoskeletal disorders. Cell isolation, experimental methods, and feasible experimental designs for single-cell studies of musculoskeletal system diseases have been reviewed based on tissue characteristics. Then, the paper summarizes the latest findings of single-cell studies in musculoskeletal disorders from three aspects: bone and ossification, joint, and muscle and tendon disorders. Recent discoveries about the cell populations involved in these diseases are highlighted. Furthermore, the therapeutic responses of musculoskeletal disorders, especially single-cell changes after the treatments of implants, stem cell therapies, and drugs are described. Finally, the application potential and future development directions of single-cell and spatial omics in research on musculoskeletal diseases are discussed.
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159
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Hong D, Lin H, Liu L, Shu M, Dai J, Lu F, Tong M, Huang J. Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis. Brief Bioinform 2023; 24:6868525. [PMID: 36464486 DOI: 10.1093/bib/bbac508] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 12/09/2022] Open
Abstract
Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.
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Affiliation(s)
- Danni Hong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Hongli Lin
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Lifang Liu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Muya Shu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jianwu Dai
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Falong Lu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengsha Tong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Jialiang Huang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
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160
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Kawaguchi RK, Tang Z, Fischer S, Rajesh C, Tripathy R, Koo PK, Gillis J. Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. Brief Bioinform 2023; 24:bbac541. [PMID: 36549922 PMCID: PMC9851328 DOI: 10.1093/bib/bbac541] [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: 05/27/2022] [Revised: 09/29/2022] [Accepted: 11/08/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner.
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Affiliation(s)
| | - Ziqi Tang
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Stephan Fischer
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Chandana Rajesh
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Rohit Tripathy
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Peter K Koo
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor 11724, USA
- Department of Physiology and Donnelly Centre for Cellular & Biomolecular Research Department, University of Toronto, Ontario M5S 3E1, Canada
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161
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Ament SA, Adkins RS, Carter R, Chrysostomou E, Colantuoni C, Crabtree J, Creasy HH, Degatano K, Felix V, Gandt P, Garden G, Giglio M, Herb BR, Khajouei F, Kiernan E, McCracken C, McDaniel K, Nadendla S, Nickel L, Olley D, Orvis J, Receveur J, Schor M, Sonthalia S, Tickle T, Way J, Hertzano R, Mahurkar A, White O. The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain. Nucleic Acids Res 2023; 51:D1075-D1085. [PMID: 36318260 PMCID: PMC9825473 DOI: 10.1093/nar/gkac962] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/30/2022] [Accepted: 10/27/2022] [Indexed: 11/06/2022] Open
Abstract
Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).
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Affiliation(s)
- Seth A Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ricky S Adkins
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Robert Carter
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elena Chrysostomou
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Carlo Colantuoni
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Departments of Neurology and Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jonathan Crabtree
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Heather H Creasy
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kylee Degatano
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Victor Felix
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Gandt
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Gwenn A Garden
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michelle Giglio
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brian R Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Farzaneh Khajouei
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elizabeth Kiernan
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Carrie McCracken
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kennedy McDaniel
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Suvarna Nadendla
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Lance Nickel
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Dustin Olley
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Joseph P Receveur
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mike Schor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shreyash Sonthalia
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Timothy L Tickle
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jessica Way
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ronna Hertzano
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Anup A Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Owen R White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
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162
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Kong S, Li R, Tian Y, Zhang Y, Lu Y, Ou Q, Gao P, Li K, Zhang Y. Single-cell omics: A new direction for functional genetic research in human diseases and animal models. Front Genet 2023; 13:1100016. [PMID: 36685871 PMCID: PMC9846559 DOI: 10.3389/fgene.2022.1100016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023] Open
Abstract
Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.
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Affiliation(s)
- Siyuan Kong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
| | - Rongrong Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yunhan Tian
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Yaqiu Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yuhui Lu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qiaoer Ou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Peiwen Gao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yubo Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,College of Life Science and Engineering, Foshan University, Foshan, China,*Correspondence: Siyuan Kong, ; Yubo Zhang,
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163
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Liu C, Wang L, Liu Z. Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss. BMC Bioinformatics 2023; 24:5. [PMID: 36600199 PMCID: PMC9812356 DOI: 10.1186/s12859-022-05126-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. RESULTS By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. CONCLUSIONS MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.
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Affiliation(s)
- Chaozhong Liu
- grid.39382.330000 0001 2160 926XGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA
| | - Linhua Wang
- grid.39382.330000 0001 2160 926XGraduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA
| | - Zhandong Liu
- grid.416975.80000 0001 2200 2638Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, USA ,grid.39382.330000 0001 2160 926XDepartment of Pediatrics, Baylor College of Medicine, Houston, USA
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164
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Cheng F, Keller MS, Qu H, Gehlenborg N, Wang Q. Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:591-601. [PMID: 36155452 PMCID: PMC10039961 DOI: 10.1109/tvcg.2022.3209408] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challenging due to the entanglement of technical (e.g., from different sequencing batches or techniques) and biological (e.g., from different cellular microenvironments) variations, only the first of which must be removed. To address this issue, we propose Polyphony, an interactive transfer learning (ITL) framework, to complement biologists' knowledge with advanced computational methods. Polyphony is motivated and guided by domain experts' needs for a controllable, interactive, and algorithm-assisted annotation process, identified through interviews with seven biologists. We introduce anchors, i.e., analogous cell populations across datasets, as a paradigm to explain the computational process and collect user feedback for model improvement. We further design a set of visualizations and interactions to empower users to add, delete, or modify anchors, resulting in refined cell type annotations. The effectiveness of this approach is demonstrated through quantitative experiments, two hypothetical use cases, and interviews with two biologists. The results show that our anchor-based ITL method takes advantage of both human and machine intelligence in annotating massive single-cell datasets.
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165
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Zhu C, Wang Z, Ren B. Single-Cell Joint Profiling of Open Chromatin and Transcriptome by Paired-Seq. Methods Mol Biol 2023; 2611:155-185. [PMID: 36807069 DOI: 10.1007/978-1-0716-2899-7_10] [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: 02/23/2023]
Abstract
Simultaneous detection of chromatin accessibility and transcription from the same cells promises to greatly facilitate the dissection of cell-type-specific gene regulatory programs in complex tissues. Paired-seq enables joint analysis of open chromatin and nuclear transcriptome from up to a million cells in parallel. It achieves ultra-high-throughput single-cell multiomics with the use of a combinatorial barcoding strategy involving sequential ligation of multiplexed DNA barcodes to chromatin DNA fragments and reverse transcription products, followed by high-throughput DNA sequencing of the resulting DNA libraries and deconvolution of single-cell multiomic maps based on cell-specific barcodes.
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Affiliation(s)
- Chenxu Zhu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Zhaoning Wang
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, Institute of Genomic Medicine, Moores Cancer Center, University of California San Diego, School of Medicine, La Jolla, California, USA.
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166
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Wang Y, Lian B, Zhang H, Zhong Y, He J, Wu F, Reinert K, Shang X, Yang H, Hu J. A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data. Bioinformatics 2023; 39:btad005. [PMID: 36622018 PMCID: PMC9857983 DOI: 10.1093/bioinformatics/btad005] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/27/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023] Open
Abstract
MOTIVATION Single-cell multimodal assays allow us to simultaneously measure two different molecular features of the same cell, enabling new insights into cellular heterogeneity, cell development and diseases. However, most existing methods suffer from inaccurate dimensionality reduction for the joint-modality data, hindering their discovery of novel or rare cell subpopulations. RESULTS Here, we present VIMCCA, a computational framework based on variational-assisted multi-view canonical correlation analysis to integrate paired multimodal single-cell data. Our statistical model uses a common latent variable to interpret the common source of variances in two different data modalities. Our approach jointly learns an inference model and two modality-specific non-linear models by leveraging variational inference and deep learning. We perform VIMCCA and compare it with 10 existing state-of-the-art algorithms on four paired multi-modal datasets sequenced by different protocols. Results demonstrate that VIMCCA facilitates integrating various types of joint-modality data, thus leading to more reliable and accurate downstream analysis. VIMCCA improves our ability to identify novel or rare cell subtypes compared to existing widely used methods. Besides, it can also facilitate inferring cell lineage based on joint-modality profiles. AVAILABILITY AND IMPLEMENTATION The VIMCCA algorithm has been implemented in our toolkit package scbean (≥0.5.0), and its code has been archived at https://github.com/jhu99/scbean under MIT license. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuwei Wang
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Bin Lian
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Haohui Zhang
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Yuanke Zhong
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Jie He
- Department of Biostatistics, School of Public Health, Peking University Health Science Center, Beijing 100191, China
| | - Fashuai Wu
- Department of Orthopaedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Knut Reinert
- Institut für Informatik, Freie Universität Berlin, 14195 Berlin, Germany
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
| | - Hui Yang
- School of Life Science, Northwestern Polytechnical University, Shaanxi 710072, China
| | - Jialu Hu
- School of Computer Science, Northwestern Polytechnical University, Shaanxi 710129, China
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167
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Preissl S, Gaulton KJ, Ren B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat Rev Genet 2023; 24:21-43. [PMID: 35840754 PMCID: PMC9771884 DOI: 10.1038/s41576-022-00509-1] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2022] [Indexed: 12/24/2022]
Abstract
Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.
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Affiliation(s)
- Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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168
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Wang Y, Sun X, Zhao H. Benchmarking automated cell type annotation tools for single-cell ATAC-seq data. Front Genet 2022; 13:1063233. [PMID: 36583014 PMCID: PMC9792779 DOI: 10.3389/fgene.2022.1063233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
As single-cell chromatin accessibility profiling methods advance, scATAC-seq has become ever more important in the study of candidate regulatory genomic regions and their roles underlying developmental, evolutionary, and disease processes. At the same time, cell type annotation is critical in understanding the cellular composition of complex tissues and identifying potential novel cell types. However, most existing methods that can perform automated cell type annotation are designed to transfer labels from an annotated scRNA-seq data set to another scRNA-seq data set, and it is not clear whether these methods are adaptable to annotate scATAC-seq data. Several methods have been recently proposed for label transfer from scRNA-seq data to scATAC-seq data, but there is a lack of benchmarking study on the performance of these methods. Here, we evaluated the performance of five scATAC-seq annotation methods on both their classification accuracy and scalability using publicly available single-cell datasets from mouse and human tissues including brain, lung, kidney, PBMC, and BMMC. Using the BMMC data as basis, we further investigated the performance of these methods across different data sizes, mislabeling rates, sequencing depths and the number of cell types unique to scATAC-seq. Bridge integration, which is the only method that requires additional multimodal data and does not need gene activity calculation, was overall the best method and robust to changes in data size, mislabeling rate and sequencing depth. Conos was the most time and memory efficient method but performed the worst in terms of prediction accuracy. scJoint tended to assign cells to similar cell types and performed relatively poorly for complex datasets with deep annotations but performed better for datasets only with major label annotations. The performance of scGCN and Seurat v3 was moderate, but scGCN was the most time-consuming method and had the most similar performance to random classifiers for cell types unique to scATAC-seq.
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Affiliation(s)
- Yuge Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States
| | - Xingzhi Sun
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States,Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States,*Correspondence: Hongyu Zhao,
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169
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Lin X, Tian T, Wei Z, Hakonarson H. Clustering of single-cell multi-omics data with a multimodal deep learning method. Nat Commun 2022; 13:7705. [PMID: 36513636 PMCID: PMC9748135 DOI: 10.1038/s41467-022-35031-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
Single-cell multimodal sequencing technologies are developed to simultaneously profile different modalities of data in the same cell. It provides a unique opportunity to jointly analyze multimodal data at the single-cell level for the identification of distinct cell types. A correct clustering result is essential for the downstream complex biological functional studies. However, combining different data sources for clustering analysis of single-cell multimodal data remains a statistical and computational challenge. Here, we develop a novel multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis. scMDC is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding for clustering analysis. Extensive simulation and real-data experiments reveal that scMDC outperforms existing single-cell single-modal and multimodal clustering methods on different single-cell multimodal datasets. The linear scalability of running time makes scMDC a promising method for analyzing large multimodal datasets.
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Affiliation(s)
- Xiang Lin
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Tian Tian
- Center of Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
| | - Hakon Hakonarson
- Center of Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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170
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Microfluidics-based single cell analysis: From transcriptomics to spatiotemporal multi-omics. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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171
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Sreenivasan VKA, Henck J, Spielmann M. Single-cell sequencing: promises and challenges for human genetics. MED GENET-BERLIN 2022; 34:261-273. [PMID: 38836091 PMCID: PMC11006387 DOI: 10.1515/medgen-2022-2156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Over the last decade, single-cell sequencing has transformed many fields. It has enabled the unbiased molecular phenotyping of even whole organisms with unprecedented cellular resolution. In the field of human genetics, where the phenotypic consequences of genetic and epigenetic alterations are of central concern, this transformative technology promises to functionally annotate every region in the human genome and all possible variants within them at a massive scale. In this review aimed at the clinicians in human genetics, we describe the current status of the field of single-cell sequencing and its role for human genetics, including how the technology works as well as how it is being applied to characterize and monitor diseases, to develop human cell atlases, and to annotate the genome.
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Affiliation(s)
- Varun K A Sreenivasan
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, 23562 Lübeck, 24105 Kiel, Germany
| | - Jana Henck
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, 23562 Lübeck, 24105 Kiel, Germany
- Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany
| | - Malte Spielmann
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck and Kiel University, 23562 Lübeck, 24105 Kiel, Germany
- Human Molecular Genomics Group, Max Planck Institute for Molecular Genetics, D-14195 Berlin, Germany
- DZHK e. V. (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck, 23538 Lübeck, Germany
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172
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Jafari E, Johnson T, Wang Y, Liu Y, Huang K, Wang Y. AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency. Bioinformatics 2022; 38:5236-5244. [PMID: 36250795 PMCID: PMC9710555 DOI: 10.1093/bioinformatics/btac683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 10/07/2022] [Accepted: 10/14/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult. RESULTS We introduce AIscEA-Alignment-based Integration of single-cell gene Expression and chromatin Accessibility-a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy. AVAILABILITY AND IMPLEMENTATION AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Elham Jafari
- Computer Science Department, Indiana University, Bloomington, IN 47408, USA
| | - Travis Johnson
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yue Wang
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yunlong Liu
- Department of Medical & Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Yijie Wang
- Computer Science Department, Indiana University, Bloomington, IN 47408, USA
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173
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Xu X, Zhang Q, Li M, Lin S, Liang S, Cai L, Zhu H, Su R, Yang C. Microfluidic single‐cell multiomics analysis. VIEW 2022. [DOI: 10.1002/viw.20220034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Xing Xu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Qiannan Zhang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Mingyin Li
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Shiyan Lin
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Shanshan Liang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Linfeng Cai
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Huanghuang Zhu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Rui Su
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Chaoyong Yang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
- Institute of Molecular Medicine Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
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174
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Liu Y, Liang S, Wang B, Zhao J, Zi X, Yan S, Dou T, Jia J, Wang K, Ge C. Advances in Single-Cell Sequencing Technology and Its Application in Poultry Science. Genes (Basel) 2022; 13:genes13122211. [PMID: 36553479 PMCID: PMC9778011 DOI: 10.3390/genes13122211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/20/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
Single-cell sequencing (SCS) uses a single cell as the research material and involves three dimensions: genes, phenotypes and cell biological mechanisms. This type of research can locate target cells, analyze the dynamic changes in the target cells and the relationships between the cells, and pinpoint the molecular mechanism of cell formation. Currently, a common problem faced by animal husbandry scientists is how to apply existing science and technology to promote the production of high-quality livestock and poultry products and to breed livestock for disease resistance; this is also a bottleneck for the sustainable development of animal husbandry. In recent years, although SCS technology has been successfully applied in the fields of medicine and bioscience, its application in poultry science has been rarely reported. With the sustainable development of science and technology and the poultry industry, SCS technology has great potential in the application of poultry science (or animal husbandry). Therefore, it is necessary to review the innovation of SCS technology and its application in poultry science. This article summarizes the current main technical methods of SCS and its application in poultry, which can provide potential references for its future applications in precision breeding, disease prevention and control, immunity, and cell identification.
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Affiliation(s)
- Yong Liu
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Shuangmin Liang
- College of Food Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Bo Wang
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Jinbo Zhao
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Xiannian Zi
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Shixiong Yan
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Tengfei Dou
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Junjing Jia
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Kun Wang
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
| | - Changrong Ge
- College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China
- Correspondence:
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175
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Yuan M, Chen L, Deng M. Clustering single-cell multi-omics data with MoClust. Bioinformatics 2022; 39:6831092. [PMID: 36383167 PMCID: PMC9805570 DOI: 10.1093/bioinformatics/btac736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 11/09/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022] Open
Abstract
MOTIVATION Single-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-omics data have the properties of inherited large dimension, high sparsity and existence of doublets. Moreover, representations of different omics from even the same cell follow diverse distributions. Without proper distribution alignment techniques, clustering methods will encounter less separable clusters easily affected by less informative omics data. RESULTS We developed MoClust, a novel joint clustering framework that can be applied to several types of single-cell multi-omics data. A selective automatic doublet detection module that can identify and filter out doublets is introduced in the pretraining stage to improve data quality. Omics-specific autoencoders are introduced to characterize the multi-omics data. A contrastive learning way of distribution alignment is adopted to adaptively fuse omics representations into an omics-invariant representation. This novel way of alignment boosts the compactness and separableness of clusters, while accurately weighting the contribution of each omics to the clustering object. Extensive experiments, over both simulated and real multi-omics datasets, demonstrated the powerful alignment, doublet detection and clustering ability features of MoClust. AVAILABILITY AND IMPLEMENTATION An implementation of MoClust is available from https://doi.org/10.5281/zenodo.7306504. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Musu Yuan
- Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Liang Chen
- To whom correspondence should be addressed. or
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176
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Zeng P, Ma Y, Lin Z. scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data. Bioinformatics 2022; 39:6831091. [PMID: 36383176 PMCID: PMC9805575 DOI: 10.1093/bioinformatics/btac739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/16/2022] [Accepted: 11/15/2022] [Indexed: 11/17/2022] Open
Abstract
MOTIVATION Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets [including parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data profiled from the same cell] are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging. The methods that integrate the multi-omics data can potentially improve the capacity of revealing the cellular heterogeneity. RESULTS We propose an adaptively weighted multi-view learning (scAWMV) method for the integrative analysis of parallel scRNA-seq and scATAC-seq data profiled from the same cell. scAWMV considers both the difference in importance across different modalities in multi-omics data and the biological connection of the features in the scRNA-seq and scATAC-seq data. It generates biologically meaningful low-dimensional representations for the transcriptomic and epigenomic profiles via unsupervised learning. Application to four real datasets demonstrates that our framework scAWMV is an efficient method to dissect cellular heterogeneity for single-cell multi-omics data. AVAILABILITY AND IMPLEMENTATION The software and datasets are available at https://github.com/pengchengzeng/scAWMV. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pengcheng Zeng
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai 201210, China
| | - Yuanyuan Ma
- School of Computer and Information Engineering, Anyang Normal University, Henan 455000, China
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177
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Han M, Li F, Zhang Y, Dai P, He J, Li Y, Zhu Y, Zheng J, Huang H, Bai F, Gao D. FOXA2 drives lineage plasticity and KIT pathway activation in neuroendocrine prostate cancer. Cancer Cell 2022; 40:1306-1323.e8. [PMID: 36332622 DOI: 10.1016/j.ccell.2022.10.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 07/10/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
Prostate cancer adeno-to-neuroendocrine lineage transition has emerged as a mechanism of targeted therapeutic resistance. Identifying the direct molecular drivers and developing pharmacological strategies using clinical-grade inhibitors to overcome lineage transition-induced therapeutic resistance are imperative. Here, using single-cell multiomics analyses, we investigate the dynamics of cellular heterogeneity, transcriptome regulation, and microenvironmental factors in 107,201 cells from genetically engineered mouse prostate cancer samples with complete time series of tumor evolution seen in patients. We identify that FOXA2 orchestrates prostate cancer adeno-to-neuroendocrine lineage transition and that Foxa2 expression is significantly induced by androgen deprivation. Moreover, Foxa2 knockdown induces the reversal of adeno-to-neuroendocrine transition. The KIT pathway is directly regulated by FOXA2 and specifically activated in neuroendocrine prostate cancer (NEPC). Pharmacologic inhibition of KIT pathway significantly suppresses mouse and human NEPC tumor growth. These findings reveal that FOXA2 drives adeno-to-neuroendocrine lineage plasticity in prostate cancer and provides a potential pharmacological strategy for castration-resistant NEPC.
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Affiliation(s)
- Ming Han
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Yehan Zhang
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Pengfei Dai
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Juan He
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunguang Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiqin Zhu
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Junke Zheng
- Hongqiao International Institute of Medicine, Shanghai Tongren Hospital, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Faculty of Basic Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hai Huang
- Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China
| | - Fan Bai
- Biomedical Pioneering Innovation Center (BIOPIC), Beijing Advanced Innovation Center for Genomics (ICG), School of Life Sciences, Peking University, Beijing 100871, China
| | - Dong Gao
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China.
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178
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Chen ZS, Kulkarni P(P, Galatzer-Levy IR, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. PATTERNS (NEW YORK, N.Y.) 2022; 3:100602. [PMID: 36419447 PMCID: PMC9676543 DOI: 10.1016/j.patter.2022.100602] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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179
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Demetci P, Santorella R, Chakravarthy M, Sandstede B, Singh R. SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation. J Comput Biol 2022; 29:1213-1228. [PMID: 36251763 PMCID: PMC9805876 DOI: 10.1089/cmb.2022.0270] [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: 11/05/2022] Open
Abstract
Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments. Our investigation revealed that these methods do not perform well for noncoassay single-cell experiments when there is disproportionate cell-type representation across measurement domains. Therefore, we extend our previous work-Single Cell alignment using Optimal Transport (SCOT)-by using unbalanced Gromov-Wasserstein optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. Our method, SCOTv2, gives state-of-the-art alignment performance across five non-coassay data sets (simulated and real world). It can also integrate multiple (M ≥ 2 ) single-cell measurements while preserving the self-tuning capabilities and computational tractability of its original version.
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Affiliation(s)
- Pinar Demetci
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Rebecca Santorella
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Manav Chakravarthy
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Bjorn Sandstede
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Ritambhara Singh
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
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180
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Zhang R, Meng-papaxanthos L, Vert JP, Noble WS. Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning. J Comput Biol 2022; 29:1198-1212. [PMID: 36251758 PMCID: PMC9700358 DOI: 10.1089/cmb.2022.0264] [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: 11/06/2022] Open
Abstract
Single-cell multi-omics technologies enable comprehensive interrogation of cellular regulation, yet most single-cell assays measure only one type of activity-such as transcription, chromatin accessibility, DNA methylation, or 3D chromatin architecture-for each cell. To enable a multimodal view for individual cells, we propose Polarbear, a semi-supervised machine learning framework that facilitates missing modality profile prediction and single-cell cross-modality alignment. Polarbear learns to translate between modalities by using data from co-assay measurements coupled with the large quantity of single-assay data available in public databases. This semi-supervised scheme mitigates issues related to low cell quantities and high sparsity in co-assay data. Polarbear first pre-trains a beta-variational autoencoder for each modality using both co-assay and single-assay profiles to learn robust representations of individual cells, and it then uses the co-assay labels to train a translator between these cell representations. This semi-supervised framework enables us to predict missing modality profiles and match single cells across modalities with improved accuracy compared with fully supervised methods, thus facilitating multimodal data integration.
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Affiliation(s)
- Ran Zhang
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | | | | | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
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181
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Wen L, Li G, Huang T, Geng W, Pei H, Yang J, Zhu M, Zhang P, Hou R, Tian G, Su W, Chen J, Zhang D, Zhu P, Zhang W, Zhang X, Zhang N, Zhao Y, Cao X, Peng G, Ren X, Jiang N, Tian C, Chen ZJ. Single-cell technologies: From research to application. Innovation (N Y) 2022; 3:100342. [PMCID: PMC9637996 DOI: 10.1016/j.xinn.2022.100342] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022] Open
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182
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Liu Z, Li H, Dang Q, Weng S, Duo M, Lv J, Han X. Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy. Cell Mol Life Sci 2022; 79:577. [DOI: 10.1007/s00018-022-04608-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/12/2022] [Accepted: 10/20/2022] [Indexed: 11/03/2022]
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183
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Mukherjee P, Park SH, Pathak N, Patino CA, Bao G, Espinosa HD. Integrating Micro and Nano Technologies for Cell Engineering and Analysis: Toward the Next Generation of Cell Therapy Workflows. ACS NANO 2022; 16:15653-15680. [PMID: 36154011 DOI: 10.1021/acsnano.2c05494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The emerging field of cell therapy offers the potential to treat and even cure a diverse array of diseases for which existing interventions are inadequate. Recent advances in micro and nanotechnology have added a multitude of single cell analysis methods to our research repertoire. At the same time, techniques have been developed for the precise engineering and manipulation of cells. Together, these methods have aided the understanding of disease pathophysiology, helped formulate corrective interventions at the cellular level, and expanded the spectrum of available cell therapeutic options. This review discusses how micro and nanotechnology have catalyzed the development of cell sorting, cellular engineering, and single cell analysis technologies, which have become essential workflow components in developing cell-based therapeutics. The review focuses on the technologies adopted in research studies and explores the opportunities and challenges in combining the various elements of cell engineering and single cell analysis into the next generation of integrated and automated platforms that can accelerate preclinical studies and translational research.
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Affiliation(s)
- Prithvijit Mukherjee
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - So Hyun Park
- Department of Bioengineering, Rice University, 6500 Main Street, Houston, Texas 77030, United States
| | - Nibir Pathak
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
| | - Cesar A Patino
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Gang Bao
- Department of Bioengineering, Rice University, 6500 Main Street, Houston, Texas 77030, United States
| | - Horacio D Espinosa
- Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States
- Theoretical and Applied Mechanics Program, Northwestern University, Evanston, Illinois 60208, United States
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184
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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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185
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Morgan DM, Shreffler WG, Love JC. Revealing the heterogeneity of CD4+ T cells through single-cell transcriptomics. J Allergy Clin Immunol 2022; 150:748-755. [DOI: 10.1016/j.jaci.2022.08.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 08/15/2022] [Accepted: 08/19/2022] [Indexed: 11/07/2022]
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186
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Xu W, Yang W, Zhang Y, Chen Y, Hong N, Zhang Q, Wang X, Hu Y, Song K, Jin W, Chen X. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods 2022; 19:1243-1249. [PMID: 36109677 DOI: 10.1038/s41592-022-01601-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/01/2022] [Indexed: 11/09/2022]
Abstract
Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA-DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.
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Affiliation(s)
- Wei Xu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Weilong Yang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yunlong Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yawen Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Qian Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yukun Hu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Kun Song
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
| | - Xi Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
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187
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Stanojevic S, Li Y, Ristivojevic A, Garmire LX. Computational Methods for Single-cell Multi-omics Integration and Alignment. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:836-849. [PMID: 36581065 PMCID: PMC10025765 DOI: 10.1016/j.gpb.2022.11.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 08/09/2022] [Accepted: 11/04/2022] [Indexed: 12/27/2022]
Abstract
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.
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Affiliation(s)
- Stefan Stanojevic
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
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188
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Jiang Y, Harigaya Y, Zhang Z, Zhang H, Zang C, Zhang NR. Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions. Cell Syst 2022; 13:737-751.e4. [PMID: 36055233 PMCID: PMC9509445 DOI: 10.1016/j.cels.2022.08.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/23/2022] [Accepted: 08/11/2022] [Indexed: 01/26/2023]
Abstract
The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.
| | - Yuriko Harigaya
- Curriculum in Bioinformatics and Computational Biology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Zhaojun Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hongpan Zhang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Chongzhi Zang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Nancy R Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.
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189
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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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190
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Hristov BH, Bilmes JA, Noble WS. Linking cells across single-cell modalities by synergistic matching of neighborhood structure. Bioinformatics 2022; 38:ii148-ii154. [PMID: 36124797 PMCID: PMC9486587 DOI: 10.1093/bioinformatics/btac481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
MOTIVATION A wide variety of experimental methods are available to characterize different properties of single cells in a complex biosample. However, because these measurement techniques are typically destructive, researchers are often presented with complementary measurements from disjoint subsets of cells, providing a fragmented view of the cell's biological processes. This creates a need for computational tools capable of integrating disjoint multi-omics data. Because different measurements typically do not share any features, the problem requires the integration to be done in unsupervised fashion. Recently, several methods have been proposed that project the cell measurements into a common latent space and attempt to align the corresponding low-dimensional manifolds. RESULTS In this study, we present an approach, Synmatch, which produces a direct matching of the cells between modalities by exploiting information about neighborhood structure in each modality. Synmatch relies on the intuition that cells which are close in one measurement space should be close in the other as well. This allows us to formulate the matching problem as a constrained supermodular optimization problem over neighborhood structures that can be solved efficiently. We show that our approach successfully matches cells in small real multi-omics datasets and performs favorably when compared with recently published state-of-the-art methods. Further, we demonstrate that Synmatch is capable of scaling to large datasets of thousands of cells. AVAILABILITY AND IMPLEMENTATION The Synmatch code and data used in this manuscript are available at https://github.com/Noble-Lab/synmatch.
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Affiliation(s)
| | - Jeffrey A Bilmes
- Department of Electrical Engineering
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - William Stafford Noble
- Department of Genome Sciences
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
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191
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Xu Y, Begoli E, McCord RP. sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network. NPJ Syst Biol Appl 2022; 8:33. [PMID: 36089620 PMCID: PMC9464763 DOI: 10.1038/s41540-022-00245-6] [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: 01/13/2022] [Accepted: 09/01/2022] [Indexed: 11/09/2022] Open
Abstract
The boom in single-cell technologies has brought a surge of high dimensional data that come from different sources and represent cellular systems from different views. With advances in these single-cell technologies, integrating single-cell data across modalities arises as a new computational challenge. Here, we present an adversarial approach, sciCAN, to integrate single-cell chromatin accessibility and gene expression data in an unsupervised manner. We benchmarked sciCAN with 5 existing methods in 5 scATAC-seq/scRNA-seq datasets, and we demonstrated that our method dealt with data integration with consistent performance across datasets and better balance of mutual transferring between modalities than the other 5 existing methods. We further applied sciCAN to 10X Multiome data and confirmed that the integrated representation preserves biological relationships within the hematopoietic hierarchy. Finally, we investigated CRISPR-perturbed single-cell K562 ATAC-seq and RNA-seq data to identify cells with related responses to different perturbations in these different modalities.
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Affiliation(s)
- Yang Xu
- grid.411461.70000 0001 2315 1184UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN USA
| | - Edmon Begoli
- grid.135519.a0000 0004 0446 2659Oak Ridge National Laboratory, Oak Ridge, TN USA ,grid.411461.70000 0001 2315 1184Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN USA
| | - Rachel Patton McCord
- Biochemistry & Cellular and Molecular Biology Department, University of Tennessee, Knoxville, TN, USA.
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192
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Lynch AW, Theodoris CV, Long HW, Brown M, Liu XS, Meyer CA. MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells. Nat Methods 2022; 19:1097-1108. [PMID: 36068320 PMCID: PMC9517733 DOI: 10.1038/s41592-022-01595-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 07/26/2022] [Indexed: 02/06/2023]
Abstract
Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here we present MIRA, probabilistic multimodal models for integrated regulatory analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along cell state trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high-fidelity cell state trees, determines key regulators of fate decisions at branch points and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.
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Affiliation(s)
- Allen W Lynch
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christina V Theodoris
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.,Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School Genetics Training Program, Boston, MA, USA
| | - Henry W Long
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Myles Brown
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA
| | - X Shirley Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. .,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Clifford A Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA. .,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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193
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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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194
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Yuan M, Chen L, Deng M. Clustering CITE-seq data with a canonical correlation-based deep learning method. Front Genet 2022; 13:977968. [PMID: 36072672 PMCID: PMC9441595 DOI: 10.3389/fgene.2022.977968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 07/22/2022] [Indexed: 12/03/2022] Open
Abstract
Single-cell multiomics sequencing techniques have rapidly developed in the past few years. Among these techniques, single-cell cellular indexing of transcriptomes and epitopes (CITE-seq) allows simultaneous quantification of gene expression and surface proteins. Clustering CITE-seq data have the great potential of providing us with a more comprehensive and in-depth view of cell states and interactions. However, CITE-seq data inherit the properties of scRNA-seq data, being noisy, large-dimensional, and highly sparse. Moreover, representations of RNA and surface protein are sometimes with low correlation and contribute divergently to the clustering object. To overcome these obstacles and find a combined representation well suited for clustering, we proposed scCTClust for multiomics data, especially CITE-seq data, and clustering analysis. Two omics-specific neural networks are introduced to extract cluster information from omics data. A deep canonical correlation method is adopted to find the maximumly correlated representations of two omics. A novel decentralized clustering method is utilized over the linear combination of latent representations of two omics. The fusion weights which can account for contributions of omics to clustering are adaptively updated during training. Extensive experiments over both simulated and real CITE-seq data sets demonstrated the power of scCTClust. We also applied scCTClust on transcriptome–epigenome data to illustrate its potential for generalizing.
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Affiliation(s)
- Musu Yuan
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- *Correspondence: Musu Yuan,
| | - Liang Chen
- Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing, China
| | - Minghua Deng
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Department of Probability and Statistics, School of Mathematical Sciences, Peking University, Beijing, China
- Center for Statistical Science, Peking University, Beijing, China
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195
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Heming M, Börsch AL, Wiendl H, Meyer zu Hörste G. High-dimensional investigation of the cerebrospinal fluid to explore and monitor CNS immune responses. Genome Med 2022; 14:94. [PMID: 35978442 PMCID: PMC9385102 DOI: 10.1186/s13073-022-01097-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/28/2022] [Indexed: 01/15/2023] Open
Abstract
The cerebrospinal fluid (CSF) features a unique immune cell composition and is in constant contact with the brain borders, thus permitting insights into the brain to diagnose and monitor diseases. Recently, the meninges, which are filled with CSF, were identified as a neuroimmunological interface, highlighting the potential of exploring central nervous system (CNS) immunity by studying CNS border compartments. Here, we summarize how single-cell transcriptomics of such border compartments advance our understanding of neurological diseases, the challenges that remain, and what opportunities novel multi-omic methods offer. Single-cell transcriptomics studies have detected cytotoxic CD4+ T cells and clonally expanded T and B cells in the CSF in the autoimmune disease multiple sclerosis; clonally expanded pathogenic CD8+ T cells were found in the CSF and in the brain adjacent to β-amyloid plaques of dementia patients; in patients with brain metastases, CD8+ T cell clonotypes were shared between the brain parenchyma and the CSF and persisted after therapy. We also outline how novel multi-omic approaches permit the simultaneous measurements of gene expression, chromatin accessibility, and protein in the same cells, which remain to be explored in the CSF. This calls for multicenter initiatives to create single-cell atlases, posing challenges in integrating patients and modalities across centers. While high-dimensional analyses of CSF cells are challenging, they hold potential for personalized medicine by better resolving heterogeneous diseases and stratifying patients.
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Affiliation(s)
- Michael Heming
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Anna-Lena Börsch
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Heinz Wiendl
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Gerd Meyer zu Hörste
- grid.16149.3b0000 0004 0551 4246Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
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196
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Ogbeide S, Giannese F, Mincarelli L, Macaulay IC. Into the multiverse: advances in single-cell multiomic profiling. Trends Genet 2022; 38:831-843. [PMID: 35537880 DOI: 10.1016/j.tig.2022.03.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
Abstract
Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.
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197
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Zhu Y, Ouyang Z, Du H, Wang M, Wang J, Sun H, Kong L, Xu Q, Ma H, Sun Y. New opportunities and challenges of natural products research: When target identification meets single-cell multiomics. Acta Pharm Sin B 2022; 12:4011-4039. [DOI: 10.1016/j.apsb.2022.08.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/06/2022] [Accepted: 08/22/2022] [Indexed: 12/12/2022] Open
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198
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Zhao X, Lan Y, Chen D. Exploring long non-coding RNA networks from single cell omics data. Comput Struct Biotechnol J 2022; 20:4381-4389. [PMID: 36051880 PMCID: PMC9403499 DOI: 10.1016/j.csbj.2022.08.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/03/2022] Open
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199
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Li Z, Seehawer M, Polyak K. Untangling the web of intratumour heterogeneity. Nat Cell Biol 2022; 24:1192-1201. [PMID: 35941364 DOI: 10.1038/s41556-022-00969-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/27/2022] [Indexed: 02/06/2023]
Abstract
Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.
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Affiliation(s)
- Zheqi Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Marco Seehawer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA.
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200
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Hung KL, Mischel PS, Chang HY. Gene regulation on extrachromosomal DNA. Nat Struct Mol Biol 2022; 29:736-744. [PMID: 35948767 PMCID: PMC10246724 DOI: 10.1038/s41594-022-00806-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/22/2022] [Indexed: 11/09/2022]
Abstract
Oncogene amplification on extrachromosomal DNA (ecDNA) is prevalent in human cancer and is associated with poor outcomes. Clonal, megabase-sized circular ecDNAs in cancer are distinct from nonclonal, small sub-kilobase-sized DNAs that may arise during normal tissue homeostasis. ecDNAs enable profound changes in gene regulation beyond copy-number gains. An emerging principle of ecDNA regulation is the formation of ecDNA hubs: micrometer-sized nuclear structures of numerous copies of ecDNAs tethered by proteins in spatial proximity. ecDNA hubs enable cooperative and intermolecular sharing of DNA regulatory elements for potent and combinatorial gene activation. The 3D context of ecDNA shapes its gene expression potential, selection for clonal heterogeneity among ecDNAs, distribution through cell division, and reintegration into chromosomes. Technologies for studying gene regulation and structure of ecDNA are starting to answer long-held questions on the distinct rules that govern cancer genes beyond chromosomes.
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Affiliation(s)
- King L Hung
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA
| | - Paul S Mischel
- Department of Pathology, Stanford University School of Medicine and ChEM-H, Stanford University, Stanford, CA, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA.
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