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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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Wen Y, Yang H, Hong Y. Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review. ACS Biomater Sci Eng 2024; 10:4175-4194. [PMID: 38934720 DOI: 10.1021/acsbiomaterials.4c00303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.
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Affiliation(s)
- Yufeng Wen
- Department of Bioengineering, University of Texas at Arlington, Arlington, Texas 76019, United States
| | - Huaxiao Yang
- Department of Biomedical Engineering, University of North Texas, Denton, Texas 76207, United States
| | - Yi Hong
- Department of Bioengineering, University of Texas at Arlington, Arlington, Texas 76019, United States
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Xiong J, Gong F, Ma L, Wan L. scVIC: deep generative modeling of heterogeneity for scRNA-seq data. BIOINFORMATICS ADVANCES 2024; 4:vbae086. [PMID: 39027640 PMCID: PMC11256938 DOI: 10.1093/bioadv/vbae086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/15/2024] [Accepted: 06/12/2024] [Indexed: 07/20/2024]
Abstract
Motivation Single-cell RNA sequencing (scRNA-seq) has become a valuable tool for studying cellular heterogeneity. However, the analysis of scRNA-seq data is challenging because of inherent noise and technical variability. Existing methods often struggle to simultaneously explore heterogeneity across cells, handle dropout events, and account for batch effects. These drawbacks call for a robust and comprehensive method that can address these challenges and provide accurate insights into heterogeneity at the single-cell level. Results In this study, we introduce scVIC, an algorithm designed to account for variational inference, while simultaneously handling biological heterogeneity and batch effects at the single-cell level. scVIC explicitly models both biological heterogeneity and technical variability to learn cellular heterogeneity in a manner free from dropout events and the bias of batch effects. By leveraging variational inference, we provide a robust framework for inferring the parameters of scVIC. To test the performance of scVIC, we employed both simulated and biological scRNA-seq datasets, either including, or not, batch effects. scVIC was found to outperform other approaches because of its superior clustering ability and circumvention of the batch effects problem. Availability and implementation The code of scVIC and replication for this study are available at https://github.com/HiBearME/scVIC/tree/v1.0.
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Affiliation(s)
- Jiankang Xiong
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fuzhou Gong
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liang Ma
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Lin Wan
- National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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4
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Zeng L, Yang K, Zhang T, Zhu X, Hao W, Chen H, Ge J. Research progress of single-cell transcriptome sequencing in autoimmune diseases and autoinflammatory disease: A review. J Autoimmun 2022; 133:102919. [PMID: 36242821 DOI: 10.1016/j.jaut.2022.102919] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 12/07/2022]
Abstract
Autoimmunity refers to the phenomenon that the body's immune system produces antibodies or sensitized lymphocytes to its own tissues to cause an immune response. Immune disorders caused by autoimmunity can mediate autoimmune diseases. Autoimmune diseases have complicated pathogenesis due to the many types of cells involved, and the mechanism is still unclear. The emergence of single-cell research technology can solve the problem that ordinary transcriptome technology cannot be accurate to cell type. It provides unbiased results through independent analysis of cells in tissues and provides more mRNA information for identifying cell subpopulations, which provides a novel approach to study disruption of immune tolerance and disturbance of pro-inflammatory pathways on a cellular basis. It may fundamentally change the understanding of molecular pathways in the pathogenesis of autoimmune diseases and develop targeted drugs. Single-cell transcriptome sequencing (scRNA-seq) has been widely applied in autoimmune diseases, which provides a powerful tool for demonstrating the cellular heterogeneity of tissues involved in various immune inflammations, identifying pathogenic cell populations, and revealing the mechanism of disease occurrence and development. This review describes the principles of scRNA-seq, introduces common sequencing platforms and practical procedures, and focuses on the progress of scRNA-seq in 41 autoimmune diseases, which include 9 systemic autoimmune diseases and autoinflammatory diseases (rheumatoid arthritis, systemic lupus erythematosus, etc.) and 32 organ-specific autoimmune diseases (5 Skin diseases, 3 Nervous system diseases, 4 Eye diseases, 2 Respiratory system diseases, 2 Circulatory system diseases, 6 Liver, Gallbladder and Pancreas diseases, 2 Gastrointestinal system diseases, 3 Muscle, Bones and joint diseases, 3 Urinary system diseases, 2 Reproductive system diseases). This review also prospects the molecular mechanism targets of autoimmune diseases from the multi-molecular level and multi-dimensional analysis combined with single-cell multi-omics sequencing technology (such as scRNA-seq, Single cell ATAC-seq and single cell immune group library sequencing), which provides a reference for further exploring the pathogenesis and marker screening of autoimmune diseases and autoimmune inflammatory diseases in the future.
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Affiliation(s)
- Liuting Zeng
- Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
| | - Kailin Yang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Tianqing Zhang
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China
| | - Xiaofei Zhu
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China.
| | - Wensa Hao
- Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Hua Chen
- Department of Rheumatology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, State Key Laboratory of Complex Severe and Rare Diseases, Beijing, China.
| | - Jinwen Ge
- Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, Hunan University of Chinese Medicine, Changsha, China; Hunan Academy of Chinese Medicine, Changsha, China.
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5
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Sardoo AM, Zhang S, Ferraro TN, Keck TM, Chen Y. Decoding brain memory formation by single-cell RNA sequencing. Brief Bioinform 2022; 23:6713514. [PMID: 36156112 PMCID: PMC9677489 DOI: 10.1093/bib/bbac412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/10/2022] [Accepted: 08/25/2022] [Indexed: 12/14/2022] Open
Abstract
To understand how distinct memories are formed and stored in the brain is an important and fundamental question in neuroscience and computational biology. A population of neurons, termed engram cells, represents the physiological manifestation of a specific memory trace and is characterized by dynamic changes in gene expression, which in turn alters the synaptic connectivity and excitability of these cells. Recent applications of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are promising approaches for delineating the dynamic expression profiles in these subsets of neurons, and thus understanding memory-specific genes, their combinatorial patterns and regulatory networks. The aim of this article is to review and discuss the experimental and computational procedures of sc/snRNA-seq, new studies of molecular mechanisms of memory aided by sc/snRNA-seq in human brain diseases and related mouse models, and computational challenges in understanding the regulatory mechanisms underlying long-term memory formation.
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Affiliation(s)
- Atlas M Sardoo
- Department of Biological & Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA
| | - Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Thomas N Ferraro
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ 08103, USA
| | - Thomas M Keck
- Department of Biological & Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA,Department of Chemistry & Biochemistry, Rowan University, Glassboro, NJ 08028, USA
| | - Yong Chen
- Corresponding author. Yong Chen, Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA. Tel.: +1 856 256 4500; E-mail:
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Källberg J, Xiao W, Van Assche D, Baret JC, Taly V. Frontiers in single cell analysis: multimodal technologies and their clinical perspectives. LAB ON A CHIP 2022; 22:2403-2422. [PMID: 35703438 DOI: 10.1039/d2lc00220e] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single cell multimodal analysis is at the frontier of single cell research: it defines the roles and functions of distinct cell types through simultaneous analysis to provide unprecedented insight into cellular processes. Current single cell approaches are rapidly moving toward multimodal characterizations. It replaces one-dimensional single cell analysis, for example by allowing for simultaneous measurement of transcription and post-transcriptional regulation, epigenetic modifications and/or surface protein expression. By providing deeper insights into single cell processes, multimodal single cell analyses paves the way to new understandings in various cellular processes such as cell fate decisions, physiological heterogeneity or genotype-phenotype linkages. At the forefront of this, microfluidics is key for high-throughput single cell analysis. Here, we present an overview of the recent multimodal microfluidic platforms having a potential in biomedical research, with a specific focus on their potential clinical applications.
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Affiliation(s)
- Julia Källberg
- Centre de Recherche des Cordeliers, INSERM, CNRS, Université Paris Cité, Sorbonne Université, USPC, Equipe labellisée Ligue Nationale contre le cancer, Paris, France.
| | - Wenjin Xiao
- Centre de Recherche des Cordeliers, INSERM, CNRS, Université Paris Cité, Sorbonne Université, USPC, Equipe labellisée Ligue Nationale contre le cancer, Paris, France.
| | - David Van Assche
- University of Bordeaux, CNRS, Centre de Recherche Paul Pascal, UMR 5031, Pessac 33600, France.
| | - Jean-Christophe Baret
- University of Bordeaux, CNRS, Centre de Recherche Paul Pascal, UMR 5031, Pessac 33600, France.
- Institut Universitaire de France, Paris 75005, France
| | - Valerie Taly
- Centre de Recherche des Cordeliers, INSERM, CNRS, Université Paris Cité, Sorbonne Université, USPC, Equipe labellisée Ligue Nationale contre le cancer, Paris, France.
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7
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Rousselle TV, McDaniels JM, Shetty AC, Bardhi E, Maluf DG, Mas VR. An optimized protocol for single nuclei isolation from clinical biopsies for RNA-seq. Sci Rep 2022; 12:9851. [PMID: 35701599 PMCID: PMC9198012 DOI: 10.1038/s41598-022-14099-9] [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: 01/06/2022] [Accepted: 06/01/2022] [Indexed: 11/10/2022] Open
Abstract
Single nuclei RNA sequencing (snRNA-seq) has evolved as a powerful tool to study complex human diseases. Single cell resolution enables the study of novel cell types, biological processes, cell trajectories, and cell-cell signaling pathways. snRNA-seq largely relies on the dissociation of intact nuclei from human tissues. However, the study of complex tissues using small core biopsies presents many technical challenges. Here, an optimized protocol for single nuclei isolation is presented for frozen and RNAlater preserved human kidney biopsies. The described protocol is fast, low cost, and time effective due to the elimination of cell sorting and ultra-centrifugation. Samples can be processed in 90 min or less. This method is effective for obtaining normal nuclei morphology without signs of structural damage. Using snRNA-seq, 16 distinct kidney cell clusters were recovered from normal and peri-transplant acute kidney injury allograft samples, including immune cell clusters. Quality control measurements demonstrated that these optimizations eliminated cellular debris and allowed for a high yield of high-quality nuclei and RNA for library preparation and sequencing. Cellular disassociation did not induce cellular stress responses, which recapitulated transcriptional patterns associated with standardized methods of nuclei isolation. Future applications of this protocol will allow for thorough investigations of small biobank biopsies, identifying cell-specific injury pathways and driving the discovery of novel diagnostics and therapeutic targets.
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Affiliation(s)
- Thomas V Rousselle
- Department of Surgery, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD, 21201, USA
| | - Jennifer M McDaniels
- Department of Surgery, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD, 21201, USA
| | - Amol C Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elissa Bardhi
- Department of Surgery, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD, 21201, USA
| | - Daniel G Maluf
- Department of Surgery, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD, 21201, USA
- Program in Transplantation, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Valeria R Mas
- Department of Surgery, University of Maryland School of Medicine, 670 W Baltimore Street, Baltimore, MD, 21201, USA.
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8
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Davies P, Jones M, Liu J, Hebenstreit D. Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision. Brief Bioinform 2021; 22:6265204. [PMID: 33959753 PMCID: PMC8574610 DOI: 10.1093/bib/bbab148] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 03/10/2021] [Accepted: 03/26/2021] [Indexed: 12/29/2022] Open
Abstract
RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.
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Affiliation(s)
- Philip Davies
- Daniel Hebenstreit's Research Group University of Warwick, CV4 7AL Coventry, UK
| | - Matt Jones
- Daniel Hebenstreit's Research Group University of Warwick, CV4 7AL Coventry, UK
| | - Juntai Liu
- Physics Department, University of Warwick, CV4 7AL Coventry, UK
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9
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Li Z, Song T, Yong J, Kuang R. Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion. PLoS Comput Biol 2021; 17:e1008218. [PMID: 33826608 PMCID: PMC8055040 DOI: 10.1371/journal.pcbi.1008218] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 04/19/2021] [Accepted: 03/19/2021] [Indexed: 12/02/2022] Open
Abstract
High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney. Biological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Due to the technical limitations, spatial transcriptomics data suffers from only sparsely measured mRNAs by in-situ capture and possibly missing spots in tissue regions that entirely failed fixing and permeabilizing RNAs. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.
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Affiliation(s)
- Zhuliu Li
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Tianci Song
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Rui Kuang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
- * E-mail:
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10
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Chen L, Wang W, Zhai Y, Deng M. Deep soft K-means clustering with self-training for single-cell RNA sequence data. NAR Genom Bioinform 2020; 2:lqaa039. [PMID: 33575592 PMCID: PMC7671315 DOI: 10.1093/nargab/lqaa039] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/24/2020] [Accepted: 05/14/2020] [Indexed: 12/18/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we combine the deep learning technique with the use of a denoising autoencoder to characterize scRNA-seq data while propose a soft self-training K-means algorithm to cluster the cell population in the learned latent space. The self-training procedure can effectively aggregate the similar cells and pursue more cluster-friendly latent space. Our method, called ‘scziDesk’, alternately performs data compression, data reconstruction and soft clustering iteratively, and the results exhibit excellent compatibility and robustness in both simulated and real data. Moreover, our proposed method has perfect scalability in line with cell size on large-scale datasets.
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Affiliation(s)
- Liang Chen
- School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Weinan Wang
- School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Yuyao Zhai
- Mathematical and Statistical Institute, Northeast Normal University, Changchun 130024, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing 100871, China
- Center for Quantitative Biology, Peking University, Beijing 100871, China
- To whom correspondence should be addressed. Tel: +86 13522856599;
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11
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Chen L, Wang W, Zhai Y, Deng M. Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm. Front Genet 2020; 11:295. [PMID: 32362908 PMCID: PMC7180207 DOI: 10.3389/fgene.2020.00295] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Accepted: 03/12/2020] [Indexed: 11/13/2022] Open
Abstract
Single-cell RNA sequencing technologies have enabled us to study tissue heterogeneity at cellular resolution. Fast-developing sequencing platforms like droplet-based sequencing make it feasible to parallel process thousands of single cells effectively. Although a unique molecular identifier (UMI) can remove bias from amplification noise to a certain extent, clustering for such sparse and high-dimensional large-scale discrete data remains intractable and challenging. Most existing deep learning-based clustering methods utilize the mean square error or negative binomial distribution with or without zero inflation to denoise single-cell UMI count data, which may underfit or overfit the gene expression profiles. In addition, neglecting the molecule sampling mechanism and extracting representation by simple linear dimension reduction with a hard clustering algorithm may distort data structure and lead to spurious analytical results. In this paper, we combined the deep autoencoder technique with statistical modeling and developed a novel and effective clustering method, scDMFK, for single-cell transcriptome UMI count data. ScDMFK utilizes multinomial distribution to characterize data structure and draw support from neural network to facilitate model parameter estimation. In the learned low-dimensional latent space, we proposed an adaptive fuzzy k-means algorithm with entropy regularization to perform soft clustering. Various simulation scenarios and the analysis of 10 real datasets have shown that scDMFK outperforms other state-of-the-art methods with respect to data modeling and clustering algorithms. Besides, scDMFK has excellent scalability for large-scale single-cell datasets.
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Affiliation(s)
- Liang Chen
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Weinan Wang
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Yuyao Zhai
- Mathematical and Statistical Institute, Northeast Normal University, Changchun, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, China
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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12
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Chen Z, Bai X, Ma L, Wang X, Liu X, Liu Y, Chen L, Wan L. A Branch Point on Differentiation Trajectory is the Bifurcating Event Revealed by Dynamical Network Biomarker Analysis of Single-Cell Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:366-375. [PMID: 29994127 DOI: 10.1109/tcbb.2018.2847690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The advance in single-cell profiling technologies and the development in computational algorithms provide the opportunity to reconstruct pseudo temporal trajectory with branch point of cellular development. On the other hand, theories such as dynamical network biomarkers (DNB) theory have been recently proposed to characterize the pre-transition state in biological systems. Few studies have validated whether the branch point identified in pseudo time is the critical point in dynamical system. In this study, the dynamical behavior of the branch point on the pseudo trajectory has been investigated. We study the pseudo temporal trajectories reconstructed by Wishbone and diffusion pseudotime analysis (DPT) algorithms, as well as the simulated trajectory. DNB theory is applied to justify the bifurcating event on the pseudo trajectories. Our results demonstrate that the branch point recovered by Wishbone and DPT algorithms is confirmed as a transition state in cell differentiation process by DNB theory. Furthermore, we show that an appropriate DNB group will amplify the comprehensive index of critical event as defined in DNB theory. Our study provides biological insights on pseudo trajectory with branch point in a dynamical view and also indicates that DNB theory may serve as a benchmark to check the validity of branch point.
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13
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Lytal N, Ran D, An L. Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey. Front Genet 2020; 11:41. [PMID: 32117453 PMCID: PMC7019105 DOI: 10.3389/fgene.2020.00041] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/14/2020] [Indexed: 02/03/2023] Open
Abstract
Data normalization is vital to single-cell sequencing, addressing limitations presented by low input material and various forms of bias or noise present in the sequencing process. Several such normalization methods exist, some of which rely on spike-in genes, molecules added in known quantities to serve as a basis for a normalization model. Depending on available information and the type of data, some methods may express certain advantages over others. We compare the effectiveness of seven available normalization methods designed specifically for single-cell sequencing using two real data sets containing spike-in genes and one simulation study. Additionally, we test those methods not dependent on spike-in genes using a real data set with three distinct cell-cycle states and a real data set under the 10X Genomics GemCode platform with multiple cell types represented. We demonstrate the differences in effectiveness for the featured methods using visualization and classification assessment and conclude which methods are preferable for normalizing a certain type of data for further downstream analysis, such as classification or differential analysis. The comparison in computational time for all methods is addressed as well.
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Affiliation(s)
- Nicholas Lytal
- Interdisciplinary Program in Statistics, Statistical Bioinformatics Laboratory, University of Arizona, Tucson, AZ, United States
| | - Di Ran
- Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, United States
| | - Lingling An
- Interdisciplinary Program in Statistics, Statistical Bioinformatics Laboratory, University of Arizona, Tucson, AZ, United States.,Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ, United States.,Statistical Bioinformatics Laboratory, Department of Biosystems Engineering, University of Arizona, Tucson, AZ, United States
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14
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A Single Cell but Many Different Transcripts: A Journey into the World of Long Non-Coding RNAs. Int J Mol Sci 2020; 21:ijms21010302. [PMID: 31906285 PMCID: PMC6982300 DOI: 10.3390/ijms21010302] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 12/17/2019] [Accepted: 12/23/2019] [Indexed: 02/07/2023] Open
Abstract
In late 2012 it was evidenced that most of the human genome is transcribed but only a small percentage of the transcripts are translated. This observation supported the importance of non-coding RNAs and it was confirmed in several organisms. The most abundant non-translated transcripts are long non-coding RNAs (lncRNAs). In contrast to protein-coding RNAs, they show a more cell-specific expression. To understand the function of lncRNAs, it is fundamental to investigate in which cells they are preferentially expressed and to detect their subcellular localization. Recent improvements of techniques that localize single RNA molecules in tissues like single-cell RNA sequencing and fluorescence amplification methods have given a considerable boost in the knowledge of the lncRNA functions. In recent years, single-cell transcription variability was associated with non-coding RNA expression, revealing this class of RNAs as important transcripts in the cell lineage specification. The purpose of this review is to collect updated information about lncRNA classification and new findings on their function derived from single-cell analysis. We also retained useful for all researchers to describe the methods available for single-cell analysis and the databases collecting single-cell and lncRNA data. Tables are included to schematize, describe, and compare exposed concepts.
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15
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Hayward RJ, Marsh JW, Humphrys MS, Huston WM, Myers GSA. Early Transcriptional Landscapes of Chlamydia trachomatis-Infected Epithelial Cells at Single Cell Resolution. Front Cell Infect Microbiol 2019; 9:392. [PMID: 31803632 PMCID: PMC6877545 DOI: 10.3389/fcimb.2019.00392] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 11/01/2019] [Indexed: 12/22/2022] Open
Abstract
Chlamydia are Gram-negative obligate intracellular bacterial pathogens responsible for a variety of disease in humans and animals worldwide. Chlamydia trachomatis causes trachoma in disadvantaged populations, and is the most common bacterial sexually transmitted infection in humans, causing reproductive tract disease. Antibiotic therapy successfully treats diagnosed chlamydial infections, however asymptomatic infections are common. High-throughput transcriptomic approaches have explored chlamydial gene expression and infected host cell gene expression. However, these were performed on large cell populations, averaging gene expression profiles across all cells sampled and potentially obscuring biologically relevant subsets of cells. We generated a pilot dataset, applying single cell RNA-Seq (scRNA-Seq) to C. trachomatis infected and mock-infected epithelial cells to assess the utility, pitfalls and challenges of single cell approaches applied to chlamydial biology, and to potentially identify early host cell biomarkers of chlamydial infection. Two hundred sixty-four time-matched C. trachomatis-infected and mock-infected HEp-2 cells were collected and subjected to scRNA-Seq. After quality control, 200 cells were retained for analysis. Two distinct clusters distinguished 3-h cells from 6- and 12-h. Pseudotime analysis identified a possible infection-specific cellular trajectory for Chlamydia-infected cells, while differential expression analyses found temporal expression of metallothioneins and genes involved with cell cycle regulation, innate immune responses, cytoskeletal components, lipid biosynthesis and cellular stress. We find that changes to the host cell transcriptome at early times of C. trachomatis infection are readily discernible by scRNA-Seq, supporting the utility of single cell approaches to identify host cell biomarkers of chlamydial infection, and to further deconvolute the complex host response to infection.
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Affiliation(s)
- Regan J. Hayward
- Faculty of Science, School of Life Sciences, The ithree Institute, University of Technology Sydney, Ultimo, NSW, Australia
| | - James W. Marsh
- Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen, Germany
| | - Michael S. Humphrys
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Wilhelmina M. Huston
- Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
| | - Garry S. A. Myers
- Faculty of Science, School of Life Sciences, The ithree Institute, University of Technology Sydney, Ultimo, NSW, Australia
- Faculty of Science, School of Life Sciences, University of Technology Sydney, Ultimo, NSW, Australia
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16
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Vans E, Sharma A, Patil A, Shigemizu D, Tsunoda T. Clustering of Small-Sample Single-Cell RNA-Seq Data via Feature Clustering and Selection. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-29894-4_36] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
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17
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Petegrosso R, Li Z, Kuang R. Machine learning and statistical methods for clustering single-cell RNA-sequencing data. Brief Bioinform 2019; 21:1209-1223. [DOI: 10.1093/bib/bbz063] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 04/04/2019] [Accepted: 04/29/2019] [Indexed: 01/08/2023] Open
Abstract
Abstract
Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics.
Availability
All the source code and data are available at https://github.com/kuanglab/single-cell-review.
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Affiliation(s)
| | - Zhuliu Li
- CREST (Ensai, Université Bretagne Loire), Bruz, France
| | - Rui Kuang
- CREST (Ensai, Université Bretagne Loire), Bruz, France
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18
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Madani Tonekaboni SA, Soltan Ghoraie L, Manem VSK, Haibe-Kains B. Predictive approaches for drug combination discovery in cancer. Brief Bioinform 2019; 19:263-276. [PMID: 27881431 DOI: 10.1093/bib/bbw104] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Indexed: 02/07/2023] Open
Abstract
Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear. These challenges call for rational, systematic approaches for drug combination discovery. Although high-throughput screening of single-agent therapeutics has been successfully implemented, it is not feasible to test all possible drug combinations, even for a reduced subset of anticancer drugs. Hence, in vitro and in vivo screening of a large number of drug combinations are not practical. Therefore, devising computational methods to efficiently explore the space of drug combinations and to discover efficacious combinations has attracted a lot of attention from the scientific community in the past few years. Nevertheless, in the absence of consensus regarding the computational approaches used to predict efficacious drug combinations, a plethora of methods, techniques and hypotheses have been developed to date, while the research field lacks an elaborate categorization of the existing computational methods and the available data sources. In this manuscript, we review and categorize the state-of-the-art computational approaches for drug combination prediction, and elaborate on the limitations of these methods and the existing challenges. We also discuss about the recent pan-cancer drug combination data sets and their importance in revising the available methods or developing more performant approaches.
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Affiliation(s)
- Seyed Ali Madani Tonekaboni
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Laleh Soltan Ghoraie
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Venkata Satya Kumar Manem
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute of Cancer Research, Toronto, Ontario, Canada
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19
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Huang XT, Li X, Qin PZ, Zhu Y, Xu SN, Chen JP. Technical Advances in Single-Cell RNA Sequencing and Applications in Normal and Malignant Hematopoiesis. Front Oncol 2018; 8:582. [PMID: 30581771 PMCID: PMC6292934 DOI: 10.3389/fonc.2018.00582] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/19/2018] [Indexed: 12/20/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has been tremendously developed in the past decade owing to overcoming challenges associated with isolation of massive quantities of single cells. Previously, cell heterogeneity and low quantities of available biological material posed significant difficulties to scRNA-seq. Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of normal and malignant hematopoietic cells; this heterogeneity has often been ignored in omics studies. The application of scRNA-seq has profoundly changed our comprehension of many biological phenomena, including organ development and carcinogenesis. Hematopoiesis, is actually a maturation process for more than ten distinct blood and immune cells, and is thought to be critically involved in hematological homeostasis and in sustaining the physiological functions. However, aberrant hematopoiesis directly leads to hematological malignancy, and a deeper understanding of malignant hematopoiesis will provide deeper insights into diagnosis and prognosis for patients with hematological malignancies. Here, we aim to review the recent technical progress and future prospects for scRNA-seq, as applied in physiological and malignant hematopoiesis, in efforts to further understand the hematopoietic hierarchy and to illuminate personalized therapy and precision medicine approaches used in the clinical treatment of hematological malignancies.
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Affiliation(s)
- Xiang-Tao Huang
- Center of Haematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xi Li
- Center of Haematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Pei-Zhong Qin
- Center of Haematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yao Zhu
- Center of Haematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Shuang-Nian Xu
- Center of Haematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jie-Ping Chen
- Center of Haematology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
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20
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Zhang H, Lee CAA, Li Z, Garbe JR, Eide CR, Petegrosso R, Kuang R, Tolar J. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa. PLoS Comput Biol 2018; 14:e1006053. [PMID: 29630593 PMCID: PMC5908193 DOI: 10.1371/journal.pcbi.1006053] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 04/19/2018] [Accepted: 02/21/2018] [Indexed: 12/31/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC. scRNA-seq enables detailed profiling of heterogeneous cell populations and can be used to reveal lineage relationships or discover new cell types. In the literature, there has been little effort directed towards developing computational methods for cross-population transcriptome analysis of multiple single-cell populations. The cross-cell-population clustering problem is different from the traditional clustering problem because single-cell populations can be collected from different patients, different samples of a tissue, or different experimental replicates. The accompanying biological and technical variation tends to dominate the signals for clustering the pooled single cells from the multiple populations. In this work, we have developed a multitask clustering method to address the cross-population clustering problem. The method simultaneously clusters each individual cell population and controls variance among the cell-type cluster centers within each cell population and across the cell populations. We demonstrate that our multitask clustering method significantly improves clustering accuracy and marker discovery in three public scRNA-seq datasets and also apply the method to an in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) dataset. Our results make it evident that multitask clustering is a promising new approach for cross-population analysis of scRNA-seq data.
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Affiliation(s)
- Huanan Zhang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Catherine A. A. Lee
- Department of Genetics, Cell Biology and Development, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Zhuliu Li
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - John R. Garbe
- Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Cindy R. Eide
- Department of Pediatrics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Raphael Petegrosso
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
| | - Rui Kuang
- Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
- * E-mail: (RK); (JT)
| | - Jakub Tolar
- Department of Pediatrics, University of Minnesota Twin Cities, Minneapolis, Minnesota, United States of America
- * E-mail: (RK); (JT)
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21
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Dal Molin A, Di Camillo B. How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives. Brief Bioinform 2018; 20:1384-1394. [DOI: 10.1093/bib/bby007] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 12/27/2017] [Indexed: 12/12/2022] Open
Abstract
Abstract
The sequencing of the transcriptome of single cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types in heterogeneous cell populations or for the study of stochastic gene expression. In recent years, various experimental methods and computational tools for analysing single-cell RNA-sequencing data have been proposed. However, most of them are tailored to different experimental designs or biological questions, and in many cases, their performance has not been benchmarked yet, thus increasing the difficulty for a researcher to choose the optimal single-cell transcriptome sequencing (scRNA-seq) experiment and analysis workflow. In this review, we aim to provide an overview of the current available experimental and computational methods developed to handle single-cell RNA-sequencing data and, based on their peculiarities, we suggest possible analysis frameworks depending on specific experimental designs. Together, we propose an evaluation of challenges and open questions and future perspectives in the field. In particular, we go through the different steps of scRNA-seq experimental protocols such as cell isolation, messenger RNA capture, reverse transcription, amplification and use of quantitative standards such as spike-ins and Unique Molecular Identifiers (UMIs). We then analyse the current methodological challenges related to preprocessing, alignment, quantification, normalization, batch effect correction and methods to control for confounding effects.
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22
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Chen J, Suo S, Tam PP, Han JDJ, Peng G, Jing N. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat Protoc 2017; 12:566-580. [PMID: 28207000 DOI: 10.1038/nprot.2017.003] [Citation(s) in RCA: 187] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Conventional gene expression studies analyze multiple cells simultaneously or single cells, for which the exact in vivo or in situ position is unknown. Although cellular heterogeneity can be discerned when analyzing single cells, any spatially defined attributes that underpin the heterogeneous nature of the cells cannot be identified. Here, we describe how to use Geo-seq, a method that combines laser capture microdissection (LCM) and single-cell RNA-seq technology. The combination of these two methods enables the elucidation of cellular heterogeneity and spatial variance simultaneously. The Geo-seq protocol allows the profiling of transcriptome information from only a small number cells and retains their native spatial information. This protocol has wide potential applications to address biological and pathological questions of cellular properties such as prospective cell fates, biological function and the gene regulatory network. Geo-seq has been applied to investigate the spatial transcriptome of mouse early embryo, mouse brain, and pathological liver and sperm tissues. The entire protocol from tissue collection and microdissection to sequencing requires ∼5 d, Data analysis takes another 1 or 2 weeks, depending on the amount of data and the speed of the processor.
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Affiliation(s)
- Jun Chen
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.,Department of Genetics and Cell Biology, College of Life Sciences, Nankai University, Tianjin, China
| | - Shengbao Suo
- Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Patrick Pl Tam
- Embryology Unit, Children's Medical Research Institute and School of MedicalSciences, Sydney Medical School, University of Sydney, Sydney, Australia
| | - Jing-Dong J Han
- Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Guangdun Peng
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Naihe Jing
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
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23
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Archer N, Walsh MD, Shahrezaei V, Hebenstreit D. Modeling Enzyme Processivity Reveals that RNA-Seq Libraries Are Biased in Characteristic and Correctable Ways. Cell Syst 2016; 3:467-479.e12. [PMID: 27840077 PMCID: PMC5167349 DOI: 10.1016/j.cels.2016.10.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 07/28/2016] [Accepted: 10/13/2016] [Indexed: 12/22/2022]
Abstract
Experimental procedures for preparing RNA-seq and single-cell (sc) RNA-seq libraries are based on assumptions regarding their underlying enzymatic reactions. Here, we show that the fairness of these assumptions varies within libraries: coverage by sequencing reads along and between transcripts exhibits characteristic, protocol-dependent biases. To understand the mechanistic basis of this bias, we present an integrated modeling framework that infers the relationship between enzyme reactions during library preparation and the characteristic coverage patterns observed for different protocols. Analysis of new and existing (sc)RNA-seq data from six different library preparation protocols reveals that polymerase processivity is the mechanistic origin of coverage biases. We apply our framework to demonstrate that lowering incubation temperature increases processivity, yield, and (sc)RNA-seq sensitivity in all protocols. We also provide correction factors based on our model for increasing accuracy of transcript quantification in existing samples prepared at standard temperatures. In total, our findings improve our ability to accurately reflect in vivo transcript abundances in (sc)RNA-seq libraries. Characterization of global RNA-seq biases specific to library preparation protocols Mathematical framework to reverse engineer enzyme reactions that cause bias Insights from reverse engineering allow optimization of RNA-seq protocols Lowered incubation temperatures during library preparation improve sensitivity
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Affiliation(s)
- Nathan Archer
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Mark D Walsh
- School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK
| | - Vahid Shahrezaei
- Department of Mathematics, Imperial College, London SW7 2AZ, UK.
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24
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Hu P, Zhang W, Xin H, Deng G. Single Cell Isolation and Analysis. Front Cell Dev Biol 2016; 4:116. [PMID: 27826548 PMCID: PMC5078503 DOI: 10.3389/fcell.2016.00116] [Citation(s) in RCA: 196] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 10/07/2016] [Indexed: 02/05/2023] Open
Abstract
Individual cell heterogeneity within a population can be critical to its peculiar function and fate. Subpopulations studies with mixed mutants and wild types may not be as informative regarding which cell responds to which drugs or clinical treatments. Cell to cell differences in RNA transcripts and protein expression can be key to answering questions in cancer, neurobiology, stem cell biology, immunology, and developmental biology. Conventional cell-based assays mainly analyze the average responses from a population of cells, without regarding individual cell phenotypes. To better understand the variations from cell to cell, scientists need to use single cell analyses to provide more detailed information for therapeutic decision making in precision medicine. In this review, we focus on the recent developments in single cell isolation and analysis, which include technologies, analyses and main applications. Here, we summarize the historical background, limitations, applications, and potential of single cell isolation technologies.
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Affiliation(s)
- Ping Hu
- The Center for Biotechnology and Biopharmaceutics, Institute of Translational Medicine, Nanchang University Nanchang, China
| | - Wenhua Zhang
- Laboratory of Fear and Anxiety Disorders, Institute of Life Science, Nanchang University Nanchang, China
| | - Hongbo Xin
- The Center for Biotechnology and Biopharmaceutics, Institute of Translational Medicine, Nanchang University Nanchang, China
| | - Glenn Deng
- The Center for Biotechnology and Biopharmaceutics, Institute of Translational Medicine, Nanchang UniversityNanchang, China; Yichang Research Center for Biomedical Industry and Central Laboratory of Yichang Central Hospital, Medical School, China Three Gorges UniversityYichang, China; Division of Surgical Oncology, Stanford University School of MedicineStanford, CA, USA
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25
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Monte E, Rosa-Garrido M, Vondriska TM, Wang J. Undiscovered Physiology of Transcript and Protein Networks. Compr Physiol 2016; 6:1851-1872. [PMID: 27783861 PMCID: PMC10751805 DOI: 10.1002/cphy.c160003] [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: 11/07/2022]
Abstract
The past two decades have witnessed a rapid evolution in our ability to measure RNA and protein from biological systems. As a result, new principles have arisen regarding how information is processed in cells, how decisions are made, and the role of networks in biology. This essay examines this technological evolution, reviewing (and critiquing) the conceptual framework that has emerged to explain how RNA and protein networks control cellular function. We identify how future investigations into transcriptomes, proteomes, and other cellular networks will enable development of more robust, quantitative models of cellular behavior whilst also providing new avenues to use knowledge of biological networks to improve human health. © 2016 American Physiological Society. Compr Physiol 6:1851-1872, 2016.
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Affiliation(s)
- Emma Monte
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, USA
| | - Manuel Rosa-Garrido
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, USA
| | - Thomas M. Vondriska
- Department of Anesthesiology & Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, USA
- Department of Medicine/Cardiology, David Geffen School of Medicine, University of California, Los Angeles, USA
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, USA
| | - Jessica Wang
- Department of Medicine/Cardiology, David Geffen School of Medicine, University of California, Los Angeles, USA
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Yu P, Lin W. Single-cell Transcriptome Study as Big Data. GENOMICS PROTEOMICS & BIOINFORMATICS 2016; 14:21-30. [PMID: 26876720 PMCID: PMC4792842 DOI: 10.1016/j.gpb.2016.01.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 01/09/2016] [Accepted: 01/10/2016] [Indexed: 12/31/2022]
Abstract
The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.
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Affiliation(s)
- Pingjian Yu
- Genomics and Bioinformatics Lab, Baylor Institute for Immunology Research, Dallas, TX 75204, USA
| | - Wei Lin
- Genomics and Bioinformatics Lab, Baylor Institute for Immunology Research, Dallas, TX 75204, USA.
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27
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Tabansky I, Stern JNH, Pfaff DW. Implications of Epigenetic Variability within a Cell Population for "Cell Type" Classification. Front Behav Neurosci 2015; 9:342. [PMID: 26733833 PMCID: PMC4679859 DOI: 10.3389/fnbeh.2015.00342] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 11/23/2015] [Indexed: 11/18/2022] Open
Abstract
Here, we propose a new approach to defining nerve “cell types” in reaction to recent advances in single cell analysis. Among cells previously thought to be equivalent, considerable differences in global gene expression and biased tendencies among differing developmental fates have been demonstrated within multiple lineages. The model of classifying cells into distinct types thus has to be revised to account for this intrinsic variability. A “cell type” could be a group of cells that possess similar, but not necessarily identical properties, variable within a spectrum of epigenetic adjustments that permit its developmental path toward a specific function to be achieved. Thus, the definition of a cell type is becoming more similar to the definition of a species: sharing essential properties with other members of its group, but permitting a certain amount of deviation in aspects that do not seriously impact function. This approach accommodates, even embraces the spectrum of natural variation found in various cell populations and consequently avoids the fallacy of false equivalence. For example, developing neurons will react to their microenvironments with epigenetic changes resulting in slight changes in gene expression and morphology. Addressing the new questions implied here will have significant implications for developmental neurobiology.
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Affiliation(s)
- Inna Tabansky
- Laboratory of Neurobiology and Behavior, The Rockefeller University New York, NY, USA
| | - Joel N H Stern
- Laboratory of Neurobiology and Behavior, The Rockefeller UniversityNew York, NY, USA; Departments of Neurology and Science Education, Hofstra North Shore-LIJ School of MedicineHempstead, NY, USA; Department of Autoimmunity, The Feinstein Institute for Medical Research, North Shore-LIJ Health SystemManhasset, NY, USA
| | - Donald W Pfaff
- Laboratory of Neurobiology and Behavior, The Rockefeller University New York, NY, USA
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28
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Recent advances and current issues in single-cell sequencing of tumors. Cancer Lett 2015; 365:1-10. [PMID: 26003306 DOI: 10.1016/j.canlet.2015.04.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Revised: 04/19/2015] [Accepted: 04/20/2015] [Indexed: 12/28/2022]
Abstract
Intratumoral heterogeneity is a recently recognized but important feature of cancer that underlies the various biocharacteristics of cancer tissues. The advent of next-generation sequencing technologies has facilitated large scale capture of genomic data, while the recent development of single-cell sequencing has allowed for more in-depth studies into the complex molecular mechanisms of intratumoral heterogeneity. In this review, the recent advances and current challenges in single-cell sequencing methodologies are discussed, highlighting the potential power of these data to provide insights into oncological processes, from tumorigenesis through progression to metastasis and therapy resistance.
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29
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Shi X, Chen CH, Gao W, Chao SH, Meldrum DR. Parallel RNA extraction using magnetic beads and a droplet array. LAB ON A CHIP 2015; 15:1059-65. [PMID: 25519439 PMCID: PMC4349128 DOI: 10.1039/c4lc01111b] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Nucleic acid extraction is a necessary step for most genomic/transcriptomic analyses, but it often requires complicated mechanisms to be integrated into a lab-on-a-chip device. Here, we present a simple, effective configuration for rapidly obtaining purified RNA from low concentration cell medium. This Total RNA Extraction Droplet Array (TREDA) utilizes an array of surface-adhering droplets to facilitate the transportation of magnetic purification beads seamlessly through individual buffer solutions without solid structures. The fabrication of TREDA chips is rapid and does not require a microfabrication facility or expertise. The process takes less than 5 minutes. When purifying mRNA from bulk marine diatom samples, its repeatability and extraction efficiency are comparable to conventional tube-based operations. We demonstrate that TREDA can extract the total mRNA of about 10 marine diatom cells, indicating that the sensitivity of TREDA approaches single-digit cell numbers.
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Affiliation(s)
- Xu Shi
- Center for Biosignatures Discovery Automation , The Biodesign Institute , Arizona State University Tempe , Arizona , USA .
| | - Chun-Hong Chen
- Center for Biosignatures Discovery Automation , The Biodesign Institute , Arizona State University Tempe , Arizona , USA .
- Department of Electrical Engineering , National Cheng Kung University Tainan , Taiwan
| | - Weimin Gao
- Center for Biosignatures Discovery Automation , The Biodesign Institute , Arizona State University Tempe , Arizona , USA .
| | - Shih-hui Chao
- Center for Biosignatures Discovery Automation , The Biodesign Institute , Arizona State University Tempe , Arizona , USA .
| | - Deirdre R. Meldrum
- Center for Biosignatures Discovery Automation , The Biodesign Institute , Arizona State University Tempe , Arizona , USA .
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Abstract
Unprecedented access to the biology of single cells is now feasible, enabled by recent technological advancements that allow us to manipulate and measure sparse samples and achieve a new level of resolution in space and time. This review focuses on advances in tools to study single cells for specific areas of biology. We examine both mature and nascent techniques to study single cells at the genomics, transcriptomics, and proteomics level. In addition, we provide an overview of tools that are well suited for following biological responses to defined perturbations with single-cell resolution. Techniques to analyze and manipulate single cells through soluble and chemical ligands, the microenvironment, and cell-cell interactions are provided. For each of these topics, we highlight the biological motivation, applications, methods, recent advances, and opportunities for improvement. The toolbox presented in this review can function as a starting point for the design of single-cell experiments.
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31
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Liu N, Liu L, Pan X. Single-cell analysis of the transcriptome and its application in the characterization of stem cells and early embryos. Cell Mol Life Sci 2014; 71:2707-15. [PMID: 24652479 PMCID: PMC11113295 DOI: 10.1007/s00018-014-1601-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2013] [Revised: 01/16/2014] [Accepted: 02/03/2014] [Indexed: 12/17/2022]
Abstract
Cellular heterogeneity within a cell population is a common phenomenon in multicellular organisms, tissues, cultured cells, and even FACS-sorted subpopulations. Important information may be masked if the cells are studied as a mass. Transcriptome profiling is a parameter that has been intensively studied, and relatively easier to address than protein composition. To understand the basis and importance of heterogeneity and stochastic aspects of the cell function and its mechanisms, it is essential to examine transcriptomes of a panel of single cells. High-throughput technologies, starting from microarrays and now RNA-seq, provide a full view of the expression of transcriptomes but are limited by the amount of RNA for analysis. Recently, several new approaches for amplification and sequencing the transcriptome of single cells or a limited low number of cells have been developed and applied. In this review, we summarize these major strategies, such as PCR-based methods, IVT-based methods, phi29-DNA polymerase-based methods, and several other methods, including their principles, characteristics, advantages, and limitations, with representative applications in cancer stem cells, early development, and embryonic stem cells. The prospects for development of future technology and application of transcriptome analysis in a single cell are also discussed.
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Affiliation(s)
- Na Liu
- State Key Laboratory of Medicinal Chemical Biology, Department of Cell Biology and Genetics, College of Life Science, Nankai University, Tianjin, 300071, China,
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32
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Ning L, Liu G, Li G, Hou Y, Tong Y, He J. Current challenges in the bioinformatics of single cell genomics. Front Oncol 2014; 4:7. [PMID: 24478987 PMCID: PMC3902584 DOI: 10.3389/fonc.2014.00007] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 01/12/2014] [Indexed: 11/13/2022] Open
Abstract
Single cell genomics is a rapidly growing field with many new techniques emerging in the past few years. However, few bioinformatics tools specific for single cell genomics analysis are available. Single cell DNA/RNA sequencing data usually have low genome coverage and high amplification bias, which makes bioinformatics analysis challenging. Many current bioinformatics tools developed for bulk cell sequencing do not work well with single cell sequencing data. Here, we summarize current challenges in the bioinformatics analysis of single cell genomic DNA sequencing and single cell transcriptomes. These challenges include calling copy number variations, identifying mutated genes in tumor samples, reconstructing cell lineages, recovering low abundant transcripts, and improving the accuracy of quantitative analysis of transcripts. Development in single cell genomics bioinformatics analysis will promote the application of this technology to basic biology and medical research.
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Affiliation(s)
- Luwen Ning
- Department of Biology, South University of Science and Technology of China , Shenzhen , China
| | | | | | | | - Yin Tong
- Department of Biology, South University of Science and Technology of China , Shenzhen , China
| | - Jiankui He
- Department of Biology, South University of Science and Technology of China , Shenzhen , China
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33
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Abstract
Cellular context can be crucial when studying developmental processes as well as responses to environmental variation. Several different tools have been developed in recent years to isolate specific tissues or cell types. Laser-assisted microdissection (LAM) allows for the isolation of such specific tissue or single cell-types purely based on morphology and cytology. This has the advantage that (1) cell types that are rare can be isolated from heterogeneous tissue, (2) no marker line with cell type-specific expression needs to be established, and (3) the method can be applied to non-model species and species that are difficult to genetically transform. The rapid development of next-generation sequencing (NGS) approaches has greatly advanced the possibilities to perform molecular analyses in diverse organisms. However, there is a mismatch between currently available cell isolation tools and their applicability to non-model organisms. Therefore, LAM will become increasingly popular in the study of diverse agriculturally or ecologically relevant plant species. Here, we describe a protocol that has been successfully used for LAM to isolate either whole floral organs or even single cell types in plants, e.g., Arabidopsis thaliana, Boechera spp., or tomato.
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Affiliation(s)
- Samuel E Wuest
- Institute of Plant Biology and Zürich-Basel Plant Science Center, University of Zürich, Zürich, Switzerland
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34
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Haselgrübler T, Haider M, Ji B, Juhasz K, Sonnleitner A, Balogi Z, Hesse J. High-throughput, multiparameter analysis of single cells. Anal Bioanal Chem 2013; 406:3279-96. [PMID: 24292433 DOI: 10.1007/s00216-013-7485-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 11/04/2013] [Accepted: 11/04/2013] [Indexed: 12/23/2022]
Abstract
Heterogeneity of cell populations in various biological systems has been widely recognized, and the highly heterogeneous nature of cancer cells has been emerging with clinical relevance. Single-cell analysis using a combination of high-throughput and multiparameter approaches is capable of reflecting cell-to-cell variability, and at the same time of unraveling the complexity and interdependence of cellular processes in the individual cells of a heterogeneous population. In this review, analytical methods and microfluidic tools commonly used for high-throughput, multiparameter single-cell analysis of DNA, RNA, and proteins are discussed. Applications and limitations of currently available technologies for cancer research and diagnostics are reviewed in the light of the ultimate goal to establish clinically applicable assays.
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Affiliation(s)
- Thomas Haselgrübler
- Center for Advanced Bioanalysis GmbH, Gruberstraße 40-42, 4020, Linz, Austria,
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35
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Kadakkuzha BM, Puthanveettil SV. Genomics and proteomics in solving brain complexity. MOLECULAR BIOSYSTEMS 2013; 9:1807-21. [PMID: 23615871 PMCID: PMC6425491 DOI: 10.1039/c3mb25391k] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The human brain is extraordinarily complex, composed of billions of neurons and trillions of synaptic connections. Neurons are organized into circuit assemblies that are modulated by specific interneurons and non-neuronal cells, such as glia and astrocytes. Data on human genome sequences predicts that each of these cells in the human brain has the potential of expressing ∼20 000 protein coding genes and tens of thousands of noncoding RNAs. A major challenge in neuroscience is to determine (1) how individual neurons and circuitry utilize this potential during development and maturation of the nervous system, and for higher brain functions such as cognition, and (2) how this potential is altered in neurological and psychiatric disorders. In this review, we will discuss how recent advances in next generation sequencing, proteomics and bioinformatics have transformed our understanding of gene expression and the functions of neural circuitry, memory storage, and disorders of cognition.
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Affiliation(s)
- Beena M Kadakkuzha
- Department of Neuroscience, The Scripps Research Institute, Scripps Florida 130 Scripps Way, Jupiter, FL 33458, USA
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36
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Gibson TM, Gersbach CA. The role of single-cell analyses in understanding cell lineage commitment. Biotechnol J 2013; 8:397-407. [PMID: 23520130 DOI: 10.1002/biot.201200201] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2012] [Revised: 01/08/2013] [Accepted: 02/26/2013] [Indexed: 12/18/2022]
Abstract
The study of cell lineage commitment is critical for improving our understanding of tissue development and regeneration, and for realizing stem cell-based therapies and engineered tissue replacements. Recently, the discovery of an unanticipated degree of variability in fundamental biological processes, including divergent responses of genetically identical cells to various stimuli, has provided mechanistic insight into cellular decision making and the collective behavior of cell populations. Therefore, the study of lineage commitment with single-cell resolution could provide greater knowledge of cellular differentiation mechanisms and the influence of noise on cellular processes. This will require the adoption of new technologies for single-cell analysis as traditional methods typically measure average values of bulk population behavior. This review discusses the recent developments in methods for analyzing the behavior of individual cells, and how these approaches are leading to a deeper understanding and better control of cellular decision making.
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Affiliation(s)
- Tyler M Gibson
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0281, USA
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