1
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Manousidaki A, Little A, Xie Y. Clustering and visualization of single-cell RNA-seq data using path metrics. PLoS Comput Biol 2024; 20:e1012014. [PMID: 38809943 DOI: 10.1371/journal.pcbi.1012014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 06/10/2024] [Accepted: 03/21/2024] [Indexed: 05/31/2024] Open
Abstract
Recent advances in single-cell technologies have enabled high-resolution characterization of tissue and cancer compositions. Although numerous tools for dimension reduction and clustering are available for single-cell data analyses, these methods often fail to simultaneously preserve local cluster structure and global data geometry. To address these challenges, we developed a novel analyses framework, Single-Cell Path Metrics Profiling (scPMP), using power-weighted path metrics, which measure distances between cells in a data-driven way. Unlike Euclidean distance and other commonly used distance metrics, path metrics are density sensitive and respect the underlying data geometry. By combining path metrics with multidimensional scaling, a low dimensional embedding of the data is obtained which preserves both the global data geometry and cluster structure. We evaluate the method both for clustering quality and geometric fidelity, and it outperforms current scRNAseq clustering algorithms on a wide range of benchmarking data sets.
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Affiliation(s)
- Andriana Manousidaki
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America
| | - Anna Little
- Department of Mathematics, University of Utah, Salt Lake City, Utah, United States of America
| | - Yuying Xie
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
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2
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Jin X, Zhang R, Fu Y, Zhu Q, Hong L, Wu A, Wang H. Unveiling aging dynamics in the hematopoietic system insights from single-cell technologies. Brief Funct Genomics 2024:elae019. [PMID: 38688725 DOI: 10.1093/bfgp/elae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 04/10/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024] Open
Abstract
As the demographic structure shifts towards an aging society, strategies aimed at slowing down or reversing the aging process become increasingly essential. Aging is a major predisposing factor for many chronic diseases in humans. The hematopoietic system, comprising blood cells and their associated bone marrow microenvironment, intricately participates in hematopoiesis, coagulation, immune regulation and other physiological phenomena. The aging process triggers various alterations within the hematopoietic system, serving as a spectrum of risk factors for hematopoietic disorders, including clonal hematopoiesis, immune senescence, myeloproliferative neoplasms and leukemia. The emerging single-cell technologies provide novel insights into age-related changes in the hematopoietic system. In this review, we summarize recent studies dissecting hematopoietic system aging using single-cell technologies. We discuss cellular changes occurring during aging in the hematopoietic system at the levels of the genomics, transcriptomics, epigenomics, proteomics, metabolomics and spatial multi-omics. Finally, we contemplate the future prospects of single-cell technologies, emphasizing the impact they may bring to the field of hematopoietic system aging research.
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Affiliation(s)
- Xinrong Jin
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Ruohan Zhang
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Yunqi Fu
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Qiunan Zhu
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Liquan Hong
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Aiwei Wu
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
| | - Hu Wang
- Zhejiang Key Laboratory of Medical Epigenetics, School of Basic Medical Sciences, The Third People's Hospital of Deqing, Deqing Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou 311121, China
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3
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Nadal-Ribelles M, Solé C, de Nadal E, Posas F. The rise of single-cell transcriptomics in yeast. Yeast 2024; 41:158-170. [PMID: 38403881 DOI: 10.1002/yea.3934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/24/2024] [Accepted: 02/15/2024] [Indexed: 02/27/2024] Open
Abstract
The field of single-cell omics has transformed our understanding of biological processes and is constantly advancing both experimentally and computationally. One of the most significant developments is the ability to measure the transcriptome of individual cells by single-cell RNA-seq (scRNA-seq), which was pioneered in higher eukaryotes. While yeast has served as a powerful model organism in which to test and develop transcriptomic technologies, the implementation of scRNA-seq has been significantly delayed in this organism, mainly because of technical constraints associated with its intrinsic characteristics, namely the presence of a cell wall, a small cell size and little amounts of RNA. In this review, we examine the current technologies for scRNA-seq in yeast and highlight their strengths and weaknesses. Additionally, we explore opportunities for developing novel technologies and the potential outcomes of implementing single-cell transcriptomics and extension to other modalities. Undoubtedly, scRNA-seq will be invaluable for both basic and applied yeast research, providing unique insights into fundamental biological processes.
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Affiliation(s)
- Mariona Nadal-Ribelles
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Carme Solé
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Eulalia de Nadal
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Francesc Posas
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
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4
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Xu C, Shao J. High-throughput omics technologies in inflammatory bowel disease. Clin Chim Acta 2024; 555:117828. [PMID: 38355001 DOI: 10.1016/j.cca.2024.117828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 02/06/2024] [Accepted: 02/10/2024] [Indexed: 02/16/2024]
Abstract
Inflammatory bowel disease (IBD) is a chronic, relapsing intestinal disease. Elucidation of the pathogenic mechanisms of IBD requires high-throughput technologies (HTTs) to effectively obtain and analyze large amounts of data. Recently, HTTs have been widely used in IBD, including genomics, transcriptomics, proteomics, microbiomics, metabolomics and single-cell sequencing. When combined with endoscopy, the application of these technologies can provide an in-depth understanding on the alterations of intestinal microbe diversity and abundance, the abnormalities of signaling pathway-mediated immune responses and functionality, and the evaluation of therapeutic effects, improving the accuracy of early diagnosis and treatment of IBD. This review comprehensively summarizes the development and advancement of HTTs, and also highlights the challenges and future directions of these technologies in IBD research. Although HTTs have made striking breakthrough in IBD, more standardized methods and large-scale dataset processing are still needed to achieve the goal of personalized medicine.
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Affiliation(s)
- Chen Xu
- Laboratory of Anti-infection and Immunity, College of Integrated Chinese and Western Medicine (College of Life Science), Anhui University of Chinese Medicine, Zhijing Building, 350 Longzihu Road, Xinzhan District, Hefei 230012, Anhui, PR China
| | - Jing Shao
- Laboratory of Anti-infection and Immunity, College of Integrated Chinese and Western Medicine (College of Life Science), Anhui University of Chinese Medicine, Zhijing Building, 350 Longzihu Road, Xinzhan District, Hefei 230012, Anhui, PR China; Institute of Integrated Traditional Chinese and Western Medicine, Anhui Academy of Chinese Medicine, Zhijing Building, 350 Longzihu Road, Xinzhan District, Hefei 230012, Anhui, PR China.
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5
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Cadwell CR, Tolias AS. Patch-seq: Multimodal Profiling of Single-Cell Morphology, Electrophysiology, and Gene Expression. Methods Mol Biol 2024; 2752:227-243. [PMID: 38194038 DOI: 10.1007/978-1-0716-3621-3_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Cells exhibit diverse morphologic phenotypes, biophysical and functional properties, and gene expression patterns. Understanding how these features are interrelated at the level of single cells has been challenging due to the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single cells. Here we present a detailed step-by-step protocol for obtaining high-quality morphological, electrophysiological, and transcriptomic data from single cells. Patch-seq enables researchers to explore the rich, multidimensional phenotypic variability among cells and to directly correlate gene expression with phenotype at the level of single cells.
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Affiliation(s)
- Cathryn R Cadwell
- Departments of Neurological Surgery and Pathology, School of Medicine, University of California San Francisco, San Francisco, CA, USA.
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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6
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Lu Y, Lee J, Li J, Allu SR, Wang J, Kim H, Bullaughey KL, Fisher SA, Nordgren CE, Rosario JG, Anderson SA, Ulyanova AV, Brem S, Chen HI, Wolf JA, Grady MS, Vinogradov SA, Kim J, Eberwine J. CHEX-seq detects single-cell genomic single-stranded DNA with catalytical potential. Nat Commun 2023; 14:7346. [PMID: 37963886 PMCID: PMC10645931 DOI: 10.1038/s41467-023-43158-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/02/2023] [Indexed: 11/16/2023] Open
Abstract
Genomic DNA (gDNA) undergoes structural interconversion between single- and double-stranded states during transcription, DNA repair and replication, which is critical for cellular homeostasis. We describe "CHEX-seq" which identifies the single-stranded DNA (ssDNA) in situ in individual cells. CHEX-seq uses 3'-terminal blocked, light-activatable probes to prime the copying of ssDNA into complementary DNA that is sequenced, thereby reporting the genome-wide single-stranded chromatin landscape. CHEX-seq is benchmarked in human K562 cells, and its utilities are demonstrated in cultures of mouse and human brain cells as well as immunostained spatially localized neurons in brain sections. The amount of ssDNA is dynamically regulated in response to perturbation. CHEX-seq also identifies single-stranded regions of mitochondrial DNA in single cells. Surprisingly, CHEX-seq identifies single-stranded loci in mouse and human gDNA that catalyze porphyrin metalation in vitro, suggesting a catalytic activity for genomic ssDNA. We posit that endogenous DNA enzymatic activity is a function of genomic ssDNA.
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Affiliation(s)
- Youtao Lu
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jaehee Lee
- Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jifen Li
- Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Srinivasa Rao Allu
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jinhui Wang
- Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - HyunBum Kim
- Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kevin L Bullaughey
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stephen A Fisher
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - C Erik Nordgren
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jean G Rosario
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Stewart A Anderson
- Department of Psychiatry, Children's Hospital of Philadelphia, ARC 517, 3615 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Alexandra V Ulyanova
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - H Isaac Chen
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - John A Wolf
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - M Sean Grady
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sergei A Vinogradov
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Junhyong Kim
- Department of Biology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James Eberwine
- Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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7
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Clark IC, Fontanez KM, Meltzer RH, Xue Y, Hayford C, May-Zhang A, D'Amato C, Osman A, Zhang JQ, Hettige P, Ishibashi JSA, Delley CL, Weisgerber DW, Replogle JM, Jost M, Phong KT, Kennedy VE, Peretz CAC, Kim EA, Song S, Karlon W, Weissman JS, Smith CC, Gartner ZJ, Abate AR. Microfluidics-free single-cell genomics with templated emulsification. Nat Biotechnol 2023; 41:1557-1566. [PMID: 36879006 PMCID: PMC10635830 DOI: 10.1038/s41587-023-01685-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/20/2023] [Indexed: 03/08/2023]
Abstract
Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.
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Affiliation(s)
- Iain C Clark
- Department of Bioengineering, University of California, Berkeley, California Institute for Quantitative Biosciences, Berkeley, CA, USA
| | | | | | - Yi Xue
- Fluent Biosciences, Watertown, MA, USA
| | | | | | | | | | | | | | | | - Cyrille L Delley
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel W Weisgerber
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Joseph M Replogle
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marco Jost
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Kiet T Phong
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Vanessa E Kennedy
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Cheryl A C Peretz
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Esther A Kim
- Division of Plastic and Reconstructive Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Siyou Song
- Division of Plastic and Reconstructive Surgery, University of California San Francisco, San Francisco, CA, USA
| | - William Karlon
- Departments of Pathology and Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Catherine C Smith
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Adam R Abate
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
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8
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Wang W, Li T, Wang Z, Yin Y, Zhang S, Wang C, Hu X, Lu S. Bibliometric analysis of research on neurodegenerative diseases and single-cell RNA sequencing: Opportunities and challenges. iScience 2023; 26:107833. [PMID: 37736042 PMCID: PMC10509354 DOI: 10.1016/j.isci.2023.107833] [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: 06/19/2023] [Revised: 07/18/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023] Open
Abstract
Neurodegeneration, characterized by the progressive deterioration in neuronal structure or function, presents an elusive mechanism. The use of single-cell RNA sequencing (scRNA-seq) technology in the clinic is becoming increasingly prevalent in recent decades. This technology offers unparalleled cell-level insights into neurodegenerative diseases, establishing itself as a potent tool for elucidating these diseases underlying mechanisms. Here, we made a deep investigation for scRNA-seq research in neurodegenerative diseases using bibliometric analysis from 2009 to 2022. We observed a robust upward trajectory in the number of publications on this subject. The United States stood out as the principal contributor to this expanding field. Specifically, the University of California System exhibited notable research prowess in this field. Alzheimer disease and Parkinson disease were the diseases most frequently investigated. Key research hotspots include the creation of a molecular brain atlas and identification of vulnerable neuronal subpopulations and potential therapeutic targets at the transcriptomic level.
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Affiliation(s)
- Wei Wang
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Tianhua Li
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Zheng Wang
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Yaxin Yin
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Sitao Zhang
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Chaodong Wang
- Department of Neurology, National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xinli Hu
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
| | - Shibao Lu
- Department of Orthopedics, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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9
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Chen S, Jiang W, Du Y, Yang M, Pan Y, Li H, Cui M. Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy. Front Genet 2023; 14:1276959. [PMID: 37900181 PMCID: PMC10602688 DOI: 10.3389/fgene.2023.1276959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
Single-cell sequencing (SCS) technology is changing our understanding of cellular components, functions, and interactions across organisms, because of its inherent advantage of avoiding noise resulting from genotypic and phenotypic heterogeneity across numerous samples. By directly and individually measuring multiple molecular characteristics of thousands to millions of single cells, SCS technology can characterize multiple cell types and uncover the mechanisms of gene regulatory networks, the dynamics of transcription, and the functional state of proteomic profiling. In this context, we conducted systematic research on SCS techniques, including the fundamental concepts, procedural steps, and applications of scDNA, scRNA, scATAC, scCITE, and scSNARE methods, focusing on the unique clinical advantages of SCS, particularly in cancer therapy. We have explored challenging but critical areas such as circulating tumor cells (CTCs), lineage tracing, tumor heterogeneity, drug resistance, and tumor immunotherapy. Despite challenges in managing and analyzing the large amounts of data that result from SCS, this technique is expected to reveal new horizons in cancer research. This review aims to emphasize the key role of SCS in cancer research and promote the application of single-cell technologies to cancer therapy.
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Affiliation(s)
- Siyuan Chen
- Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, China
| | - Weibo Jiang
- Department of Orthopaedic, The Second Hospital of Jilin University, Changchun, China
| | - Yanhui Du
- Department of Orthopaedics, Jilin Province People’s Hospital, Changchun, China
| | - Manshi Yang
- Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, China
| | - Yihan Pan
- Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, China
| | - Huan Li
- Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, China
| | - Mengying Cui
- Department of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, China
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10
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Wu YF, Chang NW, Chu LA, Liu HY, Zhou YX, Pai YL, Yu YS, Kuan CH, Wu YC, Lin SJ, Tan HY. Single-Cell Transcriptomics Reveals Cellular Heterogeneity and Complex Cell-Cell Communication Networks in the Mouse Cornea. Invest Ophthalmol Vis Sci 2023; 64:5. [PMID: 37792336 PMCID: PMC10565710 DOI: 10.1167/iovs.64.13.5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 06/30/2023] [Indexed: 10/05/2023] Open
Abstract
Purpose To generate a single-cell RNA-sequencing (scRNA-seq) map and construct cell-cell communication networks of mouse corneas. Methods C57BL/6 mouse corneas were dissociated to single cells and subjected to scRNA-seq. Cell populations were clustered and annotated for bioinformatic analysis using the R package "Seurat." Differential expression patterns were validated and spatially mapped with whole-mount immunofluorescence staining. Global intercellular signaling networks were constructed using CellChat. Results Unbiased clustering of scRNA-seq transcriptomes of 14,732 cells from 40 corneas revealed 17 cell clusters of six major cell types: nine epithelial cell, three keratocyte, two corneal endothelial cell, and one each of immune cell, vascular endothelial cell, and fibroblast clusters. The nine epithelial cell subtypes included quiescent limbal stem cells, transit-amplifying cells, and differentiated cells from corneas and two minor conjunctival epithelial clusters. CellChat analysis provided an atlas of the complex intercellular signaling communications among all cell types. Conclusions We constructed a complete single-cell transcriptomic map and the complex signaling cross-talk among all cell types of the cornea, which can be used as a foundation atlas for further research on the cornea. This study also deepens the understanding of the cellular heterogeneity and heterotypic cell-cell interaction within corneas.
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Affiliation(s)
- Yueh-Feng Wu
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Nai-Wen Chang
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
| | - Li-An Chu
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
| | - Hsin-Yu Liu
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Ophthalmology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Yu-Xian Zhou
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Yun-Lin Pai
- Department of Biochemical Science and Technology, College of Life Science, National Taiwan University, Taipei, Taiwan
| | - Yu-Sheng Yu
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Chen-Hsiang Kuan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Division of Plastic Surgery, Department of Surgery, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
- Research Center for Developmental Biology and Regenerative Medicine, National Taiwan University, Taipei, Taiwan
| | - Yu-Ching Wu
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
| | - Sung-Jan Lin
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
- Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
- Research Center for Developmental Biology and Regenerative Medicine, National Taiwan University, Taipei, Taiwan
- Department of Dermatology, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Hsin-Yuan Tan
- Department of Ophthalmology, Chang Gung Memorial Hospital, Linkou, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
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11
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Yu W, Wang C, Shang Z, Tian J. Unveiling novel insights in prostate cancer through single-cell RNA sequencing. Front Oncol 2023; 13:1224913. [PMID: 37746302 PMCID: PMC10514910 DOI: 10.3389/fonc.2023.1224913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 08/15/2023] [Indexed: 09/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technology that provides insights at the individual cell level. In contrast to traditional bulk RNA-seq, which captures gene expression at an average level and may overlook important details, scRNA-seq examines each individual cell as a fundamental unit and is particularly well-suited for identifying rare cell populations. Analogous to a microscope that distinguishes various cell types within a tissue sample, scRNA-seq unravels the heterogeneity and diversity within a single cell species, offering great potential as a leading sequencing method in the future. In the context of prostate cancer (PCa), a disease characterized by significant heterogeneity and multiple stages of progression, scRNA-seq emerges as a powerful tool for uncovering its intricate secrets.
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Affiliation(s)
| | | | - Zhiqun Shang
- Tianjin Institute of Urology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Jing Tian
- Tianjin Institute of Urology, Second Hospital of Tianjin Medical University, Tianjin, China
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12
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Madadi Y, Monavarfeshani A, Chen H, Stamer WD, Williams RW, Yousefi S. Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2837-2852. [PMID: 37294649 PMCID: PMC10631573 DOI: 10.1109/tcbb.2023.3284795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) provides a high throughput, quantitative and unbiased framework for scientists in many research fields to identify and characterize cell types within heterogeneous cell populations from various tissues. However, scRNA-seq based identification of discrete cell-types is still labor intensive and depends on prior molecular knowledge. Artificial intelligence has provided faster, more accurate, and user-friendly approaches for cell-type identification. In this review, we discuss recent advances in cell-type identification methods using artificial intelligence techniques based on single-cell and single-nucleus RNA sequencing data in vision science. The main purpose of this review paper is to assist vision scientists not only to select suitable datasets for their problems, but also to be aware of the appropriate computational tools to perform their analysis. Developing novel methods for scRNA-seq data analysis remains to be addressed in future studies.
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13
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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14
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Peng YR. Cell-type specification in the retina: Recent discoveries from transcriptomic approaches. Curr Opin Neurobiol 2023; 81:102752. [PMID: 37499619 DOI: 10.1016/j.conb.2023.102752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023]
Abstract
Understanding the formation of the complex nervous system hinges on decoding the mechanism that specifies a vast array of neuronal types, each endowed with a unique morphology, physiology, and connectivity. As a pivotal step towards addressing this problem, seminal work has been devoted to characterizing distinct neuronal types. In recent years, high-throughput, single-cell transcriptomic methods have enabled a rapid inventory of cell types in various regions of the nervous system, with the retina exhibiting complete molecular characterization across many vertebrate species. This invaluable resource has furnished a fresh perspective for investigating the molecular principles of cell-type specification, thereby advancing our understanding of retinal development. Accordingly, this review focuses on the most recent transcriptomic characterizations of retinal cells, with a particular focus on amacrine cells and retinal ganglion cells. These investigations have unearthed new insights into their cell-type specification.
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Affiliation(s)
- Yi-Rong Peng
- Department of Ophthalmology and Stein Eye Institute, UCLA David Geffen School of Medicine, Los Angeles, CA 90095, USA.
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15
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Stokes T, Cen HH, Kapranov P, Gallagher IJ, Pitsillides AA, Volmar C, Kraus WE, Johnson JD, Phillips SM, Wahlestedt C, Timmons JA. Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq. ADVANCED GENETICS (HOBOKEN, N.J.) 2023; 4:2200024. [PMID: 37288167 PMCID: PMC10242409 DOI: 10.1002/ggn2.202200024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Indexed: 06/09/2023]
Abstract
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
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Affiliation(s)
- Tanner Stokes
- Faculty of ScienceMcMaster UniversityHamiltonL8S 4L8Canada
| | - Haoning Howard Cen
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | - Iain J Gallagher
- School of Applied SciencesEdinburgh Napier UniversityEdinburghEH11 4BNUK
| | | | | | | | - James D. Johnson
- Life Sciences InstituteUniversity of British ColumbiaVancouverV6T 1Z3Canada
| | | | | | - James A. Timmons
- Miller School of MedicineUniversity of MiamiMiamiFL33136USA
- William Harvey Research InstituteQueen Mary University LondonLondonEC1M 6BQUK
- Augur Precision Medicine LTDStirlingFK9 5NFUK
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16
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Prummer M, Bertolini A, Bosshard L, Barkmann F, Yates J, Boeva V, Stekhoven D, Singer F. scROSHI: robust supervised hierarchical identification of single cells. NAR Genom Bioinform 2023; 5:lqad058. [PMID: 37332656 PMCID: PMC10273189 DOI: 10.1093/nargab/lqad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 05/10/2023] [Accepted: 06/12/2023] [Indexed: 06/20/2023] Open
Abstract
Identifying cell types based on expression profiles is a pillar of single cell analysis. Existing machine-learning methods identify predictive features from annotated training data, which are often not available in early-stage studies. This can lead to overfitting and inferior performance when applied to new data. To address these challenges we present scROSHI, which utilizes previously obtained cell type-specific gene lists and does not require training or the existence of annotated data. By respecting the hierarchical nature of cell type relationships and assigning cells consecutively to more specialized identities, excellent prediction performance is achieved. In a benchmark based on publicly available PBMC data sets, scROSHI outperforms competing methods when training data are limited or the diversity between experiments is large.
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Affiliation(s)
| | - Anne Bertolini
- Nexus Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
| | - Lars Bosshard
- Nexus Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
| | - Florian Barkmann
- Institute for Machine Learning, Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Josephine Yates
- Institute for Machine Learning, Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
| | - Valentina Boeva
- Institute for Machine Learning, Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Cochin Institute, Inserm U1016, CNRS UMR 8104, Paris Descartes University UMR-S1016, Paris 75014, France
- Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
| | | | - Daniel Stekhoven
- Nexus Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
| | - Franziska Singer
- Nexus Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Zurich, Switzerland
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17
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Lohani V, A.R A, Kundu S, Akhter MDQ, Bag S. Single-Cell Proteomics with Spatial Attributes: Tools and Techniques. ACS OMEGA 2023; 8:17499-17510. [PMID: 37251119 PMCID: PMC10210017 DOI: 10.1021/acsomega.3c00795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/12/2023] [Indexed: 05/31/2023]
Abstract
Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.
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Affiliation(s)
- Vartika Lohani
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- PG Scholar, Department of Pharmacy, Banasthali
Vidyapith, Jaipur, Rajasthan 302001, India
| | - Akhiya A.R
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- PG Scholar, Department of Computational
Biology and Bioinformatics, University of
Kerala, Thiruvananthapuram, Kerala 695034, India
| | - Soumen Kundu
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - MD Quasid Akhter
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
| | - Swarnendu Bag
- CSIR
Institute of Genomics and Integrative Biology, New Delhi, Delhi 110025, India
- Academy
of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
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18
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Alldred MJ, Ginsberg SD. Microisolation of Spatially Characterized Single Populations of Neurons for RNA Sequencing from Mouse and Postmortem Human Brain Tissues. J Clin Med 2023; 12:3304. [PMID: 37176744 PMCID: PMC10179294 DOI: 10.3390/jcm12093304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/28/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023] Open
Abstract
Single-cell and single-population RNA sequencing (RNA-seq) is a rapidly evolving new field of intense investigation. Recent studies indicate unique transcriptomic profiles are derived based on the spatial localization of neurons within circuits and regions. Individual neuronal subtypes can have vastly different transcriptomic fingerprints, well beyond the basic excitatory neuron and inhibitory neuron designations. To study single-population gene expression profiles of spatially characterized neurons, we have developed a methodology combining laser capture microdissection (LCM), RNA purification of single populations of neurons, and subsequent library preparation for downstream applications, including RNA-seq. LCM provides the benefit of isolating single neurons characterized by morphology or via transmitter-identified and/or receptor immunoreactivity and enables spatial localization within the sample. We utilize unfixed human postmortem and mouse brain tissue that is frozen to preserve RNA quality in order to isolate the desired neurons of interest. Microisolated neurons are then pooled for RNA purification utilizing as few as 250 individual neurons from a tissue section, precluding extraneous nonspecific tissue contaminants. Library preparation is performed from picogram RNA quantities extracted from LCM-captured neurons. Single-population RNA-seq analysis demonstrates that microisolated neurons from both postmortem human and mouse brain tissues are viable for transcriptomic profiling, including differential gene expression assessment and bioinformatic pathway inquiry.
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Affiliation(s)
- Melissa J. Alldred
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Stephen D. Ginsberg
- Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
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19
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Chang C, Zuo H, Li Y. Recent advances in deciphering hippocampus complexity using single-cell transcriptomics. Neurobiol Dis 2023; 179:106062. [PMID: 36878328 DOI: 10.1016/j.nbd.2023.106062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 03/07/2023] Open
Abstract
Single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) technologies have emerged as revolutionary and powerful tools, which have helped in achieving significant progress in biomedical research over the last decade. scRNA-seq and snRNA-seq resolve heterogeneous cell populations from different tissues and help reveal the function and dynamics at the single-cell level. The hippocampus is an essential component for cognitive functions, including learning, memory, and emotion regulation. However, the molecular mechanisms underlying the activity of hippocampus have not been fully elucidated. The development of scRNA-seq and snRNA-seq technologies provides strong support for attaining an in-depth understanding of hippocampal cell types and gene expression regulation from the single-cell transcriptome profiling perspective. This review summarizes the applications of scRNA-seq and snRNA-seq in the hippocampus to further expand our knowledge of the molecular mechanisms related to hippocampal development, health, and diseases.
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Affiliation(s)
- Chenxu Chang
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Hongyan Zuo
- Beijing Institute of Radiation Medicine, Beijing 100850, China.
| | - Yang Li
- Beijing Institute of Radiation Medicine, Beijing 100850, China.
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20
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Patel A, Clark KD. Characterizing RNA modifications in the central nervous system and single cells by RNA sequencing and liquid chromatography-tandem mass spectrometry techniques. Anal Bioanal Chem 2023:10.1007/s00216-023-04604-y. [PMID: 36840809 DOI: 10.1007/s00216-023-04604-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/26/2023]
Abstract
Post-transcriptional modifications to RNA constitute a newly appreciated layer of translation regulation in the central nervous system (CNS). The identity, stoichiometric quantity, and sequence position of these unusual epitranscriptomic marks are central to their function, making analytical methods that are capable of accurate and reproducible measurements paramount to the characterization of the neuro-epitranscriptome. RNA sequencing-based methods and liquid chromatography-tandem mass spectrometry (LC-MS/MS) techniques have been leveraged to provide an early glimpse of the landscape of RNA modifications in bulk CNS tissues. However, recent advances in sample preparation, separations, and detection methods have revealed that individual cells display remarkable heterogeneity in their RNA modification profiles, raising questions about the prevalence and function of cell-specific distributions of post-transcriptionally modified nucleosides in the brain. In this Trends article, we present an overview of RNA sequencing and LC-MS/MS methodologies for the analysis of RNA modifications in the CNS with special emphasis on recent advancements in techniques that facilitate single-cell and subcellular detection.
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Affiliation(s)
- Arya Patel
- Department of Chemistry, Tufts University, Medford, MA, 02155, USA
| | - Kevin D Clark
- Department of Chemistry, Tufts University, Medford, MA, 02155, USA.
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21
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Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data. Nat Commun 2023; 14:679. [PMID: 36755047 PMCID: PMC9908983 DOI: 10.1038/s41467-023-36383-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/30/2023] [Indexed: 02/10/2023] Open
Abstract
Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.
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22
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A Framework for Comparison and Assessment of Synthetic RNA-Seq Data. Genes (Basel) 2022; 13:genes13122362. [PMID: 36553629 PMCID: PMC9778097 DOI: 10.3390/genes13122362] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 12/16/2022] Open
Abstract
The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data have multiple and diverse applications. They are often aimed at benchmarking bioinformatics algorithms for purposes such as sample classification, differential expression analysis, correlation and network studies and the optimization of data integration and normalization techniques. Here, we propose a general framework to compare synthetically generated RNA-seq data and select a data-generating tool that is suitable for a set of specific study goals. As there are multiple methods for synthetic RNA-seq data generation, researchers can use the proposed framework to make an informed choice of an RNA-seq data simulation algorithm and software that are best suited for their specific scientific questions of interest.
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23
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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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24
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Carangelo G, Magi A, Semeraro R. From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis. Front Genet 2022; 13:994069. [PMID: 36263428 PMCID: PMC9575985 DOI: 10.3389/fgene.2022.994069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1–100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.
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Affiliation(s)
- Giulia Carangelo
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Florence, Italy
| | - Alberto Magi
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Roberto Semeraro
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- *Correspondence: Roberto Semeraro,
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25
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Abstract
Oral and craniofacial tissues are uniquely adapted for continuous and intricate functioning, including breathing, feeding, and communication. To achieve these vital processes, this complex is supported by incredible tissue diversity, variously composed of epithelia, vessels, cartilage, bone, teeth, ligaments, and muscles, as well as mesenchymal, adipose, and peripheral nervous tissue. Recent single cell and spatial multiomics assays-specifically, genomics, epigenomics, transcriptomics, proteomics, and metabolomics-have annotated known and new cell types and cell states in human tissues and animal models, but these concepts remain limitedly explored in the human postnatal oral and craniofacial complex. Here, we highlight the collaborative and coordinated efforts of the newly established Oral and Craniofacial Bionetwork as part of the Human Cell Atlas, which aims to leverage single cell and spatial multiomics approaches to first understand the cellular and molecular makeup of human oral and craniofacial tissues in health and to then address common and rare diseases. These powerful assays have already revealed the cell types that support oral tissues, and they will unravel cell types and molecular networks utilized across development, maintenance, and aging as well as those affected in diseases of the craniofacial complex. This level of integration and cell annotation with partner laboratories across the globe will be critical for understanding how multiple variables, such as age, sex, race, and ancestry, influence these oral and craniofacial niches. Here, we 1) highlight these recent collaborative efforts to employ new single cell and spatial approaches to resolve our collective biology at a higher resolution in health and disease, 2) discuss the vision behind the Oral and Craniofacial Bionetwork, 3) outline the stakeholders who contribute to and will benefit from this network, and 4) outline directions for creating the first Human Oral and Craniofacial Cell Atlas.
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Affiliation(s)
- A J Caetano
- Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | - I Sequeira
- Institute of Dentistry, Barts Centre for Squamous Cancer, Barts and The London School of Medicine and Dentistry, Queen Mary University London, London, UK
| | - K M Byrd
- Lab of Oral and Craniofacial Innovation, Department of Innovation and Technology Research, ADA Science and Research Institute, Gaithersburg, MD, USA
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26
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Geraldes I, Fernandes M, Fraga AG, Osório NS. The impact of single-cell genomics on the field of mycobacterial infection. Front Microbiol 2022; 13:989464. [PMID: 36246265 PMCID: PMC9562642 DOI: 10.3389/fmicb.2022.989464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/14/2022] [Indexed: 11/13/2022] Open
Abstract
Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.
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Affiliation(s)
- Inês Geraldes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Mónica Fernandes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Alexandra G. Fraga
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
| | - Nuno S. Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's—PT Government Associate Laboratory, Braga, Portugal
- *Correspondence: Nuno S. Osório
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Understanding Mammalian Hair Follicle Ecosystems by Single-Cell RNA Sequencing. Animals (Basel) 2022; 12:ani12182409. [PMID: 36139270 PMCID: PMC9495062 DOI: 10.3390/ani12182409] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/28/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Single-cell sequencing technology can reflect cell population heterogeneity at the single-cell level, leading to a better understanding of the role of individual cells in the microenvironment. Over the past few years, single-cell sequencing technology has not only made more new discoveries in the study of cellular heterogeneity of other rare cells such as stem cells, but has also become the most powerful research method for embryonic development, organ differentiation, cancer occurrence, and cell mapping. In this review, we outline the use of scRNA-seq in hair follicles. In particular, by focusing on landmark studies and the recent discovery of novel subpopulations of hair follicles, we summarize the phenotypic diversity of hair follicle cells and their links to hair follicle morphogenesis. Enhancing our understanding of the progress of hair follicle research will help to elucidate the regulatory mechanisms that determine the fate of different types of cells in the hair follicle, thereby guiding hair loss treatment and hair-producing economic animal breeding research. Abstract Single-cell sequencing technology can fully reflect the heterogeneity of cell populations at the single cell level, making it possible for us to re-recognize various tissues and organs. At present, the sequencing study of hair follicles is transiting from the traditional ordinary transcriptome level to the single cell level, which will provide diverse insights into the function of hair follicle cells. This review focuses on research advances in the hair follicle microenvironment obtained from scRNA-seq studies of major cell types in hair follicle development, with a special emphasis on the discovery of new subpopulations of hair follicles by single-cell techniques. We also discuss the problems and current solutions in scRNA-seq observation and look forward to its prospects.
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Dhapola P, Rodhe J, Olofzon R, Bonald T, Erlandsson E, Soneji S, Karlsson G. Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data. Nat Commun 2022; 13:4616. [PMID: 35941103 PMCID: PMC9360040 DOI: 10.1038/s41467-022-32097-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 07/18/2022] [Indexed: 12/11/2022] Open
Abstract
As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf’s memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf. As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Here the authors present Scarf, a modularly designed Python package that makes the analysis workflow highly memory efficient such that even the largest existing datasets can be analyzed on an average modern laptop.
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Affiliation(s)
- Parashar Dhapola
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden.
| | - Johan Rodhe
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Rasmus Olofzon
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | | | - Eva Erlandsson
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Shamit Soneji
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden
| | - Göran Karlsson
- Division of Molecular Hematology, Lund Stem Cell Center, Lund University, Lund, Sweden.
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Abstract
The single-cell revolution in the field of genomics is in full bloom, with clever new molecular biology tricks appearing regularly that allow researchers to explore new modalities or scale up their projects to millions of cells and beyond. Techniques abound to measure RNA expression, DNA alterations, protein abundance, chromatin accessibility, and more, all with single-cell resolution and often in combination. Despite such a rapidly changing technology landscape, there are several fundamental principles that are applicable to the majority of experimental workflows to help users avoid pitfalls and exploit the advantages of the chosen platform. In this overview article, we describe a variety of popular single-cell genomics technologies and address some common questions pertaining to study design, sample preparation, quality control, and sequencing strategy. As the majority of relevant publications currently revolve around single-cell RNA-seq, we will prioritize this genomics modality in our discussion. © 2022 Wiley Periodicals LLC.
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Affiliation(s)
- Claire Regan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
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30
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Kothalawala WJ, Barták BK, Nagy ZB, Zsigrai S, Szigeti KA, Valcz G, Takács I, Kalmár A, Molnár B. A Detailed Overview About the Single-Cell Analyses of Solid Tumors Focusing on Colorectal Cancer. PATHOLOGY AND ONCOLOGY RESEARCH 2022; 28:1610342. [PMID: 35928965 PMCID: PMC9344373 DOI: 10.3389/pore.2022.1610342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/15/2022] [Indexed: 11/21/2022]
Abstract
In recent years, the evolution of the molecular biological technical background led to the widespread application of single-cell sequencing, a versatile tool particularly useful in the investigation of tumor heterogeneity. Even 10 years ago the comprehensive characterization of colorectal cancers by The Cancer Genome Atlas was based on measurements of bulk samples. Nowadays, with single-cell approaches, tumor heterogeneity, the tumor microenvironment, and the interplay between tumor cells and their surroundings can be described in unprecedented detail. In this review article we aimed to emphasize the importance of single-cell analyses by presenting tumor heterogeneity and the limitations of conventional investigational approaches, followed by an overview of the whole single-cell analytic workflow from sample isolation to amplification, sequencing and bioinformatic analysis and a review of recent literature regarding the single-cell analysis of colorectal cancers.
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Affiliation(s)
- William J. Kothalawala
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
- *Correspondence: William J. Kothalawala,
| | - Barbara K. Barták
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Zsófia B. Nagy
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Sára Zsigrai
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Krisztina A. Szigeti
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Gábor Valcz
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
- Molecular Medicine Research Group, Eötvös Loránd Research Network, Budapest, Hungary
| | - István Takács
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
| | - Alexandra Kalmár
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
- Molecular Medicine Research Group, Eötvös Loránd Research Network, Budapest, Hungary
| | - Béla Molnár
- Department of Internal Medicine and Oncology, Semmelweis University, Budapest, Hungary
- Molecular Medicine Research Group, Eötvös Loránd Research Network, Budapest, Hungary
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Kelley MW. Cochlear Development; New Tools and Approaches. Front Cell Dev Biol 2022; 10:884240. [PMID: 35813214 PMCID: PMC9260282 DOI: 10.3389/fcell.2022.884240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/19/2022] [Indexed: 12/21/2022] Open
Abstract
The sensory epithelium of the mammalian cochlea, the organ of Corti, is comprised of at least seven unique cell types including two functionally distinct types of mechanosensory hair cells. All of the cell types within the organ of Corti are believed to develop from a population of precursor cells referred to as prosensory cells. Results from previous studies have begun to identify the developmental processes, lineage restrictions and signaling networks that mediate the specification of many of these cell types, however, the small size of the organ and the limited number of each cell type has hampered progress. Recent technical advances, in particular relating to the ability to capture and characterize gene expression at the single cell level, have opened new avenues for understanding cellular specification in the organ of Corti. This review will cover our current understanding of cellular specification in the cochlea, discuss the most commonly used methods for single cell RNA sequencing and describe how results from a recent study using single cell sequencing provided new insights regarding cellular specification.
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32
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Yekula A, Tracz J, Rincon-Torroella J, Azad T, Bettegowda C. Single-Cell RNA Sequencing of Cerebrospinal Fluid as an Advanced Form of Liquid Biopsy for Neurological Disorders. Brain Sci 2022; 12:brainsci12070812. [PMID: 35884620 PMCID: PMC9313114 DOI: 10.3390/brainsci12070812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/17/2022] [Accepted: 06/18/2022] [Indexed: 11/16/2022] Open
Abstract
Diagnosis and longitudinal monitoring of neurological diseases are limited by the poor specificity and limited resolution of currently available techniques. Analysis of circulating cells in cerebrospinal fluid (CSF) has emerged as a promising strategy for the diagnosis, molecular characterization, and monitoring of neurological disease. In comparison to bulk sequencing analysis, single-cell sequencing studies can provide novel insights into rare cell populations and uncover heterogeneity in gene expression at a single-cell resolution, which has several implications for understanding disease pathology and treatment. Parallel development of standardized biofluid collection protocols, pre-processing strategies, reliable single-cell isolation strategies, downstream genomic analysis, and robust computational analysis is paramount for comprehensive single-cell sequencing analysis. Here we perform a comprehensive review of studies focusing on single-cell sequencing of cells in the CSF of patients with oncological or non-oncological diseases of the central nervous system.
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Affiliation(s)
- Anudeep Yekula
- Department of Surgery, Yale School of Medicine, New Haven, CT 06510, USA;
| | - Jovanna Tracz
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (J.T.); (J.R.-T.); (T.A.)
| | - Jordina Rincon-Torroella
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (J.T.); (J.R.-T.); (T.A.)
| | - Tej Azad
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (J.T.); (J.R.-T.); (T.A.)
| | - Chetan Bettegowda
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; (J.T.); (J.R.-T.); (T.A.)
- Correspondence:
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Svoboda M, Frost HR, Bosco G. Internal oligo(dT) priming introduces systematic bias in bulk and single-cell RNA sequencing count data. NAR Genom Bioinform 2022; 4:lqac035. [PMID: 35651651 PMCID: PMC9142200 DOI: 10.1093/nargab/lqac035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/25/2022] [Accepted: 04/29/2022] [Indexed: 12/28/2022] Open
Abstract
Significant advances in RNA sequencing have been recently made possible by using oligo(dT) primers for simultaneous mRNA enrichment and reverse transcription priming. The associated increase in efficiency has enabled more economical bulk RNA sequencing methods and the advent of high-throughput single-cell RNA sequencing, already one of the most widely adopted methods in transcriptomics. However, the effects of off-target oligo(dT) priming on gene expression quantification have not been appreciated. In the present study, we describe the extent, the possible causes, and the consequences of internal oligo(dT) priming across multiple public datasets obtained from various bulk and single-cell RNA sequencing platforms. To explore and address this issue, we developed a computational algorithm for RNA counting methods, which identifies the sequencing read alignments that likely resulted from internal oligo(dT) priming and removes them from the data. Directly comparing filtered datasets to those obtained by an alternative method reveals significant improvements in gene expression measurement. Finally, we infer a list of human genes whose expression quantification is most likely to be affected by internal oligo(dT) priming and predict that when measured using these methods, the expression of most genes may be inflated by at least 10% whereby some genes are affected more than others.
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Affiliation(s)
| | - H Robert Frost
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Giovanni Bosco
- Correspondence may also be addressed to Giovanni Bosco. Tel: +1 603 650 1210; Fax: +1 603 650 1188;
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Applications of Single-Cell Sequencing Technology to the Enteric Nervous System. Biomolecules 2022; 12:biom12030452. [PMID: 35327644 PMCID: PMC8946246 DOI: 10.3390/biom12030452] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/12/2022] [Accepted: 03/13/2022] [Indexed: 02/05/2023] Open
Abstract
With recent technical advances and diminishing sequencing costs, single-cell sequencing modalities have become commonplace. These tools permit analysis of RNA expression, DNA sequence, chromatin structure, and cell surface antigens at single-cell resolution. Simultaneous measurement of numerous parameters can resolve populations including rare cells, thus revealing cellular diversity within organs and permitting lineage reconstruction in developing tissues. Application of these methods to the enteric nervous system has yielded a wealth of data and biological insights. We review recent papers applying single-cell sequencing tools to the nascent neural crest and to the developing and mature enteric nervous system. These studies have shown significant diversity of enteric neurons and glia, suggested paradigms for neuronal specification, and revealed signaling pathways active during development. As technology evolves and multiome techniques combining two or more of transcriptomic, genomic, epigenetic, and proteomic data become prominent, we anticipate these modalities will become commonplace in ENS research and may find a role in diagnostic testing and personalized therapeutics.
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35
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Ahmed R, Zaman T, Chowdhury F, Mraiche F, Tariq M, Ahmad IS, Hasan A. Single-Cell RNA Sequencing with Spatial Transcriptomics of Cancer Tissues. Int J Mol Sci 2022; 23:3042. [PMID: 35328458 PMCID: PMC8955933 DOI: 10.3390/ijms23063042] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/02/2022] [Accepted: 03/07/2022] [Indexed: 01/27/2023] Open
Abstract
Single-cell RNA sequencing (RNA-seq) techniques can perform analysis of transcriptome at the single-cell level and possess an unprecedented potential for exploring signatures involved in tumor development and progression. These techniques can perform sequence analysis of transcripts with a better resolution that could increase understanding of the cellular diversity found in the tumor microenvironment and how the cells interact with each other in complex heterogeneous cancerous tissues. Identifying the changes occurring in the genome and transcriptome in the spatial context is considered to increase knowledge of molecular factors fueling cancers. It may help develop better monitoring strategies and innovative approaches for cancer treatment. Recently, there has been a growing trend in the integration of RNA-seq techniques with contemporary omics technologies to study the tumor microenvironment. There has been a realization that this area of research has a huge scope of application in translational research. This review article presents an overview of various types of single-cell RNA-seq techniques used currently for analysis of cancer tissues, their pros and cons in bulk profiling of transcriptome, and recent advances in the techniques in exploring heterogeneity of various types of cancer tissues. Furthermore, we have highlighted the integration of single-cell RNA-seq techniques with other omics technologies for analysis of transcriptome in their spatial context, which is considered to revolutionize the understanding of tumor microenvironment.
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Affiliation(s)
- Rashid Ahmed
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
- Biomedical Research Centre, Qatar University, Doha 2713, Qatar
- Department of Biotechnology, Faculty of Natural and Applied Sciences, Mirpur University of Science and Technology, Mirpur 10250 AJK, Pakistan;
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA;
| | - Tariq Zaman
- College of Human Medicine, Michigan State University, Grand Rapids, MI 49503, USA;
| | - Farhan Chowdhury
- Department of Mechanical Engineering and Energy Processes, Southern Illinois University Carbondale, Carbondale, IL 62901, USA;
| | - Fatima Mraiche
- Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, Doha 2713, Qatar;
| | - Muhammad Tariq
- Department of Biotechnology, Faculty of Natural and Applied Sciences, Mirpur University of Science and Technology, Mirpur 10250 AJK, Pakistan;
| | - Irfan S. Ahmad
- Nick Holonyak Jr. Micro and Nanotechnology Laboratory, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA;
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
| | - Anwarul Hasan
- Department of Mechanical and Industrial Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
- Biomedical Research Centre, Qatar University, Doha 2713, Qatar
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36
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Long H, Reeves R, Simon MM. Mouse genomic and cellular annotations. Mamm Genome 2022; 33:19-30. [PMID: 35124726 PMCID: PMC8913471 DOI: 10.1007/s00335-021-09936-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/22/2021] [Indexed: 11/28/2022]
Abstract
AbstractMice have emerged as one of the most popular and valuable model organisms in the research of human biology. This is due to their genetic and physiological similarity to humans, short generation times, availability of genetically homologous inbred strains, and relatively easy laboratory maintenance. Therefore, following the release of the initial human reference genome, the generation of the mouse reference genome was prioritised and represented an important scientific resource for the mouse genetics community. In 2002, the Mouse Genome Sequencing Consortium published an initial draft of the mouse reference genome which contained ~ 96% of the euchromatic genome of female C57BL/6 J mice. Almost two decades on from the publication of the initial draft, sequencing efforts have continued to increase the completeness and accuracy of the C57BL/6 J reference genome alongside advances in genome annotation. Additionally new sequencing technologies have provided a wealth of data that has added to the repertoire of annotations associated with traditional genomic annotations. Including but not limited to advances in regulatory elements, the 3D genome and individual cellular states. In this review we focus on the reference genome C57BL/6 J and summarise the different aspects of genomic and cellular annotations, as well as their relevance to mouse genetic research. We denote a genomic annotation as a functional unit of the genome. Cellular annotations are annotations of cell type or state, defined by the transcriptomic expression profile of a cell. Due to the wide-ranging number and diversity of annotations describing the mouse genome, we focus on gene, repeat and regulatory element annotation as well as two relatively new technologies; 3D genome architecture and single-cell sequencing outlining their utility in genetic research and their current challenges.
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Affiliation(s)
- Helen Long
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, OX11 0RD, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Richard Reeves
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, OX11 0RD, UK
| | - Michelle M Simon
- MRC Harwell Institute, Mammalian Genetics Unit, Harwell Campus, Oxfordshire, OX11 0RD, UK.
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Abstract
Bladder cancer is the most common malignant tumour of the urinary system that is characterised by significant intra-tumoural heterogeneity. While large-scale sequencing projects have provided a preliminary understanding of tumour heterogeneity, these findings are based on the average signals obtained from the pooled populations of diverse cells. Recent advances in single-cell sequencing (SCS) technologies have been critical in this regard, opening up new ways of understanding the nuanced tumour biology by identifying distinct cellular subpopulations, dissecting the tumour microenvironment, and characterizing cellular genomic mutations. By integrating these novel insights, SCS technologies are expected to make powerful and meaningful changes to the current diagnosis and treatment of bladder cancer through the identification and usage of novel biomarkers as well as targeted therapeutics. SCS can discriminate complex heterogeneity in a large population of tumour cells and determine the key molecular properties that influence clinical outcomes. Here, we review the advances in single-cell technologies and discuss their applications in cancer research and clinical practice, with a specific focus on bladder cancer.
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38
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Jiang R, Sun T, Song D, Li JJ. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol 2022; 23:31. [PMID: 35063006 PMCID: PMC8783472 DOI: 10.1186/s13059-022-02601-5] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.
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Affiliation(s)
- Ruochen Jiang
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA
| | - Tianyi Sun
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA
| | - Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, 90095-7246, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, 90095-1554, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, 90095-7088, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, 90095-1766, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, 90095-1772, CA, USA.
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39
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Lee J, Eberwine J. Live Cell Genomics: Cell-Specific Transcriptome Capture in Live Single Cells from Complex Tissues. Methods Mol Biol 2022; 2383:617-626. [PMID: 34766318 DOI: 10.1007/978-1-0716-1752-6_40] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Analysis of single-cell transcriptomes shows the single-cell heterogeneity between cells within a population which is vital to our understanding of normal function and disease development. To obtain single-cell transcriptome profiling, however, the poly-A RNA must be accurately isolated from the target cell. We developed a single-cell analysis procedure called transcriptome in vivo analysis (TIVA), which will allow accurate characterization of targeted cell-specific transcriptomes from live tissue. This is accomplished using a RNA capture molecule called TIVA tag that captures the transcriptome of selected cells in their natural microenvironment. An important aspect of the TIVA approach is that the tag is delivered into the cytoplasm of live cells using cell-penetrating peptides (CPPs). Once the TIVA tag is in the cellular cytoplasm, it binds to mRNA after photoactivation of the compound. Using CPPs in combination with photoactivation is the first noninvasive access method for accurately isolating single-cell mRNA from live single cells in tissues in their natural microenvironment.
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Affiliation(s)
- Jaehee Lee
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - James Eberwine
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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40
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Ogawa S, Parhar IS. Heterogeneity in GnRH and kisspeptin neurons and their significance in vertebrate reproductive biology. Front Neuroendocrinol 2022; 64:100963. [PMID: 34798082 DOI: 10.1016/j.yfrne.2021.100963] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 10/11/2021] [Accepted: 10/31/2021] [Indexed: 02/07/2023]
Abstract
Vertebrate reproduction is essentially controlled by the hypothalamus-pituitary-gonadal (HPG) axis, which is a central dogma of reproductive biology. Two major hypothalamic neuroendocrine cell groups containing gonadotropin-releasing hormone (GnRH) and kisspeptin are crucial for control of the HPG axis in vertebrates. GnRH and kisspeptin neurons exhibit high levels of heterogeneity including their cellular morphology, biochemistry, neurophysiology and functions. However, the molecular foundation underlying heterogeneities in GnRH and kisspeptin neurons remains unknown. More importantly, the biological and physiological significance of their heterogeneity in reproductive biology is poorly understood. In this review, we first describe the recent advances in the neuroendocrine functions of kisspeptin-GnRH pathways. We then view the recent emerging progress in the heterogeneity of GnRH and kisspeptin neurons using morphological and single-cell transcriptomic analyses. Finally, we discuss our views on the significance of functional heterogeneity of reproductive endocrine cells and their potential relevance to reproductive health.
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Affiliation(s)
- Satoshi Ogawa
- Brain Research Institute, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500 Bandar Sunway, Selangor, Malaysia
| | - Ishwar S Parhar
- Brain Research Institute, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, 47500 Bandar Sunway, Selangor, Malaysia.
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OUP accepted manuscript. Brief Funct Genomics 2022; 21:159-176. [DOI: 10.1093/bfgp/elac002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 01/20/2022] [Accepted: 01/25/2022] [Indexed: 11/14/2022] Open
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Bartlett DA, Dileep V, Handa T, Ohkawa Y, Kimura H, Henikoff S, Gilbert DM. High-throughput single-cell epigenomic profiling by targeted insertion of promoters (TIP-seq). J Cell Biol 2021; 220:e202103078. [PMID: 34783858 PMCID: PMC8600797 DOI: 10.1083/jcb.202103078] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 09/15/2021] [Accepted: 10/27/2021] [Indexed: 12/16/2022] Open
Abstract
Chromatin profiling in single cells has been extremely challenging and almost exclusively limited to histone proteins. In cases where single-cell methods have shown promise, many require highly specialized equipment or cell type-specific protocols and are relatively low throughput. Here, we combine the advantages of tagmentation, linear amplification, and combinatorial indexing to produce a high-throughput single-cell DNA binding site mapping method that is simple, inexpensive, and capable of multiplexing several independent samples per experiment. Targeted insertion of promoters sequencing (TIP-seq) uses Tn5 fused to proteinA to insert a T7 RNA polymerase promoter adjacent to a chromatin protein of interest. Linear amplification of flanking DNA with T7 polymerase before sequencing library preparation provides ∼10-fold higher unique reads per single cell compared with other methods. We applied TIP-seq to map histone modifications, RNA polymerase II (RNAPII), and transcription factor CTCF binding sites in single human and mouse cells.
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Affiliation(s)
- Daniel A. Bartlett
- Department of Biological Science, Florida State University, Tallahassee, FL
- San Diego Biomedical Research Institute, La Jolla, CA
| | - Vishnu Dileep
- Department of Biological Science, Florida State University, Tallahassee, FL
| | - Tetsuya Handa
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Yasuyuki Ohkawa
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Hiroshi Kimura
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Steven Henikoff
- Basic Sciences Division and Howard Hughes Medical Institute, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - David M. Gilbert
- Department of Biological Science, Florida State University, Tallahassee, FL
- San Diego Biomedical Research Institute, La Jolla, CA
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Clark KD, Rubakhin SS, Sweedler JV. Single-Neuron RNA Modification Analysis by Mass Spectrometry: Characterizing RNA Modification Patterns and Dynamics with Single-Cell Resolution. Anal Chem 2021; 93:14537-14544. [PMID: 34672536 PMCID: PMC8608286 DOI: 10.1021/acs.analchem.1c03507] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The entire collection of post-transcriptional modifications to RNA, known as the epitranscriptome, has been increasingly recognized as a critical regulatory layer in the cellular translation machinery. However, contemporary methods for the analysis of RNA modifications are limited to the detection of highly abundant modifications in bulk tissue samples, potentially obscuring unique epitranscriptomes of individual cells with population averages. We developed an approach, single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS), that enables the detection and quantification of numerous post-transcriptionally modified nucleosides in single cells. When compared to a conventional RNA extraction approach that does not allow detection of RNA modifications in single cells, SNRMA-MS leverages an optimized sample preparation approach to detect up to 16 RNA modifications in individual neurons from the central nervous system of Aplysia californica. SNRMA-MS revealed that the RNA modification profiles of identified A. californica neurons with different physiological functions were mostly cell specific. However, functionally homologous neurons tended to demonstrate similar modification patterns. Stable isotope labeling with CD3-Met showed significant differences in RNA methylation rates that were dependent on the identity of the modification and the cell, with metacerebral cells (MCCs) displaying the fastest incorporation of CD3 groups into endogenous RNAs. Quantitative SNRMA-MS showed higher intracellular concentrations for 2'-O-methyladenosine and 2'-O-methylcytidine in homologous R2/LPl1 cell pairs than in MCCs. Overall, SNRMA-MS is the first analytical approach capable of simultaneously quantifying numerous RNA modifications in single neurons and revealing cell-specific modification profiles.
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Affiliation(s)
- Kevin D. Clark
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Stanislav S. Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jonathan V. Sweedler
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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Mittli D, Tukacs V, Micsonai A, Ravasz L, Kardos J, Juhász G, Kékesi KA. The Single-Cell Transcriptomic Analysis of Prefrontal Pyramidal Cells and Interneurons Reveals the Neuronal Expression of Genes Encoding Antimicrobial Peptides and Immune Proteins. Front Immunol 2021; 12:749433. [PMID: 34759929 PMCID: PMC8574171 DOI: 10.3389/fimmu.2021.749433] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/06/2021] [Indexed: 12/30/2022] Open
Abstract
The investigation of the molecular background of direct communication of neurons and immune cells in the brain is an important issue for understanding physiological and pathological processes in the nervous system. Direct contacts between brain-infiltrating immune cells and neurons, and the neuromodulatory effect of immune cell-derived regulatory peptides are well established. Several aspects of the role of immune and glial cells in the direct neuro-immune communication are also well known; however, there remain many questions regarding the molecular details of signaling from neurons to immune cells. Thus, we report here on the neuronal expression of genes encoding antimicrobial and immunomodulatory peptides, as well as proteins of immune cell-specific activation and communication mechanisms. In the present study, we analyzed the single-cell sequencing data of our previous transcriptomic work, obtained from electrophysiologically identified pyramidal cells and interneurons of the murine prefrontal cortex. We filtered out the genes that may be associated with the direct communication between immune cells and neurons and examined their expression pattern in the neuronal transcriptome. The expression of some of these genes by cortical neurons has not yet been reported. The vast majority of antimicrobial (~53%) and immune cell protein (~94%) transcripts was identified in the transcriptome of the 84 cells, owing to the high sensitivity of ultra-deep sequencing. Several of the antimicrobial and immune process-related protein transcripts showed cell type-specific or enriched expression. Individual neurons transcribed only a fraction of the investigated genes with low copy numbers probably due to the bursting kinetics of gene expression; however, the comparison of our data with available transcriptomic datasets from immune cells and neurons suggests the functional relevance of the reported findings. Accordingly, we propose further experimental and in silico studies on the neuronal expression of immune system-related genes and the potential role of the encoded proteins in neuroimmunological processes.
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Affiliation(s)
- Dániel Mittli
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Vanda Tukacs
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - András Micsonai
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Lilla Ravasz
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- Clinical Research Units (CRU) Hungary Ltd., Göd, Hungary
| | - József Kardos
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Gábor Juhász
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- Clinical Research Units (CRU) Hungary Ltd., Göd, Hungary
- InnoScience Ltd., Mátranovák, Hungary
| | - Katalin Adrienna Kékesi
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
- InnoScience Ltd., Mátranovák, Hungary
- Department of Physiology and Neurobiology, Institute of Biology, ELTE Eötvös Loránd University, Budapest, Hungary
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Asada K, Takasawa K, Machino H, Takahashi S, Shinkai N, Bolatkan A, Kobayashi K, Komatsu M, Kaneko S, Okamoto K, Hamamoto R. Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research. Biomedicines 2021; 9:biomedicines9111513. [PMID: 34829742 PMCID: PMC8614827 DOI: 10.3390/biomedicines9111513] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 10/06/2021] [Accepted: 10/19/2021] [Indexed: 01/14/2023] Open
Abstract
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.
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Affiliation(s)
- Ken Asada
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
| | - Ken Takasawa
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Hidenori Machino
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Satoshi Takahashi
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Norio Shinkai
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Amina Bolatkan
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Kazuma Kobayashi
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Masaaki Komatsu
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan; (K.T.); (H.M.); (S.T.); (N.S.); (A.B.); (M.K.)
| | - Syuzo Kaneko
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
| | - Koji Okamoto
- Division of Cancer Differentiation, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryuji Hamamoto
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
- Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (K.K.); (S.K.)
- Correspondence: (K.A.); (R.H.); Tel.: +81-3-3547-5271 (R.H.)
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Moulson AJ, Squair JW, Franklin RJM, Tetzlaff W, Assinck P. Diversity of Reactive Astrogliosis in CNS Pathology: Heterogeneity or Plasticity? Front Cell Neurosci 2021; 15:703810. [PMID: 34381334 PMCID: PMC8349991 DOI: 10.3389/fncel.2021.703810] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/02/2021] [Indexed: 01/02/2023] Open
Abstract
Astrocytes are essential for the development and homeostatic maintenance of the central nervous system (CNS). They are also critical players in the CNS injury response during which they undergo a process referred to as "reactive astrogliosis." Diversity in astrocyte morphology and gene expression, as revealed by transcriptional analysis, is well-recognized and has been reported in several CNS pathologies, including ischemic stroke, CNS demyelination, and traumatic injury. This diversity appears unique to the specific pathology, with significant variance across temporal, topographical, age, and sex-specific variables. Despite this, there is limited functional data corroborating this diversity. Furthermore, as reactive astrocytes display significant environmental-dependent plasticity and fate-mapping data on astrocyte subsets in the adult CNS is limited, it remains unclear whether this diversity represents heterogeneity or plasticity. As astrocytes are important for neuronal survival and CNS function post-injury, establishing to what extent this diversity reflects distinct established heterogeneous astrocyte subpopulations vs. environmentally dependent plasticity within established astrocyte subsets will be critical for guiding therapeutic development. To that end, we review the current state of knowledge on astrocyte diversity in the context of three representative CNS pathologies: ischemic stroke, demyelination, and traumatic injury, with the goal of identifying key limitations in our current knowledge and suggesting future areas of research needed to address them. We suggest that the majority of identified astrocyte diversity in CNS pathologies to date represents plasticity in response to dynamically changing post-injury environments as opposed to heterogeneity, an important consideration for the understanding of disease pathogenesis and the development of therapeutic interventions.
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Affiliation(s)
- Aaron J. Moulson
- Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries (ICORD), Vancouver, BC, Canada
| | - Jordan W. Squair
- Department of Clinical Neuroscience, Faculty of Life Sciences, Center for Neuroprosthetics and Brain Mind Institute, École Polytechnique Fédérale de Lausanne (EPFL), NeuroRestore, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland
| | - Robin J. M. Franklin
- Wellcome Trust - MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Wolfram Tetzlaff
- International Collaboration on Repair Discoveries (ICORD), Vancouver, BC, Canada
- Department of Zoology, University of British Columbia, Vancouver, BC, Canada
- Department of Surgery, University of British Columbia, Vancouver, BC, Canada
| | - Peggy Assinck
- Wellcome Trust - MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, United Kingdom
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Mohanty A, Mohanty SK, Rout S, Pani C. Liquid Biopsy, the hype vs. hope in molecular and clinical oncology. Semin Oncol 2021; 48:259-267. [PMID: 34384614 DOI: 10.1053/j.seminoncol.2021.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 05/28/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022]
Abstract
The molecular landscape of tumors has been traditionally established using a biopsy or resection specimens. These modalities result in sampling bias that offer only a single snapshot of tumor heterogeneity. Over the last decade intensive research towards alleviating such a bias and obtaining an integral yet accurate portrait of the tumors, evolved to the use of established molecular and genetic analysis using blood and several other body fluids, such as urine, saliva, and pleural effusions as liquid biopsies. Genomic profiling of the circulating markers including circulating cell-free tumor DNA (ctDNA), circulating tumor cells (CTCs) or even RNA, proteins, and lipids constituting exosomes, have facilitated the diligent monitoring of response to treatment, allowed one to follow the emergence of drug resistance, and enumerate minimal residual disease. The prevalence of tumor educated platelets (TEPs) and our understanding of how tumor cells influence platelets are beginning to unearth TEPs as a potentially dynamic component of liquid biopsies. Here, we review the biology, methodology, approaches, and clinical applications of biomarkers used to assess liquid biopsies. The current review addresses recent technological advances and different forms of liquid biopsy along with upcoming challenges and how they can be integrated to get the best possible tumor-derived genetic information that can be leveraged to more precise therapies for patient as liquid biopsies become increasingly routine in clinical practice.
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Affiliation(s)
- Abhishek Mohanty
- Rajiv Gandhi Cancer Institute and Research Centre, New Delhi, India.
| | - Sambit K Mohanty
- Advanced Medical Research Institute, Bhubaneswar, Odisha, India; CORE Diagnostics, Gurgaon, Haryana, India
| | - Sipra Rout
- Christian Medical College, Vellore, Tamil Nadu, India
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Bode D, Cull AH, Rubio-Lara JA, Kent DG. Exploiting Single-Cell Tools in Gene and Cell Therapy. Front Immunol 2021; 12:702636. [PMID: 34322133 PMCID: PMC8312222 DOI: 10.3389/fimmu.2021.702636] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Single-cell molecular tools have been developed at an incredible pace over the last five years as sequencing costs continue to drop and numerous molecular assays have been coupled to sequencing readouts. This rapid period of technological development has facilitated the delineation of individual molecular characteristics including the genome, transcriptome, epigenome, and proteome of individual cells, leading to an unprecedented resolution of the molecular networks governing complex biological systems. The immense power of single-cell molecular screens has been particularly highlighted through work in systems where cellular heterogeneity is a key feature, such as stem cell biology, immunology, and tumor cell biology. Single-cell-omics technologies have already contributed to the identification of novel disease biomarkers, cellular subsets, therapeutic targets and diagnostics, many of which would have been undetectable by bulk sequencing approaches. More recently, efforts to integrate single-cell multi-omics with single cell functional output and/or physical location have been challenging but have led to substantial advances. Perhaps most excitingly, there are emerging opportunities to reach beyond the description of static cellular states with recent advances in modulation of cells through CRISPR technology, in particular with the development of base editors which greatly raises the prospect of cell and gene therapies. In this review, we provide a brief overview of emerging single-cell technologies and discuss current developments in integrating single-cell molecular screens and performing single-cell multi-omics for clinical applications. We also discuss how single-cell molecular assays can be usefully combined with functional data to unpick the mechanism of cellular decision-making. Finally, we reflect upon the introduction of spatial transcriptomics and proteomics, its complementary role with single-cell RNA sequencing (scRNA-seq) and potential application in cellular and gene therapy.
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Affiliation(s)
- Daniel Bode
- Wellcome Medical Research Council (MRC) Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
- Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Alyssa H. Cull
- York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
| | - Juan A. Rubio-Lara
- York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
| | - David G. Kent
- York Biomedical Research Institute, Department of Biology, University of York, York, United Kingdom
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49
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Abstract
It has been known for over a century that the basic organization of the retina is conserved across vertebrates. It has been equally clear that retinal cells can be classified into numerous types, but only recently have methods been devised to explore this diversity in unbiased, scalable, and comprehensive ways. Advances in high-throughput single-cell RNA-sequencing (scRNA-seq) have played a pivotal role in this effort. In this article, we outline the experimental and computational components of scRNA-seq and review studies that have used them to generate retinal atlases of cell types in several vertebrate species. These atlases have enabled studies of retinal development, responses of retinal cells to injury, expression patterns of genes implicated in retinal disease, and the evolution of cell types. Recently, the inquiry has expanded to include the entire eye and visual centers in the brain. These studies have enhanced our understanding of retinal function and dysfunction and provided tools and insights for exploring neural diversity throughout the brain. Expected final online publication date for the Annual Review of Vision Science, Volume 7 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Karthik Shekhar
- Department of Chemical and Biomolecular Engineering; Helen Wills Neuroscience Institute; and California Institute for Quantitative Biosciences, QB3, University of California, Berkeley, California 94720, USA;
| | - Joshua R Sanes
- Center for Brain Science and Department of Molecular and Cell Biology, Harvard University, Cambridge, Massachusetts 02138, USA;
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50
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Cardona-Alberich A, Tourbez M, Pearce SF, Sibley CR. Elucidating the cellular dynamics of the brain with single-cell RNA sequencing. RNA Biol 2021; 18:1063-1084. [PMID: 33499699 PMCID: PMC8216183 DOI: 10.1080/15476286.2020.1870362] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/17/2020] [Accepted: 12/24/2020] [Indexed: 12/18/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) has emerged in recent years as a breakthrough technology to understand RNA metabolism at cellular resolution. In addition to allowing new cell types and states to be identified, scRNA-seq can permit cell-type specific differential gene expression changes, pre-mRNA processing events, gene regulatory networks and single-cell developmental trajectories to be uncovered. More recently, a new wave of multi-omic adaptations and complementary spatial transcriptomics workflows have been developed that facilitate the collection of even more holistic information from individual cells. These developments have unprecedented potential to provide penetrating new insights into the basic neural cell dynamics and molecular mechanisms relevant to the nervous system in both health and disease. In this review we discuss this maturation of single-cell RNA-sequencing over the past decade, and review the different adaptations of the technology that can now be applied both at different scales and for different purposes. We conclude by highlighting how these methods have already led to many exciting discoveries across neuroscience that have furthered our cellular understanding of the neurological disease.
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Affiliation(s)
- Aida Cardona-Alberich
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, Edinburgh University, Edinburgh, UK
| | - Manon Tourbez
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Sarah F. Pearce
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Christopher R. Sibley
- Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Sciences, Edinburgh University, Edinburgh, UK
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
- Euan MacDonald Centre for MND Research, University of Edinburgh, Edinburgh, UK
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