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Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
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Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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2
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Börner K, Teichmann SA, Quardokus EM, Gee JC, Browne K, Osumi-Sutherland D, Herr BW, Bueckle A, Paul H, Haniffa M, Jardine L, Bernard A, Ding SL, Miller JA, Lin S, Halushka MK, Boppana A, Longacre TA, Hickey J, Lin Y, Valerius MT, He Y, Pryhuber G, Sun X, Jorgensen M, Radtke AJ, Wasserfall C, Ginty F, Ho J, Sunshine J, Beuschel RT, Brusko M, Lee S, Malhotra R, Jain S, Weber G. Anatomical structures, cell types and biomarkers of the Human Reference Atlas. Nat Cell Biol 2021; 23:1117-1128. [PMID: 34750582 PMCID: PMC10079270 DOI: 10.1038/s41556-021-00788-6] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 09/29/2021] [Indexed: 02/05/2023]
Abstract
The Human Reference Atlas (HRA) aims to map all of the cells of the human body to advance biomedical research and clinical practice. This Perspective presents collaborative work by members of 16 international consortia on two essential and interlinked parts of the HRA: (1) three-dimensional representations of anatomy that are linked to (2) tables that name and interlink major anatomical structures, cell types, plus biomarkers (ASCT+B). We discuss four examples that demonstrate the practical utility of the HRA.
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Affiliation(s)
- Katy Börner
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Ellen M Quardokus
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Kristen Browne
- Department of Health and Human Services, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Bruce W Herr
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Hrishikesh Paul
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Muzlifah Haniffa
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Laura Jardine
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | | | | | - Shin Lin
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Marc K Halushka
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Avinash Boppana
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Teri A Longacre
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - John Hickey
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Yiing Lin
- Department of Surgery, Washington University in St Louis, St Louis, MO, USA
| | - M Todd Valerius
- Harvard Institute of Medicine, Harvard Medical School, Boston, MA, USA
| | - Yongqun He
- Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Gloria Pryhuber
- Department of Pediatrics, University of Rochester, Rochester, NY, USA
| | - Xin Sun
- Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Marda Jorgensen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Andrea J Radtke
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Clive Wasserfall
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Fiona Ginty
- Biology and Applied Physics, General Electric Research, Niskayuna, NY, USA
| | - Jonhan Ho
- Department of Dermatology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joel Sunshine
- Department of Dermatology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Rebecca T Beuschel
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Maigan Brusko
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Sujin Lee
- Division of Vascular Surgery and Endovascular Therapy, Massachusetts General Hospital, Boston, MA, USA
| | - Rajeev Malhotra
- Harvard Institute of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Vascular Surgery and Endovascular Therapy, Massachusetts General Hospital, Boston, MA, USA
| | - Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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4
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Slovin S, Carissimo A, Panariello F, Grimaldi A, Bouché V, Gambardella G, Cacchiarelli D. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview. Methods Mol Biol 2021; 2284:343-365. [PMID: 33835452 DOI: 10.1007/978-1-0716-1307-8_19] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields' immaturity, it may also encumber a newcomer to blend in.In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.
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Affiliation(s)
- Shaked Slovin
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy
| | - Annamaria Carissimo
- Istituto per le Applicazioni del Calcolo "Mauro Picone", Consiglio Nazionale delle Ricerche, Naples, Italy
| | - Francesco Panariello
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy
| | - Antonio Grimaldi
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy
| | - Valentina Bouché
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy
| | - Gennaro Gambardella
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy.
- Department of Chemical Materials and Industrial Engineering, University of Naples "Federico II", Naples, Italy.
| | - Davide Cacchiarelli
- Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy.
- Department of Translational Medicine, University of Naples "Federico II", Naples, Italy.
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