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Exploring and analysing single cell multi-omics data with VDJView

BACKGROUND: Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as pa...

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Autores principales: Samir, Jerome, Rizzetto, Simone, Gupta, Money, Luciani, Fabio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029546/
https://www.ncbi.nlm.nih.gov/pubmed/32070336
http://dx.doi.org/10.1186/s12920-020-0696-z
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author Samir, Jerome
Rizzetto, Simone
Gupta, Money
Luciani, Fabio
author_facet Samir, Jerome
Rizzetto, Simone
Gupta, Money
Luciani, Fabio
author_sort Samir, Jerome
collection PubMed
description BACKGROUND: Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information. RESULTS: We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8(+) T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians. CONCLUSIONS: VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview.
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spelling pubmed-70295462020-02-25 Exploring and analysing single cell multi-omics data with VDJView Samir, Jerome Rizzetto, Simone Gupta, Money Luciani, Fabio BMC Med Genomics Software BACKGROUND: Single cell RNA sequencing provides unprecedented opportunity to simultaneously explore the transcriptomic and immune receptor diversity of T and B cells. However, there are limited tools available that simultaneously analyse large multi-omics datasets integrated with metadata such as patient and clinical information. RESULTS: We developed VDJView, which permits the simultaneous or independent analysis and visualisation of gene expression, immune receptors, and clinical metadata of both T and B cells. This tool is implemented as an easy-to-use R shiny web-application, which integrates numerous gene expression and TCR analysis tools, and accepts data from plate-based sorted or high-throughput single cell platforms. We utilised VDJView to analyse several 10X scRNA-seq datasets, including a recent dataset of 150,000 CD8(+) T cells with available gene expression, TCR sequences, quantification of 15 surface proteins, and 44 antigen specificities (across viruses, cancer, and self-antigens). We performed quality control, filtering of tetramer non-specific cells, clustering, random sampling and hypothesis testing to discover antigen specific gene signatures which were associated with immune cell differentiation states and clonal expansion across the pathogen specific T cells. We also analysed 563 single cells (plate-based sorted) obtained from 11 subjects, revealing clonally expanded T and B cells across primary cancer tissues and metastatic lymph-node. These immune cells clustered with distinct gene signatures according to the breast cancer molecular subtype. VDJView has been tested in lab meetings and peer-to-peer discussions, showing effective data generation and discussion without the need to consult bioinformaticians. CONCLUSIONS: VDJView enables researchers without profound bioinformatics skills to analyse immune scRNA-seq data, integrating and visualising this with clonality and metadata profiles, thus accelerating the process of hypothesis testing, data interpretation and discovery of cellular heterogeneity. VDJView is freely available at https://bitbucket.org/kirbyvisp/vdjview. BioMed Central 2020-02-18 /pmc/articles/PMC7029546/ /pubmed/32070336 http://dx.doi.org/10.1186/s12920-020-0696-z Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Samir, Jerome
Rizzetto, Simone
Gupta, Money
Luciani, Fabio
Exploring and analysing single cell multi-omics data with VDJView
title Exploring and analysing single cell multi-omics data with VDJView
title_full Exploring and analysing single cell multi-omics data with VDJView
title_fullStr Exploring and analysing single cell multi-omics data with VDJView
title_full_unstemmed Exploring and analysing single cell multi-omics data with VDJView
title_short Exploring and analysing single cell multi-omics data with VDJView
title_sort exploring and analysing single cell multi-omics data with vdjview
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029546/
https://www.ncbi.nlm.nih.gov/pubmed/32070336
http://dx.doi.org/10.1186/s12920-020-0696-z
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