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Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4 T cell clones from murine tissues

ABSTRACT: Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4 T cell clones from murine tissues

PROVIDER: PRJNA1001964 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR25511323.fastq.gz	Fastqsanger.gz
	SRR25511324.fastq.gz	Fastqsanger.gz
	SRR25511325.fastq.gz	Fastqsanger.gz
	SRR25511326.fastq.gz	Fastqsanger.gz
	SRR25511327.fastq.gz	Fastqsanger.gz

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Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4+ T cell clones from murine tissues.

Project description:Single-cell gene expression analysis using sequencing (scRNA-seq) has gained increased attention in the past decades for studying cellular transcriptional programs and their heterogeneity in an unbiased manner, and novel protocols allow the simultaneous measurement of gene expression, T-cell receptor clonality and cell surface protein expression. In this article, we describe the methods to isolate scRNA/TCR-seq-compatible CD4+ T cells from murine tissues, such as skin, spleen, and lymph nodes. We describe the processing of cells and quality control parameters during library preparation, protocols for multiplexing of samples, and strategies for sequencing. Moreover, we describe a step-by-step bioinformatic analysis pipeline from sequencing data generated using these protocols. This includes quality control, preprocessing of sequencing data and demultiplexing of individual samples. We perform quantification of gene expression and extraction of T-cell receptor alpha and beta chain sequences, followed by quality control and doublet detection, and methods for harmonization and integration of datasets. Next, we describe the identification of highly variable genes and dimensionality reduction, clustering and pseudotemporal ordering of data, and we demonstrate how to visualize the results with interactive and reproducible dashboards. We will combine different analytic R-based frameworks such as Bioconductor and Seurat, illustrating how these can be interoperable to optimally analyze scRNA/TCR-seq data of CD4+ T cells from murine tissues.

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