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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Aug 08, 2023 |
| Title |
Using combined single-cell gene expression, TCR sequencing and cell surface protein barcoding to characterize and track CD4 T cell clones from murine tissues |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
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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| Overall design |
We performed combined single-cell RNA/TCR-sequencing of CD4 T cells from tissues of an individual mouse.
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| Contributor(s) |
Nedwed AS, Helbich SS, Braband KL, Delacher M, Marini F, Volkmar M |
| Citation(s) |
37901204 |
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| Submission date |
Aug 03, 2023 |
| Last update date |
Nov 07, 2023 |
| Contact name |
Michael Delacher |
| E-mail(s) |
delacher@uni-mainz.de
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| Phone |
+49 6131 17 6574
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| Organization name |
University Medical Center of the Johannes Gutenberg-University Mainz
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| Department |
Institute for Immunology
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| Lab |
Immunology
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| Street address |
Langenbeckstrasse 1
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| City |
Mainz |
| State/province |
Rhineland-Palatinate |
| ZIP/Postal code |
55131 |
| Country |
Germany |
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| Platforms (1) |
| GPL19057 |
Illumina NextSeq 500 (Mus musculus) |
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| Samples (3) |
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| Relations |
| BioProject |
PRJNA1001964 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE240041_Adapted_Clonotypes.xlsx |
366.1 Kb |
(ftp)(http) |
XLSX |
| GSE240041_RAW.tar |
108.7 Mb |
(http)(custom) |
TAR (of CSV, MTX, TSV) |
| GSE240041_sceRNA_manuscript.RDS.gz |
62.6 Mb |
(ftp)(http) |
RDS |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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