Can scRNAseq Reveal Differences in T Cells Between Cell Types?

March 21, 2022

Are T Cells Different Between Tissues?

It is well known that T cells are one of the essential effector cell types of the immune system, particularly for infiltrating cancerous tumors. But with all the recent discoveries in the heterogeneity of the tumor microenvironment, it begs the question- are there differences between the T cells themselves?

According to a recent paper by Szabo et al. in Nature Communications, the answer is yes: T cells differ between tissue types. The research group from Columbia University’s Center for Translational Immunology leveraged single cell RNA sequencing (scRNA-seq) to identify how T cells differ at a transcriptional level between cell types.

Using scRNA-seq to Establish a Reference T Cell Transcriptome

T cells differ dramatically in their functionality between lungs, lymph nodes, bone marrow and blood, but cells of the same lineage are similar in function. CD4 helper T cells have common functionality across tissue types, and CD8 cytotoxic T cells also share functionality across tissue types.

This scRNA-seq analysis is particularly useful because it establishes a transcriptomic reference for healthy T cells, as the group analyzed over 50,000 resting and activated T cells.

Tumor-Associated T Cells

To validate the reference transcriptome, the authors overlapped tumor-associated T cell profiles with the healthy reference transcriptomes of T cells across tissue types. They found that tumor-associated T cells exhibited markers of T cell exhaustion and abnormal proliferation, consistent with previous studies.

T cells are frequent targets of cancer immunotherapies, and thus it is vital to have a comprehensive understanding of T cell biology under normal conditions and in disease states. This study illustrates the utility of scRNA-seq for determining how cell populations vary between different tissue types and between healthy and diseased states.

Outsourcing Bioinformatics Analysis

Processing and analyzing scRNA-seq data is a complex computational task. Outsourcing your bioinformatic work can save time, eliminate common challenges, and improve reproducibility. Book a free discovery call with us at Bridge Informatics to discuss your project needs.



Jane Cook, Journalist & Content Writer, Bridge Informatics

Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email daniel.dacey@old.bridgeinformatics.com or dan.ryder@old.bridgeinformatics.com.

Sources:

https://www.nature.com/articles/s41467-019-12464-3

3-D illustration rendering of T cells, key effector cells of the immune system that are targets of cancer immunotherapy.

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