Mair, F. | Search bioRxiv

bioRxiv2019

Progressive differentiation of memory CD8 T cells in the cervicovaginal tissue

Tissue-resident memory CD8 T cells (CD8 TRM) are critical for maintaining barrier immunity. CD8 TRM have been mainly studied in the skin and gut with recent studies suggesting that the signals that control tissue-residence and phenotype are highly tissue-dependent. We examined the T cell compartment in healthy human cervicovaginal tissue (CVT) and found that most CD8 T cells were granzyme B+ and TCF-1-. To address if this phenotype is driven by CVT tissue-residence, we used a mouse model to control for environmental factors. Using localized and systemic infection models, we found that CD8 TRM in the mouse CVT gradually acquired a granzyme B+, TCF-1- phenotype as seen in human CVT. In contrast to CD8 TRM in the gut, these CD8 TRM were not stably maintained regardless of the initial infection route, which led to reductions in local immunity. Our data show that residence in the CVT is sufficient to progressively shape the size and function of its CD8 TRM compartment. SummaryThe tissue-resident memory (TRM) CD8 T cell compartment in human and mouse cervicovaginal tissue (CVT) is remarkably similar. The CVT TRM compartment is maintained autonomously and does not reach phenoypical or numerical equilibrium. The numerical decline leads to impaired viral control in a secondary challenge.

immunology↗

bioRxiv2019

A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets.\n\nHere, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

immunology↗