Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth

Abstract

CD4+ T-cells represent a heterogeneous collection of specialised sub-types and are a key cell type in the pathogenesis of many diseases due to their role in the adaptive immune system. By investigating CD4+ T-cells at the single cell level, using RNA sequencing (scRNA-seq), there is the potential to identify specific cell states driving disease or treatment response. However, the impact of sequencing depth and cell numbers, two important factors in scRNA-seq, has not been determined for a complex cell population such as CD4+ T-cells. We therefore generated a high depth, high cell number dataset to determine the effect of reduced sequencing depth and cell number on the ability to accurately identify CD4+ T-cell subtypes. Furthermore, we investigated T-cell signatures under resting and stimulated conditions to assess cluster specific effects of stimulation. We found that firstly, cell number has a much more profound effect than sequencing depth on the ability to classify cells; secondly, this effect is greater when cells are unstimulated and finally, resting and stimulated samples can be combined to leverage additional power whilst still allowing differences between samples to be observed. While based on one individual, these results could inform future scRNA-seq studies to ensure the most efficient experimental design.

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis

“…1F and G ), which is highly expressed at ADT measurement, but minimally expressed at the RNA level, mainly because mRNAs are produced at much lower rates and have much shorter half-lives than proteins ( 15 ). This observation is consistent with previous studies showing low CD4 mRNA expression compared with surface CD4 protein ( 16 ). Finally, because naïve CD8 + T cells were clustered together with CD4 + T cells based on transcriptome profiles ( Fig.…”

Single-Cell RNA Sequencing Revealed CD14+ Monocytes Increased in Patients With Takayasu’s Arteritis Requiring Surgical Management

“…Analysis of CD4 + T cells was also conducted ( Figure 2C and Supplementary Table 4 ). According to the gene expression of each subcluster, we obtained five cell populations, including memory CD4 + T cells (subcluster 0) using TMSB4X, LTB, GIMAP7, IL32, and MYL12A; naïve CD4 + T cells (subcluster 1) using CCR7, CXCR4, MAL, and SARAF; cytotoxic CD4 + T cell (subcluster 2) using GZMA, GZMB, GZMH, and CCL5; early TCR response (subcluster 3) using EGR1, FOSB, FOS, IER2; and unidentified cluster using TAOK1 and LINC01681 ( Ding et al, 2020 ; Figure 2C and Supplementary Figure 2 ).…”

See-N-Seq: RNA sequencing of target single cells identified by microscopy via micropatterning of hydrogel porosity

“…Th1 and Th2) that coordinate distinct cell mediated or humoral adaptive immune responses. T cell activation has largely been elucidated by scRNA-seq following activation with anti-CD3/CD28 beads 26 . However, previous studies have not accounted for co-regulatory receptor signaling present in a natural immunological synapse 10 , 27 , 28 .…”