A genome-scale screen of primary human CD4-positive T cells mapped how individual genes regulate immune responses at rest and following stimulation, according to a study published in Cell.
Researchers combined CRISPR interference with single-cell RNA sequencing in approximately 22 million cells from four healthy blood donors. The resulting data linked gene activity to cytokine production, T cell states, lymphocyte counts, and genetic susceptibility to autoimmune disease.
The study used Perturb-seq, which measures changes in gene expression after targeted genetic disruption. Researchers designed a library targeting 12,779 genes expressed in CD4-positive T cells, including all known transcription factors.
The cells were examined under three conditions: at rest, 8 hours following stimulation, and 48 hours following stimulation. This allowed the team to assess whether regulatory effects changed during T cell activation.
Following quality control, 22 million cells had a single guide RNA assigned and were included in the main analysis. Researchers measured transcriptome-wide effects for 11,527 disrupted genes. Of these, 7,807 affected the expression of at least three other genes under one or more conditions. Overall, the screen identified more than 2 million gene-to-gene regulatory effects.
Many of these effects depended on cellular context. Some genes regulated immune programs only at rest or at a specific time following stimulation. This finding may be relevant when interpreting immune gene-expression profiles because the same genetic change may have different effects depending on T cell activation state.
The researchers examined 30 cytokine genes and identified 1,556 gene disruptions that affected at least one cytokine. These included established components of T cell receptor signaling as well as genes involved in metabolism, mitochondrial function, messenger RNA processing, and transcription.
Follow-up experiments assessed regulators of IL10 and IL21, which encode cytokines involved in immune control and B cell function. All 12 regulatory relationships tested produced gene-expression changes in the same direction as the original screen, and 10 were statistically significant. Protein measurements by flow cytometry generally supported the RNA findings, although one IL10 result differed between the transcript and protein levels.
The team also compared the regulatory map with population-level transcriptomic and genetic data. Genes associated with autoimmune disease were enriched in 33 of 77 tested regulatory clusters. The analysis connected less-characterized genes, including LRRC25 and FAM20B, with pathways involved in T cell activation and T helper 1 polarization.
The resource may help researchers interpret immune transcriptomic signatures and prioritize genes found through genome-wide association studies. Perturb-seq is not a clinical diagnostic test, and the study did not assess its ability to diagnose or predict disease in patients.
Limitations included the use of cells from only four donors, partial gene repression rather than complete knockout, and possible off-target effects. The experiments used blood-derived T cells cultured outside the body and did not capture tissue-specific environments or all T cell polarization states.
Newsletters
Receive the latest pathologist news, personalities, education, and career development – weekly to your inbox.
