Single-Cell Analysis of T Cells – Improved Prediction of Disease Risk

The La Jolla researchers are studying how small changes in gene expression can influence the function of immune cells during infection. Single-cell analysis was performed on activated CD4+ T cells from 89 healthy donors. It turns out that the old adage of “one gene, one protein” is not precisely true. In reality, as research on gene expression has progressed, scientists have come to understand that gene and protein expression, while tightly linked, can be modulated in a cell-type and context specific manner.

Cell type specific gene regulation is particularly important for the functioning of the immune system. Immune cells need to be able to respond rapidly to their environment; when the cells are activated by the presence of a pathogen, genes are transcribed into proteins in order to carry out an immune response. It is in the best interests of scientists and doctors to discover just how this works, in order to better predict which populations may be more susceptible to disease and develop more effective treatments.

Designing a single-cell analysis study

Consequently, the La Jolla researchers designed a study in which they would use eQTL analysis to scrutinize the functional expression of  T cell genetic variants. This type of single-cell analysis is used to identify single nucleotide changes in non-coding gene sequences that affect expression of one or more genes. The team would perform single-cell analysis on close to 1 million activated CD4+ T cells for their study.

To begin the analysis, the scientists isolated immune cell samples from 89 different healthy donors (35 females and 54 males) who had agreed to provide leukapheresis samples at the San Diego Blood Bank (Excellos). The research team’s efforts focused on activation-dependent gene expression in CD4+ T cells, because this is the T cell type that initiates the body’s response to infection. CD4+ T cells were sorted into eight functionally distinct sets on the basis of cell surface protein expression. The scientists planned to mimic infection-induced immune activation by engaging the T cell receptor (TCR) and the costimulatory molecule CD28.

Single-cell analysis was performed rather than bulk analysis, because this is often the only way to detect activation-dependent gene expression from relatively rare genetic variants.

Before beginning the single-cell analysis, the scientists isolated equal numbers of cells from each donor, and sorted these cells into the eight predetermined subsets, each of which are known to be associated with different immune functions. First, the scientists performed an analysis of each pool of CD4+ cells in their resting state, and then repeated the analysis on activated CD4+ T cells. This would enable the researchers to identify activation-induced changes in gene expression. Single-cell analysis was performed rather than bulk analysis, because this is often the only way to detect activation-dependent gene expression from relatively rare genetic variants.

As the researchers expected, TCR/CD28 activation significantly increased expression of over 2500 gene transcripts across the eight CD4+ T cell subsets. Close to 500 of these transcripts also showed cell type-specific differences in their expression patterns. Deconvolution of the data revealed 19 distinct transcriptional profiles among the activated CD4+ T cells. Single-cell eQTL transcriptional analysis on these 19 T cell subsets found 4,308 genes that are significantly associated with common single nucleotide genetic variants. For most of these variants activation-dependent effects are restricted to specific T cell subtypes. The genes variants encoded proteins associated with diverse immune cell functions, including T cell activation, differentiation, survival, and effector functions.

Identifying gene variants linked to disease or infection susceptibility 

When the La Jolla research team began their study, one of their most important goals was to identify gene variants that might explain why certain people are more susceptible to disease or infection.  By comparing their functional profiling results with variants identified from genome-wide association studies, the team was able to identify new gene associations for over 600 disease risk linked gene variants from these previous studies. They were also able to pinpoint the cell types in which the activation-induced effects of these genes would be most prominent. The data also revealed that biological sex has a major impact on activation-dependent gene expression in CD4+ T cell subsets, but not on resting state gene expression. This data can help explain previously observed sex-based differences in immune response, for example in autoimmune disease.

Overall, this work represents a significant step forward in understanding disease susceptibility and clinical outcomes.  The next step will be to use this type of information to proactively identify genetic risk factors, and use that knowledge to work toward disease prevention.

Visit our website to see how high-fidelity donor characterization and single-cell analysis can be used to speed cell therapy development and help predict patient response rates.  Contact us to learn more.

Reference

  1. Schmiedel, B. J., et al. (2022). Single-cell eQTL analysis of activated T cell subsets reveals activation and cell type-dependent effects of disease-risk variants. Science immunology, 7(68), eabm2508. https://doi.org/10.1126/sciimmunol.abm2508

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