gms | German Medical Science

T helper cells play an important role in our adaptive immune system and if their regulation is out of balance, it leads to complex diseases. They coordinate adaptive immune responses by secretion of small proteins, so-called cytokines. As the body is attacked by diverse pathogens, they have specialized to fulfill different tasks leading to efficient defense against harmful invaders. T helper cells specialized for the same task are grouped into T helper cell subsets with specific functions in the human body.

We aim to describe T helper cell subsets on a deep molecular level and thus better characterize their phenotypes and their function in the immune system. In particular, we aim to identify a unique set of marker genes for each T helper cell subset in a robust and unbiased way. To achieve this aim, we analyze gene expression and protein secretion among 79 human T cell clones. T helper cell clones consist of several hundred thousand cells which all descent from one parent cell and share the same phenotype. The data set has the unique property of including a large variety of T cell subsets simultaneously which were all handled and measured using the same protocols.

We cluster T helper cell clones based on their measured cytokine secretion profile by calculating a consensus of five different algorithms to obtain robust, uniform clusters. The computationally defined clusters are associated to known, biological T helper cell subsets or combinations thereof. Next, we use whole genome gene expression data to characterize T helper cell subsets on a molecular level. We build a consensus of six differential gene expression methods which uncovers a core set of T helper cell subset specific marker genes. The differential gene expression methods comprise standard tools (limma [1]), newly introduced methods (quantile approach [2]) and regularized regression models. In regularized regression modeling, we perform feature selection using elastic net penalty [3] where features represent genes. In bionomial logistic regression, we control the per-family error rate using stability selection [4]. In multinomial regression models, we analyze features selected by both grouped and ungrouped lasso penalties. To set those uncovered subset specific marker genes in the right context, we analyze their expression levels in skin disease, perform pathway analysis and investigate protein-protein interactions to determine possible targets for experimental validation.

In summary, our approach identifies known and novel marker genes for T helper cell subsets. Their implications for immune system functions still have to be experimentally tested.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.