Single cell transcriptomics identifies immunologic priming related to extra corporeal life support survival.

Fluidics based single cell RNASeq (scRNASeq) provides a high throughput method for quantifying gene expression at single cell resolution. However, it remains unclear whether this approach is robust in dynamic clinical settings--including the extent to which new analytic tools required by the unique characteristics of scRNASeq are effective in such contexts. We report scRNASeq analysis of ~1,000 cells from each of 38 patients requiring veno-arterial extracorporeal life support (VA-ECLS)--a diverse group of critically ill patients experiencing circulatory collapse as a common endpoint to wide ranging diseases. Using existing tools including Alra for technical drop out imputation and Harmony for batch effect removal, we established an analysis pipeline capturing major biological signals from theses samples as confirmed by flow cytometry. We demonstrate that even in this very complicated clinical setting, scRNASeq can reveal novel aspects of disease biology that can be translated to and validated in subsequent patient cohorts.