Single cell transcriptomics is a robust approach to defining disease biology in complex clinical settings.
Eric J. Kort MD MS, Matthew Weiland MS, Edgars Grins MD, Emily Eugster MS, Hsiao-yun Milliron PhD, Catherine Kelty MS, Nabin Manandhar Shrestha PhD, Tomasz Timek MD, Marzia Leacche MD, Stephen J Fitch MD, Theodore J Boeve MD, Greg Marco MD, Michael Dickinson MD, Penny Wilton MD, Stefan Jovinge MD PhD
Received Date 15th April 19
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 complicated clinical setting, scRNASeq can reveal new aspects of disease biology that can be translated to and validated in subsequent patient cohorts.
Read in full at bioRxiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.