Single-cell RNA-seq of out-of-thaw mesenchymal stromal cells

Purpose: A Multiomics-based Unbiased Computational Modeling Platform Identifies Predictive Attributes of Mesenchymal Stromal Cell Immunomodulatory Potency Methods: scRNAseq from MSCs with dropseq method on BioRad ddseq platform Results: framework for identifying putative CQAs and generating MoA hypotheses for therapeutic cells and may help overcome current challenges in advancing large-scale, quality-driven manufacturing of cell therapies for broad clinical use. We used SureCell app to align the reads to human reference genome and used SEURAT for downstream analysis. Conclusions: Cell therapies are complex “living medicines” with potential to treat chronic, incurable diseases. Despite their success and promise, quality-driven reproducible manufacturing and identification of Mechanisms-of-Action (MoA) remain significant challenges. Specifically, it is difficult to identify which set of cell attributes, among the thousands of proteins, RNA, lipids, and metabolites, are most correlative to their function in a specific disease setting. Here, we report a multiomics-driven unbiased computational platform to identify multivariate features that are predictive of their immunomodulatory functions.