Leveraging RNA-seq deconvolution to improve complex in vitro model characterization.

ABSTRACT: Accelerating development of complex in vitro models (CIVMs) drives a need for additional methods to characterize these systems for use in toxicology and drug development. Relative to traditional cell culture, CIVMs can use a lower number of cells, are cultured for longer periods of time, and typically involve engineered 3-D cell organization. Standard single-cell assessment tools are not conducive to these types of complex models due to cell isolation stress, cell loss, and high cost. In this report, we benchmark RNA-seq deconvolution, which utilizes publicly available scRNA-seq datasets to predict cell proportions from bulk RNA-seq data derived from two CIVMs: a stem-cell-based human intestinal organoid CIVM and a neonatal rodent testis CIVM. We consider the impact of multiple imputation

SUBMITTER: Hansen BC 

PROVIDER: S-EPMC12391696 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature