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A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex multilayered identities or transitory states. Here the authors present devCellPy, a Python-based package that enables the automated prediction of cell types across complex cellular hierarchies, species, and experimental systems with high accuracy, particularly for developmental scRNA-seq datasets.

Abstract Generating cardiomyocytes (CMs) from human induced pluripotent stem cells (hiPSCs) has represented a significant advance in our ability to model cardiac disease. Current differentiation protocols, however, have limited use due to their production of heterogenous cell populations, primarily consisting of ventricular-like CMs. Here we describe the creation of two chamber-specific reporter hiPSC lines by site-directed genomic integration using CRISPR-Cas9 technology. In the MYL2-tdTomato reporter, the red fluorescent tdTomato was inserted upstream of the 3′ untranslated region of the Myosin Light Chain 2 (MYL2) gene in order faithfully label hiPSC-derived ventricular-like CMs while avoiding disruption of endogenous gene expression. Similarly, in the SLN-CFP reporter, Cyan Fluorescent Protein (CFP) was integrated downstream of the coding region of the atrial-specific gene, Sarcolipin (SLN). Purification of tdTomato+ and CFP+ CMs using flow cytometry coupled with transcriptional and functional characterization validated these genetic tools for their use in the isolation of bona fide ventricular-like and atrial-like CMs, respectively. Finally, we successfully generated a double reporter system allowing for the isolation of both ventricular and atrial CM subtypes within a single hiPSC line. These tools provide a platform for chamber-specific hiPSC-derived CM purification and analysis in the context of atrial- or ventricular-specific disease and therapeutic opportunities.