Guided Differentiation of Pluripotent Stem Cells for Cardiac Cell Diversity
McIntire, E.; Barr, K. A.; Gonzales, N. M.; Gilad, Y.
Show abstract
In principle, induced pluripotent stem cells (iPSCs) can differentiate into any cell type in the body. The challenge is to find a way to rapidly expand the dimensionality of cell types and cell states we can characterize. To address this, we developed a guided differentiation protocol to produce heterogeneous differentiating cultures of cardiac cell types (cardiac HDCs) in 16 days. Cardiac HDCs are three-dimensional, rhythmically contracting cell aggregates that harbor a temporally and functionally diverse range of cardiac-relevant cell types. We characterize cardiac HDCs from 47 iPSC lines using single-cell RNA-sequencing to identify cardiomyocytes, epicardial cells, cardiac fibroblasts, endothelial cells, and hematopoietic cells, along with both ectodermal and endodermal derivatives. This guided differentiation approach prioritizes simplicity by minimizing the reagents and steps required, thereby enabling rapid and cost-effective experimental throughput. We expect cardiac HDCs to provide a scalable cardiac model for population-level studies of gene regulatory variation and gene-by-environment interactions.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A comprehensive analysis of gene expression changes in a high replicate and open-source dataset of differentiating hiPSC-derived cardiomyocytes 95%
- Comparing the signaling and transcriptome profiling landscapes of human iPSC-derived and primary rat neonatal cardiomyocytes 93%
- Altered Intercellular Communication and Extracellular Matrix Signaling as a Potential Disease Mechanism in Human Hypertrophic Cardiomyopathy 93%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.