Rational Reprogramming of Cellular States by Combinatorial Perturbation.

Area of Science:

• Cell Biology
• Molecular Biology
• Regenerative Medicine

Background:

• Transcription factors (TFs) can alter cell identity, but identifying effective TF combinations for specific cell reprogramming is challenging due to the vast combinatorial possibilities.
• Efficient cell reprogramming is crucial for advancing regenerative medicine and disease modeling.

Purpose of the Study:

• To develop and validate a high-throughput screening method, Reprogram-Seq, for identifying optimal transcription factor cocktails for cell state reprogramming.
• To discover specific TF combinations capable of reprogramming mouse embryonic fibroblasts (MEFs) into functional epicardial cells.

Main Methods:

• Reprogram-Seq integrates organ-specific cell-atlas data, single-cell perturbation, and computational analysis to screen TF combinations.
• The study utilized Reprogram-Seq to test libraries of cardiac and epicardial-related TFs on MEFs.
• Characterization of reprogrammed cells involved transcriptional, molecular, morphological, and functional analyses.

Main Results:

• Reprogram-Seq successfully screened thousands of TF cocktails, identifying efficient reprogramming combinations.
• A specific combination of three TFs was identified that effectively reprogrammed MEFs into epicardial-like cells.
• The reprogrammed epicardial-like cells exhibited high similarity to primary epicardial cells across multiple biological dimensions.

Conclusions:

• Reprogram-Seq is a powerful platform for accelerating the discovery of TF combinations for targeted cell reprogramming.
• The identified TF cocktail offers a promising approach for generating specific cell types, particularly epicardial cells, for regenerative medicine applications.
• This technology has broad potential to advance cell-based therapies and research in developmental and disease biology.