Deciphering cell state transitions by logical modeling with single-cell transcriptome data

Area of Science:

• Molecular Biology
• Genomics
• Systems Biology

Background:

• Cell state transitions are fundamental to cell fate decisions but are typically irreversible.
• The regulatory mechanisms governing asymmetric forward and backward cell state transitions remain largely unknown.
• Understanding these mechanisms is critical for controlling cell behavior.

Purpose of the Study:

• To investigate the regulatory mechanisms underlying asymmetric cell state transitions.
• To develop a computational framework for analyzing single-cell transcriptome data during cell state changes.
• To identify strategies for controlling cell state transitions, particularly in the context of cancer drug resistance.

Main Methods:

• Development of scDECIPHER (single-cell dynamic explorer of complex interactions and pathway hierarchies), an integrative computational framework.
• Analysis of single-cell resolution transcriptome data from human and mouse cancer samples undergoing cell state transitions.
• Utilizing scDECIPHER to uncover hidden molecular regulatory networks.

Main Results:

• scDECIPHER successfully identified key regulatory mechanisms governing cell state transitions.
• The framework revealed molecular pathways involved in overcoming drug resistance in cancer.
• Application to cancer samples provided insights into controlling cell state plasticity.

Conclusions:

• The study presents scDECIPHER as a powerful tool for dissecting cell state dynamics.
• The findings offer novel molecular targets and strategies for combating cancer drug resistance.
• Controlling cell state transitions holds therapeutic potential for various diseases.