Identifying fate-determining transcription factors with single-cell omics

Area of Science:

• Computational biology
• Genomics
• Cellular and molecular biology

Background:

• Single-cell sequencing provides powerful tools for discovering cell fate-determining transcription factors (TFs).
• Numerous computational methods exist for identifying key TFs, but they vary in data requirements and biological focus.
• Understanding TF roles is crucial for defining cellular identity and driving cell state transitions.

Purpose of the Study:

• To systematically review and organize computational approaches for identifying key transcription factors (TFs).
• To provide a framework for understanding TF identification methods based on their biological application and modeling approach.
• To guide researchers in selecting appropriate computational tools for TF discovery and to highlight future trends in cell fate control.

Main Methods:

• Systematic literature review of computational methods for key TF identification.
• Categorization of methods based on whether they identify TFs for cell identity versus state transitions.
• Organization of methods by modeling approach (discrete vs. continuous) and TF action (individual vs. combinatorial).

Main Results:

• The review categorizes existing computational methods from three key perspectives: identity vs. transition, discrete vs. continuous processes, and individual vs. combinatorial TF action.
• Key features and application scenarios for relevant methods are summarized.
• The landscape of computational TF identification tools is presented to facilitate informed tool selection.

Conclusions:

• A structured overview of computational TF identification methods is provided, aiding researchers in tool selection.
• Emerging trends point towards programmable and active control of cell fate.
• This systematic review facilitates the advancement of research in understanding and manipulating cell fate through TF discovery.