A differential network analysis approach for lineage specifier prediction in stem cell subpopulations

Area of Science:

• Stem cell biology
• Computational biology
• Genomics

Background:

• Stem cell differentiation is complex and hindered by cellular heterogeneity.
• Single-cell gene expression technologies reveal cell subpopulations but identifying lineage specifiers remains challenging.
• Current computational and experimental workflows need advancement for analyzing transcriptional regulatory networks (TRNs) in differentiation.

Purpose of the Study:

• To propose a computational differential network analysis approach for predicting lineage specifiers in binary-fate differentiation.
• To identify genes crucial for specific cell fates during stem cell differentiation.

Main Methods:

• Reconstructs cell-subpopulation specific TRNs using single-cell gene expression data, literature knowledge, and TF-DNA binding-site prediction.
• Models stem cell subpopulations in stable states maintained by TRN stability cores.
• Predicts lineage specifiers by identifying genes within TRN stability cores of parental and daughter cell subpopulations.

Main Results:

• Successfully predicted known and novel lineage specifiers in various stem cell differentiation systems.
• Demonstrated applicability in inner cell mass differentiation (primitive endoderm/epiblast).
• Validated in hematopoietic progenitor cells and lung alveolar progenitor differentiation.

Conclusions:

• The developed method is broadly applicable to any binary-fate differentiation system with available single-cell gene expression data.
• Aids in understanding stem cell lineage specification.
• Supports the development of experimental strategies for regenerative medicine.