
Faculty, Staff and Student Publications
Publication Date
12-16-2024
Journal
Cell Reports Methods
Abstract
Decoding cellular state transitions is crucial for understanding complex biological processes in development and disease. While recent advancements in single-cell RNA sequencing (scRNA-seq) offer insights into cellular trajectories, existing tools primarily study expressional rather than regulatory state shifts. We present CellTran, a statistical approach utilizing paired-gene expression correlations to detect transition cells from scRNA-seq data without explicitly resolving gene regulatory networks. Applying our approach to various contexts, including tissue regeneration, embryonic development, preinvasive lesions, and humoral responses post-vaccination, reveals transition cells and their distinct gene expression profiles. Our study sheds light on the underlying molecular mechanisms driving cellular state transitions, enhancing our ability to identify therapeutic targets for disease interventions.
Keywords
Single-Cell Analysis, Humans, Animals, RNA-Seq, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Gene Regulatory Networks, Transcriptome, Single-Cell Gene Expression Analysis, cell transitions, gene regulatory network, dynamic systems, cell differentiation, cell development, carcinogenesis, single-cell RNA sequencing, differential equations, gene expression correlation, statistical analysis
DOI
10.1016/j.crmeth.2024.100913
PMID
39644902
PMCID
PMC11704623
PubMedCentral® Posted Date
12-6-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons