Using the simple telegraph model to decipher transcriptional burst regulation across genome-wide data

Gene transcription is a stochastic bursting process with burst frequency and size as core parameters. While complex models capture detailed biology, their computational cost limits genome-wide applications. We propose a simple telegraph model-based framework to analyze genome-wide scRNA-seq datasets. When sample size ≥500 and burst parameter change ≥ 3 fold, inferred burst frequency- and size-dominated variations reliably proxy true regulation. Analyses across mouse cells and healthy/hypertrophic cardiomyopathy (HCM) human heart tissues revealed three conserved principles: (1) over 70% of genes with altered burst regulation exhibited burst frequency- or size-dominated regulation; HCM genes show stronger bursting featuring prolonged inactivity and intense transcription; (2) TATA-initiator synergy is lost in HCM; and (3) burst frequency-dominated genes enriched in genome stability/cell cycle/apoptosis (via TFs like Foxo4/Mcm2), while burst size-dominated ones enrich in signaling/metabolism (via TFs like Zfp322a/Ppargc1a). Their interdependent dysregulation accelerated HCM. This study establishes the simple telegraph model as a scalable framework linking transcriptional burst dynamics to cell fate and pathology.