In this talk, we discuss the paper \u201cInferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes\u201d by Andrea Salati et al., bioRxiv, 2026.<\/p>\n
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Abstract:<\/p>\n
Single-cell RNA sequencing (scRNA-seq) reveals heterogeneity in circadian clock states across individual cells, yet accurately inferring circadian phase and distinguishing biological desynchrony from technical noise remains challenging. Here, we introduce\u00a0scRitmo<\/em>, a probabilistic framework that infers single-cell circadian phases from mRNA count data, providing both a point estimate and a posterior uncertainty for each cell. A simulationcalibrated variance decomposition separates the observed phase dispersion into biological and technical components, enabling direct estimation of intercellular desynchrony. We validate\u00a0scRitmo<\/em>\u00a0using deeply sequenced unsynchronized fibroblasts, where inferred transcriptomic phases accurately predict protein-level oscillations of a circadian reporter. Applied to murine scRNA-seq datasets from liver, aorta, and skin,\u00a0scRitmo<\/em>\u00a0outperforms existing methods and reveals cell-type-specific levels of phase coherence. In SABER-FISH time-series data, the method recovers the progressive accumulation of desynchrony following synchronization, and in Drosophila clock neurons it captures cell-type-specific phase shifts and the expected increase in phase dispersion under constant darkness relative to light-dark entrainment. Together,\u00a0scRitmo<\/em>\u00a0provides a principled approach for quantifying circadian (de)synchrony from transcriptomic data, decoupling biological phase variability from measurement noise across tissues, organisms, and experimental conditions.<\/p>\n","protected":false},"excerpt":{"rendered":" In this talk, we discuss the paper \u201cInferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes\u201d by Andrea Salati et al., bioRxiv, 2026. Abstract: Single-cell RNA sequencing (scRNA-seq) … <\/p>\n