Biology subjects

Salati, A.

Publications and source records attributed to Salati, A..

3 recordsLinked to original sources

bioRxiv2026

Inferring circadian phases and quantifying biological desynchrony across single-cell transcriptomes

1Single-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 scRitmo, 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 scRitmo using 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, scRitmo outperforms 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, scRitmo provides a principled approach for quantifying circadian (de)synchrony from transcriptomic data, decoupling biological phase variability from measurement noise across tissues, organisms, and experimental conditions.

bioinformatics↗

bioRxiv2025

CoPhaser: generic modeling of biological cycles in scRNA-seq with context-dependent periodic manifolds

Biological cycles are ubiquitous cellular processes operating across a wide range of time scales. Fundamental cycles such as the cell cycle, circadian rhythms, or the segmentation clock occur cell-autonomously and are typically coupled to other cellular processes, including cell-type identity, metabolic states, and disease-associated programs. In single-cell transcriptomics (scRNA-seq), disentangling these continuous periodic trajectories from other sources of cellular variability remains a major challenge. Here, we introduce CoPhaser, an algorithm that learns context-dependent periodic manifolds to decompose scRNA-seq count data into independent periodic and non-periodic sources of variation, while preserving interpretability of manifold coordinates across biological contexts. CoPhaser is based on a biologically informed variational autoencoder with a structured latent space that explicitly separates cycle phase from cellular context while controlling their mutual information. By modeling gene expression as context-modulated harmonic functions, the model captures flexible yet biologically grounded deformations of periodic manifolds. We demonstrate CoPhasers ability to yield novel biological insights across four biological cycles. It recovers accurate continuous cell-cycle phases across diverse sequencing technologies, including highly heterogeneous settings such as development and cancer, without prior knowledge of gene programs or cell-cycle states. In cancer applications, CoPhaser reveals subtype-specific proliferation dynamics, identifying quiescent primitive states in relapsed pediatric acute myeloid leukemia and distinguishing proliferation-driven from constitutive gene overexpression in triple-negative breast cancer, highlighting potential robust therapeutic targets. It further extends to spatial cancer transcriptomics, revealing spatial synchronisation of cell-cycle phases in ovarian tumors. CoPhaser generalizes to other periodic systems, enabling reconstruction of circadian clocks in the mouse aorta, and identifies cell-type and subtype-specific circadian differences. In addition, it maps continuous endometrial remodeling across the human menstrual cycle and reveals altered transcriptional dynamics in endometriosis. Finally, it reveals coupling between the cell cycle and the somite clock in the mouse embryo. Together, CoPhaser provides a versatile and interpretable framework for dissecting the interplay between cellular identity and biological cycles in single-cell data.

bioinformatics↗

bioRxiv2023

A sexually dimorphic hepatic cycle of very low density lipoprotein uptake and assembly

Recent single-cell transcriptomes revealed spatiotemporal programmes of liver function on the sublobular scale. However, how sexual dimorphism affected this space-time logic remained poorly understood. We addressed this by performing scRNA-seq in the mouse liver, which revealed that sex, space and time together markedly influence xenobiotic detoxification and lipoprotein metabolism. The very low density lipoprotein receptor (VLDLR) exhibits a pericentral expression pattern, with significantly higher mRNA and protein levels in female mice. Conversely, VLDL assembly is periportally biased, suggesting a sexually dimorphic hepatic cycle of periportal formation and pericentral uptake of VLDL. In humans, VLDLR expression is also pericentral, with higher mRNA and protein levels in premenopausal women compared to similarly aged men. Individuals with low hepatic VLDLR expression show a high prevalence of atherosis in the coronary artery already at an early age and an increased incidence of heart attack.

physiology↗