Biology subjects

Paychere, Y.

Publications and source records attributed to Paychere, Y..

4 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↗

bioRxiv2026

Cell-cycle inhibition preserves robust development but rebalances lineages in mouse gastruloids

Cell differentiation and proliferation are fundamental to the development of multicellular organ-isms. While studies in various non-mammalian species show that development can proceed despite disrupted cell cycle progression, the extent to which normal cell cycle dynamics are re-quired in mammals remains unclear. Using mouse gastruloids, we examined the effects of cell cycle inhibition on development. Despite near-complete growth arrest, gastruloids still underwent symmetry breaking, elongation, and germ layer specification, indicating that core differentiation programs are robust to growth inhibition. However, microscopy and single-cell transcriptomics revealed consistent alterations in cell type proportions, including delayed differentiation and re-duced mesodermal populations. To investigate the origin of these changes, we used the differ-ential kinetics of unspliced and spliced cycling transcripts to compare proliferation rates between cell types. While differences in proliferation partly explained the imbalance, our analysis showed that cell cycle perturbations also modulate lineage-specific differentiation timing and efficiency, highlighting a regulatory role of cell cycle dynamics in mammalian development.

developmental biology↗

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↗

bioRxiv2024

CellSeg3D: self-supervised 3D cell segmentation for microscopy

Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package called CellSeg3D that provides access to these models in Jupyter Notebooks and in a napari GUI plugin. Recognizing the scarcity of high-quality 3D ground truth data, we created a fully human-annotated mesoSPIM dataset to advance evaluation and benchmarking in the field. To assess model performance, we benchmarked our approach across four diverse datasets: the newly developed mesoSPIM dataset, a 3D platynereis-ISH-Nuclei confocal dataset, a separate 3D Platynereis-Nuclei light-sheet dataset, and a challenging and densely packed Mouse-Skull-Nuclei confocal dataset. We demonstrate that our self-supervised model, WNet3D - trained without any ground truth labels - achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts.

cell biology↗