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Boudjelthia, I. K.

Publications and source records attributed to Boudjelthia, I. K..

2 recordsLinked to original sources

Comparison of cell-cycle gene expression dynamics and mRNA kinetics across mouse and human pluripotent systems

Cell-cycle remodeling is fundamental to pluripotency and lineage commitment, yet whether its transcriptional and post-transcriptional architecture is conserved across species and developmental states has remained unresolved. Here we introduce Ciclopes, a biology-informed deep-learning framework that resolves continuous cell-cycle phase and phase-dependent mRNA transcription and degradation directly from single-cell RNA sequencing. Applying Ciclopes across six mouse and human pluripotent stem-cell systems spanning naive and primed states, we uncover striking divergence in transcriptional complexity and oscillatory control: mouse systems sustain elevated baseline expression of core cell-cycle regulators, while human systems trade higher baseline expression for larger oscillatory amplitude. Strikingly, mRNA degradation timing remain far more conserved across systems than transcription timing, exposing post-transcriptional regulation as a stable evolutionary backbone. As human iPSCs differentiate into definitive endoderm, cells progressively exit the cell cycle, cell-cycle-coupled gene networks contract, and surviving regulators oscillate with larger amplitude. Ciclopes establishes a general framework for dissecting how pluripotent cells tune proliferation across evolutionary and developmental transitions.

systems biology↗

CRAK-Velo: Chromatin Accessibility Kinetics integration improves RNA Velocity estimation

RNA velocity has recently emerged as a key tool in the analysis of single-cell transcriptomic data, yet connecting RNA velocity analyses to underlying regulatory processes has proved challenging. Here we propose CRAK-Velo, a semi-mechanistic model which integrates chromatin accessibility data in the estimation of RNA velocities. CRAK-Velo provides biologically consistent estimates of developmental flows and enables accurate cell-type deconvolution, while additionally shining light on regulatory processes at the level of interactions between genes and chromatin regions.

bioinformatics↗