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Santiago-Algarra, D.

Publications and source records attributed to Santiago-Algarra, D..

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↗

Unraveling the oscillatory dynamics of mRNA metabolism and chromatin accessibility during the cell cycle through integration of single-cell multiomic data

The cell cycle is a tightly regulated process that requires precise temporal expression of hundreds of cell cycledependent genes. However, the genome-wide dynamics of mRNA metabolism throughout the cell cycle remain uncharacterized. Here, we combined single-cell multiome sequencing, biophysical modeling, and deep learning to quantify rates of mRNA transcription, splicing, nuclear export, and degradation. Our approach revealed that both transcriptional and post-transcriptional processes exhibit distinct oscillatory waves at specific cell cycle phases, with post-transcriptional regulation playing a prominent role in shaping mRNA accumulation. We also observed dynamic changes in chromatin accessibility and transcription factor binding footprints, identifying key regulators underlying the oscillatory dynamics of mRNA. Taken together, our approach uncovered a high-resolution map of RNA metabolism dynamics and chromatin accessibility, offering new insights into the temporal control of gene expression in proliferating cells. HighlightsO_LIFourierCycle combines single-cell multiome sequencing, deep learning, and biophysical modeling to quantify gene-specific rates of mRNA metabolism during the cell cycle C_LIO_LIRates of mRNA transcription, nuclear export, and degradation show gene-specific oscillatory waves at distinct cell cycle phases. C_LIO_LIPost-transcriptional regulation, including mRNA degradation and nuclear export, plays a prominent role in shaping mRNA accumulation during the cell cycle C_LIO_LIDynamics of chromatin accessibility and transcription factor binding footprints uncover key regulators underlying the transcriptional control of gene expression C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/575159v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1d2d315org.highwire.dtl.DTLVardef@2031aaorg.highwire.dtl.DTLVardef@19cc54eorg.highwire.dtl.DTLVardef@15d9105_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗