Biology subjects

Nariya, M. K.

Publications and source records attributed to Nariya, M. K..

7 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2025

Spatiotemporal regulation of cell cycle states within the complex tumor microenvironment

Tumor growth and resistance arise from the interplay between cell-cycle dysregulation and the spatial organization of the tumor microenvironment (TME). While spatial transcriptomics now enables molecular profiling of intact tissues, it captures only static molecular states, making it challenging to reconstruct dynamic processes such as proliferation. Here, we develop SpaceCycle, a computational framework that infers the continuous cell-cycle phase and associated oscillatory gene expression dynamics from spatial transcriptomic data. Using human melanoma, breast, and lung tumors, we map both discrete and continuous cell-cycle states across entire tissue sections to reveal how proliferative activity is spatially structured within the TME. We find that cycling and non-cycling cells form distinct spatial niches reflecting differences in vascularization, immune composition, and cellular density. Continuous-phase inference further exposes tumor-specific variations in cell cycle phase durations and uncovers oscillatory programs. Together, our results provide a spatiotemporal view of cell-cycle organization in human tumors and establish a general framework for detecting dynamic transcriptional programs and spatial proliferation patterns from static tissue data.

systems biology↗

bioRxiv2024

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↗

bioRxiv2022

Paired evaluation defines performance landscapes for machine learning models

The true accuracy of a machine learning model is a population-level statistic that cannot be observed directly. In practice, predictor performance is estimated against one or more test datasets, and the accuracy of this estimate strongly depends on how well the test sets represent all possible unseen datasets. Here we present paired evaluation, a simple approach for increasing the robustness of performance evaluation by systematic pairing of test samples, and use it to evaluate predictors of drug response in breast cancer cell lines and of disease severity in patients with Alzheimers Disease. Our results demonstrate that the choice of test data can cause estimates of performance to vary by as much as 30%, and that paired evaluation makes it possible to identify outliers, improve the accuracy of performance estimates in the presence of known confounders, and assign statistical significance when comparing machine learning models.

bioinformatics↗

bioRxiv2021

MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging

Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.

bioinformatics↗

bioRxiv2019

Robustness and the evolution of length control strategies in the type III secretion system and flagellar hook

Bacteria construct many structures, like the flagellar hook and the type III secretion system, that aid in crucial processes such as locomotion and pathogenesis. Experimental work has suggested two competing mechanisms bacteria could use to regulate length in these structures: the \"ruler\" mechanism and the \"substrate switching\" mechanism. In this work, we constructed a mathematical model of length control based on the ruler mechanism, and found that the predictions of this model are consistent with experimental data not just for the scaling of the average length with the ruler protein length, but also the variance. Interestingly, we found that the ruler mechanism allows for the evolution of needles with large average lengths without the concomitant large increase in variance that occurs in the substrate switching mechanism. These findings shed new light on the trade-offs that may have lead to the evolution of different length control mechanisms in different bacterial species.

biophysics↗

bioRxiv2019

Crosstalk and Ultrasensitivity in Protein Degradation Pathways

Protein turnover is vital to protein homeostasis within the cell. Many proteins are degraded efficiently only after they have been post-translationally \"tagged\" with a polyubiquitin chain. Ubiquitylation is a form of Post-Translational Modification (PTM): addition of a ubiquitin to the chain is catalyzed by E3 ligases, and removal of ubiquitin is catalyzed by a De-UBiquitylating enzyme (DUB). Over three decades ago, Goldbeter and Koshland discovered that reversible PTM cycles function like on-off switches when the substrates are at saturating concentrations. Although this finding has had profound implications for the understanding of switch-like behavior in biochemical networks, the general behavior of PTM cycles subject to synthesis and degradation has not been studied. Using a mathematical modeling approach, we found that simply introducing protein turnover to a standard modification cycle has profound effects, including significantly reducing the switch-like nature of the response. Our findings suggest that many classic results on PTM cycles may not hold in vivo where protein turnover is ubiquitous. We also found that proteins sharing an E3 ligase can have closely related changes in their expression levels. These results imply that it may be difficult to interpret experimental results obtained from either overexpressing or knocking down protein levels, since changes in protein expression can be coupled via E3 ligase crosstalk. Understanding crosstalk and competition for E3 ligases will be key to ultimately developing a global picture of protein homeostasis.

systems biology↗