@article {165751, title = {Transcriptional noise sets fundamental limits to decoding circadian clock phase from single-cell RNA snapshots}, journal = {iScience}, volume = {29}, year = {2026}, pages = {115394}, abstract = {

Summary The circadian clock drives rhythmic physiological variations, making accurate clock-phase measurement essential. Single-sample phase inference from bulk RNA is an exciting alternative to time-series methods, but its applicability to single cells remains unclear. Using multiplexed smFISH to quantify up to six core-clock genes in mouse fibroblasts, combined with a Gaussian process-based algorithm, we show that even with minimal technical dropouts, transcriptional noise in core-clock genes prevents reliable single-cell phase estimation. Simulations predict that measuring \~{}50 low-noise, clock-like oscillatory genes is required for accurate single-cell inference. In scRNA-seq data, non-core genes were too noisy, and adding hundreds of them did not improve accuracy. Remarkably, averaging smFISH counts from only three core-clock genes across \~{}70 cells enabled robust phase estimation. In desynchronized fibroblasts, spatially heterogeneous phases indicating local inter-cellular coupling were revealed only upon averaging, not at single-cell resolution. Coarse graining is, therefore, essential for circadian phase inference from RNA measurements alone.

}, keywords = {classification of bioinformatical subject, integrative aspects of cell biology, noise control, transcriptomics}, issn = {2589-0042}, doi = {https://doi.org/10.1016/j.isci.2026.115394}, url = {https://www.sciencedirect.com/science/article/pii/S2589004226007698}, author = {Anjoom Nikhat and Taniya Mandal and Nivedha Veerasubramanian and Shaon Chakrabarti} } @article {157495, title = {Combining lineage correlations and a small molecule inhibitor to detect circadian control of the cell cycle}, journal = {Cell Press}, volume = {28}, year = {2025}, month = {18 April 2025}, abstract = {

Chronotherapy offers an exciting possibility for improving cancer treatments by leveraging the influence of the circadian clock on the cell cycle. While several molecular interactions coupling the two oscillators have been identified, whether they lead to emergent control of cellular proliferation remains unclear. Using stochastic simulations, we demonstrate that the established gene networks underlying the two oscillators are sufficient to generate lineage correlations in cell cycle times, as observed in single-cell microscopy data. The interactions also create a {\textquoteleft}therapeutic window{\textquoteright} between cancer and normal cell proliferation peaks that can be leveraged for chronotherapy. Surprisingly, our model predicts that KL001, a clock inhibitor, minimally affects population growth but significantly alters lineage correlations. Our results suggest that clock control of the cell cycle may not be detectable by measuring changes in population dynamics, but combining measurements of lineage correlations with KL001 treatment may provide a more sensitive approach to detecting the coupling.

}, doi = {https://doi.org/10.1016/j.isci.2025.112269}, url = {https://www.sciencedirect.com/science/article/pii/S2589004225005309}, author = {Anjoom Nikhat and Arsh Shaikh and Shaon Chakrabarti} }