Biology subjects

Chadha, Y.

Publications and source records attributed to Chadha, Y..

4 recordsLinked to original sources

bioRxiv2026

Retrograde signalling mediates cellular adaptation to mitochondrial DNA copy number alterations

Eukaryotic cells maintain multiple copies of the mitochondrial genome, which is essential for cellular metabolism. Accordingly, alterations in the mitochondrial DNA (mtDNA) copy number are associated with severe human diseases and ageing. However, the mechanisms through which cells regulate mtDNA copy number and the cellular consequences of altered copy number remain poorly understood. Here, using budding yeast as a model, we show that mtDNA copy number is determined by the amount of three limiting factors, Mip1, Abf2, and Rim1. By synthetically tuning the concentrations of only these three proteins, we can modulate mtDNA dosage inside the cell. This revealed that cells are surprisingly robust to mtDNA copy number alterations, with increased copy numbers even accelerating cell growth. Our findings suggest that this robustness is due to protein dosage compensation and independent regulation of mitochondrial morphology. Mechanistically, we identified a critical role of the retrograde signalling pathway for this adaptation. We show that signalling from mitochondria to the nucleus is upregulated in cells with higher mtDNA copy number, and disruption of this regulation diminishes their faster growth. Taken together, our work reveals regulatory principles that enable cells to adapt to mtDNA copy number alterations.

cell biology↗

bioRxiv2025

Division Asymmetry Drives Cell Size Variability in Budding Yeast

Cell size variability within proliferating populations reflects the interdependent regulation of cell growth and division as well as intrinsically stochastic effects. In budding yeast, the G1/S transition exerts strong size control in daughter cells, which manifests as the inverse correlation between how big a cell is when it is born and how much it grows in G1. However, mutations affecting this size control checkpoint only modestly influence population-wide size variability, often altering the coefficient of variation (CV) only by [~]10%. To resolve this paradox, we combine computational modeling and live-cell imaging to identify the principal determinants of cell size variability. Using an experimentally validated stochastic model of the yeast cell cycle, we perform parameter sensitivity analysis and find that division asymmetry between mothers and daughters is the dominant driver of CV, outweighing the effects of G1/S size control. Experimental measurements across genetic perturbations and growth conditions confirm a strong correlation between mother-daughter size asymmetry and population CV. These findings reconcile previous observations and show how asymmetric division operates in concert with G1/S size control to govern cell size heterogeneity.

cell biology↗

bioRxiv2024

SpotMAX: a generalist framework for multi-dimensional automatic spot detection and quantification

The analysis of spot-like structures is a widespread task in microscopy-based cell biology. Existing solutions are typically specific to single applications and do not use multi-dimensional information from 5D datasets. Therefore, experimental scientists often resort to subjective manual annotation. Here, we present SpotMAX, a generalist AI-driven framework for automated spot detection and quantification. SpotMAX leverages the full scope of multi-dimensional datasets with an easy-to-use interface and an embedded framework for cell segmentation and tracking. SpotMAX outperforms state-of-the-art tools, and in some cases, even expert human annotators. We applied SpotMAX across diverse experimental questions, ranging from meiotic crossover events in C. elegans to mitochondrial DNA dynamics in S. cerevisiae and telomere length in mouse stem cells, leading to new biological insights. With its flexibility in integrating AI workflows, we anticipate that SpotMAX will become the standard for spot analysis in microscopy data. Source code: https://github.com/SchmollerLab/SpotMAX

cell biology↗

bioRxiv2024

Single-cell imaging reveals a key role of Bck2 in budding yeast cell size adaptation to nutrient challenges

Cell size is tightly controlled to optimize cell function and varies broadly depending on the organism, cell type, and environment. The budding yeast S. cerevisiae has been successfully used as a model to gain insights into eukaryotic cell size control. Multiple regulators of cell size in steady-state conditions have been identified, such as the G1/S transition activators Cln3 and Bck2 and the inhibitor Whi5. Individual deletions of these regulators result in populations with altered mean cell volumes. However, size homeostasis remains largely intact. Here, we show that although the roles of Bck2 and Cln3 for cell size regulation appear largely redundant in steady-state, a switch from fermentable to non-fermentable growth media reveals a unique role for Bck2 in cell size adaptation to changing nutrients. We use live-cell microscopy and machine learning-assisted image analysis to track single cells and their progeny through the nutrient switch. We find that after the switch, bck2{Delta} cells experience longer cell cycle arrests and more arrest-associated enlargement than wild-type, whi5{Delta} or cln3{Delta} cells, indicating that Bck2 becomes the critical G1/S activator in changing nutrients. Our work demonstrates that studying size regulation during nutrient shifts to mimic the dynamic environments of free-growing microorganisms can resolve apparent redundancies observed in steady-state size regulation.

cell biology↗