Biology subjects

Redchuk, T.

Publications and source records attributed to Redchuk, T..

3 recordsLinked to original sources

bioRxiv2026

Multimodal cellular context enables molecular phenotyping from single-molecule trajectories

Single-molecule tracking (SMT) provides direct measurements of molecular dynamics in living cells, but motion alone rarely specifies the underlying biological process, without accounting for the cellular environment in which that motion occurs. Here, we present MIST (Multimodal Interpretation of Single-molecule Trajectories), a framework that brings the paradigm of image-based profiling to single molecules by integrating trajectory dynamics, emitter properties and cellular context into a common, reusable phenotype space. Using a reference panel of nine proteins in live Escherichia coli, we show that multimodal context more than doubled protein-specific information relative to trajectory features alone, a gap that learned trajectory representations did not close. MIST allowed de novo identification of shared transcription-translation phenotype at the nucleoid periphery. A fixed reference space detected effects of previously uncharacterized MukB ATPase mutations without retraining. MIST provides an interpretable and extensible framework for comparing proteins, mutants and conditions within a common space of molecular phenotypes.

bioinformatics↗

bioRxiv2026

Bacterial Hsp70 DnaK transiently samples the proteome to rapidly capture stress-induced misfolding

Protein-quality-control systems are essential for cells to maintain protein homeostasis during both steady-state growth and acute stress. Yet, how protein chaperone engagement dynamically changes as proteostasis demand increases remains poorly understood. Here, we combine rapid temperature control with single-molecule tracking to follow the bacterial heat shock protein 70 (Hsp70) DnaK in live Escherichia coli. We found that most DnaK molecules actively engage with the proteome already at the optimal growth temperature, rather than form a freely diffusing reserve. Acute heat shock reallocates the DnaK pool within seconds primarily increasing the lifetime but also frequency of client engagements. Perturbing the chaperone network reveals that this redistribution reflects proteostasis demand and network capacity: loss of small heat shock proteins IbpAB drives prolonged DnaK engagement and limits recovery, whereas overexpression of thermolabile proteins alone can produce heat shock-like dynamics. Together, our findings reveal how chaperones are dynamically reallocated during proteotoxic stress and establish DnaK mobility as a sensitive readout of proteostasis demand.

cell biology↗

bioRxiv2025

Explainable machine learning-assisted exploration of chromatin dynamics reveals chromosome-specific response to serum starvation

Chromatin is dynamic at all length scales, influencing chromatin-based processes, such as gene expression. Even large-scale reorganization of whole chromosome territories has been reported upon specific signals, but lack of suitable methods has prevented analysis of the underlying dynamic processes. Here we have used CRISPR-Sirius for time-lapse imaging of chromatin loci dynamics during serum starvation. We show that chromosome 1 loci move towards the nuclear envelope during the first hour of serum starvation in a chromosome-specific manner. Machine learning-assisted exploration of acquired multiparametric data combined with the Shapley values-based explanation approach allowed us to uncover the critical features that characterize chromatin dynamics during serum starvation. This analysis reveals that although serum starvation affects overall nuclear morphology and chromatin dynamics, chromosome 1 loci display a specific response that is characterized by maintenance of dynamics in constrained environment, and long "jumps" at the nuclear periphery. Interestingly, the two homologous chromosomes display differential behaviors, with the more peripheral homolog being more responsive to the signal than the internal one. Overall, the presented machine learning-assisted dataset exploration helps us navigate the multidimensional data to understand the underlying dynamic processes and can be applied to a wide variety of research questions in imaging and cell biology in general.

cell biology↗