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Didan, Y.

Publications and source records attributed to Didan, Y..

2 recordsLinked to original sources

Stress pathway outputs are encoded by pH-dependent phase separation of its components.

Signal processing by intracellular kinases control near all biological processes but how precise functions of signal pathways evolve with changed cellular contexts is poorly understood. Functional specificity of c-Jun N-terminal Kinases (JNK) activated in response to a broad range of pathological and physiological stimuli are partly encoded by signal strength. Here we reveal that intracellular pH (pHi) is a significant component of the JNK regulatory network and defines JNK signal response to precise stimuli. We showed that nuanced fluctuations in physiological pHi regulates JNK activity in response to cell stress. Interestingly, the relationship between pHi and JNK activity was dependent on specific stimuli and upstream kinases involved in pathway activation. Cytosolic alkalinisation promoted phase transition of upstream ASK1 to augment JNK activation. While increased pHi similarly induced JNK2 to form condensates, this led to attenuated JNK activity. Mathematical modelling of feedback signalling incorporating pHi and differential contribution by JNK2 and ASK1 condensates was sufficient to delineate the strength of JNK signal response to specific stimuli. This new knowledge of pHi regulation with consideration of JNK2 and ASK1 contribution to signal transduction may delineate oncogenic versus tumour suppressive functions of the JNK pathway and cancer cell drug responses.

systems biology↗

Multi-site assessment of reproducibility in high-content live cell imaging data

High-content image-based cell phenotyping provides fundamental insights in a broad variety of life science areas. Striving for accurate conclusions and meaningful impact demands high reproducibility standards, even more importantly with the advent of data sharing initiatives. However, the sources and degree of biological and technical variability, and thus the reproducibility and usefulness of meta-analysis of results from live-cell microscopy have not been systematically investigated. Here, using high content data describing features of cell migration and morphology, we determine the sources of variability across different scales, including between laboratories, persons, experiments, technical repeats, cells and time points. Significant technical variability occurred between laboratories, providing low value to direct meta-analysis on the data from different laboratories. However, batch effect removal markedly improved the possibility to combine image-based datasets of perturbation experiments. Thus, reproducible quantitative high-content cell image data and meta-analysis depend on standardized procedures and batch correction applied to studies of perturbation effects.

cell biology↗