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Elias, K.

Publications and source records attributed to Elias, K..

2 recordsLinked to original sources

Mechanical Stress dissipation in locally folded epithelia is orchestrated by calcium waves and nuclear tension changes

Epithelia are continuously exposed to a range of biomechanical forces such as compression, stretch and shear stress arising from their dynamic microenvironments and associated to their function. Changes in tension such as stretch are known to trigger cell rearrangements and divisions, and impact cellular transcription until mechanical stress is dissipated. How cells process, adapt and respond to mechanical stress is being intensively investigated. In here we focus on epithelial folding which is the fundamental process of transformation of flatmonolayers into 3D functional tissues. By combining the innovative method for fold generation, live imaging, mechanobiology tools and chemical screening, we uncover the role of calcium waves on mechanical adaptation of folded epithelia that occurs at the tissue and nuclear level. Folding associated tensional load results in the nuclear flattening which is recovered in the time scale of minutes and is dependent on the calcium wave that spread outwards from the channel and across the epithelium. By creating a mutant overexpressing LBR that relaxed nuclear envelope, we demonstrated that despite presence of calcium waves, nuclear tension increase was essential to trigger nuclear shape recovery post folding through the activation of cellular contractility in the cPLA2 dependent manner. Overall our results identify the molecular mechanism for nuclear shape recovery and indicate that mechanical stress dissipation program is activated at the level of nuclei which serve as internal tension sensors.

developmental biology↗

Tribus: Semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomicdata

MotivationMultiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods. ResultsWe developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets. AvailabilityTribus is available at https://github.com/farkkilab/tribus as an open-source Python package.

bioinformatics↗