Biology subjects

Hinderling, L.

Publications and source records attributed to Hinderling, L..

3 recordsLinked to original sources

bioRxiv2023

GTPase activating protein DLC1 spatio-temporally regulates Rho signaling

Tightly regulated spatio-temporal Rho GTPase activity patterns regulate morphogenetic processes such as cell migration. Emerging evidence suggests that binding of Guanine nucleotide exchange factors (GEFs) and GTPase activating proteins (GAPs) to the cytoskeleton or adhesions mediate feedback regulation to spatio-temporal Rho GTPase activation. To explore such feedback regulation, we study the Rho specific GAP Deleted in Liver Cancer 1 (DLC1) which binds to focal adhesions (FAs) through mechanosensitive interactions. Using a FRET biosensor, we show that DLC1 loss of function leads to global increase in Rho activity and contractility throughout the cell without affecting a striking lamellar RhoA activity band in fibroblasts. Although RhoA activity may appear steady at the population level, individual molecules are continually activated by GEFs, inactivated by GAPs, and diffusing between subcellular regions, a dynamic turnover referred to as "signaling flux". To directly measure this flux, we built a genetic circuit combining an optogenetic actuator to control Rho activity with a Rho activity biosensor. In spreading cells at steady state, optogenetic manipulation of Rho activity reveals that DLC1 controls the rate of Rho activation rather than duration, both at FAs and at the plasma membrane (PM). Local and reversible optogenetic control of contractility shows that DLC1 associates/dissociates with FAs during their reinforcement/relaxation. This might provide positive feedback that locally increases the rate of Rho activation at FAs that experience local tension to facilitate FA disassembly. Our results indicate that DLC1 operates both at the PM and at FAs to regulate global Rho activity levels at steady state, or to amplify local Rho activity at FAs experiencing a strong mechanical input, presumably to induce robust FA disassembly. This provides new insights in the complexity of spatio-temporal Rho GTPase signaling.

cell biology↗

bioRxiv2022

ADeS: a deep learning based Apoptosis Detection System for live cell imaging.

Intravital microscopy has revolutionized live cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy time-lapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy time-lapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in vivo, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

bioinformatics↗

bioRxiv2022

Automatic detection of spatio-temporal signalling patterns in cell collectives

An increasing experimental evidence points to physiological importance of space-time correlations in signalling of cell collectives. From wound healing to epithelial homeostasis to morphogenesis, coordinated activation of bio-molecules between cells allows the collectives to perform more complex tasks and better tackle environmental challenges. To understand this information exchange and to advance new theories of emergent phenomena, we created ARCOS, a computational method to detect and quantify collective signalling. We demonstrate ARCOS on cell and organism collectives with space-time correlations on different scales in 2D and 3D. We make a new observation that oncogenic mutations in the MAPK/ERK and PIK3CA/Akt pathways of MCF10A epithelial cells induce ERK activity waves with different size, duration, and frequency. The open-source implementations of ARCOS are available as R and Python packages, and as a plugin for napari image viewer to interactively quantify collective phenomena without prior programming experience.

cell biology↗