Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure

Quantitative volumetric assessment of filamentous actin (F-actin) fibers remains challenging due to their interconnected nature, leading researchers to utilize threshold-based or qualitative measurement methods with poor reproducibility. Herein, a novel machine learning-based methodology is introduced for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a convolutional neural network (CNN), actin filaments and nuclei from 3D confocal microscopy images are segmented and then each fiber is reconstructed by connecting intersecting contours on cross-sectional slices. This allows measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) are quantified following the disruption of the linker of nucleoskeleton and cytoskeleton (LINC) complexes. Disabling LINC in MSCs generates F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. The findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin. Quantitative volumetric assessment of filamentous actin (F-actin) fibers remains challenging. Utilizing a convolutional neural network, nuclei-associated F-actin and nuclei from 3D confocal images are segmented. This new tool introduces a novel pipeline for descriptive statistics of F-actin architecture and open ways to developing realistic computational models based on quantitative F-actin measures.image (c) 2024 WILEY-VCH GmbH