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

Abstract

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.

Keywords

• cytoskeleton
• F‐actin
• linker of nucleoskeleton and cytoskeleton
• machine learning
• mechanobiology
• nuclear envelope