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


Nikitina N., Bursa N., Goelzer M., Goldfeldt M., Crandall C., Howard S., ...More

Small Structures, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Publication Date: 2024
  • Doi Number: 10.1002/sstr.202300204
  • Journal Name: Small Structures
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: cytoskeleton, F-actin, linker of nucleoskeleton and cytoskeleton, machine learning, mechanobiology, nuclear envelope
  • Hacettepe University Affiliated: Yes

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.