Data-Driven and Cell-Specific Determination of Nuclei-Associated Actin Structure
Small Structures
2024
- 62Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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- Usage62
- Downloads60
- Abstract Views2
Article Description
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.
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