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Crandall, C.

Publications and source records attributed to Crandall, C..

2 recordsLinked to original sources

Low-intensity vibration does not induce changes in microtubule dynamics in vitro

Microtubules (MTs) are cytoskeletal filaments responsible for many vital cellular processes including intracellular organelle organization and enabling the movement of intracellular components. While MTs were shown to respond to low frequency and large mechanical signals like substrate strain, how MTs may respond to a high frequency mechanical signal like low-intensity vibrations (LIV) is unknown. Here we quantified the polymerization dynamics of MTs under an acute 1-day LIV protocol applied at 90 Hz and 0.7 xg, a signal we have shown to be effective for altering F-actin dynamics and nuclear stiffness. LIV treatments were compared against Taxol, a potent regulator of MT acetylation. Using mouse mesenchymal stem cells (MSCs) in vitro, we quantified tubulin polymerization via centrifugal fractionation and western blots as well as alpha-tubulin acetylation via immunostaining. Finally, MT growth dynamics were quantified using machine learning-assisted analysis of live cell fluorescence microscopy of MT plus end binding protein EB1. Our results were not able to detect differences between LIV and control groups while Taxol treatment was effective in all measured outcomes. Our findings indicate that LIV applied at 90 Hz and 0.7 xg does not affect MT dynamics in MSCs, suggesting a higher mechanical threshold of MTs when compared to F-actin cytoskeleton.

bioengineering↗

Data driven and cell specific determination of nuclei-associated actin structure

Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed 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, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our 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.

bioengineering↗