Nikitina Nina, Bursa Nurbanu, Goelzer Matthew, Goldfeldt Madison, Crandall Chase, Howard Sean, Rubin Janet, Satici Aykut, Uzer Gunes
Boise State University.
University of Idaho.
bioRxiv. 2023 Apr 6:2023.04.06.535937. doi: 10.1101/2023.04.06.535937.
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.
由于丝状肌动蛋白纤维（F-肌动蛋白）相互连接的特性，对其进行定量和体积评估仍然具有挑战性，这导致研究人员采用基于阈值或定性的测量方法，而这些方法的可重复性较差。在此，我们介绍一种基于机器学习的新方法，用于准确量化和重建与细胞核相关的F-肌动蛋白。利用卷积神经网络（CNN），我们从三维共聚焦显微镜图像中分割出肌动蛋白丝和细胞核，然后通过连接横截面切片上的相交轮廓来重建每根纤维。这使得我们能够以可重复的方式测量肌动蛋白丝的总数、单个肌动蛋白丝的长度和体积。着眼于F-肌动蛋白在支持核细胞骨架连接方面的作用，我们在核骨架与细胞骨架连接复合体（LINC复合体）被破坏后，对间充质干细胞（MSC）中的顶端F-肌动蛋白、基底F-肌动蛋白和核结构进行了量化。在间充质干细胞（MSC）中使LINC失活会导致核膜处的F-肌动蛋白紊乱，其特征是肌动蛋白纤维的长度和体积变短，导致核形状不那么细长。我们的研究结果不仅为力学生物学提供了一种新工具，还引入了一种基于F-肌动蛋白定量测量来开发逼真计算模型的新流程。
