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.

Heterochrony of postnatal accumulation of nonphosphorylated heavy-chain neurofilament by neurons of the cat dorsal lateral geniculate nucleus.

Accumulation of the heavy-chain neurofilaments reflects the maturation status of neuronal structures. The spatial distribution and postnatal developmental dynamic of neurons expressing nonphosphorylated heavy-chain neurofilaments (labeled by SMI-32 antibody) were analyzed in the dorsal lateral geniculate nucleus (LGNd) of the cat. Both interlaminar and intralaminar differences in the dynamic of SMI-32 staining were observed. The following results were obtained: (a) Ascending dorsoventral gradient in the density of SMI-32 immunopositive (SMI-32(+)) neurons (the greatest neuronal density in layer Cm, the minor in the top sublayer of layer A). This gradient was most prominent at the earliest stages of postnatal development (1st-2nd weeks) and slowly flattened up to adulthood; (b) Layer A1 exhibits increases in SMI-32-positive cells earlier than layer A; (c) The general transient increment in the number and density of SMI-32(+) neurons around 2-5 postnatal weeks. Since SMI-32 antibody is considered to be a putative marker for Y cells forming a motion processing stream, we suggest that peculiarities of SMI-32 staining at geniculate level could reflect the heterogeneity of Y cell subpopulations and the heterochrony of their postnatal maturation.