deep-learning-based-segmentation-of-cryo-electron-tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Cryo-electron tomography (cryo-ET) allows researchers to image cells in their native, hydrated state at the highest resolution currently possible. The ...

Pennsylvania State University-College of Medicine

We investigated the applicability of cryo-electron tomography as a method to quantify changes in the major constituents of casein micelles (i.e., casein proteins, putative colloidal calcium phosphate nanoclusters, and serum-filled voids and channels) in response to their environment. Skim milk diluted 20-fold in milk serum was used for this study. Tomograms were generated for multiple casein micelles at 2 different pH values (6.7 and 6.0) and pixel intensity thresholds were identified for each constituent. The volume of each constituent was determined using these thresholds and expressed as a fraction of micelle volume. At the given dilution, a significant decrease in the volume fractions of casein proteins (∼37%) and putative colloidal calcium phosphate nanoclusters (∼67%) was observed with the reduction of pH from 6.7 to 6.0. Assessment of casein micelle fraction obtained by ultracentrifugation of corresponding skim milk samples produced comparable results. When using such an approach, the imaging conditions, denoising methods, and thresholding approaches used can all affect the precision of the measurements, but the overall trends in constituent volumes are able to be tracked. The primary advantage of using cryo-electron tomography is that analysis can be done at the level of individual micelles, within a 3-dimensional morphological context. This workflow paves the way for high-throughput exploration of milk micelles and how their environment shapes their composition and structure.

Assessing constituent volumes and morphology of bovine casein micelles using cryo-electron tomography.

Cryo-electron tomography is fast becoming a preferred method for studying intracellular environments at the molecular scale. Increases in data collection throughput means that large numbers of tomograms can be generated at rates too fast for humans to easily explore quantitatively. Currently, there is a large effort to make data collection and segmentation tools more automated. Here, we describe a workflow for preparing cultured neurons on electron microscopy grids, batch tomographic data collection, reconstruction and automatic segmentation using freely and commercially available software.

Cryo-Electron Tomography and Automatic Segmentation of Cultured Hippocampal Neurons.

Cofilin is best known for its ability to sever actin filaments and facilitate cytoskeletal recycling inside of cells, but at higher concentrations in vitro, cofilin stabilizes a more flexible, hyper-twisted state of actin known as "cofilactin". While this filament state is well studied, a structural role for cofilactin in dynamic cellular processes has not been observed. With a combination of cryo-electron tomography and fluorescence imaging in neuronal growth cones, we observe that filopodial actin filaments switch between a fascin-linked and a cofilin-decorated state, and that cofilactin is associated with a variety of dynamic events within filopodia. The switch to cofilactin filaments occurs in a graded fashion and correlates with a decline in fascin cross-linking within the filopodia, which is associated with curvature in the bundle. Our tomographic data reveal that the hyper-twisting of actin from cofilin binding leads to a rearrangement of filament packing, which largely excludes fascin from the base of filopodia. Our results provide mechanistic insight into the fundamentals of cytoskeletal remodeling inside of confined cellular spaces, and how the interplay between fascin and cofilin regulates the dynamics of searching filopodia.

Matthew T. Swulius

Pennsylvania State University-College of Medicine

Deep Learning-Based Segmentation of Cryo-Electron Tomograms