Biology subjects

Hylton, R.

Publications and source records attributed to Hylton, R..

3 recordsLinked to original sources

bioRxiv2025

Streamlined Montage Cryo-Electron Tomography for Exploring the Ultrastructure of Cells and Tissues

Cryo-electron tomography reveals the architecture of biological specimens at molecular resolution, albeit with a limited field of view. Montage tomography overcomes this limitation, but existing strategies depend on complex dose distribution imaging schemes or hardware modifications which prevent widespread adoption of the method. To lower the barrier to entry, we developed a straightforward workflow that deviates minimally from conventional image acquisition and leverages our software MontageMaker to merge overlapping tilt series. The resulting montage tomograms are produced with high throughput, retain nanoscale detail despite the uneven accumulation of dose, and are easily acquired on a wide variety of biological samples. Most importantly, this approach can be implemented immediately by any lab with access to automated cryo-ET data collection.

cell biology↗

bioRxiv2025

Training Generalized Segmentation Networks with Real and Synthetic Cryo-ET data.

Deep learning excels at segmenting objects within noisy cryo-electron tomograms, but the approach is typically bottlenecked by access to ground truth training data. To address this issue we have developed CryoTomoSim (CTS), an open-source software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. Using CTS outputs, we demonstrate the effects of key microscope parameters (dose, defocus, and pixel size) on deep learning-based segmentation, and show that including both molecular crowding and diversity within synthetic datasets is key to training cellular segmentation networks from purely synthetic inputs. While very effective as initial models, the accuracy of these networks is currently limited, and real cellular data is necessary to train the most accurate and generalizable U-Nets. Using a co-training approach, we first segment over 100 tomograms from neuronal growth cones to quantify their cytoskeletal distributions and then we build a generalized cellular cryo-ET segmentation network called NeuralSeg that can segment a subset of cellular features in tomograms from all domains of life.

cell biology↗

bioRxiv2023

Rapid Synthesis of Cryo-ET Data for Training Deep Learning Models

Deep learning excels at cryo-tomographic image restoration and segmentation tasks but is hindered by a lack of training data. Here we introduce cryo-TomoSim (CTS), a MATLAB-based software package that builds coarse-grained models of macromolecular complexes embedded in vitreous ice and then simulates transmitted electron tilt series for tomographic reconstruction. We then demonstrate the effectiveness of these simulated datasets in training different deep learning models for use on real cryotomographic reconstructions. Computer-generated ground truth datasets provide the means for training models with voxel-level precision, allowing for unprecedented denoising and precise molecular segmentation of datasets. By modeling phenomena such as a three-dimensional contrast transfer function, probabilistic detection events, and radiation-induced damage, the simulated cryo-electron tomograms can cover a large range of imaging content and conditions to optimize training sets. When paired with small amounts of training data from real tomograms, networks become incredibly accurate at segmenting in situ macromolecular assemblies across a wide range of biological contexts. SummaryBy pairing rapidly synthesized Cryo-ET data with computed ground truths, deep learning models can be trained to accurately restore and segment real tomograms of biological structures both in vitro and in situ.

cell biology↗