Dataset Information

Cryo electron tomograms of mouse DRG axons (dataset 2)

ABSTRACT:

SUBMITTER: Helen Elizabeth Foster

PROVIDER: EMPIAR-10814 | biostudies-other |

REPOSITORIES: biostudies-other

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Publications

A cryo-ET survey of microtubules and intracellular compartments in mammalian axons.

Foster Helen E HE Ventura Santos Camilla C Carter Andrew P AP

The Journal of cell biology 20211208 2

The neuronal axon is packed with cytoskeletal filaments, membranes, and organelles, many of which move between the cell body and axon tip. Here, we used cryo-electron tomography to survey the internal components of mammalian sensory axons. We determined the polarity of the axonal microtubules (MTs) by combining subtomogram classification and visual inspection, finding MT plus and minus ends are structurally similar. Subtomogram averaging of globular densities in the MT lumen suggests they have a ...[more]

PMID: 34878519

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Project description:BackgroundDespite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexities. To date, the extent of the deep learning methods constructed for this problem is limited to conventional Convolutional Neural Networks (CNNs). Identifying macromolecules of different types and sizes is a tedious and time-consuming task. In this paper, we employ a capsule-based architecture to automate the task of macromolecule identification, that we refer to as 3D-UCaps. In particular, the architecture is composed of three components: feature extractor, capsule encoder, and CNN decoder. The feature extractor converts voxel intensities of input sub-tomograms to activities of local features. The encoder is a 3D Capsule Network (CapsNet) that takes local features to generate a low-dimensional representation of the input. Then, a 3D CNN decoder reconstructs the sub-tomograms from the given representation by upsampling.ResultsWe performed binary and multi-class localization and identification tasks on synthetic and experimental data. We observed that the 3D-UNet and the 3D-UCaps had an [Formula: see text]score mostly above 60% and 70%, respectively, on the test data. In both network architectures, we observed degradation of at least 40% in the [Formula: see text]-score when identifying very small particles (PDB entry 3GL1) compared to a large particle (PDB entry 4D8Q). In the multi-class identification task of experimental data, 3D-UCaps had an [Formula: see text]-score of 91% on the test data in contrast to 64% of the 3D-UNet. The better [Formula: see text]-score of 3D-UCaps compared to 3D-UNet is obtained by a higher precision score. We speculate this to be due to the capsule network employed in the encoder. To study the effect of the CapsNet-based encoder architecture further, we performed an ablation study and perceived that the [Formula: see text]-score is boosted as network depth is increased which is in contrast to the previously reported results for the 3D-UNet. To present a reproducible work, source code, trained models, data as well as visualization results are made publicly available.ConclusionQuantitative and qualitative results show that 3D-UCaps successfully perform various downstream tasks including identification and localization of macromolecules and can at least compete with CNN architectures for this task. Given that the capsule layers extract both the existence probability and the orientation of the molecules, this architecture has the potential to lead to representations of the data that are better interpretable than those of 3D-UNet.

Dataset Information

Cryo electron tomograms of mouse DRG axons (dataset 2)

Publications

A cryo-ET survey of microtubules and intracellular compartments in mammalian axons.

Similar Datasets

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