An open-access volume electron microscopy atlas of whole cells and tissues

Xu, Chengpei; Pang, Song; Shtengel, Gleb; Müller, Andreas; Ritter, Alex T.; Hoffman, Huxley K; Takemura, Shin-ya; Lǚ, Zhiyuan; Pasolli, H. Amalia; Iyer, Nirmala; Chung, Jeeyun; Bennett, Davis; Weigel, Aubrey V.; Freeman, Melanie; Engelenburg, Schuyler B. van; Walther, Tobias C.; Farese, Robert V.; Lippincott‐Schwartz, Jennifer; Mellman, Ira; Solimena, Michele; Hess, Harald F.

doi:10.1038/s41586-021-03992-4

Cited by 127 publications

(114 citation statements)

References 22 publications

Supporting

Mentioning

110

Contrasting

Order By: Relevance

“…Crucially, they also represented varying levels of difficulty to help identify deficiencies in the training dataset, model architectures and postprocessing algorithms. The six volumes, five of which were not previously annotated, included conventionally fixed, heavy metal stained and resin-embedded samples (different sample preparation protocols, and imaged independently) of fly 39 and mouse brain 25,40 tissue, glycolytic muscle tissue 35 , mouse salivary gland, and a HeLa cell. The sixth volume was a high-pressure frozen (HPF), freeze substituted C. elegans embryo specimen.…”

Section: Resultsmentioning

confidence: 99%

“…To assess how well models trained on CEM1.5M and CEM-MitoLab generalize to unseen images, we withheld six volumes from the above pipelines and created mitochondrial segmentations for each. These included conventionally fixed, heavy metal stained, and resin-embedded samples of fly brain 36 , mouse brain 37, 38 , glycolytic muscle 32 , mouse salivary gland, and HeLa cell in addition to a high-pressure frozen (HPF), freeze substituted C. elegans, all independently imaged by FIB-SEM and with isotropic voxel sampling. The first three were externally generated and the last three in-house.…”

Section: Resultsmentioning

confidence: 99%

“…Despite the centrality of mitochondria in renal pathology, existing studies are limited by a reliance on light microscopy, 2D EM, or semantic segmentations of limited volume EM data 51, 52 . To demonstrate the power of MitoNet for general mitochondrial instance segmentation, and to generate 3D ultrastructural correlates of known differences in mitochondrial energetics between proximal and distal tubules 53 we used the exact same model to automatically segment tens of thousands of mitochondria in two previously unseen volumes from mouse kidney tissue downloaded from OpenOrganelle 36 . The ROIs contained primarily distal tubules and proximal tubules (10 GB each).…”

Section: Resultsmentioning

confidence: 99%

“…Not only were all benchmark volumes excluded from both CEM1.5M and crowdsourced annotation, we also excluded any images related to them to rigorously test generalization, Supplementary Figure 6 . OpenOrganelle 39 , which was the source of the fly brain benchmark, included multiple volumes of fly brain tissue from different brain areas. We chose the “Drosophila brain: Fan-shaped body” volume and excluded the “Drosophila brain: Entire alpha and alpha’ lobes of a mushroom body” and “Drosophila brain: Accessory calyx” volumes.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model

Conrad

Narayan

2022

Preprint

View full text Add to dashboard Cite

Mitochondria are extremely pleomorphic in biology. Automatically identifying each one precisely and accurately from any 2D or volume electron microscopy (volume EM) dataset is an unsolved computational challenge. Current deep learning (DL) models are trained within limited contexts, restricting their widescale utility and potential as a universal or generalist solution for mitochondrial segmentation. To address this, we amass a highly heterogeneous ~1.5 x 106 unlabeled cellular EM image dataset and a ~22,000, partially crowdsource-labeled, mitochondrial instance segmentation dataset. We release MitoNet, a DL model trained on these data, which performs well on new and challenging volume EM benchmarks. An accompanying Python package and napari plugin, called empanada, can be used for efficient training, inference, and clean-up of instance segmentations on EM images.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model

Conrad

Narayan

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Only by high-resolution 3D imaging were we able to identify and characterize the nanoscale invaginations formed regularly in the nuclei of senescent fibroblasts. High-resolution 3D whole-cell data sets of non-irradiated fibroblasts can be obtained from the open access volume electron microscopy atlas repository [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Nuclear Fragility in Radiation-Induced Senescence: Blebs and Tubes Visualized by 3D Electron Microscopy

Freyter

Al-razaq

Isermann

et al. 2022

Cells

View full text Add to dashboard Cite

Irreparable DNA damage following ionizing radiation (IR) triggers prolonged DNA damage response and induces premature senescence. Cellular senescence is a permanent state of cell-cycle arrest characterized by chromatin restructuring, altered nuclear morphology and acquisition of secretory phenotype, which contributes to senescence-related inflammation. However, the mechanistic connections for radiation-induced DNA damage that trigger these senescence-associated hallmarks are poorly understood. In our in vitro model of radiation-induced senescence, mass spectrometry-based proteomics was combined with high-resolution imaging techniques to investigate the interrelations between altered chromatin compaction, nuclear envelope destabilization and nucleo-cytoplasmic chromatin blebbing. Our findings confirm the general pathophysiology of the senescence-response, with disruption of nuclear lamin organization leading to extensive chromatin restructuring and destabilization of the nuclear membrane with release of chromatin fragments into the cytosol, thereby activating cGAS-STING-dependent interferon signaling. By serial block-face scanning electron microscopy (SBF-SEM) whole-cell datasets were acquired to investigate the morphological organization of senescent fibroblasts. High-resolution 3-dimensional (3D) reconstruction of the complex nuclear shape allows us to precisely visualize the segregation of nuclear blebs from the main nucleus and their fusion with lysosomes. By multi-view 3D electron microscopy, we identified nanotubular channels formed in lamin-perturbed nuclei of senescent fibroblasts; the potential role of these nucleo-cytoplasmic nanotubes for expulsion of damaged chromatin has to be examined.

show abstract

Emerging approaches for the development of artificial islets

Li,

Sun,

Bian

et al. 2024

Smart Medicine

View full text Add to dashboard Cite

The islet of Langerhans, functioning as a “mini organ”, plays a vital role in regulating endocrine activities due to its intricate structure. Dysfunction in these islets is closely associated with the development of diabetes mellitus (DM). To offer valuable insights for DM research and treatment, various approaches have been proposed to create artificial islets or islet organoids with high similarity to natural islets, under the collaborative effort of biologists, clinical physicians, and biomedical engineers. This review investigates the design and fabrication of artificial islets considering both biological and tissue engineering aspects. It begins by examining the natural structures and functions of native islets and proceeds to analyze the protocols for generating islets from stem cells. The review also outlines various techniques used in crafting artificial islets, with a specific focus on hydrogel‐based ones. Additionally, it provides a concise overview of the materials and devices employed in the clinical applications of artificial islets. Throughout, the primary goal is to develop artificial islets, thereby bridging the realms of developmental biology, clinical medicine, and tissue engineering.

show abstract

An open-access volume electron microscopy atlas of whole cells and tissues

Cited by 127 publications

References 22 publications

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model

Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model

Nuclear Fragility in Radiation-Induced Senescence: Blebs and Tubes Visualized by 3D Electron Microscopy

Emerging approaches for the development of artificial islets

Contact Info

Product

Resources

About