ArticleCommunications biology2021
Deciphering tumour tissue organization by 3D electron microscopy and machine learning.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- vEMRec: High-Resolution Volume Electron Microscopy Reconstruction Based on Structure-Preserving and High-Fidelity 3D Alignment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- vEMINR: Ultra-Fast Isotropic Reconstruction for Volume Electron Microscopy With Implicit Neural Representation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A multimodal imaging pipeline to decipher cell-specific metabolic functions and tissue microenvironment dynamics.Nature protocols · 2025Review
- Optimized zymogram protocol from 3D spheroid cultures to study MMP-2 and -9 activities in tumor cells.BMC biotechnology · 2025Article
- The bioenergetic landscape of cancer.Molecular metabolism · 2024Review
- The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography.Frontiers in molecular biosciences · 2024Review
- Multiplexed and Millimeter-Scale Fluorescence Nanoscopy of Cells and Tissue Sections via Prism-Illumination and Microfluidics-Enhanced DNA-PAINT.Chemical & biomedical imaging · 2023Article
- Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach.The American journal of the medical sciences · 2023Review
- Volume EM: a quiet revolution takes shape.Nature methods · 2023Article
- Segmentation of cellular ultrastructures on sparsely labeled 3D electron microscopy images using deep learning.Frontiers in bioinformatics · 2023Article
- Volume electron microscopy.Nature reviews. Methods primers · 2022Article
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumour tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also found anatomical connections between the blood capillary and the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for future investigations in the emerging onconanotomy field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.