Evidence map›Paper›PMID 39927669›Full record

ArticleJournal of visualized experiments : JoVE2025

Leveraging Virtual Reality for Immersive Segmentation and Analysis of Cryo-Electron Tomography Data.

Carissa Chestnut, Jake D Johnston, Marcus Velazquez, Kasahun Neselu, Edward T Eng

Abstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Carissa Chestnut *Stony Brook University; Simons Electron Microscopy Center, New York Structural Biology Center.
Jake D Johnston *Simons Electron Microscopy Center, New York Structural Biology Center; Department of Physiology and Cellular Biophysics, Columbia University.
Marcus Velazquez *Stony Brook University; Simons Electron Microscopy Center, New York Structural Biology Center.
Kasahun NeseluSimons Electron Microscopy Center, New York Structural Biology Center.
Edward T EngSimons Electron Microscopy Center, New York Structural Biology Center; eeng@nysbc.org.

Funding

NCCAT: National Center for CryoEM Access and Training- Supplement for Windows 10 and FFIU24GM129539 · NIGMS · NEW YORK STRUCTURAL BIOLOGY CENTER · PI DE MARCO, ALEX, KIEFT, JEFFREY S · 2018 to 2023
$53.9M
NCCAT: National Center for CryoEM Access and TrainingR24GM154192 · NIGMS · NEW YORK STRUCTURAL BIOLOGY CENTER · PI EDWARD T ENG, Jeffrey S Kieft · 2024 to 2026
$21.0M
The National Center for In-situ Tomographic Ultramicroscopy (NCITU) SupplementU24GM139171 · NIGMS · NEW YORK STRUCTURAL BIOLOGY CENTER · PI DE MARCO, ALEX, KIEFT, JEFFREY S · 2020 to 2025
$8.7M
NIGMS NIH HHS R24 GM154192NIGMS NIH HHS U24 GM129539NIGMS NIH HHS U24 GM139171
6 · The paper itself

Abstract

Cryo-electron tomography (cryo-ET) is a powerful technique for visualizing the ultrastructure of cells in three dimensions (3D) at nanometer resolution. However, the manual segmentation of cellular components in cryo-ET data remains a significant bottleneck due to its complexity and time-consuming nature. In this work, we present a novel segmentation workflow that integrates advanced virtual reality (VR) software to enhance both the efficiency and accuracy of segmenting cryo-ET datasets. This workflow leverages an immersive VR tool with intuitive 3D interaction, enabling users to navigate and annotate complex cellular structures in a more natural and interactive environment. To evaluate the effectiveness of the workflow, we applied it to the segmentation of mitochondria in retinal pigment epithelium (RPE1) cells. Mitochondria, essential for cellular energy production and signaling, exhibit dynamic morphological changes, making them an ideal test sample. The VR software facilitated precise delineation of mitochondrial membranes and internal structures, enabling downstream analysis of the segmented membrane structures. We demonstrate that this VR-based segmentation workflow significantly improves the user experience while maintaining accurate segmentation of intricate cellular structures in cryo-ET data. This approach holds promise for broad applications in structural cell biology and science education, offering a transformative tool for researchers engaged in detailed cellular analysis.

Indexed as

Cryoelectron MicroscopyElectron Microscope TomographyVirtual RealityHumansMitochondriaRetinal Pigment EpitheliumSoftware

Identifiers

PMID39927669
PMCPMC12967319

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Registered trials

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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.