Evidence map›Paper›PMID 37651352›Full record

ArticlePLoS biology2023

An interactive deep learning-based approach reveals mitochondrial cristae topologies.

Shogo Suga, Koki Nakamura, Yu Nakanishi, Bruno M Humbel, Hiroki Kawai, Yusuke Hirabayashi

Open access · goldAbstract read
In one paragraph

Article in PLoS biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
3.8field-weighted citation impact, top 6% of its field
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

20 citing papers in PubMed, 26 citations in OpenAlex.

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  15. bioRxiv : the preprint server for biology · 2023
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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

6 authors at 2 institutions in 1 country.

Shogo SugaDepartment of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.
Koki NakamuraDepartment of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.ORCID 0000-0003-4885-4526
Yu NakanishiDepartment of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.
Bruno M HumbelImaging Section, Okinawa Institute of Science and Technology (OIST), Okinawa, Japan.
Hiroki KawaiDepartment of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.ORCID 0000-0002-7129-2384
Yusuke HirabayashiDepartment of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.ORCID 0000-0003-3950-6781
The University of Tokyo · JPOkinawa Institute of Science and Technology Graduate University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The convolution of membranes called cristae is a critical structural and functional feature of mitochondria. Crista structure is highly diverse between different cell types, reflecting their role in metabolic adaptation. However, their precise three-dimensional (3D) arrangement requires volumetric analysis of serial electron microscopy and has therefore been limiting for unbiased quantitative assessment. Here, we developed a novel, publicly available, deep learning (DL)-based image analysis platform called Python-based human-in-the-loop workflow (PHILOW) implemented with a human-in-the-loop (HITL) algorithm. Analysis of dense, large, and isotropic volumes of focused ion beam-scanning electron microscopy (FIB-SEM) using PHILOW reveals the complex 3D nanostructure of both inner and outer mitochondrial membranes and provides deep, quantitative, structural features of cristae in a large number of individual mitochondria. This nanometer-scale analysis in micrometer-scale cellular contexts uncovers fundamental parameters of cristae, such as total surface area, orientation, tubular/lamellar cristae ratio, and crista junction density in individual mitochondria. Unbiased clustering analysis of our structural data unraveled a new function for the dynamin-related GTPase Optic Atrophy 1 (OPA1) in regulating the balance between lamellar versus tubular cristae subdomains.

Indexed as

Deep LearningMitochondrial MembranesAcclimatizationAlgorithmsHumansMitochondria

Identifiers

PMID37651352
PMCPMC10470929
OpenAlexW4386324170

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.