Evidence map›Paper›PMID 32499316›Full record

ArticleLife science alliance2020

Quantification of cristae architecture reveals time-dependent characteristics of individual mitochondria.

Mayuko Segawa, Dane M Wolf, Nan W Hultgren, David S Williams, Alexander M van der Bliek, David B Shackelford, Marc Liesa, Orian S Shirihai

Open access · goldAbstract read
In one paragraph

Article in Life science alliance, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
2.5field-weighted citation impact, top 10% 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

34 citing papers in PubMed, 53 citations in OpenAlex.

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  10. Molecular mechanisms of mitochondrial dynamics.Nature reviews. Molecular cell biology · 2025
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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

8 authors at 2 institutions in 1 country.

Mayuko SegawaDepartment of Medicine, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-5680-9576
Dane M WolfDepartment of Medicine, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Nan W HultgrenDepartments of Ophthalmology and Neurobiology, Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
David S WilliamsDepartments of Ophthalmology and Neurobiology, Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.ORCID 0000-0002-7758-3932
Alexander M van der BliekMolecular Biology Institute at University of California, Los Angeles, Los Angeles, CA, USA.
David B ShackelfordDepartment of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
Marc LiesaDepartment of Medicine, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA mliesa@mednet.ucla.edu.ORCID 0000-0002-5909-8570
Orian S ShirihaiDepartment of Medicine, and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA OShirihai@mednet.ucla.edu.ORCID 0000-0001-8466-3431
University of California, Los Angeles · USBoston University · US

Funding

Transgenic & Knock-out MouseP30DK063491 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PETER J TONTONOZ · 2003 to 2026
$40.4M
Vision Research Core at UCLAP30EY000331 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Gregory Darin Field, DAVID S WILLIAMS · 1985 to 2026
$16.8M
UPLIFT: UCLA Postdocs' Longitudinal Investment in Faculty TrainingK12GM106996 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI CAREY, MICHAEL F, VILLANUEVA, CLAUDIO J · 2016 to 2025
$7.1M
(PQ5) Imaging mitochondrial heterogeneity in LKB1 mutant lung cancerR01CA208642 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI David B Shackelford · 2016 to 2026
$3.9M
The Photoreceptor CiliumR01EY013408 · NEI · UNIVERSITY OF CALIFORNIA SAN DIEGO · PI WILLIAMS, DAVID S · 2001 to 2017
$3.8M
RPE cell biology, aging, and diseaseR01EY027442 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WILLIAMS, DAVID S · 2017 to 2021
$2.6M
Mitochondrial calcium overload and necrosis in tauopathies caused by inhibition of Mfn2 and NCLXR01NS120690 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KOEHLER, CARLA M, VAN DER BLIEK, ALEXANDER M · 2021 to 2025
$2.2M
Contributions of mitochondrial fission proteins to mitophagyU01GM109764 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI VAN DER BLIEK, ALEXANDER M · 2014 to 2017
$1.1M
NCI NIH HHS R01 CA208642NEI NIH HHS P30 EY000331NEI NIH HHS R01 EY013408NEI NIH HHS R01 EY027442NIDDK NIH HHS P30 DK063491NIGMS NIH HHS K12 GM106996NIGMS NIH HHS U01 GM109764NINDS NIH HHS R01 NS120690
6 · The paper itself

Abstract

Recent breakthroughs in live-cell imaging have enabled visualization of cristae, making it feasible to investigate the structure-function relationship of cristae in real time. However, quantifying live-cell images of cristae in an unbiased way remains challenging. Here, we present a novel, semi-automated approach to quantify cristae, using the machine-learning Trainable Weka Segmentation tool. Compared with standard techniques, our approach not only avoids the bias associated with manual thresholding but more efficiently segments cristae from Airyscan and structured illumination microscopy images. Using a cardiolipin-deficient cell line, as well as FCCP, we show that our approach is sufficiently sensitive to detect perturbations in cristae density, size, and shape. This approach, moreover, reveals that cristae are not uniformly distributed within the mitochondrion, and sites of mitochondrial fission are localized to areas of decreased cristae density. After a fusion event, individual cristae from the two mitochondria, at the site of fusion, merge into one object with distinct architectural values. Overall, our study shows that machine learning represents a compelling new strategy for quantifying cristae in living cells.

Indexed as

Mitochondrial DynamicsCell LineHumansImage Processing, Computer-AssistedMicroscopy, FluorescenceMitochondriaMitochondrial MembranesOptical Imaging

Identifiers

PMID32499316
PMCPMC7283135
OpenAlexW3033665025

What OpenQuestion holds

Textmetadata
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.