Evidence map›Paper›PMID 31388414›Full record

ArticleCilia2019

New software for automated cilia detection in cells (ACDC).

Max C Lauring, Tianqi Zhu, Wei Luo, Wenqi Wu, Feng Yu, Derek Toomre

Open access · hybridAbstract read
In one paragraph

Article in Cilia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

22 citing papers in PubMed, 30 citations in OpenAlex.

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  16. Fluorescence imaging of beta cell primary cilia.Frontiers in endocrinology · 2022
    Article
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  19. Artificial Intelligence Approaches to Assessing Primary Cilia.Journal of visualized experiments : JoVE · 2021
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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 2 countries.

Max C Lauring1Department of Cell Biology, Yale University School of Medicine, New Haven, CT 06510 USA.
Tianqi Zhu2College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 Zhejiang China.
Wei Luo2College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 Zhejiang China.
Wenqi Wu2College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 Zhejiang China.
Feng Yu2College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027 Zhejiang China.
Derek Toomre1Department of Cell Biology, Yale University School of Medicine, New Haven, CT 06510 USA.ORCID 0000-0002-6827-8127
Zhejiang A & F University · CNYale University · US

Funding

Yale Diabetes Research CenterP30DK045735 · NIDDK · YALE UNIVERSITY · PI Kevan C Herold, GERALD I SHULMAN · 1993 to 2026
$44.0M
NIDDK NIH HHS P30 DK045735
6 · The paper itself

Abstract

backgroundPrimary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably-across different experimental data sets-without significantly compromising the accuracy of manual analysis.

methodsTo solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy 'F1' score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.

resultsThe ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1-6%, yielding high F1 scores of 0.96-0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.

conclusionThe ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.

Indexed as

Automated cilia analysisF1 scoreFalse negativeFalse positiveImage analysisPrimary ciliaSoftware

Identifiers

PMID31388414
PMCPMC6670212
OpenAlexW2966630975

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.