Evidence map›Paper›PMID 40350411›Full record

ArticleBMC biology2025

Domain-specific AI segmentation of IMPDH2 rod/ring structures in mouse embryonic stem cells.

Samuel T M Ball, Meagan J Hennessy, Yuhan Tan, Kai F Hoettges, Neil D Perkins, David J Wilkinson, Michael R H White, Yalin Zheng, David A Turner

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In one paragraph

Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

9 authors.

Samuel T M Ball *Institute of Life-Course and Medical Sciences, Faculty of Health and Life Sciences, William Henry Duncan Building, University of Liverpool, Liverpool, UK.
Meagan J Hennessy *Institute of Life-Course and Medical Sciences, Faculty of Health and Life Sciences, William Henry Duncan Building, University of Liverpool, Liverpool, UK.
Yuhan TanInstitute of Life-Course and Medical Sciences, Faculty of Health and Life Sciences, William Henry Duncan Building, University of Liverpool, Liverpool, UK.
Kai F HoettgesDepartment of Electrical and Electronic Engineering, Faculty of Science and Engineering, University of Liverpool, Liverpool, UK.
Neil D PerkinsFaculty of Medical Sciences, Biosciences Institute, University of Newcastle, Newcastle, UK.
David J WilkinsonInstitute of Life-Course and Medical Sciences, Faculty of Health and Life Sciences, William Henry Duncan Building, University of Liverpool, Liverpool, UK.
Michael R H WhiteFaculty of Biology, Medicine and Health, School of Biological Sciences, University of Manchester, Manchester, UK.
Yalin ZhengInstitute of Life-Course and Medical Sciences, Faculty of Health and Life Sciences, William Henry Duncan Building, University of Liverpool, Liverpool, UK.
David A TurnerInstitute of Life-Course and Medical Sciences, Faculty of Health and Life Sciences, William Henry Duncan Building, University of Liverpool, Liverpool, UK. david.turner@liverpool.ac.uk.

Funding

Biotechnology and Biological Sciences Research Council BB/T008695/1National Centre for the Replacement, Refinement and Reduction of Animals in Research NC/P001467/1Royal Society RGS\R2\202075University of Liverpool Department of Electrical and Electronic Engineering (Core Funding)Wellcome TrustWellcome Trust 222530/Z/21/Z
6 · The paper itself

Abstract

backgroundInosine monophosphate dehydrogenase 2 (IMPDH2) is an enzyme that catalyses the rate-limiting step of guanine nucleotides. In mouse embryonic stem cells (ESCs), IMPDH2 forms large multi-protein complexes known as rod-ring (RR) structures that dissociate when ESCs differentiate. Manual analysis of RR structures from confocal microscopy images, although possible, is not feasible on a large scale due to the quantity of RR structures present in each field of view. To address this analysis bottleneck, we have created a fully automatic RR image classification pipeline to segment, characterise and measure feature distributions of these structures in ESCs.

resultsWe find that this model can automatically segment images with a Dice score of over 80% for both rods and rings for in-domain images compared to expert annotation, with a slight drop to 70% for datasets out of domain. Important feature measurements derived from these segmentations show high agreement with the measurements derived from expert annotation, achieving an R

conclusionsWe have established for the first time a quantitative baseline for RR distribution in pluripotent ESCs and have made a pipeline available for training to be applied to other models in which RR remain an open topic of study.

Indexed as

IMP DehydrogenaseMouse Embryonic Stem CellsAnimalsMiceMicroscopy, ConfocalIMP DehydrogenaseAI segmentationConvolutional neural networksCytoophidiaDeep learningEmbryonic stem cellsImage processingIMPDH2LoukoumasomesNematosomesRods and rings

Identifiers

PMID40350411
PMCPMC12067766

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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.