Evidence map›Paper›PMID 32303261›Full record

ArticleActa neuropathologica communications2020

A high-throughput digital script for multiplexed immunofluorescent analysis and quantification of sarcolemmal and sarcomeric proteins in muscular dystrophies.

Dominic Scaglioni, Matthew Ellis, Francesco Catapano, Silvia Torelli, Darren Chambers, Lucy Feng, Caroline Sewry, Jennifer Morgan, Francesco Muntoni, Rahul Phadke

Open access · goldAbstract read
In one paragraph

Article in Acta neuropathologica communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

10 citing papers in PubMed, 13 citations in OpenAlex.

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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

10 authors at 4 institutions in 1 country.

Dominic ScaglioniDubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, London, UK.
Matthew EllisDepartment of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, London, UK.
Francesco CatapanoDubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, London, UK.
Silvia TorelliDubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, London, UK.
Darren ChambersDubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, London, UK.
Lucy FengDubowitz Neuromuscular Centre, UCL Queen Square Institute of Neurology & Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Caroline SewryDubowitz Neuromuscular Centre, UCL Queen Square Institute of Neurology & Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Jennifer MorganDubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, London, UK.
Francesco MuntoniDubowitz Neuromuscular Centre, UCL Great Ormond Street Institute of Child Health, London, UK.
Rahul PhadkeDepartment of Neurodegenerative Diseases, UCL Queen Square Institute of Neurology, London, UK. r.phadke@ucl.ac.uk.
National Hospital for Neurology and Neurosurgery · GBUniversity College London · GBGreat Ormond Street Hospital for Children NHS Foundation Trust · GBGreat Ormond Street Hospital · GB

Funding

Cancer Research UK Cl 15121 A 20256
6 · The paper itself

Abstract

The primary molecular endpoint for many Duchenne muscular dystrophy (DMD) clinical trials is the induction, or increase in production, of dystrophin protein in striated muscle. For accurate endpoint analysis, it is essential to have reliable, robust and objective quantification methodologies capable of detecting subtle changes in dystrophin expression. In this work, we present further development and optimisation of an automated, digital, high-throughput script for quantitative analysis of multiplexed immunofluorescent (IF) whole slide images (WSI) of dystrophin, dystrophin associated proteins (DAPs) and regenerating myofibres (fetal/developmental myosin-positive) in transverse sections of DMD, Becker muscular dystrophy (BMD) and control skeletal muscle biopsies. The script enables extensive automated assessment of myofibre morphometrics, protein quantification by fluorescence intensity and sarcolemmal circumference coverage, colocalisation data for dystrophin and DAPs and regeneration at the single myofibre and whole section level. Analysis revealed significant variation in dystrophin intensity, percentage coverage and amounts of DAPs between differing DMD and BMD samples. Accurate identification of dystrophin via a novel background subtraction method allowed differential assessment of DAP fluorescence intensity within dystrophin positive compared to dystrophin negative sarcolemma regions. This enabled surrogate quantification of molecular functionality of dystrophin in the assembly of the DAP complex. Overall, the digital script is capable of multiparametric and unbiased analysis of markers of myofibre regeneration and dystrophin in relation to key DAPs and enabled better characterisation of the heterogeneity in dystrophin expression patterns seen in BMD and DMD alongside the surrogate assessment of molecular functionality of dystrophin. Both these aspects will be of significant relevance to ongoing and future DMD and other muscular dystrophies clinical trials to help benchmark therapeutic efficacy.

Indexed as

Muscular DystrophiesChildChild, PreschoolDystrophinDystrophin-Associated ProteinsFluorescent Antibody TechniqueHigh-Throughput Screening AssaysHumansImage Processing, Computer-AssistedMaleSarcolemmaSarcomeresDystrophinDystrophin-Associated ProteinsDigital pathologyDystrophinGenetic therapiesImmunofluorescenceMuscular dystrophyQuantification

Identifiers

PMID32303261
PMCPMC7165405
OpenAlexW3016361753

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.