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Record W4399435643 · doi:10.1186/s44330-024-00005-4

Mini-MEndR: a miniaturized 96-well predictive assay to evaluate muscle stem cell-mediated repair

2024· article· en· W4399435643 on OpenAlexafffund
Nitya Gulati, Sadegh Davoudi, Bin Xu, Saifedine T. Rjaibi, Erik Jacques, Justin Pham, Amir Fard, Alison P. McGuigan, Penney M. Gilbert

Bibliographic record

VenueBMC Methods · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of TorontoCanada First Research Excellence FundCanada Research ChairsOntario Institute for Regenerative MedicineConnaught FundStem Cell Network
KeywordsSkeletal muscleStem cellIn vivoTissue engineeringCell biologyBiomedical engineeringMyosinEndogenyCellHigh-content screeningRegeneration (biology)ChemistryBiologyBiochemistryAnatomyMedicineBiotechnology

Abstract

fetched live from OpenAlex

Abstract Background Functional evaluation of molecules that are predicted to promote stem cell mediated endogenous repair often requires in vivo transplant studies that are low throughput and hinder the rate of discovery. To offer greater throughput for functional validation studies, we miniaturized, simplified and expanded the functionality of a previously developed muscle endogenous repair (MEndR) in vitro assay that was shown to capture significant events of in vivo muscle endogenous repair. Methods The mini-MEndR assay consists of miniaturized cellulose scaffolds designed to fit in 96-well plates, the pores of which are infiltrated with human myoblasts encapsulated in a fibrin-based hydrogel to form engineered skeletal muscle tissues. Pre-adsorbing thrombin to the cellulose scaffolds facilitates in situ tissue polymerization, a critical modification that enables new users to rapidly acquire assay expertise. Following the generation of the 3D myotube template, muscle stem cells (MuSCs), enriched from digested mouse skeletal muscle tissue using an improved magnetic-activated cell sorting protocol, are engrafted within the engineered template. Murine MuSCs are fluorescently labeled, enabling co-evaluation of human and mouse Pax7+ cell responses to drug treatments. A regenerative milieu is introduced by injuring the muscle tissue with a myotoxin to initiate endogenous repair “in a dish”. Phenotypic data is collected at endpoints with a high-content imaging system and is analyzed using ImageJ-based image analysis pipelines. Results The miniaturized format and modified manufacturing protocol cuts reagent costs in half and hands-on seeding time ~ threefold, while the image analysis pipelines save 40 h of labour. By evaluating multiple commercially available human primary myoblast lines in 2D and 3D culture, we establish quality assurance metrics for cell line selection that standardizes myotube template quality. In vivo outcomes (enhanced muscle production and Pax7+ cell expansion) to a known modulator of MuSC mediated repair (p38/β MAPK inhibition) are recapitulated in the miniaturized culture assay, but only in the presence of stem cells and the regenerative milieu. Discussion The miniaturized predictive assay offers a simple, scaled platform to co-investigate human and mouse skeletal muscle endogenous repair molecular modulators, and thus is a promising strategy to accelerate the muscle endogenous repair discovery pipeline.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.349
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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