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Record W3021835743 · doi:10.1016/j.celrep.2020.107597

Interstitial Cell Remodeling Promotes Aberrant Adipogenesis in Dystrophic Muscles

2020· article· en· W3021835743 on OpenAlexaff
J. Camps, Natacha Breuls, Alejandro Sifrim, Nefele Giarratana, Marlies Corvelyn, Laura Danti, Hanne Grosemans, Sebastiaan Vanuytven, Irina Thiry, M. Belicchi, Mirella Meregalli, Khrystyna Platko, Melissa E. MacDonald, Richard C. Austin, Rik Gijsbers, Giulio Cossu, Yvan Torrente, Thierry Voet, Maurilio Sampaolesi

Bibliographic record

VenueCell Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersMedical Research CouncilRondoufonds voor DuchenneonderzoekFonds Wetenschappelijk OnderzoekBritish Heart Foundation
KeywordsMuscular dystrophySkeletal muscleAdipogenesisEndocrinologyBiologyCellPopulationInternal medicineDystrophyITGA7FibrosisCell biologyMedicineAdipose tissueGenetics

Abstract

fetched live from OpenAlex

Fibrosis and fat replacement in skeletal muscle are major complications that lead to a loss of mobility in chronic muscle disorders, such as muscular dystrophy. However, the in vivo properties of adipogenic stem and precursor cells remain unclear, mainly due to the high cell heterogeneity in skeletal muscles. Here, we use single-cell RNA sequencing to decomplexify interstitial cell populations in healthy and dystrophic skeletal muscles. We identify an interstitial CD142-positive cell population in mice and humans that is responsible for the inhibition of adipogenesis through GDF10 secretion. Furthermore, we show that the interstitial cell composition is completely altered in muscular dystrophy, with a near absence of CD142-positive cells. The identification of these adipo-regulatory cells in the skeletal muscle aids our understanding of the aberrant fat deposition in muscular dystrophy, paving the way for treatments that could counteract degeneration in patients with muscular dystrophy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.217
Teacher spread0.205 · 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 teacher head, not a consensus.

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

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

Citations93
Published2020
Admission routes1
Has abstractyes

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