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Record W3081997836 · doi:10.1089/ten.tea.2020.0180

Adipose Stromal Cells Enhance Decellularized Adipose Tissue Remodeling Through Multimodal Mechanisms

2020· article· en· W3081997836 on OpenAlexaff
Kevin P. Robb, Laura Juignet, Pascal Morissette Martin, John T. Walker, Courtney Brooks, Christy Barreira, Gregory A. Dekaban, Lauren E. Flynn

Bibliographic record

VenueTissue Engineering Part A · 2020
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsWestern University
Fundersnot available
KeywordsDecellularizationAdipose tissueStromal cellMesenchymal stem cellStromal vascular fractionTissue engineeringChemistryCellular infiltrationCell biologyPathologyBiomedical engineeringInflammationBiologyMedicineImmunology

Abstract

fetched live from OpenAlex

Decellularized adipose tissue (DAT) scaffolds represent a promising cell-instructive platform for soft tissue engineering. While recent work has highlighted that mesenchymal stromal cells, including adipose-derived stromal cells (ASCs), can be combined with decellularized scaffolds to augment tissue regeneration, the mechanisms involved require further study. The objective of this work was to probe the roles of syngeneic donor ASCs and host-derived macrophages in tissue remodeling of DAT scaffolds within an immunocompetent mouse model. Dual transgenic reporter mouse strains were employed to track and characterize the donor ASCs and host macrophages within the DAT implants. More specifically, ASCs isolated from dsRed mice were seeded on DAT scaffolds, and the seeded and unseeded control scaffolds were implanted subcutaneously into MacGreen transgenic mice for up to 8 weeks. ASC seeding was shown to augment cell infiltration into the DAT implants at 8 weeks, and this was linked to significantly enhanced angiogenesis relative to the unseeded controls. Immunohistochemical staining demonstrated long-term retention of the syngeneic donor ASCs over the duration of the 8-week study, providing evidence that the DAT scaffolds are a cell-supportive delivery platform. Notably, newly formed adipocytes within the DAT implants were not dsRed + , indicating that the donor ASCs supported fat formation through indirect mechanisms. Immunohistochemical tracking of host macrophages through costaining for enhanced green fluorescent protein with the macrophage marker Iba1 revealed that ASC seeding significantly increased the number of infiltrating macrophages within the DAT implants at 3 weeks, while the fraction of macrophages relative to the total cellular infiltrate was similar between the groups at 1, 3, and 8 weeks. Consistent with the tissue remodeling response that was observed, western blotting demonstrated that there was significantly augmented expression of CD163 and CD206, markers of constructive M2-like macrophages, within the ASC-seeded DAT implants. Overall, our results demonstrate that exogenous ASCs enhance tissue regeneration within DAT scaffolds indirectly through multimodal mechanisms that include host cell recruitment and immunomodulation. These data provide further evidence to support the use of decellularized scaffolds as a delivery platform for ASCs in tissue engineering.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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
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

Citations22
Published2020
Admission routes1
Has abstractyes

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