Comparative proteomic analyses of human adipose extracellular matrices decellularized using alternative procedures
Bibliographic record
Abstract
Abstract Decellularized human adipose tissue has potential clinical utility as a processed biological scaffold for soft tissue cosmesis, grafting, and reconstruction. Adipose tissue decellularization has been accomplished using enzymatic‐, detergent‐, and/or solvent‐based methods. To examine the hypothesis that distinct decellularization processes may yield scaffolds with differing compositions, the current study employed mass spectrometry to compare the proteomes of human adipose‐derived matrices generated through three independent methods combining enzymatic‐, detergent‐, and/or solvent‐based steps. In addition to protein content, bioscaffolds were evaluated for deoxyribose nucleic acid depletion, extracellular matrix composition, and physical structure using optical density, histochemical staining, and scanning electron microscopy. Mass spectrometry based proteomic analyses identified 25 proteins (having at least two peptide sequences detected) in the scaffolds generated with an enzymatic approach, 143 with the detergent approach, and 102 with the solvent approach, as compared to 155 detected in unprocessed native human fat. Immunohistochemical detection confirmed the presence of the structural proteins actin, collagen type VI, fibrillin, laminin, and vimentin. Subsequent in vivo analysis of the predominantly enzymatic‐ and detergent‐based decellularized scaffolds following subcutaneous implantation in GFP+ transgenic mice demonstrated that the matrices generated with both approaches supported the ingrowth of host‐derived adipocyte progenitors and vasculature in a time dependent manner. Together, these results determine that decellularization methods influence the protein composition of adipose tissue‐derived bioscaffolds. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A:2481–2493, 2018.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".