Simple and High Yielding Method for Preparing Tissue Specific Extracellular Matrix Coatings for Cell Culture
Bibliographic record
Abstract
BACKGROUND: The native extracellular matrix (ECM) consists of a highly complex, tissue-specific network of proteins and polysaccharides, which help regulate many cellular functions. Despite the complex nature of the ECM, in vitro cell-based studies traditionally assess cell behavior on single ECM component substrates, which do not adequately mimic the in vivo extracellular milieu. METHODOLOGY/PRINCIPAL FINDINGS: We present a simple approach for developing naturally derived ECM coatings for cell culture that provide important tissue-specific cues unlike traditional cell culture coatings, thereby enabling the maturation of committed C2C12 skeletal myoblast progenitors and human embryonic stem cells differentiated into cardiomyocytes. Here we show that natural muscle-specific coatings can (i) be derived from decellularized, solubilized adult porcine muscle, (ii) contain a complex mixture of ECM components including polysaccharides, (iii) adsorb onto tissue culture plastic and (iv) promote cell maturation of committed muscle progenitor and stem cells. CONCLUSIONS: This versatile method can create tissue-specific ECM coatings, which offer a promising platform for cell culture to more closely mimic the mature in vivo ECM microenvironment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".