Matrigel® enhances 3T3-L1 cell differentiation
Bibliographic record
Abstract
Culturing cells on bio-gels are believed to provide a more in vivo-like extracellular matrix. 3T3-L1 cells cultured on Matrigel® significantly alteregd their proliferation and differentiation as compared to growth on tissue culture-coated polystyrene surfaces. Growth on a 250-μm thick layer of Matrigel® facilitated the formation of cellular aggregates of 3T3-L1 cells. Differentiation of 3T3-L1 cells cultured on Matrigel® demonstrated increased levels of mRNA levels for key adipogenic transcription factors (PPARγ, C/EBPα, SREBP1), lipogenic markers (FAS, FABP4, LPL, PLIN1) and markers of adipocyte maturity (LEP), compared to cells cultured directly on a polystyrene tissue culture surface. The gene expression of extracellular matrix proteins (FN1, COL1A1, COL4A1, COL6, LAM) was decreased in 3T3-L1 cells cultured on Matrigel®. Furthermore, growth on Matrigel® increased lipid accumulation in 3T3-L1 cells in the presence and absence of rosiglitazone, a thiazolidinedione routinely used to optimize differentiation in these cells. These changes in adipocyte gene expression and lipid accumulation patterns may be a result of the increased cell–cell and cell–ECM interactions occurring on the Matrigel®, a scenario that is more reflective of an in vivo model. Taken together, our data advance the understanding of the value of culturing 3T3-L1 cells on Matrigel®.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".