Adipose‐derived mesenchymal stem cells differentiate into pancreatic cancer‐associated fibroblasts <i>in vitro</i>
Bibliographic record
Abstract
Cancer-associated fibroblasts (CAFs) are key components of the dense, proliferating stroma observed in pancreatic ductal adenocarcinoma (PDAC), and CAF subpopulations drive tumor heterogeneity and play a major role in PDAC progression and drug resistance. CAFs consist of heterogenous subpopulations such as myoblastic CAF (myCAF) and inflammatory CAF (iCAF), and each has distinct essential roles. However, it is not clear how CAF subpopulations are formed in PDAC. Adipose-derived MSCs (AD-MSCs), which possess a high multilineage potential and self-renewal capacity, are reported to be one of the in vivo CAF sources. Here, we aimed to investigate whether AD-MSCs can act as precursors for CAFs in vitro. We recorded morphological features and collected omics data from two in vitro co-culture models for recapitulating clinical PDAC. Additionally, we tested the advantages of the co-culture model in terms of accurately modeling morphology and CAF heterogeneity. We showed that AD-MSCs differentiate into two distinct CAF subpopulations: Direct contact co-culture with PDAC cell line Capan-1 induced differentiation into myCAFs and iCAFs, while indirect co-culture induced differentiation into only iCAFs. Using these co-culture systems, we also identified novel CAF markers that may be helpful for elucidating the mechanisms of CAFs in the tumor microenvironment (TME). In conclusion, AD-MSCs can differentiate into distinct CAF subtypes depending on the different co-culture conditions in vitro, and the identification of potential CAF markers may aid in future investigations of the mechanisms underlying the role of CAFs in the TME.
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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) | 0.001 | 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".