Unveiling tissue-specific transcriptional adaptations in iPSC-derived fibroblasts via co-culture systems
Bibliographic record
Abstract
BACKGROUND: Induced pluripotent stem cell-derived fibroblasts (iFBs) hold promise for autologous disease modelling, but their ability to replicate tissue-specific fibroblast characteristics remains unclear. Fibroblasts exhibit significant heterogeneity, with distinct subtypes playing critical roles in organ function and integrity. This study investigates whether iFBs can acquire tissue-specific transcriptional profiles through co-culture with cells from different germ layers, including skin (keratinocytes), heart (cardiomyocytes), gut (intestinal cells), and lung (bronchial epithelial cells). METHODS: iFBs were co-cultured directly or indirectly with organ-specific cell types, followed by bulk RNA sequencing and pathway analysis. Transcriptional profiles were compared to primary fibroblasts using principal component analysis (PCA), large single-cell databases of over 20,000 cells for single-cell deconvolution and targeted qPCR validation. Statistical significance was assessed via one-way ANOVA. RESULTS: Transcriptomic analysis revealed that iFBs exhibit transcriptional plasticity, adopting molecular phenotypes aligned with their co-culture environment across all germ layers. Paracrine signalling induced transient tissue-specific changes in indirectly co-cultured iFBs, but sustained interactions were required for stable adaptations. Pathway analysis highlighted functional shifts, such as TGF-β activation in cardiac iFBs and ECM remodelling in dermal iFBs. However, single-cell deconvolution showed incomplete tissue specification, with iFBs retaining mixed fibroblast subpopulations. CONCLUSIONS: These findings demonstrate that iFBs can adopt tissue-specific transcriptional profiles, supporting their potential for modelling fibrotic microenvironments in 3D in vitro systems. However, the partial and transient nature of these adaptations underscores the need to validate whether transcriptional changes translate to functional fibroblast behaviours, such as ECM dysregulation or aberrant TGF-β signalling, in complex tissue models. Optimising co-culture conditions to stabilise these phenotypes will be critical for leveraging iFBs in fibrosis research, drug screening, and personalised disease modelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".