Human fibroblast-macrophage tissue spheroids demonstrate ratio-dependent fibrotic activity for <i>in vitro</i> fibrogenesis model development
Bibliographic record
Abstract
Fibrosis is a pathological accumulation of excessive collagen that underlies many of the most common diseases, representing dysfunction of the essential processes of normal tissue healing. Fibrosis research aims to limit this response without ameliorating the essential role of fibrogenesis in organ function. However, the absence of a realistic in vitro model has hindered investigation into mechanisms and potential interventions because the standard 2D monolayer culture of fibroblasts has limited applicability. We sought to develop and optimize fibrosis spheroids: a scaffold-free three-dimensional human fibroblast-macrophage spheroid system representing an improved benchtop model of human fibrosis. We created, characterized and optimized human fibroblast-only spheroids, demonstrating increased collagen deposition compared to monolayer fibroblasts, while spheroids larger than 300 μm suffered from progressively increasing apoptosis. Next, we improved the spheroid system with the addition of human macrophages to more precisely recapitulate the environment during fibrogenesis, creating a hybrid spheroid system with different ratios of fibroblasts and macrophages ranging from 2 : 1 to 64 : 1. We found that in the hybrid spheroids (particularly the 16 : 1 [F16] ratio) more fibroblasts were activated, with greater macrophage polarization towards a pro-inflammatory M1 phenotype. Hybrid spheroids containing higher ratios of macrophages showed greater macrophage heterogeneity and less fibrogenesis, while low macrophage ratios limited macrophage-induced effects and yielded less collagen deposition. The F16 group also had the highest expression levels of fibrosis-related genes (Col-1a1, Col-3a1 and TGF-β) and inflammation-related genes (TNF, IL1β and IL6). IF staining demonstrated that F16 spheroids had the highest levels of αSMA, collagen-1 and collagen-3 deposition among all groups as well as formation of a dense collagen rim surrounding the spheroid. Future studies exploring the greater fibrotic activity of F16 spheroids may provide new mechanistic insights into diseases involving excessive fibrotic activity. Microtissue fibrosis models capable of achieving greater clinical fidelity have the potential to combine the relevance of animal models with the scale, cost and throughput of in vitro testing.
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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 | 0.000 |
| 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 | 0.001 |
| 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".