Self‐Produced Brain‐Like ECM From 3D‐Cultured Dermal Fibroblasts Enhances Neuronal Growth and Survival
Bibliographic record
Abstract
Studying neurological disorders in vitro remains challenging due to the complexity of the human brain and the limited availability of primary neural cells. Tissue engineering enables the development of three-dimensional (3D) cell culture systems by generating a self-produced extracellular matrix (ECM) substrate. Culturing cells within this ECM substrate is known to more effectively mimic physiological conditions compared to traditional monolayer cultures. In this study, we analyzed the proteome and matrisome of 3D cultured dermal fibroblasts embedded in a self-produced ECM. Interestingly, in silico analysis predicted strong activation of neurogenesis-associated functions in this tissue-engineered 3D model. We showed that ECM proteins typically linked to neuronal development and maintenance were also expressed by dermal fibroblasts. Coculturing dermal fibroblasts with induced pluripotent stem cell (iPSC)-derived motor neurons notably enabled long-lasting culture periods while minimizing neuronal death, all without the need for costly media supplements. Furthermore, fibroblast-conditioned media enhanced neuronal survival. Although we demonstrated that the dermal fibroblast-derived ECM provided a rich matrix of proteins and signaling molecules that support neuronal growth and survival, the ECM alone seems insufficient to sustain the neuronal networks. These findings suggest that 3D cultured patient-derived dermal fibroblasts generate a neuro-supportive microenvironment and could serve as a cost-effective and less invasive alternative to brain biopsies for modeling complex neurological disorders. This approach offers a promising platform for studying such neural growth and survival and exploring therapeutic strategies for neurological diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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 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".