Molecular Biomimicry and the Synthetic Synapse: Engineering a Stable Synaptogenic Extracellular Matrix
Bibliographic record
Abstract
Synapses are highly specialized sites of asymmetric cell-cell contact that mediate information transfer between neurons. Many proteins have been identified that contribute to the complex processes that underlie the formation, organization, and maintenance of synapses, and the molecular biology of synapse formation is increasingly well understood. Surprisingly, synaptic differentiation does not require a cellular target. Hemisynaptic specializations form quickly at sites of neurite adhesion to microspheres coated with a synaptogenic protein or a synthetic cationic polypeptide like polylysine. This raises the possibility that functional hemisynaptic connections could be directed to form onto engineered surfaces; however, studies examining the stability of synaptic specializations formed in the brain onto polylysine-coated beads found that they were unstable and degraded within a few weeks after implantation. Here, we demonstrate that microbeads coated with dendritic polyglycerol amine (dPGA), a nonprotein macromolecular biomimetic of polylysine, promote enhanced synaptogenesis and synapse stability compared to conventional polylysine. We show that a dPGA coating is stable in long-term cell culture, resisting proteolysis, and that dPGA-coated beads cluster neurexin, which is sufficient to direct presynaptic terminal formation. We propose that synthetic synaptogenic extracellular matrices that resist proteolysis could be used to engineer electrodes with enhanced neural biocompatibility and support long-term bidirectional communication with neurons by directing the formation of stable synaptic specializations.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".