Cellular response to micropatterned growth promoting and inhibitory substrates
Bibliographic record
Abstract
BACKGROUND: Normal development and the response to injury both require cell growth, migration and morphological remodeling, guided by a complex local landscape of permissive and inhibitory cues. A standard approach for studying by such cues is to culture cells on uniform substrates containing known concentrations of these molecules, however this method fails to represent the molecular complexity of the natural growth environment. RESULTS: To mimic the local complexity of environmental conditions in vitro, we used a contact micropatterning technique to examine cell growth and differentiation on patterned substrates printed with the commonly studied growth permissive and inhibitory substrates, poly-L-lysine (PLL) and myelin, respectively. We show that micropatterning of PLL can be used to direct adherence and axonal outgrowth of hippocampal and cortical neurons as well as other cells with diverse morphologies like Oli-neu oligodendrocyte progenitor cell lines and fibroblast-like COS7 cells in culture. Surprisingly, COS7 cells exhibited a preference for low concentration (1 pg/mL) PLL zones over adjacent zones printed with high concentrations (1 mg/mL). We demonstrate that micropatterning is also useful for studying factors that inhibit growth as it can direct cells to grow along straight lines that are easy to quantify. Furthermore, we provide the first demonstration of microcontact printing of myelin-associated proteins and show that they impair process outgrowth from Oli-neu oligodendrocyte precursor cells. CONCLUSION: We conclude that microcontact printing is an efficient and reproducible method for patterning proteins and brain-derived myelin on glass surfaces in order to study the effects of the microenvironment on cell growth and morphogenesis.
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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 | 0.001 |
| 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".