A microgroove patterned multiwell cell culture plate for high‐throughput studies of cell alignment
Bibliographic record
Abstract
Grooved substrates are commonly used to guide cell alignment and produce in vitro tissues that mimic certain aspects of in vivo cellular organization. These more sophisticated tissues provide valuable in vitro models for testing drugs and for dissecting out molecular mechanisms that direct tissue organization. To increase the accessibility of these tissue models we describe a simple and yet reproducible strategy to produce 1 µm-spaced grooved well plates suitable for conducting automated analysis of cellular responses. We characterize the alignment of four human cell types: retinal epithelial cells, umbilical vein endothelial cells, foreskin fibroblasts, and human pluripotent stem-cell-derived cardiac cells on grooves. We find all cells align along the grooves to differing extents at both sparse and confluent densities. To increase the sophistication of in vitro tissue organization possible, we also created hybrid substrates with controlled patterns of microgrooved and flat regions that can be identified in real-time using optical microscopy. Using our hybrid patterned surfaces we explore: (i) the ability of neighboring cells to provide a template to organize surrounding cells that are not directly exposed to grooved topographic cues, and (ii) the distance over which this template effect can operate in confluent cell sheets. We find that in fibroblast sheets, but not epithelial sheets, cells aligned on grooves can direct alignment of neighboring cells in flat regions over a limited distance of approximately 200 μm. Our hybrid surface plate provides a novel tool for studying the collective response of groups of cells exposed to differential topographical cues.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".