High-throughput analysis of topographical cues for the expansion of murine pluripotent stem cells
Bibliographic record
Abstract
Abstract The expansion of pluripotent stem cells (PSCs) in vitro remains a critical barrier to their use in tissue engineering and regenerative medicine. Biochemical methods for PSC expansion are known to produce heterogeneous cell populations with varying states of pluripotency and are cost-intensive, hindering their clinical translation. Engineering biomaterials to physically control PSC fate offers an alternative approach. Surface or substrate topography is a promising design parameter for engineering biomaterials. Topographical cues have been shown to elicit profound effects on stem cell differentiation and proliferation. Previous reports have shown isotropic substrate topographies to be promising in expanding PSCs. However, the optimal feature to promote PSC proliferation and the pluripotent state has not yet been determined. In this work, the MultiARChitecture (MARC) plate is developed to conduct a high-throughput analysis of topographical cues in a 96-well plate format. The MARC plate is a reproducible and customizable platform for the analysis of multiple topographical patterns and features and is compatible with both microscopic assays and molecular biology techniques. The MARC plate is used to evaluate the expression of pluripotency markers Oct4, Nanog , and Sox2 and the differentiation marker LmnA as well as the proliferation of murine embryonic stem (mES) cells. Our systematic analyses identified three topographical patterns that maintain pluripotency in mES cells after multiple passages: 1 µ m pillars (1 µ m spacing, square arrangement), 2 µ m wells (c-c ( x, y ) = 4, 4 µ m), and 5 µ m pillars (c-c ( x, y ) = 7.5, 7.5 µ m). This study represents a step towards developing a biomaterial platform for controlled murine PSC expansion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".