3D niche microarrays for systems-level analyses of cell fate
Bibliographic record
Abstract
The behaviour of mammalian cells in a tissue is governed by the three-dimensional (3D) microenvironment and involves a dynamic interplay between biochemical and mechanical signals provided by the extracellular matrix (ECM), cell–cell interactions and soluble factors. The complexity of the microenvironment and the context-dependent cell responses that arise from these interactions have posed a major challenge to understanding the underlying regulatory mechanisms. Here we develop an experimental paradigm to dissect the role of various interacting factors by simultaneously synthesizing more than 1,000 unique microenvironments with robotic nanolitre liquid-dispensing technology and by probing their effects on cell fate. Using this novel 3D microarray platform, we assess the combined effects of matrix elasticity, proteolytic degradability and three distinct classes of signalling proteins on mouse embryonic stem cells, unveiling a comprehensive map of interactions involved in regulating self-renewal. This approach is broadly applicable to gain a systems-level understanding of multifactorial 3D cell–matrix interactions. 3D cell culture matrices more closely resemble the natural microenvironments of stem cells than 2D systems. Here, the authors present a 3D cell culture approach to screen for the influence of environmental parameters on self-renewal and differentiation of single mouse embryonic stem cells.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".