Control of Human Embryonic Stem Cell Colony and Aggregate Size Heterogeneity Influences Differentiation Trajectories
Bibliographic record
Abstract
To better understand endogenous parameters that influence pluripotent cell differentiation we used human embryonic stem cells (hESCs) as a model system. We demonstrate that differentiation trajectories in aggregate (embryoid body [EB])-induced differentiation, a common approach to mimic some of the spatial and temporal aspects of in vivo development, are affected by three factors: input hESC composition, input hESC colony size, and EB size. Using a microcontact printing approach, size-specified hESC colonies were formed by plating single-cell suspensions onto micropatterned (MP) extracellular matrix islands. Subsequently, size-controlled EBs were formed by transferring entire colonies into suspension culture enabling the independent investigation of colony and aggregate size effects on differentiation induction. Gene and protein expression analysis of MP-hESC populations revealed that the ratio of Gata6 (endoderm-associated marker) to Pax6 (neural-associated marker) expression increased with decreasing colony size. Moreover, upon forming EBs from these MP-hESCs, we observed that differentiation trajectories were affected by both colony and EB size-influenced parameters. In MP-EBs generated from endoderm-biased (high Gata6/Pax6) input hESCs, higher mesoderm and cardiac induction was observed at larger EB sizes. Conversely, neural-biased (low Gata6/Pax6) input hESCs generated MP-EBs that exhibited higher cardiac induction in smaller EBs. Our analysis demonstrates that heterogeneity in hESC colony and aggregate size, typical in most differentiation strategies, produces subsets of appropriate conditions for differentiation into specific cell types. Moreover, our findings suggest that the local microenvironment modulates endogenous parameters that can be used to influence pluripotent cell differentiation trajectories.
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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 | 0.000 |
| 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 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".