Temporal Changes in Nucleus Morphology, Lamin A/C and Histone Methylation During Nanotopography-Induced Neuronal Differentiation of Stem Cells
Bibliographic record
Abstract
Stem cell differentiation can be regulated by biophysical cues such as nanotopography. It involves sensing and integration of these biophysical cues into their transcriptome with a mechanism that is yet to be discovered. In addition to the cytoskeletal and focal adhesion remodeling, nanotopography has also been shown to modulate nucleus morphology. Here, we studied the effect of nanotopography on the temporal changes in nuclei of human embryonic stem cells (hESCs) and human mesenchymal stem cells (hMSCs). Using a high throughput Multi-architecture (MARC) chip analysis, the circularity of the stem cell nuclei changed significantly on different patterns. Human ESCs and MSCs showed different temporal changes in nucleus morphology, lamin A/C expression and histone methylation during topography-induced neuronal differentiation. In hESCs, the expression of nuclear matrix protein, lamin A/C during neuronal differentiation of hESCs on PDMS samples were weakly detected in the first 7 days of differentiation. The histone 3 trimethylation on Lysine 9 (H3K9me3) decreased after differentiation initiated and showed temporal changes in their expression and organization during neuronal differentiation. In hMSCs, the expression of lamin A/C was significantly increased after the first 24 h of cell culture. The quantitative analysis of histone methylation also showed a significant increase in hMSCs histone methylation on 250 nm anisotropic nanogratings within the first 24 h of seeding. This reiterates the importance of cell-substrate sensing within the first 24 h for adult stem cells. The lamin A/C expression and histone methylation shows a correlation of epigenetic changes in early events of differentiation, giving an insight on how extracellular nanotopographical cues are transduced into nuclear biochemical signals. Collectively, these results provide more understanding into the nuclear regulation of the mechanotransduction of nanotopographical cues in stem cell differentiation.
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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) | 0.001 | 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".