Potential Factors for the Differentiation of ESCs/iPSCs into Insulin-Producing Cells
Bibliographic record
Abstract
The low efficiency of in vitro differentiation of human embryonic stem cells (ESCs) or human induced pluripotent stem cells (iPSCs) into insulin-producing cells thus creates a crucial hurdle for the clinical implementation of human pluripotent stem cells (PSCs). In this study, we investigated the key factors for the differentiation of PSCs into insulin-producing cells. We obtained microarray data of HUES8 and HUES6 from two GeneChips (GPL3921: Affymetrix HT Human Genome U133A Array, GPL570: Affymetrix Human Genome U133 Plus 2.0 Array) in a database of GEO (NCBI), since HUES8 can differentiate into pancreatic cells, while HUES6 hardly demonstrates any differentiation at all. The genes with more than fourfold higher expressions in HUES8 compared to HUES6 included RPS4Y1, DDX3Y, EIF1AY, GREM1, GATA6, and NLGN4Y. Since there were four genes, RPS4Y1, DDX3Y, EIF1AY, and NLGN4Y, on the Y chromosome and HUES8 was a male cell line and HUES6 was a female cell line, we excluded these genes in this study. On the other hand, genes with more than fourfold higher expressions in HUES6 compared to HUES8 included NLRP2, EGR1, and SMC3. We next compared iPSCs derived from pancreatic cells (PiPSCs) and iPSCs derived from fibroblasts (FiPSCs). PiPSCs differentiated into insulin-producing cells more easily than FiPSCs because of their epigenetic memory. The gene expressions of GREM1, GATA6, NLRP2, EGR1, and SMC3 in PiPSCs and FiPSCs were also investigated. The expression level of GREM1 and GATA6 in PiPSCs were higher than in FiPSCs. On the other hand, EGR1, which was lower in HUES8 than in HUES6, was predictably lower in PiPSCs than FiPSCs, while NLRP2 and SMC3 were higher in PiPSCs than FiPSCs. These data suggest that the expression of GATA6 and GREM1 and the inhibition of EGR1 may be important factors for the differentiation of PSCs into insulin-producing cells.
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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 | 0.000 | 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 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".