Determining epigenetic memory in kidney proximal tubule cell derived induced pluripotent stem cells using a quadruple transgenic reprogrammable mouse
Bibliographic record
Abstract
The majority of nucleated somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs). The process of reprogramming involves epigenetic remodelling to turn on pluripotency-associated genes and turn off lineage-specific genes. Some evidence shows that iPSCs retain epigenetic marks of their cell of origin and this "epigenetic memory" influences their differentiation potential, with a preference towards their cell of origin. Here, we reprogrammed proximal tubule cells (PTC) and tail tip fibroblasts (TTF), from a reprogrammable mouse to iPSCs and differentiated the iPSCs to renal progenitors to understand if epigenetic memory plays a role in renal differentiation. This model allowed us to eliminate experimental variability due to donor genetic differences and transfection of the reprogramming factors such as copy number and integration site. In this study we demonstrated that early passage PTC iPSCs and TTF iPSCs expressed low levels of renal progenitor genes and high levels of pluripotency-associated genes, and the transcriptional levels of these genes were not significantly different between PTC iPSCs and TTF iPSCs. We used ChIP-seq of H3K4me3, H3K27me3, H3K36me3 and global DNA methylation profiles of PTC iPSCs and TTF iPSCs to demonstrate that global epigenetic marks were not different between the cells from the two different sets of tissue samples. There were also no epigenetic differences observed when kidney developmental genes and pluripotency-associated genes were closely examined. We did observe that during differentiation to renal progenitor cells the PTC iPSC-derived renal cells expressed higher levels of three renal progenitor genes compared to progenitors derived from TTF iPSCs but the underlying DNA methylation and histone methylation patterns did not suggest an epigenetic memory basis for this.
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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".