Concise Review: Molecular Cytogenetics and Quality Control: Clinical Guardians for Pluripotent Stem Cells
Bibliographic record
Abstract
Now that induced pluripotent stem cell (iPSC)-based transplants have been performed in humans and organizations have begun producing clinical-grade iPSCs, it is imperative that strict quality control standards are agreed upon. This is essential as both ESCs and iPSCs have been shown to accumulate genomic aberrations during long-term culturing. These aberrations can include copy number variations, trisomy, amplifications of chromosomal regions, deletions of chromosomal regions, loss of heterozygosity, and epigenetic abnormalities. Moreover, although the differences between iPSCs and ESCs appear largely negligible when a high enough n number is used for comparison, the reprogramming process can generate further aberrations in iPSCs, including copy number variations and deletions in tumor-suppressor genes. If mutations or epigenetic signatures are present in parental cells, these can also be carried over into iPSCs. To maximize patient safety, we recommend a set of standards to be utilized when preparing iPSCs for clinical use. Reprogramming methods that do not involve genomic integration should be used. Cultured cells should be grown using feeder-free and serum-free systems to avoid animal contamination. Karyotyping, whole-genome sequencing, gene expression analyses, and standard sterility tests should all become routine quality control tests. Analysis of mitochondrial DNA integrity, whole-epigenome analyses, as well as single-cell genome sequencing of large cell populations may also prove beneficial. Furthermore, clinical-grade stem cells need to be produced under accepted regulatory good manufacturing process standards. The creation of haplobanks that provide major histocompatibility complex matching is also recommended to improve allogeneic stem cell engraftment. Stem Cells Translational Medicine 2018;7:867-875.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".