X-Chromosome Inactivation in Rett Syndrome Human Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Rett syndrome (RTT) is a neurodevelopmental disorder that affects girls due primarily to heterozygous mutations in the X-linked gene encoding methyl-CpG binding protein 2 (MECP2). Random X-chromosome inactivation (XCI) results in cellular mosaicism in which some cells express wild-type (WT) MECP2 while other cells express mutant MECP2. The generation of patient-specific human induced pluripotent stem cells (hiPSCs) facilitates the production of RTT-hiPSC-derived neurons in vitro to investigate disease mechanisms and identify novel drug treatments. The generation of RTT-hiPSCs has been reported by many laboratories, however, the XCI status of RTT-hiPSCs has been inconsistent. Some report RTT-hiPSCs retain the inactive X-chromosome (post-XCI) of the founder somatic cell allowing isogenic RTT-hiPSCs that express only the WT or mutant MECP2 allele to be isolated from the same patient. Post-XCI RTT-hiPSCs-derived neurons retain this allele-specific expression pattern of WT or mutant MECP2. Conversely, others report RTT-hiPSCs in which the inactive X-chromosome of the founder somatic cell reactivates (pre-XCI) upon reprogramming into RTT-hiPSCs. Pre-XCI RTT-hiPSC-derived neurons exhibit random XCI resulting in cellular mosaicism with respect to WT and mutant MECP2 expression. Here we review and attempt to interpret the inconsistencies in XCI status of RTT-hiPSCs generated to date by comparison to other pluripotent systems in vitro and in vivo and the methods used to analyze XCI. Finally, we discuss the relative strengths and weaknesses of post- and pre-XCI hiPSCs in the context of RTT, and other X-linked and autosomal disorders for translational medicine.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".