Extensive cellular heterogeneity of X inactivation revealed by single-cell allele-specific expression in human fibroblasts
Bibliographic record
Abstract
ABSTRACT In eutherian mammals, X chromosome inactivation (XCI) provides a dosage compensation mechanism where in each female cell one of the two X chromosomes is randomly silenced. However, some genes on the inactive X chromosome and outside the pseudoautosomal regions escape from XCI and are expressed from both alleles (escapees). Given the relevance of the escapees in biology and medicine, we investigated XCI at an unprecedented single-cell resolution. We combined deep single-cell RNA sequencing with whole genome sequencing to examine allelic specific expression (ASE) in 935 primary fibroblast and 48 lymphoblastoid single cells from five female individuals. In this framework we integrated an original method to identify and exclude doublets of cells. We have identified 55 genes as escapees including 5 novel escapee genes. Moreover, we observed that all genes exhibit a variable propensity to escape XCI in each cell and cell type, and that each cell displays a distinct expression profile of the escapee genes. We devised a novel metric, the Inactivation Score (IS), defined as the mean of the allelic expression profiles of the escapees per cell, and discovered a heterogeneous and continuous degree of cellular XCI with extremes represented by “inactive” cells, i.e., exclusively expressing the escaping genes from the active X chromosome, and “escaping” cells, expressing the escapees from both alleles. Intriguingly we found that XIST is the major genetic determinant of IS, and that XIST expression, higher in G0 phase, is negatively correlated with the expression of escapees, inactivated and pseudoautosomal genes. In this study we use single-cell allele specific expression to identify novel escapees in different tissues and provide evidence of an unexpected cellular heterogeneity of XCI driven by a possible regulatory activity of XIST .
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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) | 0.000 | 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 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".