Back

Integrated single-nuclei and spatial transcriptomic profiling of human sacrococcygeal teratomas reveals heterogeneity in cellular composition and X-chromosome inactivation

Rojas, E. J.; Giannikou, K.; Huang, B. J.; Cho, S.-J.; Cordero, M.; Pena, D.; Vu, L.; Bagrodia, A.; Derderian, S. C.; MacKenzie, T. C.; Laird, D. J.

2025-07-24 cancer biology
10.1101/2025.07.21.665156 bioRxiv
Show abstract

Sacrococcygeal teratomas (SCTs) are the most common neonatal tumors, yet their cellular origins, clinical stratification, and sex bias-occurring three times more in XX than XY individuals--remain poorly understood. To address these gaps, we examined six postnatal (one male and five female) and two prenatal (both female) SCTs by single nuclei RNA-seq and spatial transcriptomics. We identified five broad cellular lineages in SCTs: stroma, epithelia, endothelia, neuroectoderm, and immune. The transcriptomes and lineage compositions showed significant heterogeneity, which offer a framework for future molecular stratification. SCTs are thought to originate from and be propagated by pluripotent cells, notably however, we did not detect these populations. Among female tumors, a subset of cells exhibited biallelic expression of X-linked genes, consistent with X-inactivation failure or reactivation of the once inactivated X-chromosome. These biallelic cells were enriched for developmental and neuronal programs, whereas cells with single-allelic X-chromosome preferentially expressed immune-related genes. Biallelic X-chromosome activation, which can occur only in female cells, may result in transcriptomic features that favor survival of tumor cells, contributing to the sex bias of SCTs. Our findings reveal a link between X-chromosome inactivation and SCT cell identity, suggesting that X-dosage dysregulation may influence SCT heterogeneity and immune landscape.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.