Dissecting Small Noncoding RNA Landscapes in Mouse Preimplantation Embryos and Human Blastoids for Modeling Early Human Embryogenesis
Bibliographic record
Abstract
Summary Small non-coding RNAs (sncRNA) exert regulatory functions in mammalian cells; however, their expression dynamics and contribution during preimplantation development are not entirely understood. In this study, we apply Small-Seq to generate a comprehensive single-cell atlas of sncRNA expression in mouse oocytes, sperm, and embryos (2-cell to 64-cell stages), and compare these dynamics with human embryos at equivalent stages. In both species, all sncRNA subtypes are expressed, but only microRNAs (miRNAs) and small nucleolar RNAs (snoRNAs) display cell type-specific patterns. In mice and humans, miRNAs and snoRNAs from the Dlk1-Dio3 locus were upregulated in the inner cell mass (ICM). Human trophectoderm (TE) is enriched with primate-specific C19MC miRNAs, which show relatively low expression in the ICM. In contrast, the mouse lacks a TE-specific hotspot, as C2MC miRNAs are higher in the ICM. Nonetheless, differentiation-associated miRNAs (e.g., miR-24-3p, miR-200c-3p) are high in the TE of both species. Further, we profile the sncRNAs of human blastoids (stem-cell-based embryo model) and determine that they largely recapitulate the human blastocyst. We envision broad utility of this dataset as a resource for future studies seeking to dissect the functions of individual sncRNAs in early development, and for advancing applications in stem cell-based embryo models and assisted reproductive technologies.
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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".