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Record W4381619799 · doi:10.1093/humrep/dead093.075

O-061 Single cell atlas of small RNAs in the human preimplantation embryo reveals the miRNA dynamics of lineage segregation

2023· article· en· W4381619799 on OpenAlexaff
Stewart J. Russell, K Menezes, Chunyan Zhao, Clifford Librach, S Petropoulos

Bibliographic record

VenueHuman Reproduction · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsUniversité de MontréalSunnybrook HospitalUniversity of TorontoCentre Hospitalier de l’Université de MontréalCReATe Fertility Centre
Fundersnot available
KeywordsBiologyEmbryomicroRNATranscriptomeSmall RNAGeneticsGeneRNAGene expressionPhenotypeGene expression profilingEmbryonic stem cellLineage (genetic)Regulation of gene expressionComputational biologyCell biology

Abstract

fetched live from OpenAlex

Abstract Study question How does the small non-coding RNA (sncRNA) expression profile of the human embryo change from day three (E3) to seven (E7) of preimplantation development? Summary answer From E3 to E7, micro-RNA expression becomes more dynamic, targeting genes important for lineage segregation. What is known already The drivers of lineage specification [SP1] in the human embryo remain unknown. SncRNAs regulate gene expression in most biological systems studied to date, with critical roles in cell development, differentiation, and disease. The most well-studied class of sncRNA, micro-RNAs (miRNAs), are required for embryo development to the peri-implantation stage in animal models, and their expression regulates embryonic stem cell differentiation in vitro. Study design, size, duration Eighty-seven E3-E5 embryos donated for research to the CReATe Fertility Centre (Veritas IRB#16580) between 2002 and 2021 were thawed and cultured to E3-E7. A total of 1279 cells were profiled for sncRNAs. Of these, 463 had known gender and 189 cells were co-sequenced for their mRNA complement. Participants/materials, setting, methods Good-quality embryos by morphology were enzymatically and mechanically dissociated into single cells and subjected to small and large RNA sequencing, as outlined in Petropoulos et al. 2016 and Hagemann-Jensen et al. 2018. Gene expression was analyzed with the ‘Seurat’ package. MiRNA targeting and pathway analysis was performed with Mienturnet. Main results and the role of chance We identified the complete complement of small RNAs present in the human preimplantation embryo, with piRNAs being the most abundant in E3 and miRNAs increasing in diversity and abundance to E7. Uniform Manifold Approximation and Projection (UMAP) analysis revealed progression with developmental stage and lineage. Split-cell co-sequencing identified canonical transcriptional markers of preimplantation development, allowing the classification of the small RNA profiles of 16-cell, early blast, inner cell mass (ICM), mural TE, and polar TE populations. Enriched miRNAs in the E5 ICM included miR-302b-5p and miR-302c-3p, which have known functions in stem cell programming. Conversely, TE cells were enriched for miR-519b-3p, miR-519c-3p, and miR-516a-5p, all members of the imprinted primate-specific microRNA gene cluster (C19MC), which is required for placenta formation. Targeting analysis identified miRNA-gene networks involving the Hedgehog, Hippo, and FoxO signalling pathways, all of which are developmentally significant. Limitations, reasons for caution This study was performed on embryos from a single center – embryo handling and culture conditions may influence sncRNA expression. Furthermore, the ability of these embryos to implant was unknown, therefore we likely profiled some non-viable embryos. Wider implications of the findings The epigenomic changes which drive lineage segregation are poorly understood, and there are vast differences in embryonic development between conventional model systems and humans. We report the first sncRNA profile of single cells between human E3 and E7, providing a resource for further exploration of their role in preimplantation development. Trial registration number not applicable

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.295
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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