Proteomic Analyses Reveal Common Promiscuous Patterns of Cell Surface Proteins on Human Embryonic Stem Cells and Sperms
Bibliographic record
Abstract
BACKGROUND: It has long been proposed that early embryos and reproductive organs exhibit similar gene expression profiles. However, whether this similarity is propagated to the protein level remains largely unknown. We have previously characterised the promiscuous expression pattern of cell surface proteins on mouse embryonic stem (mES) cells. As cell surface proteins also play critical functions in human embryonic stem (hES) cells and germ cells, it is important to reveal whether a promiscuous pattern of cell surface proteins also exists for these cells. METHODS AND PRINCIPAL FINDINGS: Surface proteins of hES cells and human mature sperms (hSperms) were purified by biotin labelling and subjected to proteomic analyses. More than 1000 transmembrane or secreted cell surface proteins were identified on the two cell types, respectively. Proteins from both cell types covered a large variety of functional categories including signal transduction, adhesion and transporting. Moreover, both cell types promiscuously expressed a wide variety of tissue specific surface proteins, and some surface proteins were heterogeneously expressed. CONCLUSIONS/SIGNIFICANCE: Our findings indicate that the promiscuous expression of functional and tissue specific cell surface proteins may be a common pattern in embryonic stem cells and germ cells. The conservation of gene expression patterns between early embryonic cells and reproductive cells is propagated to the protein level. These results have deep implications for the cell surface signature characterisation of pluripotent stem cells and germ cells and may lead the way to a new area of study, i.e., the functional significance of promiscuous gene expression in pluripotent and germ cells.
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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 | 0.000 |
| 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".