Systematic Proteome and Transcriptome Analysis of Stem Cell Populations
Bibliographic record
Abstract
Methods for relative assessment of the transcriptional activity of the cell are now routinely employed and obtain large amounts of information regarding process such as transformation or development. These approaches have great impact and are of significant value. Nonetheless, mRNA is an intermediate in the process of protein synthesis and changes in mRNA expression do not reflect absolute or relative changes in protein levels. The mechanisms which translate mRNA to protein are highly regulated, and it remains unclear how the transcriptome reflects the functional state of the cell, as defined by its protein output. Large scale analyses of the proteome are now becoming a reality due to technical advances in protein arrays and mass spectrometry. Thus for the first time data on large numbers of mRNA transcripts and the levels of expression of their associated proteins is available in dynamic systems. Analysis of one such comparison, the transcriptome and proteome of primary haematopoietic stem cells, reveals post-translational regulation of the proteome in stem cell populations. The factors which must be considered when comparing two systematically acquired 'omics' datasets are reviewed and the relative merits of transcriptome and proteome approaches are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".