Monitoring Early Differentiation Events in Human Embryonic Stem Cells by Massively Parallel Signature Sequencing and Expressed Sequence Tag Scan
Why this work is in the frame
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Bibliographic record
Abstract
To identify genes that may be involved in the process of human embryonic stem cell (hESC) differentiation, we profiled gene expression by expressed sequenced tag (EST) enumeration and massively parallel signature sequencing (MPSS) using RNA samples from feeder-free cultures of undifferentiated (passages 40-50) and differentiated (day 14) H1, H7, and H9 lines. MPSS and EST scan analysis showed good concordance and identified a large number of genes that changed rapidly as cultures transition from a pluripotent to a differentiated state. These included known and unknown ES cell-specific genes as well as a large number of known genes that were altered as cells differentiate. A subset of genes that were either up- or down-regulated were selected and their differential expression confirmed by a variety of independent methods, including comparison of expression after further differentiation, publicly available databases, and direct assessments by reverse transcriptase (RT)-PCR and immunocytochemistry. The analysis identified markers unique to the hESC and embryoid bodies (hEBs) stage as well as signaling pathways that likely regulate differentiation. The data generated can be used to monitor the state of hESC isolated by different laboratories using independent methods and maintained under differing culture conditions.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it