Histone Modification Profiling in Normal and Transformed Human Embryonic Stem Cells Using Micro Chromatin Immunoprecipitation, Scalable to Genome-Wide Microarray Analyses
Bibliographic record
Abstract
Comparing normal human embryonic stem cells (hESCs) to those that have acquired cellular properties of neoplasm provides a unique opportunity to study the distinguishing molecular features of human cellular transformation. As global alterations in the epigenetic landscape are a common feature of cancer, we sought to investigate the loci-specific and global differences between normal and transformed hESCs using ChIP-PCR and ChIP-microarray (also known as ChIP-chip). Here, specific emphasis was placed on optimizing ChIP for low cell numbers (termed micro-ChIP; μChIP) towards applications where the target population is rare, such as the case for somatic human tumors containing a low frequency of cancer stem cell populations and for single-colony analysis of embryonic and induced pluripotent stem cells emerging from initial derivation. Using these methods, we suggest that μChIP-PCR and microarray analysis is thus a powerful technology for epigenetic profiling of cell populations relevant to developmental biology, cancer, and regenerative medicine where target populations regulating the biological process can only be isolated in small numbers.
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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".