The study of epigenetic mechanisms based on the analysis of histone modification patterns by flow cytoametry
Bibliographic record
Abstract
Epigenetic regulation of genes involved in cell growth, survival, or differentiation through histone modifications is an important determinant of cancer development and outcome. The basic science of epigenetics uses analytical tools that, although powerful, are not well suited to the analysis of heterogeneous cell populations found in human cancers, or for monitoring the effects of drugs designed to modulate epigenetic mechanisms in patients. To address this, we selected three clinically relevant histone marks (H3K27me3, H3K9ac, and H3K9me2), modulated their expression levels by in vitro treatments to generate high and low expressing control cells, and tested the relative sensitivity of candidate antibodies to detect the differences in expression levels by flow cytoametry using a range of sample preparation techniques. We identified monoclonal antibodies to all three histone marks that were suitable for flow cytoametry. Staining intensities were reduced with increasing formaldehyde concentration, and were not affected by ionic strength or by alcohol treatment. A protocol suitable for clinical samples was then developed, to allow combined labeling of histone marks and surface antigens while preserving light scatter signals. This was applied to normal donor blood, and to samples obtained from 25 patients with leukemia (predominantly acute myeloid leukemia). Significant cellular heterogeneity in H3K9ac and H3K27me3 staining was seen in normal peripheral blood, but the patterns were very similar between individual donors. In contrast, H3K27me3 in particular showed considerable inter-patient heterogeneity in the leukemia cell populations. Although further refinements are likely needed to fully optimize sample staining protocols, "flow epigenetics" appears to be technically feasible, and to have potential both in basic research, and in clinical application.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".