Bibliographic record
Abstract
Gene expression control occurs partially by modifications in chromatin structure, including the addition and removal of posttranslational modifications to histone tails. Histone post-translational modifications (HPTMs) can either facilitate gene expression or repression. For example, acetylation of histone tail lysine residues neutralizes the positive charge and reduces interactions between the tail and negatively charged DNA. The decrease in histone tail-DNA interactions results in increased accessibility of the underlying DNA, allowing for increased transcription factor access. The acetylation mark also serves as a recognition site for bromodomain-containing transcriptional activators, together resulting in enhanced gene expression. Histone marks can be dynamically regulated during cell differentiation and in response to different cellular environments and stimuli. While next-generation sequencing approaches have begun to characterize genomic locations for individual histone modifications, only one modification can be examined concurrently. Given that there are hundreds of different HPTMs, we have developed a high throughput, quantitative measure of global HPTMs that can be used to screen histone modifications prior to conducting more extensive genome sequencing approaches. This protocol describes a flow cytometry-based method to detect global HPTMs and can be conducted using cells in culture or isolated cells from in vivo tissues. We present example data from isolated mouse brain microglia to demonstrate the sensitivity of the assay to detect global shifts in HPTMs in response to a bacteria-derived immune stimulus (lipopolysaccharide). This protocol allows for the rapid and quantitative assessment of HPTMs and can be applied to any transcriptional or epigenetic regulator that can be detected by an antibody.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.560 | 0.362 |
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".