Ultra-low-input native ChIP-seq for rare cell populations
Bibliographic record
Abstract
Combined chromatin immunoprecipitation and next generation sequencing \(ChIP-seq) has become an extremely popular method to generate genome-wide epigenetic pro les from numerous cell lines and tissue types.Typical ChIP-seq experiments require large number of cells, making them ill-adapted to the study of rare cell populations.This procedure describes an ultra-low-input \(ULI) micrococcal nucleasebased native ChIP \(NChIP) and sequencing library construction method to generate genome-wide chromatin pro les from as few as 10 3 cells \(Brind'Amour et al., 10.1038/ncomms7033).In addition, ULI-NChIP-seq has been validated in vivo, by generation of H3K9me3 and H3K27me3 pro les from E13.5 primordial germ cells isolated from single embryos \(Liu, Brind'Amour et al., 10.1101/gad.244848.114).ULI-NChIP-seq should be useful to generate high quality and complexity libraries from rare cell populations, allowing to decrease colony breeding size or to analyze rare clinical samples.Due to often variable cell numbers obtained during isolation of in vivo cell population, the procedure described here allows for exibility, with some suggestions on adaptation of buffer or volume conditions at various points during the procedure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".