An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations
Why this work is in the frame
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Bibliographic record
Abstract
Combined chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) has enabled genome-wide epigenetic profiling of numerous cell lines and tissue types. A major limitation of ChIP-seq, however, is the large number of cells required to generate high-quality data sets, precluding the study of rare cell populations. Here, we present an ultra-low-input micrococcal nuclease-based native ChIP (ULI-NChIP) and sequencing method to generate genome-wide histone mark profiles with high resolution from as few as 103 cells. We demonstrate that ULI-NChIP-seq generates high-quality maps of covalent histone marks from 103 to 106 embryonic stem cells. Subsequently, we show that ULI-NChIP-seq H3K27me3 profiles generated from E13.5 primordial germ cells isolated from single male and female embryos show high similarity to recent data sets generated using 50–180 × more material. Finally, we identify sexually dimorphic H3K27me3 enrichment at specific genic promoters, thereby illustrating the utility of this method for generating high-quality and -complexity libraries from rare cell populations. Standard ChIP-seq protocols require large numbers of cells for high-quality datasets, limiting the application of this technique on rare cell types. Here, Brind’Amour et al. introduce an ultra-low-input ChIP-seq protocol to generate maps of covalent histone marks from as few as 1,000 cells.
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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 | 0.001 | 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