Refined ChIP‐Seq Protocol for High‐Quality Chromatin Profiling in Solid Tissues Using the Complete Genomics/MGI Sequencing Platform
Bibliographic record
Abstract
The chromatin immunoprecipitation followed by sequencing (ChIP-seq) assay is an instrumental and accurate method for understanding chromatin dynamics in eukaryotic cells. It provides critical insights into the regulation of gene expression and enables identification of regulatory elements, patterns of histone modifications, and chromatin states in health and disease conditions. Although cell cultures are great models to study molecular mechanisms associated with pathologies, studying tissues provides a physiologically native environment that reflects the cellular heterogeneity and spatial organization that are missing in an in vitro model. Several ChIP-seq protocols have been published; however, performing ChIP-seq in tissues remains a challenge in many settings due to the heterogeneity of tissues, complexity of cell matrices, low input material and intricacy of chromatin fragmentation and handling. Here, we present an optimized ChIP-seq protocol for solid tissues, with a focus on colorectal cancer. In this article, we incorporate simplified and efficient procedures for tissue preparation, chromatin extraction, immunoprecipitation, and library construction. The refined protocols overcome common limitations related to tissue processing and allows for highly reproducible, sensitive, and scalable analysis of disease-relevant chromatin states in vivo. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Frozen tissues preparation Basic Protocol 2: Chromatin immunoprecipitation from tissues Basic Protocol 3: Library construction for DNA sequencing Basic Protocol 4: DNA nanoballs preparation for the DNBSEQ-G99RS sequencing platform and data quality control.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.021 |
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".