This manuscript presents a comprehensive protocol for low-input ChIP-seq to profile histone modifications and ATAC-seq to assess chromatin accessibility in small amounts of primary mouse cholangiocytes.
Method Article
This manuscript presents a comprehensive protocol for low-input ChIP-seq to profile histone modifications and ATAC-seq to assess chromatin accessibility in small amounts of primary mouse cholangiocytes.
Polycystic liver disease (PLD) is a hereditary disorder characterized by the formation of fluid-filled cysts derived from cholangiocytes, leading to progressive disease and a significant reduction in patients' quality of life. Current treatments for PLD are inadequate, emphasizing the need for novel therapeutic strategies. The role of epigenetic regulation in PLD progression, particularly chromatin accessibility and histone modifications, remains underexplored. Traditional epigenetic profiling techniques, such as ChIP-seq and DNase-seq, require large numbers of cells, which are difficult to obtain from primary cholangiocytes. To address this, we optimized low-input ChIP-seq and ATAC-seq protocols for low numbers of primary cholangiocytes. These approaches allow for the analysis of histone modifications and chromatin accessibility with minimal cell input. Low-input ChIP-seq utilizes micrococcal nuclease (MNase) for DNA fragmentation, while ATAC-seq employs Tn5 transposase to capture open chromatin regions. These multi-omics techniques provide valuable insights into chromatin state dynamics during cholangiocyte fate transitions in PLD and other biliary diseases. Importantly, the optimized protocols are confidently applicable to other low-input primary cells, enabling the exploration of epigenetic mechanisms across various cellular contexts. This work presents a systematic approach for studying chromatin state alterations, contributing to the development of epigenetic-based therapeutic strategies for PLD and related diseases.
PLD is an inherited disorder characterized by the development of multiple fluid-filled cysts derived from cholangiocytes. As these cysts progressively expand, they severely impact patients' quality of life1,2. Existing treatment strategies for PLD are inadequate, providing only limited benefits while frequently leading to high recurrence rates and complications3,4. Therefore, there is a pressing need for safer and more effective therapeutic approaches to meet the unresolved clinical challenges in PLD treatment.
Under normal conditions, cholangiocytes remain quiescent, whereas in PLD, they exhibit excessive proliferation, a key driver of disease progression5,6. The molecular mechanisms underlying this cystic transition remain unclear. While epigenetic regulation, including chromatin accessibility and histone modifications, plays a vital role in cell fate transitions7,8, its role in PLD progression remains understudied. However, many epigenetic profiling techniques require a large number of cells. Traditional ChIP-seq, which relies on chromatin fragmentation by sonication, as well as chromatin accessibility assays such as DNase-seq and MNase-seq, typically require over 106 cells-far exceeding the number obtainable from primary cholangiocytes.
This manuscript provides a comprehensive protocol for low-input ChIP-seq9,10,11 to profile histone modifications and ATAC-seq11,12,13 to assess chromatin accessibility in low numbers of primary cholangiocytes. Low-input ChIP-seq employs MNase to fragment DNA9, while ATAC-seq captures DNA fragments from open chromatin regions using Tn5 transposase12. Multi-omics analysis utilizing these approaches offers valuable insights into chromatin state dynamics during cholangiocyte fate transitions in PLD and other biliary diseases, thereby facilitating the development of epigenetic-targeted therapies.
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1. Preparation of solutions and cholangiocytes for ATAC-seq
2. ATAC-seq
NOTE: Use 100,000 cholangiocytes for ATAC-seq. Keep the samples and solutions on ice. For the ATAC-seq experiment, the entire procedure takes approximately 2 days.
3. Preparation of solutions and cholangiocytes for Low-input ChIP-seq
4. Day 1 of Low-input ChIP-seq: Cell lysis, MNase digestion, and Antibody incubation
NOTE: For the low-input ChIP-seq experiment, the entire procedure takes approximately 3 days.
5. Day 2 of Low-input ChIP-seq: Immunoprecipitation, Wash and Elution
NOTE: For IP samples, follow steps 5.1-5.7. For Input samples, follow steps 5.8-5.12.
6. Day 3 of low-input ChIP-seq: Library construction and Sequencing
7. Data analysis of ATAC-seq and Low-input ChIP-seq
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To generate the chromatin landscape of primary cholangiocytes, we optimized the low-input ChIP-seq and ATAC-seq protocols for low numbers (~100,000) of primary cholangiocytes. Agarose gel electrophoresis results for primary cholangiocytes indicated that for 1 × 105 primary cholangiocytes, 0.02 U MNase at 37 °C for 5 min resulted in the production of mononucleosomes, which was identified as the optimal concentration (Figure 1).
Overview of ATAC-s...
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To systematically and comprehensively map the chromatin state dynamics underlying the state transition of cystic cholangiocytes, we successfully optimized low-input ChIP-seq and ATAC-seq for a limited number of primary cholangiocytes. Although this study focused on primary cholangiocytes, we are confident that the protocol can also be applied to other high-viability primary cells with limited availability. Similarly, while this study only presents the ChIP-seq analysis results of H3K9ac and H3K9me3, the protocol is equal...
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The authors have no conflicts of interest to declare.
This work was supported by grants from the National Natural Science Foundation of China (82402166 to R.J.).
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| 0.5 M EDTA | Solarbio | E1170 | |
| 1 M Tris-HCl (pH=7.5) | Solarbio | T1140 | |
| 1 M Tris-HCl (pH=8.0) | Solarbio | T1150 | |
| 3 M NaAc | Beyotime | ST342 | |
| 8 M LiCl | Sigma | L7026 | |
| Agarose gel | Biosharp | BS081 | |
| ATAC DNA Library Prep Kit | Vazyme | TD501 | |
| CaCl2 | Sangon Biotech | A501330 | 1 M stock |
| ChIP DNA Library Prep Kit | Vazyme | ND607 | |
| DNA Clean beads | Vazyme | N411 | |
| DNA Extraction Reagent | Solarbio | P1012 | |
| EGTA | Solarbio | E8050 | 100 mM (pH = 8) stock |
| Fluorometer | Invitrogen | Q33226 | |
| Glycogen | Thermo Scientific | R0561 | |
| Hemocytometer | QIUJING | XB.K.25. | |
| Igepal CA-630 | Sigma | I8896 | 10% stock |
| Magnetic separator | Promega | Z5342 | |
| MgCl2 | Sangon Biotech | A100288 | 1.5 M stock |
| MNase | Sigma | N3755 | 0.01 U/µL stock |
| NaCl | Sangon Biotech | A610476 | 5 M stock |
| NP40 | Solarbio | N8030 | |
| Nuclease-free water | Life Technologies | AM9937 | |
| PCR instrument | Applied Biosystems | 4484073 | |
| PCR Purification Kit | QIAGEN | 28106 | |
| Protease Inhibitor | Roche | 04693132001 | |
| Protein G beads | Invitrogen | 10004D | |
| Proteinase K | TransGen | GE201-01 | |
| Rotator | Kylin-Bell | QB-528 | |
| SDS | Solarbio | S8010 | 10% stock |
| Sodium deoxycholate | Sigma | S1827 | |
| Thermomixer comfort | Eppendorf | 5355 | |
| Triton X-100 | Solarbio | T8200 | |
| Tween-20 | Solarbio | T8220 | |
| Software | Citation (PMID)/Company | Version | Website |
| Bowtie2 | 22388286 | 2.3.5.1 | https://github.com/BenLangmead/bowtie2 |
| Deeptools | 27079975 | 3.4.3 | https://deeptools.readthedocs.io/en/latest/ |
| FastQC | 0.12.1 | https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ | |
| IGV | 21221095 | 2.12.3 | https://igv.org/ |
| MACS2 | 18798982 | 2.2.7.1 | https://hbctraining.github.io/Intro-to-ChIPseq/lessons/05_peak_calling_macs.html |
| Miniconda | Anaconda | 4.7.12.1 | https://www.anaconda.com/ |
| MultiQC | 27312411 | 1.23 | https://seqera.io/multiqc/ |
| Picard | 2.27.5 | https://broadinstitute.github.io/picard/ | |
| samtools | 33590861 | 1.6 | https://www.htslib.org/ |
| trim-galore | 0.6.6 | https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ |
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