All tags Epigenetics Going low in ChIP

Going low in ChIP

Novel strategies for chromatin immunoprecipitation (ChIP) with low input samples.

Standard ChIP workflows require a starting sample quantity of around 106 to 107 cells, below which the assay is hindered by high background binding, poor enrichment efficiencies and loss of enriched library complexity.

Improving enrichment efficiency and sample loss

To counter this, several adjustments to the ChIP workflow have been proposed for low input samples to increase enrichment efficiency1 and minimize sample loss2​.

  • The quality and properties of the sample itself are important considerations, and often the starting material is formalin-fixed paraffin embedded (FFPE) in which over-cross-linking can cause problems. Methods to extract soluble chromatin from FFPE samples have been developed3.
  • The many variables that affect the kinetics of the immunoprecipitation with low concentration antigen can be optimized, including buffer pH, ionic strength and time of incubation4.
  • Micrococcal nuclease (MNase) is an endo-exonuclease that digests DNA until it meets some obstruction, such as a nucleosome or bound protein. MNase can be used instead of, or in addition to limited sonication to shear the cross-linked DNA and avoid the broad fragment size distribution that hinders low-input ChIP5.
  • While bacterial DNA is sometimes used as a blocking agent for standard ChIP, it is not advised for low-input ChIP as it carries through the assay and confounds data analysis. Other blocking agents such as inert proteins or mRNA can reduce background binding in low-input ChIP, without contaminating the data2.
  • Miniaturization of the assay into microwell formats facilitates automation and increases concentration of the antigen (target transcription factor) during the IP workflow –  this avoids the “dilution effect” of low antigen concentrations that favor dissociation of the antibody:antigen complex4 and decrease the efficiency of ChIP.
  • Single-tube assay formats and the use of magnetic or SPRI bead purification rather than phenol-based extraction after each assay step maximizes sample retention.
  • Often overlooked steps are the immobilization of antibody and washes to remove non-antibody bound material. Standard protocols use Protein A/G for this purpose. Alternatives have suggested the use of epitope tagged proteins6, but these run the risk of over-expression and the introduction of artefacts.

    ​The Abcam high-sensitivity ChIP assay employs a unique chimeric protein to capture the antibody-bound protein:DNA complex and this offers significant advantage: the capture protein is smaller than Protein A or G and is coated at high density on the surface of microtiter plate wells, providing a much higher number of IgG immobilization sites in a smaller area, which in turn ensures efficiency and concentration of eluted DNA. In addition, the chimeric capture protein shows superior stability across a wider range of pH and salt concentrations, which allows higher stringency wash conditions to be used.

Platform choice and downstream sample processing

In addition to the assay itself, the choice and optimization of downstream processing (ie sequencing, array, or PCR) and bioinformatic analysis are also important.

See also Advances in ChIP-seq analysis with small samples 

  • The choice of detection platform will impact the sensitivity of the assay. ChIP-sequencing (ChIP-seq) has become the gold standard platform for high sensitivity ChIP, because it gives consistently lower noise than ChIP-on-chip7
  • The most common issues in low-input ChIP-seq are high numbers of unmappable reads and PCR duplicates, and poor library complexity. While the latter will be helped by maximizing the efficiency of ChIP enrichment as described above, library preparation for sequencing must also be optimized for low input samples, key steps being the optimization of adapter ligation 8,9 and avoidance of amplification-derived error and bias.
  • Bioinformatic workflows should be adapted to take into account likely process-derived biases in the data10​​​.

​The Abcam high-sensitivity ChIP assay

Embarking on ChIP with low sample input quantities can be a daunting prospect. Our high-sensitivity ChIP kit (ab185913) has been developed specifically for this application:

  • Successful ChIP starting with just 2 x 103 cells or 0.5 mg tissue
  • Relative enrichment factors >500x
  • Rapid, 5-hour protocol from cells/tissue to enriched DNA
  • Microplate assay format for flexibility in sample throughput and automation (can be used in single-well, 8-well strip or 96-well plate format)
Successful ChIP starting with just 2 x 103 cells or 0.5 mg tissue
Relative enrichment factors >500x
Rapid, 5-hour protocol from cells/tissue to enriched DNA
Microplate assay format for flexibility in sample throughput and automation (can be used in single-well, 8-well strip or 96-well plate format)

Find more ChIP resources


1. Mao. Accounting for immunoprecipitation inefficiences in the statistical analysis of ChIP-Seq data. BMC Bionformatics. 14, 169 (2013).

2. Dirks, R. Genome-wide epigenomic profiling for biomarker discovery. Clinical Epigenetics. 8, 122. (2016).

3. Cejas, P. Chromatin Immunoprecipitation from fixed clinical tissues reveals tumor-specific enhancer profiles. Nature Medicine. 22, 685. (2016).

4. Reverberi, R. Factors affecting the antigen-antibody reaction. Blood transfusion. 5, 227. (2007).

5. Gilfillian, G. Limitations and possibilities of low cell number CHIP-SEQ. BMC Genomcis. 13, 645. (2012).

6. Xiong, X. A scalable epitope tagging approach for high throughput ChIP-Seq analysis. ACS Synth biol . (2017, Feb 19).

7. ENCODE. (n.d.). ENCODE Platform Comparison. Retrieved from

8. Schmidl, C. ChIPmentation: fast, robust, low-input ChIP-Seq for histones and transcription factors. Nature Methods. 12, 963. (2015).

9. Bolduc, N. Preparation of low-input and ligation-free librarIes using template-switching technology. In Current protocols in molecular biology (Vol. Unit 7.26). Wiley & Sons. (2016).

10. Kidder, B. ChIP-Seq: Technical considerations for obtaining high quality data. Nature Immunology. 12, 918. (2011).

11. Stelloo, S. Androgen receptor profiling predicts prostate cancer outcome. EMBO Mol Med. 7, 1450. (2015).

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