Limitations and possibilities of low cell number ChIP-seq

BMC Genomics

Methodology Article

Dag E Undlien¹  Kristina Gervin¹  Robert Lyle²  Gregor D Gilfillan²  Ying Sheng²  Hanne S Hjorthaug²  Timothy Hughes²  Jennifer R Harris³  Tobias Straub⁴ 

[1]Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway

[2]Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

[3]Division of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway

[4]Ludwig Maximilians Universität, Adolf Butenandt Institut, Lehrstuhl für Molekularbiologie, Schillerstraße 44, 80336, München, Germany

关键词: PCR duplicates;    Redundant reads;    HTS;    NGS;    Next generation sequencing;    Micro-ChIP;    N-ChIP;    Native ChIP;    Location analysis;    Histone;   

DOI  :  10.1186/1471-2164-13-645

 received in 2012-04-30, accepted in 2012-11-05,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundChromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-seq) offers high resolution, genome-wide analysis of DNA-protein interactions. However, current standard methods require abundant starting material in the range of 1–20 million cells per immunoprecipitation, and remain a bottleneck to the acquisition of biologically relevant epigenetic data. Using a ChIP-seq protocol optimised for low cell numbers (down to 100,000 cells / IP), we examined the performance of the ChIP-seq technique on a series of decreasing cell numbers.ResultsWe present an enhanced native ChIP-seq method tailored to low cell numbers that represents a 200-fold reduction in input requirements over existing protocols. The protocol was tested over a range of starting cell numbers covering three orders of magnitude, enabling determination of the lower limit of the technique. At low input cell numbers, increased levels of unmapped and duplicate reads reduce the number of unique reads generated, and can drive up sequencing costs and affect sensitivity if ChIP is attempted from too few cells.ConclusionsThe optimised method presented here considerably reduces the input requirements for performing native ChIP-seq. It extends the applicability of the technique to isolated primary cells and rare cell populations (e.g. biobank samples, stem cells), and in many cases will alleviate the need for cell culture and any associated alteration of epigenetic marks. However, this study highlights a challenge inherent to ChIP-seq from low cell numbers: as cell input numbers fall, levels of unmapped sequence reads and PCR-generated duplicate reads rise. We discuss a number of solutions to overcome the effects of reducing cell number that may aid further improvements to ChIP performance.

【 授权许可】

CC BY   
© Gilfillan et al.; licensee BioMed Central Ltd. 2012