Limitations and possibilities of low cell number ChIP-seq
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE42147 | GEO | 2012/12/03
SECONDARY ACCESSION(S): PRJNA179207
REPOSITORIES: GEO
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Project description:ChIP-seq experiments using low numbers of input cells, scaled down to the point where data quality is unnacceptably compromised, reveals limits of the technique. Two-part ChIPseq study using native chromatin (non-crosslinked) generated with MNase from human CD4+ cells. Part 1: Previously published protocol (Barski et al, 2007, Cell 129:823, hereafter called "Benchmark") used with 2x10e7 cells / ChIP with H3K4me3 antibody, compared to modified method ("new") with decreasing input cell numbers spanning 1000-fold range from 2x10e7 cells / IP to 2x10e4 cells/ IP. Part 2: reproducibility of new method studied using triplicate samples at lowest two cell numbers tested: 1x10e5 and 2x10e4 / ChIP, using H3K4me3 and H3K27me3 antisera.
2012-12-03 | E-GEOD-42147 | biostudies-arrayexpress
Project description: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.
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