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Old 06-20-2011, 03:01 PM   #1
Theorbe27
Junior Member
 
Location: Boston

Join Date: Feb 2011
Posts: 3
Question ChIPseq on tissue - eliminating background

Hi,

I am trying to do ChIPseq on tissue for a transcription factor that is expressed in a single cell type within a tissue. Therefore, specific signal is only going to be derived from about 3% of the input chromatin.
By ChIP-qPCR, I have managed to consistently get 15 to 100x enrichment (calculated in both ways, foldchange of specific IP vs IgG IP, and %input candidate region/%input negative control region) for six different candidate genes, while negative control regions were not enriched.
So I went forward to ChIP-seq which turned out somewhat disappointing as there is a lot of non-specific signal in the IP sample that looks similar to an input signal. However, I do get peaks that reflect the enrichment at my candidate genes but these peaks don't look great. I don't know yet how to summarize peak statistics in an appropriate fashion, but when you plot the read count there is like one or two reads in almost any 100bp window, and the count just goes up to like 6-7 reads within a peak. My feeling is that I would be able to get much better peaks if I was not having all those unspecific fragments lingering around and interfering with library amplification.
Has anybody come across a similar problem and would be able to give some suggestions?

Thanks

Theorbe

Here is what I did for this sample:
Dissect tissue, xlink.
Lyse and sonicate to ~300bp on average (>75% of signal smaller than 500bp).
IP (antibody is good and has been giving us excellent results in any application it has been used for including ChIPchip and ChIP-qpCR). Capture Ab with magnetic beads (blocked with BSA and tRNA). Multiple washes, including several high-salt washes with both NaCl and LiCl containing buffers. Elute DNA and clean up with PCI x3.
Pooled six of such ChIP-DNAs and checked for enrichment of best candidate gene by PCR(100fold). Library prep done on a SPRIworks system followed by Illumina sequencing.
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