Search

Background ChIP-seq is highly utilized for mapping histone modifications that are informative about gene regulation and genome annotations. For example, applying ChIP-seq to histone modifications such as H3K4me1 has facilitated generating epigenomic maps of putative enhancers. This powerful technology, however, is limited in its application by the large number of cells required. ChIP-seq involves extensive manipulation of sample material and multiple reactions with limited quality control at each step, therefore, scaling down the number of cells required has proven challenging. Recently, several methods have been proposed to overcome this limit but most of these methods require extensive optimization to tailor the protocol to the specific antibody used or number of cells being profiled. Results Here we describe a robust, yet facile method, which we named carrier ChIP-seq (cChIP-seq), for use on limited cell amounts. cChIP-seq employs a DNA-free histone carrier in order to maintain the working ChIP reaction scale, removing the need to tailor reactions to specific amounts of cells or histone modifications to be assayed. We have applied our method to three different histone modifications, H3K4me3, H3K4me1 and H3K27me3 in the K562 cell line, and H3K4me1 in H1 hESCs. We successfully obtained epigenomic maps for these histone modifications starting with as few as 10,000 cells. We compared cChIP-seq data to data generated as part of the ENCODE project. ENCODE data are the reference standard in the field and have been generated starting from tens of million of cells. Our results show that cChIP-seq successfully recapitulates bulk data. Furthermore, we showed that the differences observed between small-scale ChIP-seq data and ENCODE data are largely to be due to lab-to-lab variability rather than operating on a reduced scale. Conclusions Data generated using cChIP-seq are equivalent to reference epigenomic maps from three orders of magnitude more cells. Our method offers a robust and straightforward approach to scale down ChIP-seq to as low as 10,000 cells. The underlying principle of our strategy makes it suitable for being applied to a vast range of chromatin modifications without requiring expensive optimization. Furthermore, our strategy of a DNA-free carrier can be adapted to most ChIP-seq protocols. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2285-7) contains supplementary material, which is available to authorized users.

Background:Discovering patient subtypes and molecular drivers of a subtype are difficult and driving problems underlying most modern disease expression studies collected across patient populations. Expression patterns conserved across multiple expression datasets from independent disease studies are likely to represent important molecular events underlying the disease.Methods:We present the INSPIRE (INferring Shared modules from multiPle gene expREssion datasets) method to infer highly coherent and robust modules of co-expressed genes and the dependencies among the modules from multiple expression datasets. Focusing on inferring modules and their dependencies conserved across multiple expression datasets is important for several reasons. First, using multiple datasets will increase the power to detect robust and relevant patterns (modules and dependencies among modules). Second, INSPIRE enables the use of multiple datasets that contain different sets of genes due to, e.g., the difference in microarray platforms. Many methods designed for expression data analysis cannot integrate multiple datasets with variable discrepancy to infer a single combined model, whereas INSPIRE can naturally model the dependencies among the modules even when a large proportion of genes are not observed on a certain platform.Results:We evaluated INSPIRE on synthetically generated datasets with known underlying network structure among modules, and gene expression datasets from multiple ovarian cancer studies. We show that the model learned by INSPIRE can explain unseen data better and can reveal prior knowledge on gene functions more accurately than alternative methods. We demonstrate that applying INSPIRE to nine ovarian cancer datasets leads to the identification of a new marker and potential molecular driver of tumor-associated stroma - HOPX. We also demonstrate that the HOPXmodule strongly overlaps with the genes defining the mesenchymal patient subtype identified in The Cancer Genome Atlas (TCGA) ovarian cancer data. We provide evidence for a previously unknown molecular basis of tumor resectability efficacy involving tumor-associated mesenchymal stem cells represented by HOPX.Conclusions:INSPIRE extracts a low-dimensional description from multiple gene expression data, which consists of modules and their dependencies. The discovery of a new tumor-associated stroma marker, HOPX, and its module suggests a previously unknown mechanism underlying tumor-associated stroma.