Techniques used in genomic profiling have come a long way in the past few decades. Researchers can now combine low-cost sequencing technologies to an upstream workflow such as chromatin immunoprecipitation (ChIP-seq) to produce an extraordinary amount of data in a short period of time. ChIP-seq is an assay designed to measure direct binding of a protein to target genes in an in vivo system. Proteins are cross-linked to the DNA with formaldehyde and the chromatin is digested into small pieces which are then precipitated out with a specific antibody. The proteins are then digested away and the remaining DNA fragments sequenced. This technique allows researchers to ask questions that were previously impossible.
With the ability to directly measure protein-DNA interactions, a researcher can show causal evidence of gene expression. It is one thing to show that a transcription factor is overexpressed in a certain cancer type, but showing how that factor is now physically bound to a new genomic location takes the data to the next level. In fact, more and more journals are requiring researchers to show proof of direct binding. This is a major reason why genomic profiling is not just relegated to epigenetics lab, and that many academic institutions have their own sequencing cores.
At their most basic, ChIP-seq experiments can be designed simply to detect levels of histone modifications or map transcription factor binding in a cell or tissue type. By looking at multiple variables at once, whether that be the mapping multiple histone modifications or transcription factors, ChIP-seq can be extremely versatile. For example, Chip-Seq has enabled researchers to predict active, inactive, and bivalent genes using the histone code by measuring H3K4me3 and H3K27me3 levels. Enhancer elements have been mapped by measuring H3K27ac levels, and even chromatin architecture can be inferred by mapping the chromatin insulator CTCF. By using antibodies against the transcription factors OCT-4, Sox2, and Nanog in ChIP assays, Boyer et al (2005) were able to identify many target genes co-occupied by all factors. They used this data to postulate what is now a core tenet of stem cell biology: these factors form an autoregulatory loop that is responsible for pluripotency and self-renewal.
ChIP-seq assays can also be useful as a comparison tool. Different cell types, tissues, or disease states can be mapped to determine the underlying transcriptional circuitry. This type of data has been instrumental in discovering mechanisms in key cancer drivers. Zanconato et al (2015) were able to map oncogenic transcription factors YAP + TAZ in breast cancer cells and found a previously undiscovered function of YAP/TAZ through its association with enhancers. Additionally, by comparing across wildtype and siRNA treated cells, they found a key link between YAP/TAZ-mediated tumor growth and the AP-1 factors.
ChIP and ChIP-seq, however are not without limitations. In many cases, you need a massive amount of cells, making the assays harder to perform on patient samples and primary cells. This is due to the fact the immunoprecipitation step is extremely inefficient and that the chromatin needs to be cross-linked and fragmented beforehand. A new technique developed by Steve Henikoff’s lab, termed CUT&RUN (Cleavage Under Target & Release Using Nuclease), performs the same function of a ChIP-seq experiment, but does it in situ, relying on a targeted antibody-based fragmentation of the genome. In this assay there is no cross-linking or precipitation steps, which enables a researcher to use a far fewer cells in their experiment and see less background in the sequencing assay.
The impact of using fewer cells could lead to amazing advancements in research and medicine. For stem cell biologists, genomic profiling of IPS cells has been extremely challenging as getting the amount of cells required for ChIP-seq experiments would require an incredible amount of time and effort. In immunology, there are lingering questions on how epigenetics affects T-cell exhaustion. CUT&RUN could allow studies to maximize the limited supply of isolated immune cells and compare the genomic landscape across cell types. Individual patient tumor could be mapped to determine key transcription factor activity and to help enable personalized medicine. The prospects are exciting, and experiments are seemingly only limited by one’s imagination (and one’s bioinformatics department!).
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