Single-molecule imaging combined with single DNA sequencing was also applied to study combinatory histone modifications ( 9). Single-cell ChIP-seq technique (that is, Drop-ChIP) offers insights into cell-to-cell variation but provides only a low number of unique reads per cell (~1000). There have been various strategies developed in recent years to minimize the input of ChIP-seq. Thus, there is a pressing need for profiling histone modifications with both low input and high throughput.Ĭhromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) is the gold standard for mapping in vivo genome-wide histone modifications. Comprehensive large-scale data sets on various histone marks permit accurate description of chromatin states using advanced computational algorithms ( 3). There are more than 100 distinct histone modifications, and tens of these modifications are studied routinely ( 1, 2). This limitation of sample size is often further compounded by the fact that a large number of histone modifications may need to be examined. On the other hand, a homogeneous population of cells extracted from primary tissues is often in very small quantity because of low abundance and tedious isolation. Mapping histone modifications using tissue homogenates with mixed cell types creates ambiguity and confusion in identifying molecular drivers. The genome-wide profile of a specific histone mark is highly specific to a particular cell type. Histone modifications play critical roles in normal development and disease processes by dynamically tuning chromatin conformations and regulating gene expressions. Our cell type–specific data revealed that neuronal and glial fractions exhibited profound epigenomic differences across the two functionally distinct brain regions. We applied the technology to profile epigenomes using nuclei isolated from prefrontal cortex and cerebellum of mouse brain. We demonstrate a simple microfluidic technology, SurfaceChIP-seq, for profiling genome-wide histone modifications using as few as 30 to 100 cells per assay and with up to eight assays running in parallel. Thus, technologies that permit both ultralow input and high throughput are desired. This difficulty is further compounded by the need to profile a myriad of epigenetic marks. The low abundance of some tissue types and the isolation procedure required to generate a homogenous cell population often yield a small quantity of cells for examination. A list of differentially expressed genes (fold change > 4 P < 0.05, t test) with correlated differential histone modification in H3K4me3 or H3K27me3 at promoters across PNeuN+ and CNeuN+ populations.Įxtensive effort is under way to survey the epigenomic landscape of primary ex vivo tissues to establish normal reference data and to discern variation associated with disease. A list of genes with differential H3K27me3 mark at promoters across PNeuN+ and CNeuN+ populations. A list of genes with differential H3K27ac mark at promoters across PNeuN+ and CNeuN+ populations. A list of genes with differential H3K4me3 mark at promoters across PNeuN+ and CNeuN+ populations. Metadata of SurfaceChIP-seq and mRNA-seq. Rank of H3K4me3, H3K27ac, and H3K27me3 signals at promoters plotted against mRNA rank (generated by mRNA-seq). Reproducibility of SurfaceChIP-seq data by eight-channel devices produced in different batches.įig. Fabrication, operation, and setup of the SurfaceChIP microfluidic device.įig. Supplementary material for this article is available at įig. Our cell type-specific data revealed that neuronal and glial fractions exhibited profound epigenomic differences across the two functionally distinct brain regions. Extensive effort is under way to survey the epigenomic landscape of primary ex vivo tissues to establish normal reference data and to discern variation associated with disease.
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