Chromatin plays a crucial role in gene regulation, and chromatin immunoprecipitation followed by sequencing (ChIP–seq) has been the standard technique for examining protein–DNA interactions across the whole genome. However, it is difficult to obtain epigenomic information from limited numbers of cells by ChIP–seq because of sample loss during chromatin preparation and inefficient immunoprecipitation. In this study, we established an immunoprecipitation-free epigenomic profiling method named chromatin integration labelling (ChIL), which enables the amplification of genomic sequences closely associated with the target molecules before cell lysis. Using ChIL followed by sequencing (ChIL–seq), we reliably detected the distributions of histone modifications and DNA-binding factors in 100–1,000 cells. In addition, ChIL–seq successfully detected genomic regions associated with histone marks at the single-cell level. Thus, ChIL–seq offers an alternative method to ChIP–seq for epigenomic profiling using small numbers of cells, in particular, those attached to culture plates and after immunofluorescence.
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Deep-sequencing (ChIP–seq and ChIL–seq) data in this study have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE115047. Previously published ChIP–seq, ATAC-seq and mRNA-seq data reanalysed in this work are available under accession codes GSE36023 (ChIP–seq C2C12 cells; H3K4me3 and H3K27me3), GSE65493 (ChIP–seq C2C12 cells; H3K9me3), GSE36024 (ChIP–seq C2C12 cells; CTCF and MyoD), GSE37525 (ChIP–seq C2C12 cells; H3K27ac), GSE89977 (ChIP–seq C2C12 cells; H3K4me3, H3K27ac and H3K27me3), GSE75169 (ChIP–seq MCF-7 cells; H3K4me3; ChIP–seq MDA-MB-231cells; H3K4me3), GSE63523 (ULI-NChIP TT2 cells; H3K4me3 for 5K, 10K and 100K cells)7, GSE74359 (Mint-ChIP K562 cells; H3K4me3 for 500, 1K, 10K and 100K cells)14, GSE72784 (Micro-ChIP E14 cells; H3K4me3 for 500, 1K, 10K and 100K cells)13, GSE71434 (STAR-ChIP R1 cells; H3K4me3 for 200 and 5K cells)15 and GSE104389 (ATAC-seq and mRNA-seq for C2C12 growth cells). Source data for Figs. 1e, 2a,b, 3c, 4d–f and 6c and Supplementary Figs. 1g, 3a, 4a,c,e, 5b and 6a,c have been provided as Supplementary Table 7. All other data supporting the findings of this work are available from the corresponding authors upon reasonable request.
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We are grateful to the staff of Ohkawa Lab and S. Sekine (Waseda University, Tokyo, Japan) for technical supports, and Y. Sato (Tokyo Tech, Yokohama, Japan) for discussion and drawing the ChIL scheme illustrations. We also thank the Advanced Computational Scientific Program of the Research Institute for Information Technology, Kyushu University and the National Institute of Genetics, for providing high-performance computing resources. This work was in part supported by MEXT/JSPS KAKENHI (JP25116010, JP17H03608, JP17K19356, JP18H04802 and JP18H05527 to Y.O., JP16H01219, JP15K18457 and JP18K19432 to A.H., JP16K18479, JP16H01577, JP16H01550 and JP18H04904 to K.M., JP25116002, JP17H01408 and JP18H05534 to H.Kurumizaka and JP25116005, JP26291071, JP17H01417 and JP18H05527 to H.Kimura), JST CREST (JPMJCR16G1 to Y.O., H.Kurumizaka, and H.Kimura), the Platform for Drug Discovery, Informatics, and Structural Life Science, and the Platform Project for Supporting Drug Discovery and Life Science Research from the Japan Agency for Medical Research and Development (to K.S., H.Kimura and H.Kurumizaka). H.Kurumizaka was also supported by the Waseda Research Institute for Science and Engineering and by programs of Waseda University.