核酸酶靶向切割和释放 (CUT&RUN)技术是由Steven henikoff博士团队开发的一种染色质图谱分析方法，基于Ulrich Laemmli博士的染色质免疫切割技术 (ChIC)，融合蛋白A与微球菌核酸酶 (pA-MNase)，选择性原位切割与抗体结合的染色质。在CUT&RUN中，细胞或细胞核固定化在固相载体上，从溶液中分离出pAG-MNase裂解的DNA片段。该方法与二代测序(NGS)兼容，可提供高质量的组蛋白翻译后修饰(PTMs)和染色质相关蛋白(如转录因子；Figure 1)。

FIGURE 1 Overview of the CUTANA™ CUT&RUN protocol.

ChIP-seq是组蛋白PTMs和染色质相关蛋白全基因组定位的主要方法。在这种方法中，染色质通过超声或酶消化破碎，然后免疫沉淀目标特异性片段。尽管进行优化，但ChIP-seq需要大量细胞(通常为105 - 106个细胞)而且需要深度测序input 染色质与免疫沉淀物质(通常为 >3000万 reads/次)来从背景中解析信号。

ChIC和CUT&RUN通过将基因组片段靶向释放到溶液中，彻底改变了染色质调控。通过这一创新，背景显著减少，允许使用少量细胞且每个反应仅需300 - 800万reads/次对组蛋白PTMs和染色质相关蛋白进行高分辨率基因组图谱的绘制(Figure 2)。简化的工作流程和节省的成本使ChIC/CUT&RUN适用于高通量研究表观遗传生物学。

FIGURE 2 Representative genome browser tracks show CUTANA™ CUT&RUN results using 500,000 K562   cells.  Clear peaks with the expected distribution profile are observed using 3-8 million sequencing  reads per reaction for a variety of epigenetic targets, including histone PTMs (H3K4me3, H3K27me3,  H3K27ac), transcription factors (CTCF), epigenetic reader proteins (BRD4), writer enzymes (MLL1),   and chromatin remodelers (SMARCA4).  Rabbit IgG antibody shown as a negative control (top track).

CUTANA™ChIC/CUT&RUN试剂盒包含48个反应的材料，专为多通道移液而设计，以实现CUT&RUN的高通量优势。该试剂盒包括阳性(H3K4me3)和阴性(Rabbit IgG)对照抗体，以及SNAP-CUTANA™ K-MetStat Panel (16个DNA条形码设计核小体携带广泛研究的赖氨酸甲基化PTMs)的分装分量。K-MetStat Panel加入到对照反应中，直接监测实验成功与否并帮助排除故障。此外，在pAG-MNase切割后，将剪切的E. coli DNA添加到所有反应中，以控制文库构建并使NGS标准化。该试剂盒与细胞和细胞核兼容，包括冻存和交联样品(Figure 3)。

FIGURE 3 Heatmaps show CUT&RUN signal (red) and background (blue) of H3K4me3-enriched regions flanking annotated transcription start sites (TSS, +/- 2 kb). Gene rows are aligned across conditions, showing that genome-wide enrichment is preserved across sample types.

尽管建议从500,000个细胞开始，但只需使用5,000个细胞即可生成可比较的数据(Figure 4)。对照组的加入以及与不同靶类型、样本和低细胞数的兼容性，使该试剂盒成为染色质图谱实验的首选解决方案。                                 

FIGURE 4 Representative genome browser tracks for H3K4me3 (low abundance target) and H3K27me3  (high abundance target) CUT&RUN experiments using decreasing amounts of K562 cells. At 5,000   cells, data quality is largely indistinguishable from standard conditions (500,000 cells).

保存条件

OPEN KIT IMMEDIATELY and store components at room temperature, 4℃, and -20℃ as indicated (see User Manual corresponding to Kit Version 3). Stable for 6 months upon date of receipt.

数据示例

FIGURE 1： CUT&RUN DNA fragment size  distribution analysis.  CUT&RUN was performed  as described above.   Library DNA was analyzed  by Agilent Tapestation® .  This analysis confirmed  that mononucleosomes  were predominantly  enriched in CUT&RUN (~300 bp peaks represent 150 bp nucleosomes + sequencing  adapters).

FIGURE 2： SNAP-CUTANA™ K-MetStat Spikein Controls. DNA-barcoded designer  nucleosomes (dNucs)  representing 16 K-methyl PTMs: mono-, di-, and tri-methylation at H3K4, H3K9, H3K27, H3K36,  and H4K20, as well as  unmodified control, were spiked into CUT&RUN  reactions prior to the  addition of antibodies (IgG, H3K4me3). Spike-in barcodes were counted and  normalized from  raw fastq files using the shell  script and analysis sheet available at  epicypher.com/19-1002.  Barcodes for IgG (top;  normalized to total reads) and H3K4me3 (bottom; normalized to on-target)  antibodies are  shown. The spike-ins confirmed optimal  experimental conditions (H3K4me3  antibody  specifically recovered the target dNuc, while IgG  showed no preferential enrichment).

FIGURE 3： CUT&RUN genome-wide heatmaps. CUT&RUN was performed as described above. Peaks were  called with MACS2. Heatmaps show  two replicates (“Rep”) of IgG (left) and H3K4me3 (right) kit  control antibodies in aligned rows  ranked by intensity (top to bottom) and colored  such that red  indicates high localized enrichment  and blue denotes background signal.

FIGURE 4： Representative gene browser tracks. CUT&RUN was performed as described above. A representative  168 kb window at the TRMT2A gene is shown for two replicates (“Rep”) of IgG and H3K4me3 kit control   antibodies. Representative tracks are also  shown for antibodies to H3K27me3 and the  transcription  factor CTCF. The CUT&RUN kit  produced the expected genomic distribution for  each target. Images  were generated using the Integrative Genomics Viewer (IGV, Broad Institute).

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