核酸酶靶向切割和釋放 (CUT&RUN)技術是由Steven henikoff博士團隊開發的一種染色質圖譜分析方法，基于Ulrich Laemmli博士的染色質免疫切割技術 (ChIC)，融合蛋白A與微球菌核酸酶 (pA-MNase)，選擇性原位切割與抗體結合的染色質。在CUT&RUN中，細胞或細胞核固定化在固相載體上，從溶液中分離出pAG-MNase裂解的DNA片段。該方法與二代測序(NGS)兼容，可提供高質量的組蛋白翻譯后修飾(PTMs)和染色質相關蛋白(如轉錄因子；Figure 1)。

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

ChIP-seq是組蛋白PTMs和染色質相關蛋白全基因組定位的主要方法。在這種方法中，染色質通過超聲或酶消化破碎，然后免疫沉淀目標特異性片段。盡管進行優化，但ChIP-seq需要大量細胞(通常為105 - 106個細胞)而且需要深度測序input 染色質與免疫沉淀物質(通常為 >3000萬 reads/次)來從背景中解析信號。

ChIC和CUT&RUN通過將基因組片段靶向釋放到溶液中，徹-底改變了染色質調控。通過這一創新，背景顯著減少，允許使用少量細胞且每個反應僅需300 - 800萬reads/次對組蛋白PTMs和染色質相關蛋白進行高分辨率基因組圖譜的繪制(Figure 2)。簡化的工作流程和節省的成本使ChIC/CUT&RUN適用于高通量研究表觀遺傳生物學。

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試劑盒包含48個反應的材料，專為多通道移液而設計，以實現CUT&RUN的高通量優勢。該試劑盒包括陽性(H3K4me3)和陰性(Rabbit IgG)對照抗體，以及SNAP-CUTANA™ K-MetStat Panel (16個DNA條形碼設計核小體攜帶廣泛研究的賴氨酸甲基化PTMs)的分裝分量。K-MetStat Panel加入到對照反應中，直接監測實驗成功與否并幫助排除故障。此外，在pAG-MNase切割后，將剪切的E. coli DNA添加到所有反應中，以控制文庫構建并使NGS標準化。該試劑盒與細胞和細胞核兼容，包括凍存和交聯樣品(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.

盡管建議從500,000個細胞開始，但只需使用5,000個細胞即可生成可比較的數據(Figure 4)。對照組的加入以及與不同靶類型、樣本和低細胞數的兼容性，使該試劑盒成為染色質圖譜實驗的首-選解決方案。                                 

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).

保存條件

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.

數據示例

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/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).

訂購詳情

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