Figure 1 Yingzi Gene H3K27ac CUT&Tag Library Agilent 2100 Quality Inspection Peak Chromatogram                                

Figure 2 Enrichment Heat Maps of Yingzi Gene H3K4me3 and H3K27ac CUT&Tag Signals inGene Promoter Regions

Potential Limitation

Although CUT&Tag technology features many advantages, researchers have still identified some application limitations, such as:

1. Chromatin cross-linking and sonication are necessary steps for detecting protein-DNA interactions, especially for transcription factors with weak or transient binding to DNA or indirect binding to chromatin.

2. Most ChIP-validated antibodies are verified under cross-linking conditions, and their performance may not be as effective under non-cross-linking conditions because the accessibility of protein epitopes may differ from non-cross-linking conditions. Therefore, transitioning from ChIP to CUT&Tag requires confirming the antibody's specificity and sensitivity under non-fixed conditions.

3. Since the CUT&Tag library preparation process significantly differs from that of ChIP-seq, differences between the both need to be considered when conducting joint analysis.

4. Tn5 transposase used in CUT&Tag has a higher affinity for open chromatin regions. Hence, CUT&Tag may be more suitable for analyzing histone modification or transcription factors associated with active transcription regions rather than analyzing silent or heterochromatin-contained regions in the genome.

5. Digestion time and the quantity of pA-Tn5 need to be optimized for each target protein to avoid non-specific tagmentation.

1. CUT&Tag Facilitating the Analysis of Regulatory Effects of Genetic Variations on Human Immune Trait Risk

Background: Most of the genomic variations associated with traits identified through Genome-Wide Association Studies (GWAS) are located in non-coding regions. Consequently, these variations are often believed to influence biological phenotypes through the regulation of gene expression and splicing. However, most trait-associated loci are difficult to support as quantitative trait loci (eQTLs and sQTLs) for gene expression and splicing quantities. In this study, 26,271 eQTLs and 23,121 sQTLs were identified in various immune cell types through GWAS. Compared with the loci selected from immune trait-associated GWAS, it is revealed that approximately 40.4% of eQTLs and sQTLs are co-located with them. To explore the regulatory effects of unexplained eQTLs and sQTLs, researchers obtained H3K27ac CUT&Tag sequencing data from different types of immune cells, including B cells, CD4+ T lymphocytes, CD8+ T lymphocytes, regulatory T lymphocytes, and monocytes, from rheumatoid arthritis (RA) and healthy control groups, and found significant differences in the H3K27ac histone modification maps in different immune environments and disease states in various immune cell types. It was also observed that risk SNPs for RA were significantly enriched in the regulatory regions of immune cells in the synovial fluid of RA, highlighting the critical importance of selecting appropriate tissue and cell types in disease research.

(Figure a) CUT&Tag Experiment Sampling Design Flowchart (RA: Rheumatoid Arthritis; PBMC: Peripheral Blood Mononuclear Cells; SF: Synovial Fluid; FACS: Fluorescence-Activated Cell Sorting; Treg: Regulatory T Lymphocytes).

(Figure b) UMAP nonlinear dimensionality reduction was performed on healthy (H) and disease (RA) samples using 30,00 peaks with the most abundant variation, and the analysis is revealed that the histone modification maps can cluster well by cell type and disease state and the immune cell in the synovial fluid of the disease group significantly differs from PBMC immune cells in the disease group.
(Figure c) Volcano plot illustrating differences in H3K27ac histone modification between synovial fluid from the disease group and PBMC monocytes from the healthy group.

(Figure d and e) H3K27ac histone modification signals at FCLR3 promoter [d] and ETV7 promotor [e] were enhanced in CD4+ T cells from RA synovial fluid compared with RA and healthy PBMC CD4+ T cells, and the regulatory regions overlapped with RA risk SNPs that were unexplained by eQTLs and sQTLs.
(Figure f) Stratified LD score regression analysis indicated that the enrichment level of RA risk SNPs in H3K27ac peaks in RA synovial fluid B cells, CD4+ T lymphocytes, and regulatory T lymphocytes was significantly higher than their enrichment in H3K27ac peaks of immune cells in RA and healthy PBMC and stimulated immune cell ATAC-seq peaks.

2. CUT&Tag Facilitating the Analysis of the Regulatory Mechanisms of H3K36me2 Methyltransferase NSD1 at Active Enhancer Regions

Background: Previous studies in mouse embryonic stem cells have shown that NSD1-mediated H3K36me2 can inhibit the expansion of H3K27me3 (a repressive histone marker), thereby indirectly regulating gene expression. However, the specific mechanism by which NSD1 regulates gene expression remains unclear. In this study, NSD1 knockout in mouse embryonic stem cells revealed a significant correlation between enhanced H3K27ac signaling and reduced H3K36me2 signaling within active enhancers, resulting in upregulation of mesoderm-associated genes. To investigate the above phenomenon from the molecular mechanism, researchers overexpressed NSD1-tagged proteins in mouse embryonic stem cells, and used immunoprecipitation coupled with mass spectrometry technology and functional analysis to identify NSD1's interacting protein, HDAC1 (H3K27ac deacetylase). Therefore, the researchers hypothesized that NSD1 might recruit HDAC1 to cooperatively regulate H3K27ac signaling within active enhancers. This hypothesis was subsequently validated through a combination of CUT&Tag and functional experiments.

(Figure A) NSD1 knockout resulted in a significant negative correlation between changes in H3K27ac and H3K36me2 within active enhancer regions (overlapping with H3K36me2 peaks in wild-type cells).
(Figure B) NSD1 knockout showed no correlation between changes in H3K27ac and H3K27me3 within active enhancer regions (overlapping with H3K36me2 peaks in wild-type cells), suggesting that changes in H3K27ac are unlikely to be caused by changes in H3K27me3.
(Figure C) There were significant interactions between bromine domain proteins and histone acetylation-related proteins with NSD1 proteins.
(Figure D) Western blot validated the interaction between HDAC1 and BRD4 with NSD1.
(Figure E) Enrichment of HDAC1 in a 1.5 kb region upstream and downstream of transcription start sites of genes in different groups. Group 1 includes genes with high H3K27ac and low H3K27me3, while Group 2 includes genes with low H3K27ac and high H3K27me3.
(Figure F) Enrichment of HDAC1 in Active and Repressive Enhancers.
(Figure E) and (Figure F) suggest that, after NSD1 knockout, the enrichment of HDAC1 is significantly reduced in promoters and enhancers compared with the wild type.
(Figure G) Example IGV shows conclusions supporting E and F, where the red background represents intergenic region, and the blue background represents gene promoters and gene regions.
(Figure H) ChIP-qPCR validated HDAC1 enrichment in different classes of promoters and enhancers, with inhibitory Hoxa9 used as a negative control.
(Figure I) NSD1 knockout resulted in a significant positive correlation between changes in HDAC1 and H3K36me2 within active promoter and repressive promoter regions (overlapping with H3K36me2 peaks in wild-type cells)
(Figure J) NSD1 knockout resulted in a significant positive correlation between changes in HDAC1 and H3K36me2 within active enhancer and repressive enhancer regions (overlapping with H3K36me2 peaks in wild-type cells).
Figure I) and (Figure J) suggested a strong correlation between the changes in HDAC1 and H3K36me2 in enhancer and promoter regions. Therefore, the loss of NSD1 may lead to impaired recruitment of HDAC1 to enhancers and promoters, thereby regulating gene expression.

Literature Citation: Fang,Y,Tang,Y,Zhang,Y,Pan,Y,Jia,J,Sun,Z...& Fang, D. (2021). The H3K36me2 methyltransferase NSD1 modulates H3K27ac at active enhancers to safeguard gene expression, Nucleic acids research,49(11),6281-6295.