CUT&Tag robustly maps G4s
First, we established G4-CUT&Tag using human chronic myelogenous leukaemia (K562) and osteosarcoma (U2OS) cell lines to demonstrate applicability of the method for both suspension and adherent cell lines. We employed buffers using potassium salts, rather than sodium, to mimic intracellular ionic conditions, since cation coordination with K+ can influence G4 stability16. 100,000 fixed permeabilised cells17 were incubated with a FLAG-tagged G4 structure-specific single-chain variable fragment (BG4)12. Next, a rabbit anti-FLAG secondary antibody followed by an anti-rabbit tertiary antibody were added followed by an adapter-loaded protein A-Tn5 transposome (with FLAG epitope removed, see Supplementary Information). Tagmentation was then performed with magnesium-induced activation of Tn5 to insert mosaic-end adapters at target sites. Tagmented fragments were subsequently enriched by PCR from purified genomic DNA, pooled and sequenced (Fig. 1a).
Characterisation of bulk G4-CUT&Tag. (a) Schematic diagram of G4-CUT&Tag workflow. Fixed permeabilised cells are incubated with the G4 structure-specific BG4 single-chain variable fragment (scFv), followed the secondary antibodies, and then adapter-loaded proteinA-Tn5 to enable the integration of adapters at G4 target loci for next-generation sequencing library preparation. (b) Example genome browser view of G4-CUT&Tag signals and G4 peaks called obtained from 100,000 K562 (red) and 100,000 U2OS (blue) cells with comparisons to published G4-ChIP-seq data21 (pink, light blue respectively), and sites that fold into G4 structures in vitro (called observed quadruplex sequences OQs, orange)20 Light grey shading highlights G4 peaks in the MYC locus. Gene annotations are shown in black below. (c) Hierarchical clustering of the Spearman correlation matrix for G4-CUT&Tag replicates. G4-CUT&Tag was performed on three biological replicates (b1, b2, b3) with two technical replicates (t1, t2) for 100,000 K562 and 100,000 U2OS cells. Spearman correlations between samples were computed at G4 peaks on read coverage normalised to library size. (d) Venn diagram showing the number of G4 peaks and their overlap from 100,000 (100 k), 50,000 (50 k) and 10,000 (10 k) K562 cells.
G4 peaks were defined as local enrichment in G4-CUT&Tag data using the SEACR peak caller18,19 (see Supplementary Information), and showed high overlap (> 60%) with G4 structures identified in vitro in purified human genomic DNA (referred to as observed quadruplex sequences, OQs)20. When compared to G4-ChIP-seq data, > 97% of G4-ChIP-seq peaks were seen in G4-CUT&Tag data in both cell lines21,22 (Fig. 1b and Supplementary Table S1). We observed low background, which is a characteristic of CUT&Tag data6, with the average fraction of reads in G4 peaks being above 40% (Fig. 1b and Supplementary Table S1). Technical and biological replicates showed high reproducibility (Spearman’s rank correlation coefficient, rs > 0.9) within the same cell line (Fig. 1c). Compared to samples incubated with negative-control IgG antibodies, G4-CUT&Tag libraries had a higher library concentration, supporting G4 enrichment via antibody-directed tagmentation (Supplementary Fig. S1). We observed G4s unique to each cell line with 6161 (34%) of K562 and 23,072 (65%) of U2OS G4 peaks. Taken together, our data shows that G4-CUT&Tag is a robust and reproducible technique to probe G4 structures at the genomic scale. Compared to conventional G4-ChIP-seq23, G4-CUT&Tag method does not require sonication and requires a 100-fold less cellular input (100,000 cells for G4-CUT&Tag and 1 × 107 cells for chromatin preparation in G4-ChIP-seq23).
To profile G4s in situations where cell number is limiting, we performed G4-CUT&Tag on 50,000 and 10,000 K562 cells. While the total number of G4 peaks was lower, both cell profiles represented an almost complete subset of the 100,000-cell G4-CUT&Tag landscape (98% for 50,000 and 99% for 10,000 cells respectively), and had high specificity for G4s (> 85% overlap with OQs) (Fig. 1d and Supplementary Table S1).
G4 mapping at the single-cell level
We next sought to deploy G4-CUT&Tag to profile G4s at the single-cell level using single nuclei (sn). We prepared two biological replicates of fixed nuclei from MCF7 breast adenocarcinoma and U2OS osteosarcoma adherent cell lines, as this enabled parallel processing under the same conditions thus allowing for a fair comparison of data obtained from different cell types. Each set of nuclei was subjected to G4-CUT&Tag followed by single nuclei partitioning and barcoding using a 10X Genomics microfluidic platform (Fig. 2a). Using 10X Genomics Cell Ranger software, we obtained G4 profiles of an average of 593 MCF7 and 2,071 U2OS cells with a median of 739 and 939 unique fragments per cell, respectively (Supplementary Table S2). Compared to other studies, albeit in different cell types, we observed a greater number of median unique fragments per cell (~ 650–1200) compared to scCUT&Tag for transcription factors (< 300)7 but similar to that of H3K27me3 (~ 400–6000)8. The tracks of normalised read coverage from aggregated single cells correlate well (rs > 0.8, Supplementary Fig. S2) with that of bulk G4-CUT&Tag from ~ 10,000 cells (Fig. 2b), confirming that snG4-CUT&Tag data recapitulates the observations from bulk G4-CUT&Tag. Also, there is high reproducibility between biological replicates of snG4-CUT&Tag experiment (rs > 0.7, Supplementary Fig. S2). Single-cell G4 peaks (local enrichments called by Cell Ranger from aggregated single-cell profiles), showed good overlap with OQs and ensemble G4-ChIP-seq maps (Supplementary Table S2). These observations confirm the veracity of snG4-CUT&Tag to probe G4 landscapes in individual cells with high specificity and reproducibility.
Characterisation of single-nuclei G4-CUT&Tag. (a) Schematic diagram of single-nuclei G4 CUT&Tag (snG4-CUT&Tag) workflow. In fixed nuclei, G4-CUT&Tag is used to integrate adapters at G4 sites. Single intact nuclei are then partitioned with barcoded gel beads to index tagmented fragments from individual nuclei using a 10X Genomics Chromium platform with Next GEM Single Cell ATAC Reagents Kits (see “Methods”). (b) Two example G4 landscapes in the human genome generated by G4-CUT&Tag. Blue genomic tracks: normalised read coverages of bulk G4-CUT&Tag libraries (50,000 MCF7 cells and 100,000 U2OS cells); red genomic tracks: normalised signals from aggregated snG4-CUT&Tag libraries; dark red tiles: top 50 single nuclei based on the total number of fragments in the genomic region; pink: Cell Ranger peaks called; black: gene annotations. (c) Clustering of snG4-CUT&Tag data reveals two groups in mixed U2OS and MCF7 sample. t-SNE plot showing dimensionality reduced snG4-CUT&Tag data from mixed cell lines with 671 imputed U2OS (blue) and 467 imputed MCF7 (red) cells. (d) Graphical representation of G4 distribution across promoter G4 peaks in single cells. As in (c) for each t-SNE plot, the top cluster represents individual MCF7 cells while the bottom cluster represents individual U2OS cells. Each dot shows example data covering single U2OS or MCF7 cells for the indicated genes. Grey colouring indicates that no G4 is detected in that cell for Cell Ranger-called promoter peak(s) (1000 bases upstream or 100 bases downstream from the TSS) of the specified genes while orange to red shading quantifies the number of G4s (i.e. pA-Tn5 cut sites) detected in a single cell at the promoter peak(s) of the specified gene. Two example genes (CNGA4 and ALDH6A1) are shown where the G4 promoter peaks are observed in common in the two cell lines, and two example genes (CAPN5 and HMGN1) are shown in which the with G4 promoter peaks specific to only one cell line.
snG4-CUT&Tag readily resolves cell identity in a mixed cell population
We next explored whether snG4-CUT&Tag could distinguish cell types based on single-cell G4 profiles. We mixed tagmented MCF7 and U2OS nuclei in a 1:1 ratio and subjected them to single nuclei partitioning and sequencing. Two distinct clusters of cells (n = 467 and 671) were observed after dimensionality reduction using latent sematic indexing followed by graph-based clustering and visualization via t-SNE projection, using the 10X Genomics Cell Ranger ATAC pipeline (Fig. 2c). For the aggregated single-cell profiles of each cluster, through hierarchical clustering with aforementioned bulk G4-CUT&Tag and snG4-CUT&Tag dataset, we imputed the cell identity of the two clusters and successfully resolved them as either MCF7 or U2OS cells (Supplementary Fig. S2).
As many G4s are found in promoters of active and amplified genes3, we next investigated if we could observe any difference between the two clusters based on promoter G4 peaks called by Cell Ranger. For the MCF7 cluster, we found that 28% of the top 50 differentially enriched promoter G4 peaks were associated with genes, such as BCAS1 and BRIP1, showing increased copy number (log2(relative to ploidy + 1) > 1.5, Supplementary Table S3)24. This result from unsupervised single-cell analysis is consistent with earlier observations of G4s in amplified regions in breast cancers3.
snG4-CUT&Tag reveals cell-to-cell variation of G4 formation
We further investigated the cell-to-cell variation of G4 formation within each cluster by quantifying the number of cells displaying a G4 structure at a given genomic locus within each cell type. We counted the number of supporting cells, defined as a cell having at least one unique read identified per G4 peak called by MACS2, for both U2OS and MCF7 clusters independently (Supplementary Fig. S3a,b). We found that G4 peaks with a higher number of supporting cells tended to be common to both cell lines, whereas G4 peaks with fewer supporting cells tend to be cell line-specific (Supplementary Fig. S3a,c). However, data from many more cells may be needed to further validate this observation. Nevertheless, our snG4-CUT&Tag data showed variable frequency of G4s at certain loci within a cell population (Fig. 2d). Such findings provide a snapshot the co-occurrence of G4s in individual cells thus providing information previously unobtainable by ensemble measurements.
Implications of snG4-CUT&Tag findings
This study is the first example that we are aware of that has mapped DNA secondary structure at single-cell level. The separation of cell types by snG4-CUT&Tag demonstrates that the pattern of G4s reflect cellular identity right down to the single-cell level. We observe gene-specific incidence of G4s in sub-populations of cells, previously inaccessible by ensemble measurements. In future, snG4-CUT&Tag will be applied to a variety of sample types and cell states, to provide more insights into the roles of DNA secondary structures in biology. Potential applications of the approach include the determination of cellular heterogeneity in tumour cell populations and also biological changes during developmental processes and disease states. Moreover, we envisage that the incorporation of G4s into single-cell multimodal omics approaches will provide a more complete understanding of how chromatin structure and gene expression are regulated at individual loci25,26.
