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. 2022 Aug;40(8):1220-1230.
doi: 10.1038/s41587-022-01250-0. Epub 2022 Mar 24.

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Bingjie Zhang #  1   2 , Avi Srivastava #  1   2 , Eleni Mimitou  3 , Tim Stuart  1   2 , Ivan Raimondi  3 , Yuhan Hao  1   2 , Peter Smibert  3 , Rahul Satija  4   5
Affiliations

Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro

Bingjie Zhang et al. Nat Biotechnol. 2022 Aug.

Abstract

Technologies that profile chromatin modifications at single-cell resolution offer enormous promise for functional genomic characterization, but the sparsity of the measurements and integrating multiple binding maps represent substantial challenges. Here we introduce single-cell (sc)CUT&Tag-pro, a multimodal assay for profiling protein-DNA interactions coupled with the abundance of surface proteins in single cells. In addition, we introduce single-cell ChromHMM, which integrates data from multiple experiments to infer and annotate chromatin states based on combinatorial histone modification patterns. We apply these tools to perform an integrated analysis across nine different molecular modalities in circulating human immune cells. We demonstrate how these two approaches can characterize dynamic changes in the function of individual genomic elements across both discrete cell states and continuous developmental trajectories, nominate associated motifs and regulators that establish chromatin states and identify extensive and cell-type-specific regulatory priming. Finally, we demonstrate how our integrated reference can serve as a scaffold to map and improve the interpretation of additional scCUT&Tag datasets.

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Conflict of interest statement

Competing interests:

In the past three years, R.S. has worked as a consultant for Bristol-Myers Squibb, Regeneron, and Kallyope and served as an SAB member for ImmunAI, Resolve Biosciences, Nanostring, and the NYC Pandemic Response Lab. P.S. is co-inventor of a patent related to this work. The other authors declare no competing interests.

Figures

Figure 1:
Figure 1:. scCUT&Tag-pro enables simultaneous profiling of CUT&Tag and protein levels
(A) Schematic of experimental workflow, which is compatible with the 10x Chromium system. (B) Distribution of unique fragments obtained per-cell for six histone modifications, profiled in separate PBMC scCUT&Tag-pro experiments (left to right n = 12,770, 9,575, 10,386, 8,304, 15,609 and 8,232 cells). The center, bounds and whiskers of the boxplot show median, quartiles and data points that lie within 1.5× interquartile range of the lower and upper quartiles, respectively. Data beyond the end of the whiskers are plotted individually. (C) Pseudobulk profiles of scCUT&Tag are well-correlated with bulk CUT&Tag profiles of human PBMC. (D) Tornado plots of genomic regions ordered by chromatin accessibility. We observe no enrichment of H3K27me3 in accessible regions, indicating minimal open chromatin bias. (E) Relationship between the number of cells included in a pseudobulk CUT&Tag profile of human PBMC, and the pearson correlation with a bulk experiment. (F) UMAP visualizations of 12,770 single cells profiled with H3K4me1 scCUT&Tag-pro and clustered on the basis of CUT&Tag profiles, cell surface protein levels, and weighted nearest neighbor (WNN) analysis which combines both modalities. Cluster labels are derived from WNN analysis. (G) Comparing pseudobulk CUT&Tag-pro profiles with ChIP-seq data from ENCODE. Including all cells assigned to each cell type results in pseudobulk tracks that closely mirror ENCODE profiles. However, even when downsampling to 300 cells per cluster, cell type-specific patterns can still be observed
Figure 2:
Figure 2:. Protein measurements facilitate integrated analysis across modalities
(A) Schematic workflow for integrated analysis. Datasets produced by scCUT&Tag-pro, ASAP-seq, and CITE-seq are integrated together on the basis of a shared panel of cell surface protein measurements. (B) Left: UMAP visualization of 230,597 total cells projected onto the reference dataset from (Hao et al, Cell 2021). Right: In addition to a harmonized visualization, cells from all experiments are annotated with a unified set of labels. (C) We learned a unified pseudotime trajectory based on all experiments, representing the CD8 T cell transition from naive to effector states. We observe identical molecular dynamics for naive (CD55), memory (CD103), and effector (CD57) markers across all experiments, demonstrating that integrative analysis accurately identifies cells in matched biological states across experiments. (D) Visualization of nine molecular modalities at the CD8A locus in B cell (B), CD4 T cell (CD4 T), CD8 T cell (CD8 T), dendritic cell (DC), monocyte (Mono) and natural killer cell (NK) groups.
Figure 3:
Figure 3:. scChromHMM annotates chromatin states at single-cell resolution
(A) Chromatin states returned by ChromHMM, which was run on 25 pseudobulk tracks for six histone marks. States can be broadly grouped into five categories. (B) Correlation comparing cell type-specific pseudobulk profiles of H3K4me1 in CD14 monocytes, generated from the original experiment, or from the interpolated values. Each point corresponds to a 200bp genomic window (Supplementary Methods) (C) For each cell type, the interpolated and original profiles are highly correlated and clustered together. (D) scChromHMM outputs at the PAX5 locus. (Top) Pseudobulk profiles for six chromatin marks in three cell types. Yellow bar highlights a 200bp genomic window near the TSS. (Bottom) scChromHMM posterior probabilities representing the annotation for the highlighted window in each cell. The region is uniformly annotated with a promoter state in B cells where PAX5 is transcriptionally active, and as a repressive state in other cell types. (E) Metaplots exhibiting the enrichment of chromatin accessibility and histone modifications at functional regions identified by chromHMM (left) and scChromHMM (right) in CD14 monocytes.
Figure 4:
Figure 4:. Extensive heterogeneity in repressive chromatin encodes cellular identity.
(A) Remodeling of repressive chromatin during CD8 T cell maturation. Heatmap shows the posterior probabilities (repressive state) in single cells for 14,585 genomic loci, as returned by scChromHMM. Cells are ordered by their progression along pseudotime (Figure 2C). (B) ChromVar deviation scores for the TBX21 and LEF1 motifs in single cells, ordered by their progression along pseudotime. We used the scChromHMM-derived posterior probabilities as input to ChromVar, instead of chromatin accessibility levels. (C) Unsupervised analysis of scChromHMM-derived probabilities (repressive state) separates granular cell types. (D) Single-cell correlation matrix based on repressive chromatin at TSS (Supplementary Methods) when using all TSS (left heatmap), or after excluding the top 3,000 transcriptionally variable genes (right heatmap). In each case, the observed correlation structure is fully consistent with cell type labels, suggesting that there is extensive heterogeneity in repressive chromatin even for genes that do not vary transcriptionally. (E) Scatter plot showing average gene expression levels for all genes in CD14 monocytes (x-axis) and other cell types (y-axis). Colored points represent 1,597 loci where we detect changes in repressive chromatin for monocytes (Supplementary Methods). Blue points represent 1,340 loci where we do not detect an accompanying transcriptional change. Red points represent 257 genes where we detect a transcriptional shift. TPM: Transcripts Per Kilobase Million. (F) Four representative examples of individual genes shown as blue points in (E).
Figure 5:
Figure 5:. Supervised mapping of scCUT&Tag datasets
(A) UMAP visualization of 8,362 H3K27me3 scCUT&Tag profiles of human PBMC, from (Wu et al, 2021), based on an unsupervised analysis and clustering. (B) Same cells as in (A), but after mapping to the multimodal reference defined in this paper. Cells are colored by their reference-derived level 2 annotations. (C)-(E) Coverage plots showing the cell type-specific binding patterns of H3K27me3 at three loci. Plots are shown for our dataset (reference), as well as the scCUT&Tag profiles from the query dataset (query, Wu et al, 2021). Cells in the query dataset are grouped by their predicted labels. We observe highly concordant patterns across datasets for all loci, supporting the accuracy of our predictions. Four representative cell types are shown at each locus. TEM: T effector memory.
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