Structural and functional properties of mSWI/SNF chromatin remodeling complexes revealed through single-cell perturbation screens

Abstract

The mammalian SWI/SNF (mSWI/SNF or BAF) family of chromatin remodeling complexes play critical roles in regulating DNA accessibility and gene expression. The three final-form subcomplexes-cBAF, PBAF, and ncBAF-are distinct in biochemical componentry, chromatin targeting, and roles in disease; however, the contributions of their constituent subunits to gene expression remain incompletely defined. Here, we performed Perturb-seq-based CRISPR-Cas9 knockout screens targeting mSWI/SNF subunits individually and in select combinations, followed by single-cell RNA-seq and SHARE-seq. We uncovered complex-, module-, and subunit-specific contributions to distinct regulatory networks and defined paralog subunit relationships and shifted subcomplex functions upon perturbations. Synergistic, intra-complex genetic interactions between subunits reveal functional redundancy and modularity. Importantly, single-cell subunit perturbation signatures mapped across bulk primary human tumor expression profiles both mirror and predict cBAF loss-of-function status in cancer. Our findings highlight the utility of Perturb-seq to dissect disease-relevant gene regulatory impacts of heterogeneous, multi-component master regulatory complexes.

Keywords: ATAC-seq; ATP-dependent chromatin remodeling; BAF complex; Perturb-seq; SHARE-Seq; cancer; chromatin accessibility; gene expression; mammalian SWI/SNF complexes; pediatric cancer; rare diseases.

Figure 1.. An integrative, single-cell Perturb-seq approach to define mSWI/SNF regulatory function.

A. Experimental strategy for single mSWI/SNF gene Perturb-Seq in MOLM-13 cells. B-C. Distribution of (B) number of cells with 0–4 distinct guides captured per cell and (C) number of cells recovered per guide across n= 30,891 cells. D. Distribution of expression (x axis) of each gene (y-axis) in cells targeted by its guide (blue) or control cells (gray). Median (+/− 25% quartiles) with whiskers representing additional 1.5 interquartile ranges, and dots as outliers. **** P< 10⁻⁴, ns – non significant, two-sided Mann-Whitney-test with Bonferroni correction. E. Extent (dot color) and significance (dot size, -lop₁₀(B-H FDR), Kolmogorov-Smirnov (KS) test) of differential RNA expression vs. control cells (color bar) for each mSWI/SNF subunit for each subunit gene (columns) in all cells carrying guides targeting a specific subunit gene (rows). See also Figure S1, Table S1, S2.

Figure 2.. Distinct single-cell gene expression outcomes following differential mSWI/SNF subcomplex and paralog subunit perturbations.

A. Spearman correlation coefficient (color bar) between mean profiles of cells with each perturbation. Color boxes, bars: PBAF (red), ncBAF (green), and cBAF/functional core (blue) subunits. B. UMAP embedding of cell profiles (dots), colored by the density of cells with a perturbation specific to each of cBAF/Core (ARID1A, SMARCA4, SMARCB1, SMARCC1, SMARCD2), PBAF (PBRM1, BRD7, ARID2), ncBAF (SMARCD1, BRD9, GLTSCR1), or controls. C. UMAP embedding of cell profiles (dots, as in B) colored by clusters and labeled with perturbations whose cells are enriched in the respective cluster. D. Significance (signed -log₁₀(q-value), Fisher’s exact test) of enrichment (red) or depletion (blue) of cells with each perturbation (columns) in each cluster (rows) from C. E. Subunits (nodes) from the Core BAF (blue), ncBAF (green), PBAF (red) complexes, and peripheral BAF subunits (purple) connected by significant similarity in viability across perturbed cell lines based on Project Achilles (edges, left) or correlation (Spearman’s $\text{ρ}$) in mean expression profiles in Perturb-seq (right, edges). Edge width: rank-normalized Pearson correlation for fitness network, rank-normalized Spearman correlation for Perturb-seq network. F. Top: UMAP embedding of all cell profiles (dots, as in B) colored by the density of cells with each perturbation (red and blue). Top: Significance (-log₁₀(P-value), x axis, Hotelling’s T² test) of difference in mean profiles between cells with perturbation of each paralog and control cells or cells with the other paralog perturbed (y axis). See also Figure S2.

Figure 3.. mSWI/SNF subcomplexes and their constituent subunits regulate distinct gene regulatory programs.

A. Left: Regulatory model coefficients (color bar) for the regulatory impact of each perturbation (row) on the expression of each gene (columns), clustered into programs of genes with similar patterns across perturbations. Right: Mean magnitude (dot color, color bar) and significance (dot size, -(log₁₀ (B-H FDR), KS-test) of changes in activity of each program (columns) in cells with each perturbation (rows) vs. control cells. B. Schematic effect (edges) of sets of perturbations (left) on gene programs labeled by their enriched GO terms (right). C. Shared and distinct genes (C) or biological processes (GO terms) regulated by perturbation in the indicated subcomplex subunits (C) or subcomplexes (D). See also Figure S3, S1.

Figure 4.. Chromatin states and TF regulatory events coupled with mSWI/SNF perturbation-induced expression changes.

A. Schematic for SHARE-seq experiment. B. UMAP embedding of single-cell ATAC-Seq profiles (from SHARE-Seq, dots) colored by density of cells perturbed for different mSWI/SNF subunits or controls (color bar), or by Louvain-based clusters. C. Proportion of single cells (color bar) from each perturbation (rows, clustered) in each chromatin accessibility cluster (columns, as defined in B). D. Change in fragment length distribution between perturbed and control cells (y axis) for reads within (top, proximal) or beyond (bottom, distal) 1kb upstream of TSS. E. Schematic of strategy for identifying subcomplex-specific target sites by combining ChIP-seq and accessibility scores. F. Top: UMAP embedding of scATAC-Seq profiles (from SHARE-Seq, as in B) colored by cBAF- (left), ncBAF- (middle) or PBAF- (right) specific ChromVAR accessibility scores (color bar). Bottom: Distribution of complex-specific accessibility scores in individual cells, computed via ChromVAR (y axis, median +−25% quartiles, with whiskers representing 1.5 interquartile ranges and outliers shown as dots) for cells with different perturbations (x axis). *** P<10⁻³, **** P<10⁻⁴, ns – not significant. Mann-Whitney-Wilcoxon test, Bonferroni adjusted p-values. G. cBAF (x axis), ncBAF (y axis, left) and PBAF (y axis right) accessibility scores (ChromVAR) in individual cells (dots) perturbed by different perturbations (color legend). Top: Spearman correlation coefficient. H. Mean ChromVAR accessibility scores for different genomic regions (left, columns) or motifs significantly differing between any perturbation and control cells (middle, columns) and mean program scores in the SHARE-seq experiment (right, columns) in cells perturbed with different perturbations (rows). I. Spearman correlation coefficient (color bar) across single cells between gene program score profiles (columns) and different TF and chromatin feature accessibility score profiles (rows). CTCF motifs in loop anchors as defined in Rao et al., 2015). J. Summary of changes caused by perturbations to cBAF. K. Principal Components Analysis (PCA) of chromatin feature accessibility (acc.) score profiles and gene program score profiles across single cells. L. Significance (Bonferroni corrected signed -log₁₀(P-value), t-test) of chromatin accessibility changes at TF binding sites (x axis) and of changes in the expression level of the respective TF upon ARID1A knockout (y axis). See also Figure S4.

Figure 5.. Combinatorial Perturb-Seq reveals additive and synergistic roles for mSWI/SNF paralogs.

A. Overview of combinatorial Perturb-seq experiment. B. UMAP embedding of all cell profiles from the combinatorial Perturb-Seq experiment colored by density of cells with single perturbations from each subcomplex/functional group or controls. C. Magnitude (dot color) and significance (dot size, -log₁₀(B-H FDR), KS test) for score changes in cells from each perturbation condition (rows) vs. control cells in each of 18 gene programs (columns) derived from Combo-Perturb-seq. D-F. Top: UMAP embedding of all cell profiles from a combinatorial Perturb-Seq experiment colored by density of cells with specific perturbations. Bottom left: Mean expression of each gene (dot) as observed in the combinatorial perturbation data (y axis) or predicted under an additive linear model from the constituent single perturbations experiment data (x axis). Bottom Right: Linear model coefficients (color bar) for the overall regulatory effect (sum) and its portion attributed to single perturbations or their combination (interaction term) (columns) for each gene (rows). Side color bar: orange: synergistic interactions; gray: additive effect. Black horizontal lines separate interaction classes. G. Spearman correlation coefficient (color bar) between ncBAF single and combinatorial perturbation profiles. See also Figure S5, S6.

Figure 6.. Intra-complex combinatorial perturbations yield assembly-related genetic interactions, but inter-complex combinations are mostly additive.

A. Chart depicting combination perturbations examined. B. PCA of intra-complex combinatorial perturbation pseudo-bulk expression profiles, with vectors representing difference between each single or combinatorial perturbations and control, colored by complex (as in A). C,D.. Top: UMAP embedding of all cell profiles from a combinatorial Perturb-Seq experiment colored by density of cells with specific perturbations. Bottom left: Mean expression of each gene (dot) as observed in the combinatorial perturbation data (y axis) or predicted under an additive linear model from the constituent single perturbations experiment data (x axis). Bottom Right: Linear model coefficients (color bar) for the overall regulatory effect (sum) and its portion attributed to single perturbations or their combination (interaction term) (columns) for each gene (rows). Side color bar: orange: synergistic interactions; gray: additive effect, purple: buffering effect. Black horizontal lines separate interaction classes. E. Magnitude (dot color) and significance (dot size, -log₁₀(B-H FDR), KS-test) of difference in activity scores between perturbed and control cells (columns) for gene sets up- or down-regulated by different subcomplexes (rows). See also Figure S6.

Figure 7.. mSWI/SNF subunit perturbation signatures classify human cancers with subunit disruptions or convergent mechanisms.

A. Schematic of classification strategy. B. Distribution of cBAF loss Perturb-seq signatures scores (y axis, median (+/− 25% quartiles), whiskers represent additional 1.5 interquartile ranges, and dots represent outliers) in cBAF KO or control single cells (x axis, blue) or in bulk RNA-seq profiles of mSWI/SNF perturbation-driven rare cancers (x axis, orange). *** P<10⁻³, one-tailed Welch’s t-test. C. True (y axis) and false (x axis) positive rate for classifying cBAF mutant tumors across all TCGA tumors based on cBAF loss Perturb-seq signatures. D. Distribution of cBAF loss Perturb-Seq signature scores (x-axis) for all (blue) and cBAF mutant (yellow) TCGA tumors. Dashed line: threshold for calling tumors with cBAF loss signatures. E. Significance of enrichment (Benjamini-Hochberg FDR, Fisher’s exact test) of mutation rate (vs. mutational background within tumor class) and proportion (green/white color bar) of mutations in different categories (columns) for genes (rows) with increased mutation rate in tumors with high cBAF loss signature scores but no BAF mutations. F. Significance (y axis, -log₁₀(P-value), one-sided Wilcoxon rank-sum test) of association of TF regulators with tumors with high cBAF loss signature scores but no BAF mutations (x axis, rank ordered by significance). G. Log fold-change in viability (y axis) in response to drugs that specifically reduce viability in BAF mutant cell lines for cell lines with BAF mutations (red), BAF loss-like mutations (pink) and all other cell lines (grey) (* * * q < 0.001, * * 0.001 < q < 0.01, * 0.01 < q < 0.05, t-test). See also Figure S7, Table S3-S5.