Enhancer elements in the human genome control how genes are expressed in specific cell types and harbor thousands of genetic variants that influence risk for common diseases1,2,3,4. Yet, we still do not know how enhancers regulate specific genes, and we lack general rules to predict enhancer–gene connections across cell types5,6. We developed an experimental approach, CRISPRi-FlowFISH, to perturb enhancers in the genome, and we applied it to test >3,500 potential enhancer–gene connections for 30 genes. We found that a simple activity-by-contact model substantially outperformed previous methods at predicting the complex connections in our CRISPR dataset. This activity-by-contact model allows us to construct genome-wide maps of enhancer–gene connections in a given cell type, on the basis of chromatin state measurements. Together, CRISPRi-FlowFISH and the activity-by-contact model provide a systematic approach to map and predict which enhancers regulate which genes, and will help to interpret the functions of the thousands of disease risk variants in the noncoding genome.
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Genome-wide ABC predictions for the six cell types considered in this study (K562, mESC, GM12878, NCCIT, LNCAP, hepatocytes) and raw counts from CRISPRi-FlowFISH are available on the Open Science Framework at https://osf.io/uhnb4/. ChIP–seq, ATAC-seq, Hi-C and RNA-seq data from this study are available at GSE118912.
Code to calculate the ABC model is available at https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction.
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We thank J. Chen, A. Chow, B. Cleary, C. de Boer, A. Dixit, M. Guttman, R. Herbst, K. Mualim, S. Rao, J. Ray, S. Reilly, R. Tewhey, J. Ulirsch and C. Vockley for discussions, and J. Marshall for assistance with fluorescence microscopy. FACS sorting was performed at the Broad Institute FACS Core by P. Rogers, S. Saldi and C. Otis. This work was supported by funds from the Broad Institute (to E.S.L.) and by a NIH NHGRI grant (no. 1K99HG009917-01 to J.M.E). J.M.E. is supported by the Harvard Society of Fellows. S.R.G. is supported by National Institute of General Medical Sciences (grant no. T32GM007753). E.L.A. was supported by an NSF Physics Frontiers Center Award (no. PHY1427654), the Welch Foundation (grant no. Q-1866), a USDA Agriculture and Food Research Initiative Grant (no. 2017-05741), a NIH 4D Nucleome Grant (no. U01HL130010) and a NIH Encyclopedia of DNA Elements Mapping Center Award (no. UM1HG009375).
E.S.L. serves on the Board of Directors for Codiak BioSciences and Neon Therapeutics, and serves on the Scientific Advisory Board of F-Prime Capital Partners and Third Rock Ventures; he is also affiliated with several nonprofit organizations including serving on the Board of Directors of the Innocence Project, Count Me In and Biden Cancer Initiative, and the Board of Trustees for the Parker Institute for Cancer Immunotherapy. He has served, and continues to serve, on various federal advisory committees. C.P.F., E.S.L. and J.M.E. are inventors on a patent application (no. WO2018064208A1) filed by the Broad Institute related to this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, K562 cells labeled with FlowFISH probesets against RPL13A (control gene) and GATA1 (gene of interest) imaged by fluorescence microscopy. b, Histograms of FlowFISH signal (arbitrary units of fluorescence) for GATA1 (left) and RPL13A (right) in unlabeled K562s (red), K562s stained for GATA1 expressing a gRNA against the GATA1-TSS (orange), or a non-targeting Ctrl gRNA (blue). Results typical of cells across 2 independent samples (a,b). c, Scatterplot of FlowFISH fluorescent signal for RPL13A versus GATA1. d, Cells in c with cells unstained for RPL13A (below dotted line in c) removed and using the color compensation tool to reduce the correlation between the control gene and gene of interest (see Methods). e, Binning strategy for sorting FlowFISH-labeled cells into 6 bins each containing 10% of the cells. Typical results from 3 independent GATA1 CRISPRi-FlowFISH screens (c-e). f, Effect on gene expression as measured by CRISPRi-FlowFISH (dark grey) and RT-qPCR (light grey). Error bars: 95% confidence intervals for the mean of 2 gRNAs per target, 3505 Ctrl gRNAs for FlowFISH (a random 50 shown), and 6 Ctrl gRNAs for RT-qPCR. n = 3 independent experiments per gRNA for CRISPRi-FlowFISH screens. n = 4 independent samples per gRNA for RT-qPCR. *P < 0.05 in 2-sided t-test versus Ctrl. P-values, test statistics, confidence intervals, effect sizes, and degrees of freedom are available in Supplementary Table 3. g, Counts in each of the 6 bins for single gRNAs targeting the GATA1 TSS, two GATA1 enhancers (DE1 and DE2) identified in Fulco et al., and representative negative controls (Ctrl).
a, Cumulative distribution plot of the number of gRNAs in each tested candidate element. b, Cumulative distribution plot of the width of each tested candidate element. c, Correlation between independent CRISPRi-FlowFISH screens for GATA1. Red points denote elements significantly affecting expression. Pearson R = 0.94 for significant elements, 0.37 for all elements. d, Quantile-quantile plot for GATA1 CRISPRi-FlowFISH screen. Red points denote elements significantly affecting expression. Vertical axis capped at 10-20. e, Pearson correlation between effect on gene expression as measured by CRISPRi-FlowFISH screening and RT-qPCR for 42 E-G pairs tested by both methods. Value is the mean effect of the two gRNAs for each element. f, Pearson correlation between effects on gene expression for all significant E-G pairs measured in biologically independent CRISPRi-FlowFISH screens. P-values, test statistics, confidence intervals, effect sizes, and degrees of freedom for all panels are available in Supplementary Table 3.
a, Precision-recall curves for classifying regulatory DE-G pairs, comparing each of the components of the ABC score. b, Scatterplot of Activity and Contact frequency for each tested DE-G pair. KR-normalized Hi-C contact frequencies are scaled for each gene so that the maximum score of an off-diagonal bin is 100 (see Methods). c, Precision-recall curves comparing different measures of Activity. ActivityFeature1,Feature2 = sqrt(Feature1 RPM x Feature2 RPM). (ABC score corresponds to ActivityDHS,H3K27ac x Contact). d, Precision-recall curves for the ABC model using H3K27ac HiChIP. ABCDHS x H3K27ac Hi-ChIP corresponds to a predictive model whose score is proportional to the DHS signal at the candidate element multiplied by the H3K27ac Hi-ChIP signal between the element and gene promoter (see Supplementary Methods). ABCH3K27ac Hi-ChIP is the same as above but only uses the existence of the DHS peak as opposed to the quantitative signal in the DHS peak. H3K27ac HiChIP HiCCUPS Loops is the HiCCUPS loop calls derived from the H3K27ac HiChIP experiment (see Supplementary Methods). ABC corresponds to ABCsqrt(DHS x H3K27ac) x Hi-C. These results suggest that the ABC score computed using H3K27ac HiChIP data is an effective predictor of regulatory enhancer-gene connections.
Extended Data Fig. 4 Tissue-specific genes have more distal enhancers than ubiquitously expressed genes.
a, Left: Comparison of ABC scores (predicted effect) with observed changes in gene expression upon CRISPR perturbations. Each dot represents one tested DE-G pair where G is a ubiquitously expressed gene. Right: precision-recall curve for ABC score in classifying regulatory DE-G pairs where each G is a ubiquitously expressed gene. b, Same as a for tissue-specific genes. All panels include only the subset of our dataset for which we have CRISPRi tiling data to comprehensively identify all enhancers that regulate each gene (30 genes from this study, 2 from previous studies; see Supplementary Methods).
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Fulco, C.P., Nasser, J., Jones, T.R. et al. Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat Genet 51, 1664–1669 (2019). https://doi.org/10.1038/s41588-019-0538-0
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