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A single-cell massively parallel reporter assay detects cell-type-specific gene regulation

Nature Genetics volume 55, pages 346–354 (2023)Cite this article

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Abstract

Massively parallel reporter gene assays are key tools in regulatory genomics but cannot be used to identify cell-type-specific regulatory elements without performing assays serially across different cell types. To address this problem, we developed a single-cell massively parallel reporter assay (scMPRA) to measure the activity of libraries of cis-regulatory sequences (CRSs) across multiple cell types simultaneously. We assayed a library of core promoters in a mixture of HEK293 and K562 cells and showed that scMPRA is a reproducible, highly parallel, single-cell reporter gene assay that detects cell-type-specific cis-regulatory activity. We then measured a library of promoter variants across multiple cell types in live mouse retinas and showed that subtle genetic variants can produce cell-type-specific effects on cis-regulatory activity. We anticipate that scMPRA will be widely applicable for studying the role of CRSs across diverse cell types.

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Fig. 1: scMPRA measures CRS activity at single-cell resolution.
Fig. 2: scMPRA detects cell-type-specific CRS activity.
Fig. 3: scMPRA detects substate-specific CRS activity.
Fig. 4: scMPRA design and workflow in mouse retina.
Fig. 5: scMPRA recapitulates Gnb3 expression patterns.
Fig. 6: Mutations in the Gnb3 promoter display cell-type-specific effects.

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Data availability

Next-generation sequencing data that support the findings of the study are available in the Gene Expression Omnibus using accession code GSE188639.

Code availability

The code that supports the findings of this study is available in Zenodo52.

References

  1. Schaub, M. A., Boyle, A. P., Kundaje, A., Batzoglou, S. & Snyder, M. Linking disease associations with regulatory information in the human genome. Genome Res. 22, 1748–1759 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Hindorff, L. A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA 106, 9362–9367 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Vattikuti, S., Guo, J. & Chow, C. C. Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 8, e1002637 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Aygün, N. et al. Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis. Am. J. Hum. Genet. 108, 1647–1668 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Nott, A. et al. Brain cell type-specific enhancer–promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Spielmann, M. & Mundlos, S. Looking beyond the genes: the role of non-coding variants in human disease. Hum. Mol. Genet. 25, R157–R165 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ong, C.-T. & Corces, V. G. Enhancer function: new insights into the regulation of tissue-specific gene expression. Nat. Rev. Genet. 12, 283–293 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Arnold, C. D. et al. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science 339, 1074–1077 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Kwasnieski, J. C., Mogno, I., Myers, C. A., Corbo, J. C. & Cohen, B. A. Complex effects of nucleotide variants in a mammalian cis-regulatory element. Proc. Natl Acad. Sci. USA 109, 19498–19503 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ireland, W.T. et al. Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time. eLife 9, e55308 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Patwardhan, R. P. et al. Massively parallel functional dissection of mammalian enhancers in vivo. Nat. Biotechnol. 30, 265–270 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kinney, J. B., Murugan, A., Callan, C. G. Jr & Cox, E. C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA 107, 9158–9163 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Melnikov, A. et al. Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay. Nat. Biotechnol. 30, 271–277 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. White, M. A. et al. A simple grammar defines activating and repressing cis-regulatory elements in photoreceptors. Cell Rep. 17, 1247–1254 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kwasnieski, J. C., Fiore, C., Chaudhari, H. G. & Cohen, B. A. High-throughput functional testing of ENCODE segmentation predictions. Genome Res. 24, 1595–1602 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chaudhari, H. G. & Cohen, B. A. Local sequence features that influence AP-1 cis-regulatory activity. Genome Res. 28, 171–181 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hughes, A. E. O., Myers, C. A. & Corbo, J. C. A massively parallel reporter assay reveals context-dependent activity of homeodomain binding sites in vivo. Genome Res. 28, 1520–1531 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Tewhey, R. et al. Direct identification of hundreds of expression-modulating variants using a multiplexed reporter assay. Cell 165, 1519–1529 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hong, C. K. Y. & Cohen, B. A. Genomic environments scale the activities of diverse core promoters. Genome Res. 32, 85–96 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Haberle, V. et al. Transcriptional cofactors display specificity for distinct types of core promoters. Nature 570, 122–126 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zabidi, M. A. et al. Enhancer-core-promoter specificity separates developmental and housekeeping gene regulation. Nature 518, 556–559 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Moudgil, A. et al. Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells. Cell 182, 992–1008 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Litzenburger, U. M. et al. Single-cell epigenomic variability reveals functional cancer heterogeneity. Genome Biol. 18, 15 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Min, M. & Spencer, S. L. Spontaneously slow-cycling subpopulations of human cells originate from activation of stress-response pathways. PLoS Biol. 17, e3000178 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bonnet, D. & Dick, J. E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730–737 (1997).

    Article  CAS  PubMed  Google Scholar 

  33. Ishikawa, F. et al. Chemotherapy-resistant human AML stem cells home to and engraft within the bone-marrow endosteal region. Nat. Biotechnol. 25, 1315–1321 (2007).

    Article  CAS  PubMed  Google Scholar 

  34. Friedman, R. Z. et al. Information content differentiates enhancers from silencers in mouse photoreceptors. eLife 10, e67403 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Murphy, D. P., Hughes, A. E., Lawrence, K. A., Myers, C. A. & Corbo, J. C. Cis-regulatory basis of sister cell type divergence in the vertebrate retina. eLife 8, e48216 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Reese, B. E. Development of the retina and optic pathway. Vis. Res. 51, 613–632 (2011).

    Article  PubMed  Google Scholar 

  38. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bryant, D. H. et al. Deep diversification of an AAV capsid protein by machine learning. Nat. Biotechnol. 39, 691–696 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Chan, Y. K. et al. Engineering adeno-associated viral vectors to evade innate immune and inflammatory responses. Sci. Transl. Med. 13, eabd3438 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Byrne, L. C. et al. In vivo-directed evolution of adeno-associated virus in the primate retina. JCI Insight 5, e135112 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Wang, D., Tai, P. W. L. & Gao, G. Adeno-associated virus vector as a platform for gene therapy delivery. Nat. Rev. Drug Discov. 18, 358–378 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shen, S. Q. et al. Massively parallel cis-regulatory analysis in the mammalian central nervous system. Genome Res. 26, 238–255 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cohen, R. N., van der Aa, M. A. E. M., Macaraeg, N., Lee, A. P. & Szoka, F. C. Jr. Quantification of plasmid DNA copies in the nucleus after lipoplex and polyplex transfection. J. Control. Release 135, 166–174 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hsiau, T. H.-C. et al. The cis-regulatory logic of the mammalian photoreceptor transcriptional network. PLoS One 2, e643 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Montana, C. L., Myers, C. A. & Corbo, J. C. Quantifying the activity of cis-regulatory elements in the mouse retina by explant electroporation. J. Vis. Exp.(52), 2821 (2011).

  47. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Bailey, T. L. & Gribskov, M. Combining evidence using P values: application to sequence homology searches. Bioinformatics 14, 48–54 (1998).

    Article  CAS  PubMed  Google Scholar 

  50. Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Waskom, M. Seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).

    Article  Google Scholar 

  52. Zhao, S. et al. A single-cell massively parallel reporter assay detects cell type specific cis-regulatory activity. https://doi.org/10.5281/zenodo.7338678 (2022).

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Acknowledgements

We thank the members of the Cohen laboratory for their critical feedback on the manuscript. We thank J. Hoisington-Lopez and M. Crosby for assistance with high-throughput sequencing. This work is supported by grants to B.A.C. from the National Institutes of Health (R01 GM140711 and R01 GM092910) and to J.C.C. from the National Institutes of Health (R01 EY030075). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. Siqi Zhao

    Present address: Ginkgo Bioworks, Boston, MA, USA

Authors and Affiliations

  1. Edison Family Center for Systems Biology and Genome Sciences, Washington University School of Medicine, St. Louis, MO, USA

    Siqi Zhao, Clarice K. Y. Hong, David M. Granas, Michael A. White & Barak A. Cohen

  2. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    Siqi Zhao, Clarice K. Y. Hong, David M. Granas, Michael A. White & Barak A. Cohen

  3. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA

    Connie A. Myers & Joseph C. Corbo

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Contributions

S.Z. and B.A.C. conceived and designed the project. All experiments and analyses were performed by S.Z. with technical contributions from C.K.Y. H. and D.M.G., except for the electroporation and culturing of mouse retinas, which was performed by C.A.M. J.C.C and M.A.W provided critical input into the design of the Gnb3 promoter variant library. S.Z., C.K.Y.H. and B.A.C. wrote the manuscript with input and feedback from all authors.

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Correspondence to Barak A. Cohen.

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Competing interests

S.Z. and B.A.C. are inventors on a pending patent filed by Washington University in St. Louis which may encompass the methods, reagents and data disclosed in this manuscript. B.A.C. is on the scientific advisory board of Patch Biosciences. The remaining authors declare no competing interests.

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Nature Genetics thanks Bas van Steensel, Rickard Sandberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 scMPRA measures cell-type specific CRS activity.

(a) UMAP of the single-cell transcriptome from the mixed-cell experiment. 105 out of 3417 cells (3%) are labeled by both K562 and HEK293 cell genes. (b) UMAP of the mixed-cell experiment with cells marked by other representative markers for K562 and HEK293 cell expression. (c, d) Histogram of the number of plasmids (unique cBC-rBC pairs) transfected into K562 cells and HEK293 cells. (e, f) Histogram of the mean number of rBC per cBC (CRS) per cell for K562 cells and HEK293 cells. (g, h) Correlation of bulk MPRA versus scMPRA where only the scMPRA data has been UMI normalized (i, j) Scatterplot of scMPRA reproducibility for housekeeping and developmental promoters in K562 cells and HEK293 cells.

Extended Data Fig. 2 scMPRA measures CRS activity in K562 cell substates.

(a) Reproducibility for mean expression of core promoters in K562 cells. (b) Correlation of bulk and scMPRA (non-UMI corrected) in K562 cells (c) Different dynamics of expression. For UBA52, the promoter is most highly expressed in S phase, whereas for CSF1, the promoter is most highly expressed in G1 phase. For CXCL10, the promoter is expressed evenly through cell cycle (Stars indicate significance from two-sided Wilcoxon rank sum test, *: p < 0.05) (d) Cells no longer cluster together based on cell cycle genes after the effects of the cell cycle are removed.

Extended Data Fig. 3 Robust measurements of Gnb3 promoter library in ex vivo retina.

(a) Expression of marker genes by scRNA-seq used to identify cell types in the retina. (b) Percentage of the total cells recovered represented by each retinal cell type. (c) Plot showing the relationship between the mean activity of a Gnb3 promoter variant in a given cell type (x-axis) and the proportion of cells in which that promoter variant is silent (y-axis). Individual cells in which a given Gnb3 variant is silent are identified as cells with U6-expressed cBC, but no Gnb3-expressed cBC. (d) The correlation between biological replicates (n = 2) is plotted as a function of the number of cells used in the analysis. The bounds of the box represent the upper and lower quartiles respectively, and the center line represents the median. The whiskers extend to the maxima/minima except for points determined to be outliers using a method that is a function of the interquartile range.

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Supplementary Tables

Supplementary Table 1—Mixed-cell experiment expression. Supplementary Table 2—Differential expression of the core promoter library between K562 and HEK293 cells. Supplementary Table 3—K562 cell cycle expression. Supplementary Table 4—K562 cell substate expression. Supplementary Table 5—Gnb3 promoter variant library. Supplementary Table 6—Gnb3 library expression in retina. Supplementary Table 7—Oligos used in this study.

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Zhao, S., Hong, C.K.Y., Myers, C.A. et al. A single-cell massively parallel reporter assay detects cell-type-specific gene regulation. Nat Genet 55, 346–354 (2023). https://doi.org/10.1038/s41588-022-01278-7

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