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Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase

Nature Methods (2025)Cite this article

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Abstract

Genome editing enables sequence-function profiling of endogenous cis-regulatory elements, driving understanding of their mechanisms. However, these approaches lack direct, scalable readouts of chromatin accessibility across long single-molecule chromatin fibers. Here we leverage double-stranded DNA cytidine deaminases to profile chromatin accessibility at endogenous loci of interest through targeted PCR and long-read sequencing, a method we term targeted deaminase-accessible chromatin sequencing (TDAC-seq). With high sequence coverage at targeted loci, TDAC-seq can be integrated with CRISPR perturbations to link genetic edits and their effects on chromatin accessibility on the same single chromatin fiber at single-nucleotide resolution. We employed TDAC-seq to parse CRISPR edits that activate fetal hemoglobin in human CD34+ hematopoietic stem and progenitor cells (HSPCs) during erythroid differentiation as well as in pooled CRISPR and base-editing screens tiling an enhancer controlling the globin locus. We further scaled the method to interrogate 947 variants in a GFI1B-linked enhancer associated with myeloproliferative neoplasm risk in a single pooled CRISPR experiment in CD34+ HSPCs. Together, TDAC-seq enables high-resolution sequence-function mapping of single-molecule chromatin fibers by genome editing.

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Fig. 1: TDAC-seq detects chromatin accessibility across kilobase regions through targeted PCR.
Fig. 2: TDAC-seq measures CRISPR perturbation and chromatin structure from the same single DNA molecule.
Fig. 3: TDAC-seq detects chromatin-accessibility dynamics at the β-globin locus in erythroid-differentiated HSPCs.
Fig. 4: TDAC-seq can measure the impact of pooled CRISPR perturbations on chromatin accessibility in parallel.
Fig. 5: TDAC-seq can measure the impact of pooled ABE perturbations on chromatin accessibility across distinct genotypes.
Fig. 6: TDAC-seq detects different CRISPR perturbations changing GFI1B enhancer accessibility in CD34+ HSPCs.

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Data availability

ATAC-seq and Oxford nanopore sequencing data have been deposited to GSE284031 and GSE284033. Source data are provided with this paper.

Code availability

Custom software for analyzing TDAC-seq data and producing figures are available at github.com/liaulab/TDAC-seq.

References

  1. Wittkopp, P. J. & Kalay, G. Cis-regulatory elements: molecular mechanisms and evolutionary processes underlying divergence. Nat. Rev. Genet. 13, 59–69 (2011).

    Article  PubMed  Google Scholar 

  2. Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Thurman, R. E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wang, Y. et al. Single-molecule long-read sequencing reveals the chromatin basis of gene expression. Genome Res. 29, 1329–1342 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Stergachis, A. B., Debo, B. M., Haugen, E., Churchman, L. S. & Stamatoyannopoulos, J. A. Single-molecule regulatory architectures captured by chromatin fiber sequencing. Science 368, 1449–1454 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Lee, I. et al. Simultaneous profiling of chromatin accessibility and methylation on human cell lines with nanopore sequencing. Nat. Methods 17, 1191–1199 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Shipony, Z. et al. Long-range single-molecule mapping of chromatin accessibility in eukaryotes. Nat. Methods 17, 319–327 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Abdulhay, N. J. et al. Massively multiplex single-molecule oligonucleosome footprinting. eLife 9, e59404 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Battaglia, S. et al. Long-range phasing of dynamic, tissue-specific and allele-specific regulatory elements. Nat. Genet. 54, 1504–1513 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Doughty, B. R. et al. Single-molecule states link transcription factor binding to gene expression. Nature 636, 745–754 (2024).

    Article  CAS  PubMed  Google Scholar 

  12. Rubin, A. J. et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell 176, 361–376 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Canver, M. C. et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis. Nature 527, 192–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Martin-Rufino, J. D. et al. Massively parallel base editing to map variant effects in human hematopoiesis. Cell 186, 2456–2474 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Piel, F. B., Steinberg, M. H. & Rees, D. C. Sickle cell disease. N. Engl. J. Med. 377, 305 (2017).

    PubMed  Google Scholar 

  16. Ye, L. et al. Genome editing using CRISPR–Cas9 to create the HPFH genotype in HSPCs: an approach for treating sickle cell disease and β-thalassemia. Proc. Natl Acad. Sci. USA 113, 10661–10665 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sharma, A. et al. CRISPR–Cas9 editing of the HBG1 and HBG2 promoters to treat sickle cell disease. N. Engl. J. Med. 389, 820–832 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bohaczuk, S. C. et al. Resolving the chromatin impact of mosaic variants with targeted Fiber-seq. Genome Res. 34, 2269–2278 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mok, B. Y. et al. A bacterial cytidine deaminase toxin enables CRISPR-free mitochondrial base editing. Nature 583, 631–637 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gallagher, L. A. et al. Genome-wide protein–DNA interaction site mapping in bacteria using a double-stranded DNA-specific cytosine deaminase. Nat. Microbiol. 7, 844–855 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Vaisvila, R. et al. Discovery of cytosine deaminases enables base-resolution methylome mapping using a single enzyme. Mol. Cell 84, 854–866.e7 (2024).

    Article  CAS  PubMed  Google Scholar 

  22. Mok, B. Y. et al. CRISPR-free base editors with enhanced activity and expanded targeting scope in mitochondrial and nuclear DNA. Nat. Biotechnol. 40, 1378–1387 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Helbo, A. S., Lay, F. D., Jones, P. A., Liang, G. & Grønbæk, K. Nucleosome positioning and NDR structure at RNA polymerase III promoters. Sci. Rep. 7, 41947 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. He, R. et al. Genome-wide single-cell and single-molecule footprinting of transcription factors with deaminase. Proc. Natl Acad. Sci. USA 121, e2423270121 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hu, Y. et al. Multiscale footprints reveal the organization of cis-regulatory elements. Nature 638, 779–786 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Luger, K., Mäder, A. W., Richmond, R. K., Sargent, D. F. & Richmond, T. J. Crystal structure of the nucleosome core particle at 2.8 Å resolution. Nature 389, 251–260 (1997).

    Article  CAS  PubMed  Google Scholar 

  27. Kornberg, R. D. & Lorch, Y. Twenty-five years of the nucleosome, fundamental particle of the eukaryote chromosome. Cell 98, 285–294 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Vierstra, J. et al. Global reference mapping of human transcription factor footprints. Nature 583, 729–736 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  30. Luo, H. et al. CTCF boundary remodels chromatin domain and drives aberrant HOX gene transcription in acute myeloid leukemia. Blood 132, 837–848 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Xu, B. et al. Acute depletion of CTCF rewires genome-wide chromatin accessibility. Genome Biol. 22, 244 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Verheul, T. C. J., van Hijfte, L., Perenthaler, E. & Barakat, T. S. The why of YY1: mechanisms of transcriptional regulation by Yin Yang 1. Front. Cell Dev. Biol. 8, 592164 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Ludwig, L. S. et al. Transcriptional states and chromatin accessibility underlying human erythropoiesis. Cell Rep. 27, 3228–3240 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Schulz, V. P. et al. A unique epigenomic landscape defines human erythropoiesis. Cell Rep. 28, 2996–3009 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Borg, J. et al. Haploinsufficiency for the erythroid transcription factor KLF1 causes hereditary persistence of fetal hemoglobin. Nat. Genet. 42, 801–805 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Steinberg, M. H. Fetal hemoglobin in sickle hemoglobinopathies: high HbF genotypes and phenotypes. J. Clin. Med. 9, 3782 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Liu, N. et al. Author correction: Transcription factor competition at the γ-globin promoters controls hemoglobin switching. Nat. Genet. 53, 586 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kim, S., Kim, D., Cho, S. W., Kim, J. & Kim, J.-S. Highly efficient RNA-guided genome editing in human cells via delivery of purified Cas9 ribonucleoproteins. Genome Res. 24, 1012–1019 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Thakore, P. I. et al. Highly specific epigenome editing by CRISPR-Cas9 repressors for silencing of distal regulatory elements. Nat. Methods 12, 1143–1149 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Moi, P. & Kan, Y. W. Synergistic enhancement of globin gene expression by activator protein-1-like proteins. Proc. Natl Acad. Sci. USA 87, 9000–9004 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ney, P. A., Sorrentino, B. P., McDonagh, K. T. & Nienhuis, A. W. Tandem AP-1-binding sites within the human beta-globin dominant control region function as an inducible enhancer in erythroid cells. Genes Dev. 4, 993–1006 (1990).

    Article  CAS  PubMed  Google Scholar 

  42. Tuan, D., Kong, S. & Hu, K. Transcription of the hypersensitive site HS2 enhancer in erythroid cells. Proc. Natl Acad. Sci. USA 89, 11219–11223 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Zhou, Z. et al. USF and NF-E2 cooperate to regulate the recruitment and activity of RNA polymerase II in the beta-globin gene locus. J. Biol. Chem. 285, 15894–15905 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kim, A. & Dean, A. Chromatin loop formation in the β-globin locus and its role in globin gene transcription. Mol. Cells 34, 1–5 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Huang, T. P., Newby, G. A. & Liu, D. R. Precision genome editing using cytosine and adenine base editors in mammalian cells. Nat. Protoc. 16, 1089–1128 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Bao, E. L. et al. Inherited myeloproliferative neoplasm risk affects haematopoietic stem cells. Nature 586, 769–775 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Katsumura, K. R. & Bresnick, E. H. & GATA Factor Mechanisms Group. The GATA factor revolution in hematology. Blood 129, 2092–2102 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Iwasaki, H. et al. Distinctive and indispensable roles of PU.1 in maintenance of hematopoietic stem cells and their differentiation. Blood 106, 1590–1600 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Liebermann, D. A., Gregory, B. & Hoffman, B. AP-1 (Fos/Jun) transcription factors in hematopoietic differentiation and apoptosis. Int. J. Oncol. 12, 685–700 (1998).

    CAS  PubMed  Google Scholar 

  50. Kelly, T. K. et al. Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res. 22, 2497–2506 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sun, Z. et al. Nondestructive enzymatic deamination enables single-molecule long-read amplicon sequencing for the determination of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Genome Res 31, 291–300 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ma, C.-J. et al. Single-nucleotide resolution mapping of N6-methyladenine in genomic DNA. ACS Cent. Sci. 9, 1799–1809 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mahdavi-Amiri, Y., Chung Kim Chung, K. & Hili, R. Single-nucleotide resolution of N6-adenine methylation sites in DNA and RNA by nitrite sequencing. Chem. Sci. 12, 606–612 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Swanson, E. G. et al. Deaminase-assisted single-molecule and single-cell chromatin fiber sequencing. Preprint at bioRxiv https://doi.org/10.1101/2024.11.06.622310 (2024).

  55. Yu, T. et al. Deaminase-mediated chromatin accessibility profiling with single-allele resolution. Preprint at bioRxiv https://doi.org/10.1101/2024.12.17.628768 (2024).

  56. Puig, R. R., Boddie, P., Khan, A., Castro-Mondragon, J. A. & Mathelier, A. UniBind: maps of high-confidence direct TF-DNA interactions across nine species. BMC Genomics 22, 482 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Gheorghe, M. et al. A map of direct TF–DNA interactions in the human genome. Nucleic Acids Res. 47, 7715 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Giani, F. C. et al. Targeted application of human genetic variation can improve red blood cell production from stem cells. Cell Stem Cell 18, 73–78 (2016).

    Article  CAS  PubMed  Google Scholar 

  59. Nandakumar, S. K. et al. Gene-centric functional dissection of human genetic variation uncovers regulators of hematopoiesis. eLife 8, e44080 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.1–21.29.9 (2015).

  61. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Li, H. New strategies to improve minimap2 alignment accuracy. Bioinformatics 37, 4572–4574 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank J. Nelson and C. Hartmann at the Bauer Core Facility of Harvard University for assistance with FACS and Nanopore sequencing, and members of the Liau lab, in particular F. Najm, E. Gaskell and B. Bernstein, and others for insightful comments and discussion. We thank R. Zhang and M. Horlbeck for helpful discussions as well as technical and computational support. We thank B.Y. Mok and D. Liu (Merkin Institute of Transformative Technologies in Healthcare, Broad Institute of MIT and Harvard/Department of Chemistry and Chemical Biology, Harvard University/Howard Hughes Medical Institute, Harvard University) for providing the DddA expression plasmids. This work was supported by funds from National Science Foundation grant DGE1745303 (S.P.S.), NIH F31 grant F31HL174076 (S.P.S.), the Charles A. King Trust Postdoctoral Research Fellowship from Sara Elizabeth O’Brien Trust/Simeon J. Fortin Charitable Foundation, Bank of America Private Bank, Co-Trustees (H.S.K.), NIH grant R01DK103794 (V.G.S.), the Howard Hughes Medical Institute (V.G.S.), NIH grant R35GM153476 (B.B.L.), Harvard University (B.B.L.), the Alfred P. Sloan Research Foundation (B.B.L.), the Camille Dreyfus Teacher-Scholar Award (B.B.L.) and from the Gene Regulation Observatory at the Broad Institute (J.D.B., B.B.L.).

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Author notes

  1. These authors contributed equally: Heejin Roh, Simon P. Shen, Yan Hu.

Authors and Affiliations

  1. Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA

    Heejin Roh, Simon P. Shen, Hui Si Kwok, Allison P. Siegenfeld, Ceejay Lee, Marcanthony U. Zepeda, Shelby A. Roseman & Brian B. Liau

  2. Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Heejin Roh, Simon P. Shen, Yan Hu, Hui Si Kwok, Allison P. Siegenfeld, Ceejay Lee, Marcanthony U. Zepeda, Chun-Jie Guo, Shelby A. Roseman, Caroline Comenho, Vijay G. Sankaran, Jason D. Buenrostro & Brian B. Liau

  3. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA

    Yan Hu, Caroline Comenho & Jason D. Buenrostro

  4. Division of Hematology/Oncology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA

    Chun-Jie Guo & Vijay G. Sankaran

  5. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Chun-Jie Guo & Vijay G. Sankaran

  6. Howard Hughes Medical Institute, Boston, MA, USA

    Chun-Jie Guo & Vijay G. Sankaran

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Contributions

H.R., A.P.S. and B.B.L. conceived of the study. H.R. designed the experiments and performed protein purification, biochemical assays and cell-biology experiments with assistance from H.S.K. H.R., S.P.S. and Y.H. performed computational analysis, with input from J.D.B. and B.B.L. A.P.S., C.L., M.U.Z., C.-J.G., S.A.R. and C.C. provided experimental support with input from V.G.S. H.R., S.P.S., Y.H., H.S.K. and B.B.L. wrote and edited the paper with input from all other authors. B.B.L. held overall responsibility for the study.

Corresponding author

Correspondence to Brian B. Liau.

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Competing interests

B.B.L. is a cofounder, member of the scientific advisory board and holds equity in Light Horse Therapeutics, and receives financial support from Ono Pharmaceuticals. J.D.B. is on the scientific advisory board for Camp4 and seqWell and is a consultant at the Treehouse Family Foundation. V.G.S. is an advisor for Ensoma. H.R., S.P.S., Y.H. and B.B.L. are co-inventors on a U.S. provisional patent application 63/734,670 regarding technologies described in this paper. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Supporting Data for Fig. 1.

a) Sequence logo showing the sequence context preference for DddA11 on naked DNA. b) Aggregate profile of cytidine deamination fractions (y axis) around DNase I hypersensitive sites (DHSs) from whole-genome sequencing (WGS) of DddA11-treated K562 cells. c) Bar plots comparing modified cytosine profiles between DddA11-WGS and NOMe-seq23. Left: Number of modified cytosines in open chromatin. Middle: Total number of modified cytosines detected genome-wide. Right: Fraction of modified cytosines overlapping CREs.d) Bar plot showing C•G-to-T•A editing rates at unmethylated, methylated, or untreated cytidines in CpG sites of varying sequence contexts in the chr7:27158522–27163197 region. e) Histogram showing the number of C•G-to-T•A mutations per read at the HS2 locus (chr11:5279265-5282582) in DddA11-free and DddA11-treated samples. The dashed line represents a 2% threshold of all candidate cytosines. f) Histogram of edit differences between randomly sampled read pairs at the HS2 locus in DddA11-free and DddA11-treated samples. The dashed line represents a 2% threshold of all candidate cytosines. g) Genome tracks displaying ATAC-seq, TDAC-seq, and DADs across indicated genomic regions. TDAC-seq signal represents the average number of DddA11 edits in a 50-bp window. h) TDAC-seq at the HS2 locus under varying DddA11 concentrations and reaction times, compared with ATAC-seq. TDAC-seq signal represents the average number of DddA11 edits in a 50-bp window. i) Schematic of hidden Markov model used to call DADs. j) Scatter plot showing the percentage of bases covered by both DAD signal and ATAC-seq peaks (x-axis) versus bases covered only by DAD signal (y-axis). The dashed diagonal line represents equal coverage. Each point represents TDAC-seq from a different locus. k) Top: genome tracks showing chromatin profiles, TDAC-seq, and DADs at the ZBTB38 locus. Bottom: heatmap showing DAD co-accessibility, shown as observed minus expected, calculated as the product of individual accessibility fractions. Data in b,g,j, and k are representative examples of n = 2 replicates. Correlation between TDAC-seq replicates is presented in Supplementary Fig. 1.

Extended Data Fig. 2 Supporting Data for Fig. 2.

a) Schematic of DddA11 footprint calling method. b) Heatmap showing DddA11 footprints from DddA11-WGS data centered around CTCF (top) and YY1 (bottom) binding sites calculated with varying footprint radii (y axis). c) Line plot showing average DddA11 edit profile around the nucleosome center using the same dataset as Fig. 2a. d) Scatter plot comparing DNase-seq TF footprint scores with DddA11 footprint scores quantified for different TFs. e) Genome tracks showing ATAC-seq, TDAC-seq, MNase-seq, CTCF ChIP-seq and DddA11 footprint scores at indicated genomic locus in K562 cells. TDAC-seq signal represents the average number of DddA11 edits in a 50-bp window. The footprint window radii are 73 and 25 bp for nucleosome and TF footprints, respectively. f) Genome tracks showing ATAC-seq, TDAC-seq, MNase-seq, and DddA11 footprint scores at the GORAB-AS1/GORAB locus in K562 cells. Accessibility and footprint scores are similarly defined as e). g) Heatmaps showing aggregate DddA11 footprints across the same reads from Fig. 2a and Fig. 2c calculated with varying footprint radii (y axis). h) Genome tracks at the HOXA locus showing ATAC-seq and TDAC-seq after CRISPR perturbation with individual sgRNAs. TDAC-seq tracks show signals from DNA reads with or without CRISPR deletions near each sgRNA target site. Data in b-h are representative examples of n = 2 replicates. Correlation between TDAC-seq replicates is presented in Supplementary Fig. 1.

Extended Data Fig. 3 Supplementary Data for Fig. 3.

a) Flow-cytometry measurements of CD71 and CD235a levels in erythroid-differentiated HSPCs on day 5, 9 and 13. b) Flow-cytometry measurements of CD71 and CD235a levels in erythroid-differentiated HSPCs (day 5), transduced with indicated sgRNAs and electroporated with Cas9 protein. c) DNA gel of TDAC-seq library amplified at the HBG1/2 locus from erythroid-differentiated HSPCs (day 5), transduced with sgRNA-68 or non-targeting sgRNA and electroporated with Cas9 protein. d) Top six abundant genotypes (middle) at the HBG1/2 locus from erythroid-differentiated HSPCs (day 5) transduced with sgRNA-68, along with their relative abundance in sequencing reads (left) and corresponding TDAC-seq signals (right) compared to the non-targeting sgRNA control. TDAC-seq signal represents the average number of DddA11 edits in a 100-bp window. e) Scatter plots showing the number of DddA11 edits in 150-bp sliding windows across the amplicon (chr11:5245964-5257447) for the top six most abundant genotypes in samples transduced with sgRNA-68. Each axis represents replicate 1 (x axis) and replicate 2 (y axis). Experiments for data in a and b were performed once. Data c-e present n = 2 replicates. See Supplementary Fig. 2 for flow gating schemes.

Source data

Extended Data Fig. 4 Supplementary Data for Fig. 4.

a) Line plot showing the fraction of reads (y axis) containing a deletion at the indicated position across the HS2 region (x axis), with expected cut sites for each sgRNA indicated with red dotted lines. b) The number of deduplicated reads (y axis) per genotypes (x axis) from HS2 CRISPR deletions. In the HS2 Cas9 TDAC-seq sgRNA tiling library, 541 genotypes have at least 400 reads (red line). c) 2D histogram of UMIs plotted by the number of reads of the most abundant CRISPR genotype per UMI (x axis) and all genotypes (y axis). Both values would be equal for pure UMIs, indicated by red boxes along the diagonal. Data are representative of a sample of 10,000 sequencing reads. d) Heatmaps of TDAC-seq using CRISPR-Cas9 cutting on the HS2 enhancer in K562 cells. Individual sequencing reads were assigned to one of 541 genotypes (y axis), with black lines indicating the CRISPR deletion position and length on the HS2 region. For each genotype, the average number of DddA11 edits in a 100-bp sliding window is shown across the HS2 region (x axis). e) Heatmap showing the statistical significance (Welch’s two-sided t-test) of comparing DddA11 accessibility between reads that contain a deletion spanning the entire deletion window and all other reads. This value is calculated for all possible deletion windows indicated by their start position (horizontal axis) and length (vertical axis). Those with less than 100× coverage were excluded. Arrow indicates a 10-bp deletion window corresponding to the minimal key motif discussed in Fig. 4. f) The proportion of reads from gDNA with and without CRISPR deletions after individual transduction of sgRNA-1 and sgRNA-11 into K562 cells. Data in a-e are representative examples of n = 2 replicates. Correlation between TDAC-seq replicates is presented in Supplementary Fig. 3. Experiments for data in f were performed once.

Extended Data Fig. 5 Supplementary Data for Fig. 5.

a) Line plot showing the ratio of A•T-to-G•C edit coverage in ABE(+) versus ABE(-) samples with sgRNA predicted editing sites based on the ABE8e editing window (red dotted lines) across the HS2 region (x axis). b) Box plot of A•T-to-G•C mutation rate at adenosines on the non-target strand and target strand for the HS2 pooled ABE screen. Target strand is defined as the strand containing the same sequence as the sgRNA, and non-target strand is the strand containing complementary sequence to the sgRNA. The boxes define the first quartile (Q1), median, and third quartile of data (Q3), and whiskers extend to the farthest data point within Q1 - 1.5x the inter-quartile range (IQR) and Q3 + 1.5x IQR. Data points outside of the whisker range are shown as fliers. c) Number of reads (y axis) per genotype (x axis) from HS2 ABE edits. In the HS2 ABE TDAC-seq sgRNA tiling library, 49 genotypes have at least 100 reads. d) Heatmaps of TDAC-seq integrated with pooled ABE base editing on the HS2 enhancer in K562 cells. Individual sequencing reads were assigned into one of 49 genotypes (y axis), with black lines indicating the A•T-to-G•C mutation sites on the HS2 region. For each genotype, the average number of DddA11 edits in a 100-bp sliding window are shown across the HS2 region (x axis). Genotypes were grouped by the number of ABE edits (right). e) Swarm plots showing the number of DddA11 edits in the accessible region (Fig. 5a) per genotype, stratified by the number of ABE mutations. Horizontal line represents wild-type. Data in a-e are representative examples of n = 2 replicates. Correlation between TDAC-seq replicates is presented in Supplementary Fig. 3.

Extended Data Fig. 6 MGYFPDa829 enhances sensitivity for detecting chromatin accessibility.

a) Genome tracks at HS2 locus showing ATAC-seq and two TDAC-seq replicates under different conditions, including changes in deaminase concentration and reaction time, addition of UGI, and substitution of DddA11 with MGYFPDa829. TDAC-seq signal represents the average number of DddA11 or MGYFPDa829 edits in a 100-bp window. b) CTCF binding sites (left) and transcription start sites (TSS, right) from WGS in K562 cells treated with MGYFPDa829. c) TDAC-seq using MGYFPDa829 and chromatin tracks at the DSCR4/8 locus (left) and ZBTB38 locus (right) in K562 cells. TDAC-seq signal represents the average number of MGYFPDa829 edits in a 100-bp window. C•G-to-T•A edits are depicted in purple. d) Genome tracks showing chromatin accessibility changes measured by TDAC-seq using MGYFPDa829 at indicated stages of CD34+ HSPC erythroid differentiation at the HBD-HBB locus. Partially created in BioRender. Liau, B. (2025) https://BioRender.com/8kl7dtq. Data in b-d are representative examples of n = 2 replicates. Correlation between TDAC-seq replicates for the data shown in c and d is presented in Supplementary Fig. 1.

Extended Data Fig. 7 Supplementary Data for Fig. 6.

a) Line plot showing the fraction of reads (y axis) containing a deletion at the indicated position across the 138 GFI1B enhancer region (x axis), with expected cut sites for each sgRNA indicated with red dotted lines. b) The number of deduplicated reads (y axis) per genotypes (x axis) from GFI1B CRISPR deletions. In the GFI1B Cas9 TDAC-seq sgRNA tiling library, 947 genotypes have at least 3000 reads (red line). c) Bar plot comparing target locus coverage by CRISPR-Cas9-based enrichment and PCR-based enrichment (TDAC-seq). Coverage was adjusted by sequencing depth and duplication rate. d) Top: ATAC-seq signal from unperturbed CD34+ HSPCs (y axis) across the GFI1B enhancer region (x axis). Bottom: Heatmaps of TDAC-seq using CRISPR-Cas9 cutting on the GFI1B enhancer in CD34+ HSPCs. Individual sequencing reads were assigned to one of 947 genotypes (y axis), with black lines indicating the CRISPR deletion position and length on the GFI1B enhancer region. For each genotype, either the average number of MGYFPDa829 edits in a 100-bp sliding window (left) or the TDAC-seq signal after subtracting the wild type background (right) is shown across the GFI1B enhancer region (x axis). Genotype hits from Extended Data Fig. 7e are labeled on the right. e) Heatmap showing the statistical significance (Welch’s two-sided t-test) of comparing the number of MGYFPDa829 edits in the accessible region between reads that contain a deletion spanning the entire deletion window and all other reads. This value is calculated for all possible deletion windows indicated by their start position (x axis) and length (y axis). Those with less than 3000× coverage were excluded. Arrows indicate deletions associated with the most significant decreases in accessibility (P < 1.0 × 10−4 each), corresponding to the minimal key motifs discussed in Fig. 6b. Data in a,b,d, and e are representative examples of n = 2 replicates. Correlation between TDAC-seq replicates is presented in Supplementary Fig. 3.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Tables 1–4.

Reporting Summary

Supplementary Table 1

Sequences of primers used in TDAC-seq library preparation.

Supplementary Table 2

sgRNA library sequences targeting the HS2 or GFI1B enhancer.

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Roh, H., Shen, S.P., Hu, Y. et al. Coupling CRISPR scanning with targeted chromatin accessibility profiling using a double-stranded DNA deaminase. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02811-2

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