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Simultaneous quantification of protein–DNA interactions and transcriptomes in single cells with scDam&T-seq

Abstract

Protein–DNA interactions are essential for establishing cell type–specific chromatin architecture and gene expression. We recently developed scDam&T-seq, a multi-omics method that can simultaneously quantify protein–DNA interactions and the transcriptome in single cells. The method effectively combines two existing methods: DNA adenine methyltransferase identification (DamID) and CEL-Seq2. DamID works through the tethering of a protein of interest (POI) to the Escherichia coli DNA adenine methyltransferase (Dam). Upon expression of this fusion protein, DNA in proximity to the POI is methylated by Dam and can be selectively digested and amplified. CEL-Seq2, in contrast, makes use of poly-dT primers to reverse transcribe mRNA, followed by linear amplification through in vitro transcription. scDam&T-seq is the first technique capable of providing a combined readout of protein–DNA contact and transcription from single-cell samples. Once suitable cell lines have been established, the protocol can be completed in 5 d, with a throughput of hundreds to thousands of cells. The processing of raw sequencing data takes an additional 1–2 d. Our method can be used to understand the transcriptional changes a cell undergoes upon the DNA binding of a POI. It can be performed in any laboratory with access to FACS, robotic and high-throughput-sequencing facilities.

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Fig. 1: Overview of the method.
Fig. 2: CEL-Seq2 primer and DamID adapter structure.
Fig. 3: Bioinformatics workflow.
Fig. 4: Examples of aRNA bioanalyzer plots.
Fig. 5: Technical statistics of a scDam&T-seq run.
Fig. 6: Examples of DNA bioanalyzer plots.

Data availability

A test dataset is available from GitHub (https://github.com/KindLab/scDamAndTools).

Code availability

All codes are available from GitHub (https://github.com/KindLab/scDamAndTools). The code in this manuscript has been peer reviewed.

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Acknowledgements

We thank the members of the Kind laboratory for their comments on the manuscript. S.S.D. acknowledges support from the Center for Scientific Computing at UCSB: an NSF MRSEC (DMR-1720256) and NSF CNS-1725797. S.S.D. was also supported by the NIH grant R01HG011013. This work was funded by a European Research Council Starting grant (ERC-StG 678423-EpiID), a Nederlandse Organisatie voor Wetenschappelijk Onderzoek37 Open (824.15.019) and ALW/VENI grant (016.181.013). The Oncode Institute is supported by KWF Dutch Cancer Society.

Author information

Affiliations

Authors

Contributions

K.R., S.S.D. and J.K. designed the study. S.S.D. developed the method with input and assistance from D.M. K.R. supervised and performed bioinformatic analyses and developed the scDam&T computational pipeline. F.J.R. performed cloning and bioinformatic analyses on mESC scDam&T data and developed the clonal selection strategy. C.M.M. optimized the method, performed experiments, generated cell lines and designed the protocol for scDamID2. S.S.d.V. generated cell lines, assisted with experiments and designed the protocol for bulk DamID2. S.J.A.L. generated cell lines. K.L.d.L. assisted with experiments. A.C. assisted with analyses. J.K. and S.S.D. conceived and supervised the study. C.M.M. and F.J.R. wrote the manuscript with input from J.K.

Corresponding authors

Correspondence to Siddharth S. Dey or Jop Kind.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks John Arne Dahl, Madeleine Fosslie, Tamar Hashimshony and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key reference using this protocol

Rooijers, K. et al. Nat. Biotechnol. 37, 766–772 (2019): https://doi.org/10.1038/s41587-019-0150-y

Integrated supplementary information

Supplementary Fig. 1 Gating strategy for FACS.

a, Dot plot of ungated mESCs showing gating strategy to exclude debris in FSC (forward scatter) versus SSC (side scatter). The percentage of events in Gate 1 is indicated. b, Dot plot of mESCs passing Gate 1, showing the gating strategy to exclude dead cells in propidium iodide versus FSC. The percentage of events in Gate 2 is indicated. c, Dot plot of mESCs passing Gate 2, showing the gating strategy to exclude duplet cells in Hoechst versus Hoechst area. The percentage of events in Gate 3 is indicated. d, Dot plot of mESCs passing Gate 3 were gated for DNA content in G1 and G2/M phase of the cell cycle. The gate for the G2/M population was defined by doubling the intensity value of the G1 peak maximum. e, DNA content histogram events in Gate 3, showing counted events versus Hoechst area. Only cells passing gate G2/M were sorted. f, Table indicating gate hierarchy, percentage of events in each gate relative to parent population and total numbers of events within each gate. g, Table indicating the mean value for the G1 and G2/M populations. All measurements were done on the BD FACSJazz and analyzed with FlowJo software, version 10.1r5.

Supplementary information

Supplementary Information

Supplementary Fig. 1, Supplementary Methods, Supplementary Manual and Supplementary Tables 1 and 2.

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Markodimitraki, C.M., Rang, F.J., Rooijers, K. et al. Simultaneous quantification of protein–DNA interactions and transcriptomes in single cells with scDam&T-seq. Nat Protoc 15, 1922–1953 (2020). https://doi.org/10.1038/s41596-020-0314-8

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