Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing

Nature Methods volume 13, pages 1013–1020 (2016)Cite this article

Subjects

Abstract

Spatial organization of the genome plays a central role in gene expression, DNA replication, and repair. But current epigenomic approaches largely map DNA regulatory elements outside of the native context of the nucleus. Here we report assay of transposase-accessible chromatin with visualization (ATAC-see), a transposase-mediated imaging technology that employs direct imaging of the accessible genome in situ, cell sorting, and deep sequencing to reveal the identity of the imaged elements. ATAC-see revealed the cell-type-specific spatial organization of the accessible genome and the coordinated process of neutrophil chromatin extrusion, termed NETosis. Integration of ATAC-see with flow cytometry enables automated quantitation and prospective cell isolation as a function of chromatin accessibility, and it reveals a cell-cycle dependence of chromatin accessibility that is especially dynamic in G1 phase. The integration of imaging and epigenomics provides a general and scalable approach for deciphering the spatiotemporal architecture of gene control.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: ATAC-see visualizes the accessible genome in situ.
Figure 2: ATAC-see enables imaging and sequencing of the accessible genome in the same cells.
Figure 3: Cell-type-specific accessible-chromatin organization in the intact nucleus.
Figure 4: ATAC-see and ATAC-seq reveal the dynamic chromatin organization during human NETosis.
Figure 5: ATAC-see reveals cell-cycle-specific genome accessibility.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

References

  1. Bickmore, W.A. & van Steensel, B. Genome architecture: domain organization of interphase chromosomes. Cell 152, 1270–1284 (2013).

    CAS  PubMed  Google Scholar 

  2. Misteli, T. Self-organization in the genome. Proc. Natl. Acad. Sci. USA 106, 6885–6886 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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 

  4. Fullwood, M.J. et al. An oestrogen-receptor-alpha-bound human chromatin interactome. Nature 462, 58–64 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Markaki, Y. et al. The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture: 3D structured illumination microscopy of defined chromosomal structures visualized by 3D (immuno)-FISH opens new perspectives for studies of nuclear architecture. BioEssays 34, 412–426 (2012).

    Article  PubMed  Google Scholar 

  8. Deng, W., Shi, X., Tjian, R., Lionnet, T. & Singer, R.H. CASFISH: CRISPR/Cas9-mediated in situ labeling of genomic loci in fixed cells. Proc. Natl. Acad. Sci. USA 112, 11870–11875 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Amini, S. et al. Haplotype-resolved whole genome sequencing by contiguity preserving transposition and combinatorial indexing. Nat. Genet. 46, 1343–1349 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Buenrostro, J.D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Cusanovich, D.A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gibcus, J.H. & Dekker, J. The hierarchy of the 3D genome. Mol. Cell 49, 773–782 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Buys, C.H., Anders, G.J., Gouw, W.L., Borkent-Ypma, J.M. & Blenkers-Platter, J.A. A comparison of constitutive heterochromatin staining methods in two cases of familial heterochromatin deficiencies. Hum. Genet. 52, 133–138 (1979).

    Article  CAS  PubMed  Google Scholar 

  14. Lanctôt, C., Cheutin, T., Cremer, M., Cavalli, G. & Cremer, T. Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. Nat. Rev. Genet. 8, 104–115 (2007).

    Article  PubMed  Google Scholar 

  15. Kolaczkowska, E. & Kubes, P. Neutrophil recruitment and function in health and inflammation. Nat. Rev. Immunol. 13, 159–175 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. Qu, K. et al. Individuality and variation of personal regulomes in primary human T cells. Cell Syst. 1, 51–61 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Guelen, L. et al. Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature 453, 948–951 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Amendola, M. & van Steensel, B. Nuclear lamins are not required for lamina-associated domain organization in mouse embryonic stem cells. EMBO Rep. 16, 610–617 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Solovei, I. et al. Nuclear architecture of rod photoreceptor cells adapts to vision in mammalian evolution. Cell 137, 356–368 (2009).

    Article  CAS  PubMed  Google Scholar 

  20. Brinkmann, V. et al. Neutrophil extracellular traps kill bacteria. Science 303, 1532–1535 (2004).

    Article  CAS  PubMed  Google Scholar 

  21. Li, P. et al. PAD4 is essential for antibacterial innate immunity mediated by neutrophil extracellular traps. J. Exp. Med. 207, 1853–1862 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lewis, H.D. et al. Inhibition of PAD4 activity is sufficient to disrupt mouse and human NET formation. Nat. Chem. Biol. 11, 189–191 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Christophorou, M.A. et al. Citrullination regulates pluripotency and histone H1 binding to chromatin. Nature 507, 104–108 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hinde, E., Cardarelli, F., Digman, M.A. & Gratton, E. Changes in chromatin compaction during the cell cycle revealed by micrometer-scale measurement of molecular flow in the nucleus. Biophys. J. 102, 691–697 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Aleem, E., Kiyokawa, H. & Kaldis, P. Cdc2-cyclin E complexes regulate the G1/S phase transition. Nat. Cell Biol. 7, 831–836 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chen, X. et al. Chromatin in situ proximity (ChrISP): single-cell analysis of chromatin proximities at a high resolution. Biotechniques 56, 117–124 (2014).

    Article  PubMed  Google Scholar 

  28. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  30. McLean, C.Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  32. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank S. Kim (Stanford) for FACS access. This work was supported by NIH grant P50-HG007735 (to H.Y.C. and W.J.G.), the Life Extension Foundation (to H.Y.C.), NCI Physical Sciences Oncology Center grant U54CA143836 (to J.T.L.), and National Institute of Biomedical Imaging and Bioengineering (NIBIB)/4D Nucleome Roadmap Initiative grant 1U01EB021237 (to J.T.L.).

Author information

Authors and Affiliations

  1. Center for Personal Dynamic Regulomes, Stanford University, Stanford, California, USA

    Xingqi Chen, Ying Shen, Jason D Buenrostro, Ulrike Litzenburger, Seung Woo Cho, Ansuman T Satpathy, Ava C Carter, William J Greenleaf & Howard Y Chang

  2. Department of Bioengineering, Stanford University, Stanford, California, USA

    Will Draper, Rajarshi P Ghosh & Jan T Liphardt

  3. Department of Genetics, Stanford University, Stanford, California, USA

    Jason D Buenrostro & William J Greenleaf

  4. Department of Molecular and Cell Biology, University of California, Berkeley, California, USA

    Alexandra East-Seletsky & Jennifer A Doudna

  5. Department of Chemistry, University of California, Berkeley, Berkeley, California, USA.,

    Alexandra East-Seletsky & Jennifer A Doudna

  6. Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, California, USA.,

    Jennifer A Doudna

  7. Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Jennifer A Doudna

  8. Department of Applied Physics, Stanford University, Stanford, California, USA

    William J Greenleaf

Authors
  1. Xingqi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Will Draper
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jason D Buenrostro
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ulrike Litzenburger
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Seung Woo Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ansuman T Satpathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ava C Carter
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rajarshi P Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Alexandra East-Seletsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jennifer A Doudna
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. William J Greenleaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jan T Liphardt
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Howard Y Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X.C., W.J.G., and H.Y.C. conceived and designed the study. X.C., J.D.B., U.L., A.T.S., A.C.C., and R.P.G. performed experiments. Y.S. and X.C. performed genomic data analysis. W.D., X.C. and J.T.L. conducted image analyses. S.W.C., A.E.-S., and J.A.D. generated reagents. X.C. and H.Y.C. wrote the manuscript with input from all authors. H.Y.C. supervised all aspects of this work.

Corresponding author

Correspondence to Howard Y Chang.

Ethics declarations

Competing interests

H.Y.C. and W.J.G. are cofounders of Epinomics. Stanford University has filed a patent on ATAC-see, on which X.C., J.D.B., W.J.G., and H.Y.C. are coinventors.

Integrated supplementary information

Supplementary Figure 1 Validation of ATAC-seq library from Atto-Tn5

a, genome-wide comparisons of ATAC-seq reproducibility with Atto-Tn5 in GM12878 cells (GM). b, genome-wide comparisons of ATAC-seq reproducibility with Nextera Tn5 in GM12878 cells (GM). c, Insert size distribution of GM12878 ATAC-seq libraries transposed by Nextera Tn5. d, Insert size distribution of GM12878 cells ATAC-seq libraries from Atto-Tn5. e, Calculation of regulatory element enrichment from ChromHMM in previously published GM12878 ATAC-seq libraries and new ATAC-seq libraries using either Atto-Tn5 or Illumina Nextera Tn5. f: density plot of differential ATAC-seq peaks fold change (FC)(log2 value) in GM12878 from Atto-Tn5 and Nextera Tn5 among technical replicates and different enzyme.

Supplementary Figure 2 Validation of ATAC-seq library in fixed cells

a, Insert size distribution of HT1080 cells ATAC-seq with standard protocol. b, Insert size distribution of fixed HT1080 cells ATAC-seq without reverse crosslinking step. c, Genomic tracks of ATAC-seq data from HT1080 cells among different conditions: No fixation, with fixation. X-axis is genomic coordinates; Y-axis is normalized ATAC-seq read counts. d, Insert size distribution of fixed HT1080 cells ATAC-seq with reverse crosslinking step. e, genome-wide comparisons of ATAC-seq reproducibility with Nextera Tn5 in non-fixed HT1080 cells (HT). f, genome-wide comparisons of ATAC-seq reproducibility with Nextera Tn5 in fixed HT1080 cells (HT) with reverse crosslinking.

Supplementary Figure 3 Confirmation of ATAC-see principle

a, Left: representative whole frame images of ATAC-see from normal reaction and with 50 mM EDTA treated in HT1080 cells. The white squares in the DAPI channel indicate the cropped cells in Figure 2a. Scale bar=2 μm. Right: Signal intensity of ATAC-see was quantified in both normal reaction and 50 mM EDTA control samples. 20 cells were counted in each condition with independent replication, ** (p<0.005, student t-test), error bar is standard deviation. b, Representative whole frame images of ATAC-see co-staining with Lamin B1 and mitochondria protein marker (Mito) in HT1080 cells. The white square in the DAPI channel indicates the cropped cells in Figure 2b. Scale bar=2 μm. c, Correlation coefficient of ATAC-see with different epigenetic markers and active form of RNAP II in HT1080 cells (n= cell number); RNAPII Ser-2 P = RNA polymerase II phosphorylation ser-2, and RNAPII Ser-5 P= RNA polymerase II phosphorylation ser-5. d, Representative images of ATAC-see co-staining with different epigenetic markers and active form of RNAP II in HT1080 cells. e, ATAC-see and XIST RNA-FISH in mouse Neural Progenitor Cells. Upper panel: representative images (the white arrows in the ATAC-see panel indicate the location of XIST RNA FISH signal); lower panel: signal intensity quantification of ATAC-see within and outside of XIST area. 30 cells were counted in each condition with independent replicate, ** (p<0.005, student t-test), error bar is standard deviation.

Supplementary Figure 4 Validation of ATAC-seq library after ATAC-see imaging

a, Schematic workflow of ATAC-seq library preparation after ATAC-see imaging. b, genome-wide comparisons of ATAC-seq reproducibility with Atto-Tn5 on slides from HT1080 cells (HT). c, density plot of differential ATAC-seq peaks fold change (FC)(log2 value) in HT1080(HT) from Atto-Tn5 (on slide) and Nextera Tn5 (in solution) among technical replicates and different enzyme. d, Sensitivity assay (with different input) of global DNA accessibility of ATAC-seq after imaging (on slides) from HT1080 cells.

Supplementary Figure 5 ATAC-see in different cell types

a, Representative whole frame images of ATAC-see from different cell types. Scale bar=2 μm. Multi images (n>5) were taken in each cell type with independent replicates. b, Cell type specific accessible chromatin organization in the intact nucleus. The violin plot represents of the correlation coefficients between ATAC-see signal and DAPI signal per nucleus in different cell types. Each cell type has a unique profile, and neutrophils stand out as an outlier. B-cell = B-lymphoblastoid GM12878 cells.

Supplementary Figure 6 Systematic imaging processing of ATAC-see

a, Schematic of image processing workflow of making mitochondria masks from ATAC-see and define the bright area of ATAC-see signal in the nucleus. b, Analysis of two different ATAC-see patterns in CD4+ T cells. The two plots in the upper panel represent the radial distribution of ATAC-see signal intensity and DAPI signal intensity in two different groups of CD4+ T cells. The blue line shows the ATAC-see signal intensity is low at the nucleus periphery, the green line illustrates the ATAC-see signal form a rim structure at the nucleus periphery (“Cap pattern”) and the red line indicates the means of all signal intensity. The line plot in the middle panel shows the ratio of ATAC-see signal intensity and DAPI signal intensity. The scatter plot in the lower panel represents the correlation of ATAC-see signal intensity and DAPI signal intensity among the population cells. The red lines in the plot represent the average values of single intensity from all groups.

Supplementary Figure 7 Cell type specific accessible chromatin organization in the intact nucleus.

For each cell type (organized in rows), we display from left to right columns: (i) A representative ATAC-see image (red color is ATAC-see and blue is DAPI, scale bar = 2 μm). (ii) Signal intensity of ATAC-see and DAPI as a function of distance from nuclear periphery. Each trace is one nucleus; n= number of nuclei analyzed. (iii) Correlation of ATAC-see and DAPI signal intensity; Pearson correlation (r) is indicated. (iv) ATAC-see clusters, quantified as the ratio of ATAC-see bright areas vs total nucleus area.

Supplementary Figure 8 Unique pattern of ATAC-see in the human neutrophil

a, Representative whole frame images show ATAC-see co-staining with Lamin B1 and mitochondria protein marker (Mito) in human neutrophils. The dotted lines in the Lamin B1 panel show the location of the nucleus periphery based on DAPI staining. Multi images (n>5) were taken with independent replicates. Scale bar=2 μm. b, The similarity of ATAC-seq libraries after imaging (on slide) with ATTO-590 from different donors. c, The boxplot represents the ATAC-seq peaks enrichment within NKI LADs and outside of NKI LADs, 0= outside of LADs, 1= within LADs. d, the black squares show the location of BAC clones chosen for DNA FISH according to the genomic tracks of ATAC-seq data in human neutrophil; BAC clones were chosen from both LAD region (RP11-626N18, RP11-832P24) and none LAD regions (RP11-63J14, RP11-637D5, RP11-368K11, RP11-116A9). e, Representative DNA FISH image in human neutrophils: the left panel: extended focus, right four panels: side view of 3D images. X-Y= X dimension and Y dimension, X-Z= X dimension and Z dimension. Scale bar=2 μm.

Supplementary Figure 9 Immunostaining in human neutrophils and NETosis

a, Immunostaining of epigenetic markers in human neutrophil. RNAPII Ser-5 P= RNA polymerase II phosphorylation ser-5. Multi images (n>5) were taken with independent replicates in each staining. b, The representative images show DAPI staining of control, PMA and PAD4 inhibitor (PAD4i) treated human neutrophils. Scale bar=2 μm. c, The bar graph presents the quantification of NETosis in PMA and PAD4 inhibitor treated human neutrophils based on DAPI staining, Error bar= standard deviation; n=60x2 for each condition. d, Representative whole frame images of H3 citrullination staining in control, 5 h PMA treated and 5 h PAD4 inhibitor (PAD4i) treated human neutrophils. Multi images (n=5x2) were taken in each condition. Scale bar=2 μm.

Supplementary Figure 10 ATAC-see and –seq in the human NETosis.

a, Representative images ATAC-see in 3h PMA stimulation human neutrophils. Scale bar=2 μm.

b, Epigenomic landscape of 3h PMA stimulation human neutrophils. Left column: Genomic tracks of ATAC-seq data. Locations of NKI Lamin associated domains (LADs) are indicated. X-axis is genomic coordinates; Y-axis is ATAC-seq normalized read counts. Middle: Metagene plot of ATAC-seq signal centered on the boundary between NKI LADs and neighboring sequences. Right: ATAC-seq insert size distribution for the corresponding samples. Diagnostic insert sizes for accessible DNA, mononucleosome, and di-nucleosome are labeled.

Supplementary Figure 11 FACS analysis of ATAC-see and DAPI dual staining in GM12878 cells.

a, Left panel: cell cycle histogram from DAPI staining. Right panel: ATAC-see signal intensity histogram. b, violin plots show the ATAC-see signal intensity of the sorted cells measured by confocal microscopy. n= cell number measured under the confocal microscopy. *** (p<0.001, ANOVA test). c, Heatmap of the correlation coefficient of different FACS sorted groups. Each group contains independent replicate. d, Heatmap represents the cluster of accessible regions (FD>2, FDR<0.05) in FACS sorted groups: G1 low, G1 high, S phase and G2. Each group contains independent replicate. e, genome-wide comparison of accessible regions between different ATAC-see sorted groups: G1 low vs. G1 high; S vs. G1 high; G2 vs. S; G1 low vs. G2.

Supplementary Figure 12 Classification of accessible regions in G1

a, Genomic tracks of ATAC-seq data from ATAC-see G1 low and G1 high group after FACS sorting. X-axis is genomic coordinates; Y-axis is normalized ATAC-seq read counts. b, ChromHMM classification of accessible regions in asynchronized GM12878 cells (left), more accessible G1 high (middle) and more accessible G1 low regions (right). Each chromatin state was color-coded.

Supplementary Figure 13 FACS analysis of ATAC-see and cell surface marker staining in mouse bone marrow progenitor cells.

a: FACS plots was gated (see Methods) as common myeloid progenitors (CMP), granulocyte-macrophage progenitors (GMP), and band neutrophils; b: Representative images of sorted populations shown in (a) by confocal microscopy, and multi images (n>5) were taken with independent replicates in each group.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13 and Supplementary Tables 1 and 2. (PDF 5257 kb)

Supplementary Video 1

HT1080 cells 3D movie of ATAC-see HT1080 cells 3D movie of ATAC-see (red) and DAPI staining (blue). (MOV 441 kb)

Supplementary Video 2

HeLa cells 3D movie of ATAC-see HeLa cells 3D movie of ATAC-see (red) and DAPI staining (blue). (MOV 663 kb)

Supplementary Video 3

CD4+ T cells 3D movie of ATAC-see CD4+ T cells 3D movie of ATAC-see (red) and DAPI staining (blue). (MOV 473 kb)

Supplementary Video 4

B lymphocyte cell line GM12878 cells 3D movie of ATAC-see B lymphocyte cell line GM12878 cells 3D movie of ATAC-see (red) and DAPI staining (blue). (MOV 584 kb)

Supplementary Video 5

Human neutrophils 3D movie of ATAC-see Human neutrophils 3D movie of Lamin B1 (green), mitochondria protein staining (yellow), ATAC-see (red) and DAPI staining (blue). (MOV 944 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Shen, Y., Draper, W. et al. ATAC-see reveals the accessible genome by transposase-mediated imaging and sequencing. Nat Methods 13, 1013–1020 (2016). https://doi.org/10.1038/nmeth.4031

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4031

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing