SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues


Fluorescence in situ hybridization (FISH) reveals the abundance and positioning of nucleic acid sequences in fixed samples. Despite recent advances in multiplexed amplification of FISH signals, it remains challenging to achieve high levels of simultaneous amplification and sequential detection with high sampling efficiency and simple workflows. Here we introduce signal amplification by exchange reaction (SABER), which endows oligonucleotide-based FISH probes with long, single-stranded DNA concatemers that aggregate a multitude of short complementary fluorescent imager strands. We show that SABER amplified RNA and DNA FISH signals (5- to 450-fold) in fixed cells and tissues. We also applied 17 orthogonal amplifiers against chromosomal targets simultaneously and detected mRNAs with high efficiency. We then used 10-plex SABER-FISH to identify in vivo introduced enhancers with cell-type-specific activity in the mouse retina. SABER represents a simple and versatile molecular toolkit for rapid and cost-effective multiplexed imaging of nucleic acid targets.

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Fig. 1: SABER-FISH design and workflow.
Fig. 2: SABER effectively amplifies fluorescent signals.
Fig. 3: Transcript detection and quantification in retina tissue.
Fig. 4: SABER enables spectrally multiplexed imaging.
Fig. 5: Exchange-SABER for detection of cell types in retina tissue.
Fig. 6: Sequential imaging of chromosomal targets using Exchange-SABER.
Fig. 7: SABER-FISH enables detection of in vivo RNA reporters for analysis of enhancer activity.

Data availability

All raw and processed data are available from the authors upon reasonable request.

Code availability

The complete set of CellProfiler38,60 pipelines used and example input images for each are available at PD3D, a package of MATLAB functions for detecting SABER puncta (or other fluorescent puncta) in 3D and assigning puncta to cells in a watershed segmentation, is available at Functions used for image processing are available at or


  1. 1.

    Pardue, M. L. & Gall, J. G. Molecular hybridization of radioactive DNA to the DNA of cytological preparations. Proc. Natl Acad. Sci. USA 64, 600–604 (1969).

    CAS  Article  Google Scholar 

  2. 2.

    Riegel, M. Human molecular cytogenetics: from cells to nucleotides. Genet. Mol. Biol. 37, 194–209 (2014).

    Article  Google Scholar 

  3. 3.

    Bolzer, A. et al. Three-dimensional maps of all chromosomes in human male fibroblast nuclei and prometaphase rosettes. PLoS Biol. 3, 0826–0842 (2005).

    CAS  Article  Google Scholar 

  4. 4.

    Femino, A. M., Fay, F. S., Fogarty, K. & Singer, R. H. Visualization of single RNA transcripts in situ. Science 280, 585–590 (1998).

    CAS  Article  Google Scholar 

  5. 5.

    Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A. & Tyagi, S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879 (2008).

    CAS  Article  Google Scholar 

  6. 6.

    Schröck, E. et al. Multicolor spectral karyotyping of human chromosomes. Science 273, 494–497 (1996).

    Article  Google Scholar 

  7. 7.

    Lubeck, E. & Cai, L. Single-cell systems biology by super-resolution imaging and combinatorial labeling. Nat. Methods 9, 743–748 (2012).

    CAS  Article  Google Scholar 

  8. 8.

    Jungmann, R. et al. Multiplexed 3D cellular super-resolution imaging with DNA-PAINT and Exchange-PAINT. Nat. Methods 11, 313–318 (2014).

    CAS  Article  Google Scholar 

  9. 9.

    Schueder, F. et al. Universal super-resolution multiplexing by DNA exchange. Angew. Chem. Int. Ed. Engl. 56, 4052–4055 (2017).

    CAS  Article  Google Scholar 

  10. 10.

    Wang, Y. et al. Rapid sequential in situ multiplexing with DNA exchange imaging in neuronal cells and tissues. Nano Lett. 17, 6131–6139 (2017).

    CAS  Article  Google Scholar 

  11. 11.

    Wang, S. et al. Spatial organization of chromatin domains and compartments in single chromosomes. Science 353, 598–602 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Bintu, B. et al. Super-resolution chromatin tracing reveals domains and cooperative interactions in single cells. Science 362, eaau1783 (2018).

    Article  Google Scholar 

  13. 13.

    Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932–935 (2018).

    CAS  Article  Google Scholar 

  14. 14.

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    CAS  Article  Google Scholar 

  15. 15.

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  Google Scholar 

  16. 16.

    Levesque, M. J. & Raj, A. Single-chromosome transcriptional profiling reveals chromosomal gene expression regulation. Nat. Methods 10, 246–248 (2013).

    CAS  Article  Google Scholar 

  17. 17.

    Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    Kerstens, H. M., Poddighe, P. J. & Hanselaar, A. G. A novel in situ hybridization signal amplification method based on the deposition of biotinylated tyramine. J. Histochem. Cytochem. 43, 347–352 (1995).

    CAS  Article  Google Scholar 

  19. 19.

    Player, A. N., Shen, S. P., Kenny, D., Antao, V. P. & Kolberg, J. A. Single-copy gene detection using branched DNA (bDNA) in situ hybridization. J. Histochem. Cytochem. 49, 603–611 (2001).

    CAS  Article  Google Scholar 

  20. 20.

    Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

    CAS  Article  Google Scholar 

  21. 21.

    Beliveau, B. J. et al. Single-molecule super-resolution imaging of chromosomes and in situ haplotype visualization using Oligopaint FISH probes. Nat. Commun. 6, 7147 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Lizardi, P. et al. Mutation detection and single-molecule counting using isothermal rolling-circle amplification. Nat. Genet. 19, 225–232 (1998).

    CAS  Article  Google Scholar 

  23. 23.

    Dirks, R. M. & Pierce, N. A. Triggered amplification by hybridization chain reaction. Proc. Natl Acad. Sci. USA 101, 15275–15278 (2004).

    CAS  Article  Google Scholar 

  24. 24.

    Choi, H. M. T. et al. Programmable in situ amplification for multiplexed imaging of mRNA expression. Nat. Biotechnol. 28, 1208–1212 (2010).

    CAS  Article  Google Scholar 

  25. 25.

    Choi, H. M., Beck, V. A. & Pierce, N. A. Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability. ACS Nano 8, 4284–4294 (2014).

    CAS  Article  Google Scholar 

  26. 26.

    Shah, S. et al. Single-molecule RNA detection at depth via hybridization chain reaction and tissue hydrogel embedding and clearing. Development 92, 2862–2867 (2016).

    Article  Google Scholar 

  27. 27.

    Rouhanifard, S. H. et al. ClampFISH detects individual nucleic acid molecules using click chemistry–based amplification. Nat. Biotechnol. 37, 84–89 (2018).

    Article  Google Scholar 

  28. 28.

    Nagendran, M., Riordan, D. P., Harbury, P. B. & Desai, T. J. Automated cell-type classification in intact tissues by single-cell molecular profiling. eLife 7, e30510 (2018).

    Article  Google Scholar 

  29. 29.

    Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  Google Scholar 

  30. 30.

    Kishi, J. Y., Schaus, T. E., Gopalkrishnan, N., Xuan, F. & Yin, P. Programmable autonomous synthesis of single-stranded DNA. Nat. Chem. 10, 155–164 (2018).

    CAS  Article  Google Scholar 

  31. 31.

    Beliveau, B. J. et al. Versatile design and synthesis platform for visualizing genomes with Oligopaint FISH probes. Proc. Natl Acad. Sci. USA 109, 21301–21306 (2012).

    CAS  Article  Google Scholar 

  32. 32.

    Lee, C. S., Davis, R. W. & Davidson, N. A physical study by electron microscopy of the terminally repetitious, circularly permuted DNA from the coliphage particles of Escherichia coli 15. J. Mol. Biol. 48, 1–22 (1970).

    CAS  Article  Google Scholar 

  33. 33.

    Beliveau, J. et al. OligoMiner provides a rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes. Proc. Natl Acad. Sci. USA 115, E2183–E2192 (2018).

    CAS  Article  Google Scholar 

  34. 34.

    Xu, Q., Schlabach, M. R., Hannon, G. J. & Elledge, S. J. Design of 240,000 orthogonal 25mer DNA barcode probes. Proc. Natl Acad. Sci. USA 106, 2289–2294 (2009).

    CAS  Article  Google Scholar 

  35. 35.

    Dirks, R. M. & Pierce, N. A. A partition function algorithm for nucleic acid secondary structure including pseudoknots. J. Comput. Chem. 24, 1664–1677 (2003).

    CAS  Article  Google Scholar 

  36. 36.

    Dirks, R. M. & Pierce, N. A. An algorithm for computing nucleic acid base-pairing probabilities including pseudoknots. J. Comput. Chem. 25, 1295–1304 (2004).

    CAS  Article  Google Scholar 

  37. 37.

    Dirks, R. M., Bois, J. S., Schaeffer, J. M., Winfree, E. & Pierce, N. A. Thermodynamic analysis of interacting nucleic acid strands. SIAM Rev. 49, 65–88 (2007).

    Article  Google Scholar 

  38. 38.

    Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article  Google Scholar 

  39. 39.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  Article  Google Scholar 

  40. 40.

    Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Mosaliganti, K. R., Noche, R. R., Xiong, F., Swinburne, I. A. & Megason, S. G. ACME: automated cell morphology extractor for comprehensive reconstruction of cell membranes. PLoS Comput. Biol. 8, e1002780 (2012).

    CAS  Article  Google Scholar 

  42. 42.

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

    CAS  Article  Google Scholar 

  43. 43.

    Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).

    CAS  Article  Google Scholar 

  44. 44.

    Emerson, M. M. & Cepko, C. L. Identification of a retina-specific Otx2 enhancer element active in immature developing photoreceptors. Dev. Biol. 360, 241–255 (2011).

    CAS  Article  Google Scholar 

  45. 45.

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

    Article  Google Scholar 

  46. 46.

    Matsuda, T. & Cepko, C. L. Electroporation and RNA interference in the rodent retina in vivo and in vitro. Proc. Natl Acad. Sci. USA 101, 16–22 (2004).

    CAS  Article  Google Scholar 

  47. 47.

    Saka, S. K. et al. Highly multiplexed in situ protein imaging with signal amplification by Immuno-SABER. Nat. Biotechnol. (in the press).

  48. 48.

    Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    CAS  Article  Google Scholar 

  49. 49.

    Yildirim, E., Sadreyev, R. I., Pinter, S. F. & Lee, J. T. X-chromosome hyperactivation in mammals via nonlinear relationships between chromatin states and transcription. Nat. Struct. Mol. Biol. 19, 56–61 (2011).

    Article  Google Scholar 

  50. 50.

    Kent, W. J. et al. The Human Genome Browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  Article  Google Scholar 

  51. 51.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    CAS  Article  Google Scholar 

  52. 52.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  53. 53.

    Marçais, G. & Kingsford, C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27, 764–770 (2011).

    Article  Google Scholar 

  54. 54.

    Casanova, M. et al. Heterochromatin reorganization during early mouse development requires a single-stranded noncoding transcript. Cell Rep. 4, 1156–1167 (2013).

    CAS  Article  Google Scholar 

  55. 55.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Article  Google Scholar 

  56. 56.

    Beliveau, B. J., Apostolopoulos, N. & Wu, C. Visualizing genomes with Oligopaint FISH probes. Curr. Protoc. Mol. Biol. 2014, 14.23.1–14.23.20 (2014).

    Article  Google Scholar 

  57. 57.

    Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  Article  Google Scholar 

  58. 58.

    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    CAS  Article  Google Scholar 

  59. 59.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Article  Google Scholar 

  60. 60.

    McQuin, C. et al. CellProfiler 3.0: next-generation image processing for biology. PLoS Biol. 16, 1–17 (2018).

    Article  Google Scholar 

  61. 61.

    Linkert, M. et al. Metadata matters: access to image data in the real world. J. Cell Biol. 189, 777–782 (2010).

    CAS  Article  Google Scholar 

  62. 62.

    Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).

    Article  Google Scholar 

  63. 63.

    Marr, D. & Hildreth, E. Theory of edge detection. Proc. R. Soc. Lond. B Biol. Sci. 207, 187–217 (1980).

    CAS  Article  Google Scholar 

  64. 64.

    Plaisier, S., Taschereau, R., Wong, J. & Graeber, T. Rank-rank hypergeometric overlap: identification of statistically significant overlap between gene-expression signatures. Nucleic Acids Res. 38, e169 (2010).

    Article  Google Scholar 

  65. 65.

    Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Article  Google Scholar 

  66. 66.

    Waskom, M. et al. mwaskom/seaborn: v0.8.1. (2017).

  67. 67.

    Oliphant, T. E. A Guide to NumPy (Trelgol Publishing, 2006).

  68. 68.

    McKinney, W. Data structures for statistical computing in Python. in Proc. 9th Python in Science Conference (eds. van der Walt, S. & Millman, J.) 51–56 (SciPy, 2010).

  69. 69.

    Cock, P. J. A. et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25, 1422–1423 (2009).

    CAS  Article  Google Scholar 

  70. 70.

    Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots, version 0.1.7. (2018).

  71. 71.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  72. 72.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

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The authors thank B. Fields, S. Kennedy, J.A. Abed, T. Wu, T. Ferrante, N. Liu, F. Dannenberg, M. Cicconet, P.M. Llopis and the Microscopy Resources on the North Quad (MicRoN) at Harvard Medical School for discussions and technical support. We also thank the ENCODE consortium and J. Stamatoyannopoulos (University of Washington) for retina DHS data. This work was supported by the National Institutes of Health (under grants 1R01EB018659-01 to P.Y., 1UG3HL145600 to P.Y., 1R01GM124401 to P.Y., 1U01MH106011-01 to P.Y., 1DP1GM133052 to P.Y., 5K99EY028215-02 to S.W.L. and a T32 training grant GM096911 supporting E.R.W.), the Office of Naval Research (under grants N00014-16-1-2410 to P.Y. and N00014-18-1-2549 to P.Y.), the National Science Foundation (under grant CCF-1317291 to P.Y. and a Graduate Research Fellowship to J.Y.K.), the Howard Hughes Medical Institute (C.L.C.), the Damon Runyon Cancer Research Foundation (under a fellowship to B.J.B.), the Uehara Memorial Foundation (under a fellowship to H.M.S.), the Human Frontier Science Program (under fellowship LT000048/2016-L to S.K.S.), EMBO (under fellowship ALTF 1278-2015 to S.K.S.) and the Wyss Institute’s Molecular Robotics Initiative (MRI) (P.Y., J.Y.K. and B.J.B.).

Author information




J.Y.K., S.W.L., B.J.B., E.R.W., C.L.C. and P.Y. conceived the study. J.Y.K. and B.J.B. designed SABER probes, designed and executed cell experiments and analyzed cell data. S.W.L. designed and executed tissue experiments. E.R.W. developed the analytical pipeline and methods for tissue cell segmentation and puncta quantification. J.Y.K., S.W.L., B.J.B., E.R.W., A.Z., S.K.S., H.M.S. and Y.W. contributed to optimizing and performing experimental protocols and obtaining data. J.Y.K., S.W.L., B.J.B., E.R.W., C.L.C. and P.Y. wrote the manuscript. All authors edited and approved the manuscript. C.L.C. and P.Y. supervised the work.

Corresponding authors

Correspondence to Brian J. Beliveau or Constance L. Cepko or Peng Yin.

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Competing interests

A provisional US patent has been filed based on this work (PCT/US2018/013019). P.Y. is cofounder of Ultivue, Inc. and NuProbe Global.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Supplementary Note

Reporting Summary

Supplementary Protocols

Step-by-step instructions for designing and applying SABER-FISH

Supplementary Table 1

SABER sequences. PER, imager, and probe pool sequences.

Supplementary Table 2

SABER simulated parameters. Melting temperatures of PER, probe and bridge sequences under different formamide conditions, as well as cross-talk probabilities.

Supplementary Table 3

SABER experimental conditions. Detailed PER, ISH and fluorescent-hybridization conditions for each experiment.

Supplementary Table 4

SABER puncta counts. Cell counts, puncta counts, puncta sizes, signal-to-noise values, cross-correlation values and biological replicate information for experiments.

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Kishi, J.Y., Lapan, S.W., Beliveau, B.J. et al. SABER amplifies FISH: enhanced multiplexed imaging of RNA and DNA in cells and tissues. Nat Methods 16, 533–544 (2019).

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