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.

  • Protocol
  • Published:

HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging

Nature Protocols volume 18, pages 157–187 (2023)Cite this article

Subjects

Abstract

The ability to visualize RNA in its native subcellular environment by using single-molecule fluorescence in situ hybridization (smFISH) has reshaped our understanding of gene expression and cellular functions. A major hindrance of smFISH is the difficulty to perform systematic experiments in medium- or high-throughput formats, principally because of the high cost of generating the individual fluorescent probe sets. Here, we present high-throughput smFISH (HT-smFISH), a simple and cost-efficient method for imaging hundreds to thousands of single endogenous RNA molecules in 96-well plates. HT-smFISH uses RNA probes transcribed in vitro from a large pool of unlabeled oligonucleotides. This allows the generation of individual probes for many RNA species, replacing commercial DNA probe sets. HT-smFISH thus reduces costs per targeted RNA compared with many smFISH methods and is easily scalable and flexible in design. We provide a protocol that combines oligo pool design, probe set generation, optimized hybridization conditions and guidelines for image acquisition and analysis. The pipeline requires knowledge of standard molecular biology tools, cell culture and fluorescence microscopy. It is achievable in ~20 d. In brief, HT-smFISH is tailored for medium- to high-throughput screens that image RNAs at single-molecule sensitivity.

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

Fig. 1: Overview of the HT-smFISH pipeline.
Fig. 2: Oligo pool design.
Fig. 3: RNA probe set generation from an oligo pool.
Fig. 4: In situ hybridization and imaging.
Fig. 5: HT-smFISH images highlighting the localization of mRNAs to various subcellular locations.
Fig. 6: HT-smFISH probe sets hybridized in mouse colon tissue sections and primary neurons.
Fig. 7: Images of negative and positive control wells routinely included in HT-smFISH experiments.
Fig. 8: Examples of capillary gel electrophoresis used to quality control various steps during probe set generation.

Similar content being viewed by others

Data availability

The main data discussed in this protocol were generated as part of the studies published in the supporting primary research papers6,13. Example barcodes, barcode primers and probe sets are provided in Supplementary Tables 1 and 2.

Code availability

Oligostan-HT is available at https://hub.docker.com/r/oligostan/oligostan_ht_rna alongside documentation and test data. FISH-quant v2 for image analysis is available at https://github.com/fish-quant.

References

  1. Chin, A. & Lécuyer, E. RNA localization: making its way to the center stage. Biochim. Biophys. Acta Gen. Subj. 1861, 2956–2970 (2017).

    Article  CAS  Google Scholar 

  2. Buxbaum, A. R., Haimovich, G. & Singer, R. H. In the right place at the right time: visualizing and understanding mRNA localization. Nat. Rev. Mol. Cell Biol. 16, 95–109 (2015).

    Article  CAS  Google Scholar 

  3. Kejiou, N. S. & Palazzo, A. F. mRNA localization as a rheostat to regulate subcellular gene expression. Wiley Interdiscip. Rev. RNA 8, (2017).

  4. Lee, C.-D. & Tu, B. P. Glucose-regulated phosphorylation of the PUF protein Puf3 regulates the translational fate of its bound mRNAs and association with RNA granules. Cell Rep. 11, 1638–1650 (2015).

    Article  CAS  Google Scholar 

  5. Böckler, S. & Westermann, B. Mitochondrial ER contacts are crucial for mitophagy in yeast. Dev. Cell 28, 450–458 (2014).

    Article  Google Scholar 

  6. Safieddine, A. et al. A choreography of centrosomal mRNAs reveals a conserved localization mechanism involving active polysome transport. Nat. Commun. 12, 1352 (2021).

    Article  CAS  Google Scholar 

  7. Lécuyer, E. et al. Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function. Cell 131, 174–187 (2007).

    Article  Google Scholar 

  8. Yoon, Y. J. et al. Glutamate-induced RNA localization and translation in neurons. Proc. Natl Acad. Sci. USA 113, E6877–E6886 (2016).

    Article  CAS  Google Scholar 

  9. Singer-Krüger, B. & Jansen, R.-P. Here, there, everywhere. mRNA localization in budding yeast. RNA Biol. 11, 1031–1039 (2014).

    Article  Google Scholar 

  10. Chang, P. et al. Localization of RNAs to the mitochondrial cloud in Xenopus oocytes through entrapment and association with endoplasmic reticulum. Mol. Biol. Cell 15, 4669–4681 (2004).

    Article  CAS  Google Scholar 

  11. Medioni, C., Mowry, K. & Besse, F. Principles and roles of mRNA localization in animal development. Dev. Camb. Engl. 139, 3263–3276 (2012).

    CAS  Google Scholar 

  12. Chouaib, R. et al. A dual protein–mRNA localization screen reveals compartmentalized translation and widespread co-translational RNA targeting. Dev. Cell 54, 773–791.e5 (2020).

    Article  CAS  Google Scholar 

  13. Kwon, O. S. et al. Exon junction complex dependent mRNA localization is linked to centrosome organization during ciliogenesis. Nat. Commun. 12, 1351 (2021).

    Article  CAS  Google Scholar 

  14. Sepulveda, G. et al. Co-translational protein targeting facilitates centrosomal recruitment of PCNT during centrosome maturation in vertebrates. eLife 7, e34959 (2018).

    Article  Google Scholar 

  15. Chen, L.-L. Linking long noncoding RNA localization and function. Trends Biochem. Sci. 41, 761–772 (2016).

    Article  CAS  Google Scholar 

  16. Cabili, M. N. et al. Localization and abundance analysis of human lncRNAs at single-cell and single-molecule resolution. Genome Biol. 16, 20 (2015).

    Article  Google Scholar 

  17. Kloc, M., Dallaire, P., Reunov, A. & Major, F. Structural messenger RNA contains cytokeratin polymerization and depolymerization signals. Cell Tissue Res. 346, 209–222 (2011).

    Article  CAS  Google Scholar 

  18. Garcia-Jove Navarro, M. et al. RNA is a critical element for the sizing and the composition of phase-separated RNA–protein condensates. Nat. Commun. 10, 3230 (2019).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  20. Pichon, X., Lagha, M., Mueller, F. & Bertrand, E. A growing toolbox to image gene expression in single cells: sensitive approaches for demanding challenges. Mol. Cell 71, 468–480 (2018).

    Article  CAS  Google Scholar 

  21. Tsanov, N. et al. smiFISH and FISH-quant - a flexible single RNA detection approach with super-resolution capability. Nucleic Acids Res. 44, e165 (2016).

    Article  Google Scholar 

  22. Battich, N., Stoeger, T. & Pelkmans, L. Image-based transcriptomics in thousands of single human cells at single-molecule resolution. Nat. Methods 10, 1127–1133 (2013).

    Article  CAS  Google Scholar 

  23. Sinnamon, J. R. & Czaplinski, K. RNA detection in situ with FISH-STICs. RNA 20, 260–266 (2014).

    Article  CAS  Google Scholar 

  24. Marx, V. Method of the year: spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).

    Article  CAS  Google Scholar 

  25. Method of the year 2020: spatially resolved transcriptomics. Nat. Methods. 18, 1–1 (2021).

  26. Wang, G., Moffitt, J. R. & Zhuang, X. Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847 (2018).

    Article  Google Scholar 

  27. 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).

    Article  CAS  Google Scholar 

  28. Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235 (2019).

    Article  CAS  Google Scholar 

  29. Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376.e16 (2018).

    Article  CAS  Google Scholar 

  30. Eng, C.-H. L., Shah, S., Thomassie, J. & Cai, L. Profiling the transcriptome with RNA SPOTs. Nat. Methods 14, 1153–1155 (2017).

    Article  CAS  Google Scholar 

  31. Goh, J. J. L. et al. Highly specific multiplexed RNA imaging in tissues with split-FISH. Nat. Methods 17, 689–693 (2020).

    Article  CAS  Google Scholar 

  32. Chen, X., Teichmann, S. A. & Meyer, K. B. From tissues to cell types and back: single-cell gene expression analysis of tissue architecture. Annu. Rev. Biomed. Data Sci. 1, 29–51 (2018).

    Article  Google Scholar 

  33. Close, J. L., Long, B. R. & Zeng, H. Spatially resolved transcriptomics in neuroscience. Nat. Methods 18, 23–25 (2021).

    Article  CAS  Google Scholar 

  34. Zhuang, X. Spatially resolved single-cell genomics and transcriptomics by imaging. Nat. Methods 18, 18–22 (2021).

    Article  CAS  Google Scholar 

  35. Larsson, L., Frisén, J. & Lundeberg, J. Spatially resolved transcriptomics adds a new dimension to genomics. Nat. Methods 18, 15–18 (2021).

    Article  CAS  Google Scholar 

  36. Lee, J. H. et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442–458 (2015).

    Article  CAS  Google Scholar 

  37. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    Article  CAS  Google Scholar 

  38. Chen, X., Sun, Y.-C., Church, G. M., Lee, J. H. & Zador, A. M. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 46, e22 (2018).

    Article  CAS  Google Scholar 

  39. Liu, S. et al. Barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analyses. Nucleic Acids Res. 49, e58 (2021).

    Article  CAS  Google Scholar 

  40. Alon, S. et al. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  42. Mueller, F. et al. FISH-quant: automatic counting of transcripts in 3D FISH images. Nat. Methods 10, 277–278 (2013).

    Article  CAS  Google Scholar 

  43. Samacoits, A. et al. A computational framework to study sub-cellular RNA localization. Nat. Commun. 9, 4584 (2018).

    Article  Google Scholar 

  44. Imbert, A. et al. FISH-quant v2: a scalable and modular tool for smFISH image analysis. RNA 28, 786–795 (2022).

    Article  CAS  Google Scholar 

  45. Bahry, E. et al. RS-FISH: Precise, interactive, fast, and scalable FISH spot detection. Preprint at bioRxiv https://doi.org/10.1101/2021.03.09.434205 (2021).

  46. Perkel, J. M. Starfish enterprise: finding RNA patterns in single cells. Nature 572, 549–551 (2019).

    Article  CAS  Google Scholar 

  47. Eichenberger, B. T., Zhan, Y., Rempfler, M., Giorgetti, L. & Chao, J. A. deepBlink: threshold-independent detection and localization of diffraction-limited spots. Nucleic Acids Res. 49, 7292–7297 (2021).

    Article  CAS  Google Scholar 

  48. Savulescu, A. F. et al. Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH. Cell Rep. Methods 1, 100068 (2021).

    CAS  Google Scholar 

  49. Rouhanifard, S. H. et al. ClampFISH detects individual nucleic-acid molecules using click chemistry-based amplification. Nat. Biotechnol. 37, 84–89 (2019).

    Article  CAS  Google Scholar 

  50. Yaroslavsky, A. I. & Smolina, I. V. Fluorescence imaging of single-copy DNA sequences within the human genome using PNA-directed padlock probe assembly. Chem. Biol. 20, 445–453 (2013).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  52. Pichon, X. et al. The kinesin KIF1C transports APC-dependent mRNAs to cell protrusions. RNA 27, 1528–1544 (2021).

    Article  CAS  Google Scholar 

  53. Ouyang, W., Mueller, F., Hjelmare, M., Lundberg, E. & Zimmer, C. ImJoy: an open-source computational platform for the deep learning era. Nat. Methods 16, 1199–1200 (2019).

    Article  CAS  Google Scholar 

  54. Rio, D. C. Expression and purification of active recombinant T7 RNA polymerase from E. coli. Cold Spring Harb. Protoc. 2013, pdb.prot078527 (2013).

    Article  Google Scholar 

  55. 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).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

A.S. was supported by the Agence Nationale de la Recherche (ANR, grant no. ANR 19-CE12-0024-01) and fellowships from the Ministère de l’Enseignement Supérieur, de la Recherche et de l’Innovation (MESRI) and the Fondation pour la Recherche Médicale (FRM). This project was supported by France BioImaging (ANR-10-INBS-04), the ANR (grants ANR-11-BSV8-018-02 and ANR-14-CE10-0018-01), the FRM ‘Bioinformatics’ grant, the Pasteur Institut, the Ligue Nationale Contre le Cancer and the Labex EpiGenMed from the framework ‘Investissements d’avenir.’

Author information

Authors and Affiliations

  1. Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France

    Adham Safieddine, Emeline Coleno, Abdel-Meneem Traboulsi, Soha Salloum, Charles-Henri Lecellier, Thierry Gostan, Marion Peter & Edouard Bertrand

  2. Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie du Développement, Paris, France

    Adham Safieddine

  3. Institut de Génétique Humaine, University of Montpellier, CNRS, Montpellier, France

    Emeline Coleno, Soha Salloum & Edouard Bertrand

  4. BioCampus Montpellier, Montpellier, Cedex, France

    Frederic Lionneton, Virginie Georget & Cédric Hassen-Khodja

  5. LIRMM, University of Montpellier, CNRS, Montpellier, France

    Charles-Henri Lecellier

  6. Centre for Computational Biology (CBIO), Mines Paris, PSL University, Paris, France

    Arthur Imbert & Thomas Walter

  7. Institut Curie, PSL University, Paris, France

    Arthur Imbert & Thomas Walter

  8. INSERM, U900, 75005, Paris, France

    Arthur Imbert & Thomas Walter

  9. Institut Pasteur, Université de Paris, Computational Imaging and Modeling Unit, F-75015, Paris, France

    Florian Mueller

  10. Institut de Recherche en Cancérologie de Montpellier U1194, University of Montpellier, INSERM, ICM, CNRS, Montpellier, France

    Marion Peter

  11. Equipe Labélisée Ligue Nationale Contre le Cancer, University of Montpellier, CNRS, Montpellier, France

    Edouard Bertrand

Authors
  1. Adham Safieddine
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emeline Coleno
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frederic Lionneton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdel-Meneem Traboulsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Soha Salloum
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Charles-Henri Lecellier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Thierry Gostan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Virginie Georget
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Cédric Hassen-Khodja
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Arthur Imbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Florian Mueller
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Thomas Walter
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Marion Peter
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Edouard Bertrand
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The HT-smFISH methodology was conceived by E.B. and developed by A.M-T., C.H.-K., C.-H.L., E.B., E.C., F.L., M.P., T.G. and V.G. Oligostan-HT was developed by T.G. with input from E.B., and C.-H.L. for barcoding and target selection. The RNA localization analysis pipeline FISH-quant v2 was developed by A.I., F.M. and T.W. S.S. performed experiments in tissue. A.S. wrote the manuscript with input from E.B. A.S. made the figures. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Adham Safieddine or Edouard Bertrand.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Protocols thanks Mona Batish and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Related links

Key references using this protocol

Safieddine, A. et al. Nat. Commun. 12, 1352 (2021): https://doi.org/10.1038/s41467-021-21585-7

Kwon, O. S. et al. Nat. Commun. 12, 1351 (2021): https://doi.org/10.1038/s41467-021-21590-w

Supplementary information

Reporting Summary

Supplementary Table 1

Barcodes and PCR barcode primers to be ordered. The primers are organized by plate and ID. Oligostan-HT uses this order such that the first two primers of plate 1 (wells A1 and A2) are used to PCR the first probe set in the Oligostan-HT output file, the next two (wells A3 and A4) for the second probe set, and so on.

Supplementary Table 2

A sample of Oligostan-HT’s output. The output is shown in an Excel sheet with several columns. ENST, ensemble transcript identifier; ENSGx, ensemble gene identifier; GeneName, name of gene or RNA; SET, the experiment to which the probe set belongs; dGOpt, ΔG37°C reference value (yielding the highest number of probes); Start/End Pos, the start and end position of the probe with respect to the target transcript’s coding sequence; ProbeSize, the length of the probe’s hybridization sequence; Seq, the probe’s hybridization sequence; dGScore and dG37, ΔG37°C score (higher is better) and value; GCpc, percentage of GC in the hybridization sequence; GCFilter, whether the probe satisfies the specified % GC filter; aCompFilter, whether the probe contains an A nucleotide composition <28%; aStackFilter, whether the probe does not contain AAAA stacks; cCompFilter, whether the probe contains an C nucleotide composition between 22% and 28%, cStackFilter, whether the probe does not contain CCCC stacks; cSpecStackFilter, whether the probe does not contain four nonconsecutive Cs in any six consecutive nucleotides in the first twelve positions; NbOfPNAS, the number of filters satisfied (from ref. 55); PNASFilter, whether the probe satisfies the assigned number of PNAS filters; RSESeqFilter, whether probes contain a restriction enzyme site, filter not used; InsideUTR, whether the probe is inside a UTR; BC1ID, ID of the first barcode to be used to amplify the probe; BC1PN, the number of the plate in which the barcode is stored; BC1WP, position of the barcode in the 96-well plate; BC1, sequence of barcode 1; BC2ID, BC2PN, BC2WP, BC2, same as barcode 1, but for barcode 2; BC1-Y-Hyb-X-BC2, sequence resulting from concatenation of barcode 1– readout Y–hybridization sequence–readout X–barcode 2; WithComplementSeq, same but with a few additional nucleotides to normalize all oligonucleotide length to facilitate synthesis. This column has the oligo pool sequences to be ordered.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safieddine, A., Coleno, E., Lionneton, F. et al. HT-smFISH: a cost-effective and flexible workflow for high-throughput single-molecule RNA imaging. Nat Protoc 18, 157–187 (2023). https://doi.org/10.1038/s41596-022-00750-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-022-00750-2

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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