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Spatially resolved transcriptomics and beyond

Nature Reviews Genetics volume 16, pages 57–66 (2015)Cite this article

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Abstract

Considerable progress in sequencing technologies makes it now possible to study the genomic and transcriptomic landscape of single cells. However, to better understand the complexity of multicellular organisms, we must devise ways to perform high-throughput measurements while preserving spatial information about the tissue context or subcellular localization of analysed nucleic acids. In this Innovation article, we summarize pioneering technologies that enable spatially resolved transcriptomics and discuss how these methods have the potential to extend beyond transcriptomics to encompass spatially resolved genomics, proteomics and possibly other omic disciplines.

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Figure 1: Imaging-based methods for spatially resolved transcriptomics.
Figure 2: Sequencing-based methods for spatially resolved transcriptomics.
Figure 3: A future technology for spatially resolved omics: tissue-to-nanopore in situ sequencing.

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  • 05 December 2014

    This article has been updated to include the current institutional affiliations of Nicola Crosetto and Magda Bienko. These authors are currently at Science for Life Laboratory, Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, S-171 21 Stockholm, Sweden.

References

  1. Mazzarello, P. A unifying concept: the history of cell theory. Nature Cell Biol. 1, E13–E15 (1999).

    Article  CAS  PubMed  Google Scholar 

  2. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Zong, C., Lu, S., Chapman, A. R. & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. de Bourcy, C. F. A. et al. A quantitative comparison of single-cell whole genome amplification methods. PLoS ONE 9, e105585 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods 10, 1096–1098 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Junker, J. P. & van Oudenaarden, A. Every cell is special: genome-wide studies add a new dimension to single-cell biology. Cell 157, 8–11 (2014).

    Article  CAS  PubMed  Google Scholar 

  13. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nature Rev. Genet. 14, 618–630 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nature Methods 11, 41–46 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Yuste, R. Fluorescence microscopy today. Nature Methods 2, 902–904 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Agard, D. A., Hiraoka, Y., Shaw, P. & Sedat, J. W. Fluorescence microscopy in three dimensions. Methods Cell Biol. 30, 353–377 (1989).

    Article  CAS  PubMed  Google Scholar 

  17. Tsien, R. Y. The green fluorescent protein. Annu. Rev. Biochem. 67, 509–544 (1998).

    Article  CAS  PubMed  Google Scholar 

  18. Langer-Safer, P. R., Levine, M. & Ward, D. C. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl Acad. Sci. USA 79, 4381–4385 (1982).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Fredriksson, S. et al. Protein detection using proximity-dependent DNA ligation assays. Nature Biotech. 20, 473–477 (2002).

    Article  CAS  Google Scholar 

  20. Boyle, S., Rodesch, M. J., Halvensleben, H. A., Jeddeloh, J. A. & Bickmore, W. A. Fluorescence in situ hybridization with high-complexity repeat-free oligonucleotide probes generated by massively parallel synthesis. Chromosome Res. 19, 901–909 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Bienko, M. et al. A versatile genome-scale PCR-based pipeline for high-definition DNA FISH. Nature Methods 10, 122–124 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. 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  PubMed  Google Scholar 

  24. Luengo-Oroz, M. A., Ledesma-Carbayo, M. J., Peyriéras, N. & Santos, A. Image analysis for understanding embryo development: a bridge from microscopy to biological insights. Curr. Opin. Genet. Dev. 21, 630–637 (2011).

    Article  CAS  PubMed  Google Scholar 

  25. Matos, L. L. de, Trufelli, D. C., de Matos, M. G. L. & da Silva Pinhal, M. A. Immunohistochemistry as an important tool in biomarkers detection and clinical practice. Biomark Insights 5, 9–20 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Reeves, G. T. et al. Dorsal–ventral gene expression in the Drosophila embryo reflects the dynamics and precision of the dorsal nuclear gradient. Dev. Cell 22, 544–557 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Trisnadi, N., Altinok, A., Stathopoulos, A. & Reeves, G. T. Image analysis and empirical modeling of gene and protein expression. Methods 62, 68–78 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Ramel, M.-C. & Hill, C. S. The ventral to dorsal BMP activity gradient in the early zebrafish embryo is determined by graded expression of BMP ligands. Dev. Biol. 378, 170–182 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Hayat, M. A. Handbook of Immunohistochemistry and in situ Hybridization of Human Carcinomas. (Academic Press, 2006).

    Google Scholar 

  30. Cremer, T. & Cremer, M. Chromosome territories. Cold Spring Harb. Perspect. Biol. 2, a003889 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Schekman, R. Merging cultures in the study of membrane traffic. Nature Cell Biol. 6, 483–486 (2004).

    Article  CAS  PubMed  Google Scholar 

  32. Mor, A. et al. Dynamics of single mRNP nucleocytoplasmic transport and export through the nuclear pore in living cells. Nature Cell Biol. 12, 543–552 (2010).

    Article  CAS  PubMed  Google Scholar 

  33. Söderberg, O. et al. Direct observation of individual endogenous protein complexes in situ by proximity ligation. Nature Methods 3, 995–1000 (2006).

    Article  CAS  PubMed  Google Scholar 

  34. Querido, E. & Chartrand, P. Using fluorescent proteins to study mRNA trafficking in living cells. Methods Cell Biol. 85, 273–292 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Saad, H. et al. DNA dynamics during early double-strand break processing revealed by non-intrusive imaging of living cells. PLoS Genet. 10, e1004187 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR–Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Bertrand, E. et al. Localization of ASH1 mRNA particles in living yeast. Mol. Cell 2, 437–445 (1998).

    Article  CAS  PubMed  Google Scholar 

  38. Martin, R. M., Rino, J., Carvalho, C., Kirchhausen, T. & Carmo-Fonseca, M. Live-cell visualization of pre-mRNA splicing with single-molecule sensitivity. Cell Rep. 4, 1144–1155 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Chen, B. et al. Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell 155, 1479–1491 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Lassadi, I. & Bystricky, K. Tracking of single and multiple genomic loci in living yeast cells. Methods Mol. Biol. 745, 499–522 (2011).

    Article  CAS  PubMed  Google Scholar 

  41. van Dijk, E. L., Auger, H., Jaszczyszyn, Y. & Thermes, C. Ten years of next-generation sequencing technology. Trends Genet. 30, 418–426 (2014).

    Article  CAS  PubMed  Google Scholar 

  42. Koboldt, D. C., Steinberg, K. M., Larson, D. E., Wilson, R. K. & Mardis, E. R. The next-generation sequencing revolution and its impact on genomics. Cell 155, 27–38 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Zentner, G. E. & Henikoff, S. High-resolution digital profiling of the epigenome. Nature Rev. Genet. 15, 814–827 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Martin, J. A. & Wang, Z. Next-generation transcriptome assembly. Nature Rev. Genet. 12, 671–682 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Marusyk, A., Almendro, V. & Polyak, K. Intra-tumour heterogeneity: a looking glass for cancer? Nature Rev. Cancer 12, 323–334 (2012).

    Article  CAS  Google Scholar 

  46. Bedard, P. L., Hansen, A. R., Ratain, M. J. & Siu, L. L. Tumour heterogeneity in the clinic. Nature 501, 355–364 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Almendro, V. et al. Genetic and phenotypic diversity in breast tumor metastases. Cancer Res. 74, 1338–1348 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Almendro, V. et al. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity. Cell Rep. 6, 514–527 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Zhang, J. et al. Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing. Science 346, 256–259 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. de Bruin, E. C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. De Robertis, E. M., Morita, E. A. & Cho, K. W. Gradient fields and homeobox genes. Development 112, 669–678 (1991).

    CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Lyubimova, A. et al. Single-molecule mRNA detection and counting in mammalian tissue. Nature Protoc. 8, 1743–1758 (2013).

    Article  CAS  Google Scholar 

  55. Itzkovitz, S. et al. Single-molecule transcript counting of stem-cell markers in the mouse intestine. Nature Cell Biol. 14, 106–114 (2012).

    Article  CAS  Google Scholar 

  56. Waks, Z., Klein, A. M. & Silver, P. A. Cell-to-cell variability of alternative RNA splicing. Mol. Syst. Biol. 7, 506 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Semrau, S. et al. FuseFISH: robust detection of transcribed gene fusions in single cells. Cell Rep. 16, 18–23 (2013).

    Google Scholar 

  58. Markey, F. B., Ruezinsky, W., Tyagi, S. & Batish, M. Fusion FISH imaging: single-molecule detection of gene fusion transcripts in situ. PLoS ONE 9, e93488 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nature Methods 11, 637–640 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Hansen, C. H. & van Oudenaarden, A. Allele-specific detection of single mRNA molecules in situ. Nature Methods 10, 869–871 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Levesque, M. J., Ginart, P., Wei, Y. & Raj, A. Visualizing SNVs to quantify allele-specific expression in single cells. Nature Methods 10, 865–867 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Larsson, C. et al. In situ genotyping individual DNA molecules by target-primed rolling-circle amplification of padlock probes. Nature Methods 1, 227–232 (2004).

    Article  CAS  PubMed  Google Scholar 

  65. Nilsson, M. et al. Padlock probes: circularizing oligonucleotides for localized DNA detection. Science 265, 2085–2088 (1994).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  67. Zhong, X. B., Lizardi, P. M., Huang, X. H., Bray-Ward, P. L. & Ward, D. C. Visualization of oligonucleotide probes and point mutations in interphase nuclei and DNA fibers using rolling circle DNA amplification. Proc. Natl Acad. Sci. USA 98, 3940–3945 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Melin, J. et al. Ligation-based molecular tools for lab-on-a-chip devices. N. Biotechnol. 25, 42–48 (2008).

    Article  CAS  PubMed  Google Scholar 

  69. Larsson, C., Grundberg, I., Söderberg, O. & Nilsson, M. In situ detection and genotyping of individual mRNA molecules. Nature Methods 7, 395–397 (2010).

    Article  CAS  PubMed  Google Scholar 

  70. Kern, D. et al. An enhanced-sensitivity branched-DNA assay for quantification of human immunodeficiency virus type 1 RNA in plasma. J. Clin. Microbiol. 34, 3196–3202 (1996).

    PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  72. Espina, V. et al. Laser-capture microdissection. Nature Protoc. 1, 586–603 (2006).

    Article  CAS  Google Scholar 

  73. Liotta, L. & Petricoin, E. Molecular profiling of human cancer. Nature Rev. Genet. 1, 48–56 (2000).

    Article  CAS  PubMed  Google Scholar 

  74. Luzzi, V., Holtschlag, V. & Watson, M. A. Expression profiling of ductal carcinoma in situ by laser capture microdissection and high-density oligonucleotide arrays. Am. J. Pathol. 158, 2005–2010 (2001).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Schütze, K. & Lahr, G. Identification of expressed genes by laser-mediated manipulation of single cells. Nature Biotech. 16, 737–742 (1998).

    Article  Google Scholar 

  76. Morton, M. L. et al. Identification of mRNAs and lincRNAs associated with lung cancer progression using next-generation RNA sequencing from laser micro-dissected archival FFPE tissue specimens. Lung Cancer 85, 31–39 (2014).

    Article  PubMed  Google Scholar 

  77. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Combs, P. A. & Eisen, M. B. Sequencing mRNA from cryo-sliced Drosophila embryos to determine genome-wide spatial patterns of gene expression. PLoS ONE 8, e71820 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Junker, J. P. et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662–675 (2014).

    Article  CAS  PubMed  Google Scholar 

  81. Okamura-Oho, Y. et al. Transcriptome tomography for brain analysis in the web-accessible anatomical space. PLoS ONE 7, e45373 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nature Methods 11, 190–196 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  84. Bedard, P. L. & Cardoso, F. Can some patients avoid adjuvant chemotherapy for early-stage breast cancer? Nature Rev. Clin. Oncol. 8, 272–279 (2011).

    Article  CAS  Google Scholar 

  85. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods 11, 417–422 (2014).

    Article  CAS  PubMed  Google Scholar 

  88. Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nature Med. 20, 436–442 (2014).

    Article  CAS  PubMed  Google Scholar 

  89. Rimm, D. L. Next-gen immunohistochemistry. Nature Methods 11, 381–383 (2014).

    Article  CAS  PubMed  Google Scholar 

  90. Shaffer, S. M., Wu, M.-T., Levesque, M. J. & Raj, A. Turbo FISH: a method for rapid single molecule RNA FISH. PLoS ONE 8, e75120 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Hyun, B.-R., McElwee, J. L. & Soloway, P. D. Single molecule and single cell epigenomics. Methods http://dx.doi.org/10.1016/j.ymeth.2014.08.015 (2014).

  92. Shankaranarayanan, P. et al. Single-tube linear DNA amplification (LinDA) for robust ChIP–seq. Nature Methods 8, 565–567 (2011).

    Article  CAS  PubMed  Google Scholar 

  93. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  94. Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nature Protoc. 9, 1682–1697 (2014).

    Article  CAS  Google Scholar 

  95. Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Susaki, E. A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by a European Research Council Advanced grant (ERC-AdG 294325-GeneNoiseControl) and by a Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) Vici award to A.v.O. M.B. was sponsored by the Human Frontier Science Program.

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Author notes

  1. Nicola Crosetto & Magda Bienko

    Present address: Present address: Science for Life Laboratory, Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institutett, S-171 21 Stockholm, Sweden.,

  2. Nicola Crosetto and Magda Bienko: These authors contributed equally to this work.

Authors and Affiliations

  1. Hubrecht Institute–KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Utrecht, 3584 CT, The Netherlands

    Nicola Crosetto, Magda Bienko & Alexander van Oudenaarden

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  1. Nicola Crosetto
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Correspondence to Nicola Crosetto or Alexander van Oudenaarden.

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Glossary

Cluster analysis

A set of statistical methods used in many scientific disciplines to group objects within a data set based on similarity.

CRISPR–Cas system

(Clustered regularly interspaced short palindromic repeat–CRISPR-associated protein system). A genome editing technique based on the bacterial immune system. The Cas9 protein and an RNA guide are used to specifically edit or visualize a given sequence of DNA.

DNA fluorescence in situ hybridization

(DNA FISH). An in situ hybridization technique in which probes consisting of short (<500 nucleotides) fluorescently labelled DNA fragments are hybridized to the complementary target sequences in the nucleus.

High-content imaging

A subfield of microscopy using fast and automated imaging systems that can acquire thousands of cell micrographs from different regions on a microscope slide or separate wells of multiwell plates. It is frequently applied to detect morphological cellular changes in drug and small interfering RNA (siRNA) library screens.

High-dimensional data analysis

A collection of statistical methods and visualization tools used to analyse and represent data with dozens to thousands of dimensions. In spatially resolved omics, the intrinsic high dimensionality of omic data is multiplied by the number of spatial locations analysed, producing mega- or giga-dimensional data sets that need to be correlated among each other in order to identify spatially organized expression patterns and regulatory networks. Efficient handling of these mega-dimensional omic data sets requires extraordinary computational efforts and novel statistical tools that are being developed for the analysis of 'big data' in many areas of science and business.

Immunocytological and immunohistological techniques

A group of methods that visualize specific proteins or other antigens directly in cells (immunocytology) or tissues (immunohistology) using antibodies coupled to fluorophores or enzymes.

In situ hybridization techniques

A set of methods that use DNA or RNA probes which bind selectively to specific DNA or RNA target sequences by Watson–Crick or Hoogsteen base pairing. Probes can be conjugated to fluorescent dyes (such as in fluorescence in situ hybridization (FISH)) or to enzymes that catalyse a chromogenic reaction (such as the horseradish peroxidase in chromogenic in situ hybridization (CISH)).

In situ methods

A vast collection of methods used for the detection of nucleic acids or proteins directly in cells or tissues while preserving spatial information.

Laser capture microdissection

(LCM). A technique in which a laser is used to ablate small groups of cells or even single cells up to relatively large regions within a tissue section mounted on a special support matrix, and subsequently to transfer the captured material into test tubes for downstream processing.

Locked nucleic acid

(LNA). A synthetic RNA nucleotide in which the ribose group is modified with an extra bridge connecting the 2′ oxygen and the 4′ carbon. Incorporating LNAs into DNA or RNA oligonucleotides increases the specificity and sensitivity of detection in a number of assays.

Mass cytometry

A technology that combines flow cytometry with mass spectrometry, which enables simultaneous detection of dozens of antigens in thousands of single cells using antibodies tagged with isotopically pure metal reporters. Based on current isotope availability, up to ~100 proteins or protein modifications can be detected simultaneously in thousands of single cells using mass cytometry.

MS2 tagging

A technique that uses the MS2 coat protein of the MS2 bacteriophage to detect RNA molecules owing to the high binding affinity of MS2 for a specific RNA stem–loop structure. Upon insertion of the MS2 recognition sequence in the RNA of interest, the intracellular localization of the RNA can be monitored in live cells using GFP-tagged MS2.

Padlock probes

Oligonucleotide probes consisting of target-complementary homology arms connected by linker sequences. Upon recognition of the target, the extremities of the probe are ligated, and the circle created is amplified through rolling circle amplification (RCA). The resulting product is detected using fluorescence in situ hybridization (FISH) probes. Padlock probes and RCA combine high specificity of detection with high signal amplification, and they have been used in numerous applications in vitro and in situ.

ParB–INT system

A technique used to investigate the position and dynamics of DNA in living cells based on the interaction between the bacterial ParB protein and the parS sequence. Insertion of a cluster of parS sequences in a genomic region of interest allows its visualization using fluorescently tagged ParB.

Proximity ligation assays

(PLAs). A method that can detect proteins and protein–protein interactions with high specificity. The protein of interest is recognized by two primary antibodies raised in different species, followed by binding of species-specific secondary antibodies conjugated to a short DNA oligonucleotide. Upon specific binding of both primary antibodies to the target (two epitopes either on the same protein or on two distinct interacting proteins), the two oligonucleotides are brought together, which aids the formation of a circle from an extra pair of oligonucleotides added to the mixture by the use of in situ ligation. Fluorescent detection is then achieved using rolling circle amplification.

Rolling circle amplification

(RCA). A process by which circularized padlock probes are amplified through the continuous action of an isothermal DNA polymerase. The result is a single-stranded DNA concatemer containing many copies of the original padlock probe sequence in tandem.

Sequence barcodes

Short unique DNA sequences, usually inserted in synthetic oligonucleotides, that are used to keep track of sample identity during nucleic acid amplification procedures prior to sequencing. A particular type of sequence barcode are Unique Molecular Identifiers used for counting absolute numbers of DNA or RNA molecules that are originally present in a sample before nucleic acid amplification by methods such as PCR.

Sequencing-by-ligation

(SBL). A next-generation sequencing method based on sequential ligation of fluorescently labelled probes starting from an adaptor primer bound to a template DNA fragment. Multiple rounds of ligation, detection and fluorophore cleavage are performed using a pool of differently labelled oligonucleotides. In each cycle, only the oligonucleotide that is complementary to the query sequence is ligated and its specific fluorescence detected. SBL constitutes the core of the SOLiD and Complete Genomics technology.

Single-molecule RNA fluorescence in situ hybridization

(smFISH). An in situ hybridization method by which individual RNA molecules are specifically visualized in cells or tissues using pools of 20–50 complementary short DNA oligonucleotides, each of which is coupled to the same fluorophore. Single RNA molecules appear as diffraction-limited fluorescence spots, which can be accurately counted using unbiased automated procedures.

Spatially resolved omics

A new technology-driven field in which high-throughput genomic, epigenomic, transcriptomic, proteomic and possibly other omic data are collected from cells or tissues by means that preserve positional information and analysed using high-dimensional data analysis techniques. So far, most of pioneering efforts in this newly developed field have focused on RNA measurements, but we predict that measurements at the DNA and protein levels will soon ensue.

Super-resolution microscopy

A form of light microscopy that allows the acquisition of images with a resolution not limited by the diffraction limit of light (that is, in the range of dozens of nanometres or less). Super-resolution microscopy methods include STED (stimulated emission depletion), SSIM (saturated structured illumination microscopy), STORM (stochastic optical reconstruction microscopy) and PALM (photoactivated localization microscopy).

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Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat Rev Genet 16, 57–66 (2015). https://doi.org/10.1038/nrg3832

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