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Spatial omics and multiplexed imaging to explore cancer biology

Nature Methods volume 18, pages 997–1012 (2021)Cite this article

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Abstract

Understanding intratumoral heterogeneity—the molecular variation among cells within a tumor—promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

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Fig. 1: Outstanding questions in the field of spatial cancer biology.
Fig. 2: Spatial technologies.
Fig. 3: Spatial proteomic approaches.
Fig. 4: FISH-based spatial transcriptomic methods.
Fig. 5: Sequencing-based spatial transcriptomic methods.
Fig. 6: Timeline of clonal, spatial transcriptomics and proteomics methods.

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References

  1. Hooke, R. Micrographia: Or Some Physiological Descriptions of Minute Bodies Made by Magnifying Glasses, with Observations and Inquiries Thereupon (Courier, 2003).

  2. Hajdu, S. I. A note from history: landmarks in history of cancer, part 3. Cancer 118, 1155–1168 (2012).

    Article  PubMed  Google Scholar 

  3. Coons, A. H., Creech, H. J. & Jones, R. N. Immunological properties of an antibody containing a fluorescent group. Proc. Soc. Exp. Biol. Med. 47, 200–202 (1941).

    Article  CAS  Google Scholar 

  4. Cobb, M. Who discovered messenger RNA? Curr. Biol. 25, R526–R532 (2015).

    Article  CAS  PubMed  Google Scholar 

  5. Berger, M. F. & Mardis, E. R. The emerging clinical relevance of genomics in cancer medicine. Nat. Rev. Clin. Oncol. 15, 353–365 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Kashima, Y. et al. Single-cell sequencing techniques from individual to multiomics analyses. Exp. Mol. Med. 52, 1419–1427 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Klein, C. A. Parallel progression of primary tumours and metastases. Nat. Rev. Cancer 9, 302–312 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Marusyk, A., Janiszewska, M. & Polyak, K. Intratumor heterogeneity: the Rosetta Stone of therapy resistance. Cancer Cell 37, 471–484 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. DeGregori, J. Adaptive Oncogenesis: A New Understanding of How Cancer Evolves Inside Us (Harvard University Press, 2018).

  12. Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Marine, J. C., Dawson, S. J. & Dawson, M. A. Non-genetic mechanisms of therapeutic resistance in cancer. Nat. Rev. Cancer 20, 743–756 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Casasent, A. K., Edgerton, M. & Navin, N. E. Genome evolution in ductal carcinoma in situ: invasion of the clones. J. Pathol. 241, 208–218 (2017).

    Article  PubMed  Google Scholar 

  16. Lan, X. et al. Fate mapping of human glioblastoma reveals an invariant stem cell hierarchy. Nature 549, 227–232 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nguyen, L. V. et al. DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts. Nat. Commun. 5, 5871 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Merino, D. et al. Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer. Nat. Commun. 10, 766 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Echeverria, G. V. et al. High-resolution clonal mapping of multi-organ metastasis in triple negative breast cancer. Nat. Commun. 9, 5079 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. Snippert, H. J. et al. Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010). The authors developed a multicolor reporter mouse termed R26R-Confetti based on the Brainbow-2.1 cassette. After Cre-mediated recombination, one of four fluorescent marker proteins is stochastically expressed, allowing clonal lineage tracing of stem cells.

    Article  CAS  PubMed  Google Scholar 

  22. Weber, K., Bartsch, U., Stocking, C. & Fehse, B. A multicolor panel of novel lentiviral ‘gene ontology’ (LeGO) vectors for functional gene analysis. Mol. Ther. 16, 698–706 (2008). A panel of LeGO vectors provide a flexible tool for investigating clonality and gene networks using simultaneous ectopic expression of fluorescent proteins, transgenes and shRNAs.

    Article  CAS  PubMed  Google Scholar 

  23. Weissman, T. A. & Pan, Y. A. Brainbow: new resources and emerging biological applications for multicolor genetic labeling and analysis. Genetics 199, 293–306 (2014).

    Article  CAS  Google Scholar 

  24. El-Nachef, D. et al. A rainbow reporter tracks single cells and reveals heterogeneous cellular dynamics among pluripotent stem cells and their differentiated derivatives. Stem Cell Rep. 15, 226–241 (2020).

    Article  CAS  Google Scholar 

  25. Boone, P. G. et al. A cancer rainbow mouse for visualizing the functional genomics of oncogenic clonal expansion. Nat. Commun. 10, 5490 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim, G. B. et al. Rapid generation of somatic mouse mosaics with locus-specific, stably integrated transgenic elements. Cell 179, 251–267 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Schepers, A. G. et al. Lineage tracing reveals Lgr5+ stem cell activity in mouse intestinal adenomas. Science 337, 730–735 (2012).

    Article  CAS  PubMed  Google Scholar 

  28. Fumagalli, A. et al. Plasticity of Lgr5-negative cancer cells drives metastasis in colorectal cancer. Cell Stem Cell 26, 569–578 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Reeves, M. Q., Kandyba, E., Harris, S., Del Rosario, R. & Balmain, A. Multicolour lineage tracing reveals clonal dynamics of squamous carcinoma evolution from initiation to metastasis. Nat. Cell Biol. 20, 699–709 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zomer, A. et al. Intravital imaging of cancer stem cell plasticity in mammary tumors. Stem Cells 31, 602–606 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Rios, A. C. et al. Intraclonal plasticity in mammary tumors revealed through large-scale single-cell resolution 3D imaging. Cancer Cell 35, 618–632 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Tiede, S. et al. Multi-color clonal tracking reveals intra-stage proliferative heterogeneity during mammary tumor progression. Oncogene 40, 12–27 (2021).

    Article  CAS  PubMed  Google Scholar 

  33. Weber, K. et al. RGB marking facilitates multicolor clonal cell tracking. Nat. Med. 17, 504–509 (2011).

    Article  CAS  PubMed  Google Scholar 

  34. Roh, V. et al. Cellular barcoding identifies clonal substitution as a hallmark of local recurrence in a surgical model of head and neck squamous cell carcinoma. Cell Rep. 25, 2208–2222 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Lamprecht, S. et al. Multicolor lineage tracing reveals clonal architecture and dynamics in colon cancer. Nat. Commun. 8, 1406 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Mohme, M. et al. Optical barcoding for single-clone tracking to study tumor heterogeneity. Mol. Ther. 25, 621–633 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lambert, T. J. FPbase: a community-editable fluorescent protein database. Nat. Methods 16, 277–278 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Anzalone, A. V., Jimenez, M. & Cornish, V. W. FRAME-tags: genetically encoded fluorescent markers for multiplexed barcoding and time-resolved tracking of live cells. Preprint at bioRxiv https://doi.org/10.1101/2021.04.09.436507 (2021).

  39. Weber, K., Thomaschewski, M., Benten, D. & Fehse, B. RGB marking with lentiviral vectors for multicolor clonal cell tracking. Nat. Protoc. 7, 839–849 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Medaglia, C. et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 358, 1622–1626 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yamashita, R. et al. Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol. 22, 132–141 (2021).

    Article  PubMed  Google Scholar 

  42. Naik, N. et al. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nat. Commun. 11, 5727 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tóth, Z. E. & Mezey, É. Simultaneous visualization of multiple antigens with tyramide signal amplification using antibodies from the same species. J. Histochem. Cytochem. 55, 545–554 (2007). Microwave-mediated antibody stripping in combination with tyramide signal amplification enables use of multiple antibodies from the same host, and both rare and abundant antigens can be detected.

    Article  PubMed  CAS  Google Scholar 

  44. Banik, G. et al. High-dimensional multiplexed immunohistochemical characterization of immune contexture in human cancers. Methods Enzymol. 635, 1–20 (2020).

  45. Tsujikawa, T. et al. Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep. 19, 203–217 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Stack, E. C., Wang, C., Roman, K. A. & Hoyt, C. C. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70, 46–58 (2014).

    Article  CAS  PubMed  Google Scholar 

  47. Lin, J. R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Herbel, C. et al. Abstract 4694: Evaluation of tumor-associated antigen expression with the MACSima high-content imaging platform. Cancer Res. 79, 4694 (2019).

  50. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 (2018). An indexable tagging system whereby antibodies are labeled with uniquely designed oligonucleotide duplexes. Pairs of antibodies are visualized in a multicycle protocol to allow antigen multiplexing.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Saka, S. K. et al. Immuno-SABER enables highly multiplexed and amplified protein imaging in tissues. Nat. Biotechnol. 37, 1080–1090 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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  CAS  PubMed  PubMed Central  Google Scholar 

  54. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014). IMC combines CyTOF mass cytometry and a high-resolution laser ablation system with immunocytochemistry and IHC techniques to image 32 proteins simultaneously. Here IMC is used to study microenvironmental heterogeneity in human breast cancer, while in ref. 55 it is used for multimodal RNA and protein imaging.

    Article  CAS  PubMed  Google Scholar 

  55. Schulz, D. et al. Simultaneous multiplexed imaging of mrna and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Angelo, M. et al. Multiplexed ion beam imaging (MIBI) of human breast tumors. Nat. Med. 20, 436–442 (2014). MIBI uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters. Here MIBI is used to study pathogenesis in human breast cancer, and it has also been used in several cancer studies, including in ref. 57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Keren, L. et al. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 5, eaax5851 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Beechem, J. M. High-plex spatially resolved RNA and protein detection using digital spatial profiling: a technology designed for immuno-oncology biomarker discovery and translational research. Methods Mol. Biol. 2055, 563–583 (2020).

  60. Brady, L. et al. Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling. Nat. Commun. 12, 1426 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Gall, J. G. & Pardue, M. L. Formation and detection of RNA–DNA hybrid molecules in cytological preparations. Proc. Natl Acad. Sci. USA 63, 378–383 (1969).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Rudkin, G. T. & Stollar, B. D. High resolution detection of DNA–RNA hybrids in situ by indirect immunofluorescence. Nature 265, 472–473 (1977).

    Article  CAS  PubMed  Google Scholar 

  64. Bauman, J. G. J., Wiegant, J., Borst, P. & van Duijn, P. A new method for fluorescence microscopical localization of specific DNA sequences by in situ hybridization of fluorochrome-labelled RNA. Exp. Cell. Res. 128, 485–490 (1980).

    Article  CAS  PubMed  Google Scholar 

  65. Takei, Y. et al. Integrated spatial genomics reveals global architecture of single nuclei. Nature 590, 344–350 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Su, J. H., Zheng, P., Kinrot, S. S., Bintu, B. & Zhuang, X. Genome-scale imaging of the 3D organization and transcriptional activity of chromatin. Cell 182, 1641–1659 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Payne, A. C. et al. In situ genome sequencing resolves DNA sequence and structure in intact biological samples. Science 371, eaay3446 (2021).

    Article  CAS  PubMed  Google Scholar 

  68. Nguyen, H. Q. et al. 3D mapping and accelerated super-resolution imaging of the human genome using in situ sequencing. Nat. Methods 17, 822–832 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. 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 

  70. 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). A sensitive smFISH method for detecting individual mRNA molecules in fixed cells via hybridization of multiple short probes. This paved the way for future multiplexed smFISH methods.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  74. 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). MERFISH is a temporal barcoded smFISH method that measures 100–1,001 genes with high spatial resolution and detection efficiency. Xia et al.76 build on this to enable detection of 10,000+ mRNAs in situ, leading to the identification of approximately 1,600 cell cycle-dependent genes.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Xia, C., Fan, J., Emanuel, G., Hao, J. & Zhuang, X. Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl Acad. Sci. USA 116, 19490–19499 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  78. 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). seqFISH uses temporal barcoding to measure 12 genes. Eng et al.80 expand this to achieve multiplexing of 10,000 genes in single cells using pseudocolors to circumvent the problem of optical crowding.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  82. Askary, A. et al. In situ readout of DNA barcodes and single base edits facilitated by in vitro transcription. Nat. Biotechnol. 38, 66–75 (2020).

    Article  CAS  PubMed  Google Scholar 

  83. Chow, K.-H. K. et al. Imaging cell lineage with a synthetic digital recording system. Science 372, eabb3099 (2021).

    Article  CAS  PubMed  Google Scholar 

  84. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Nichterwitz, S. et al. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat. Commun. 7, 12139 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Chen, J. et al. Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566–580 (2017).

    Article  CAS  PubMed  Google Scholar 

  87. Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Pennycuick, A. et al. Immune surveillance in clinical regression of preinvasive squamous cell lung cancer. Cancer Discov. 10, 1489–1499 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  91. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016). An oligonucleotide array with positional barcodes to capture mRNA from histological tissue sections, demonstrating that individual experiments can recover thousands of transcript-coupled spatial barcodes at 10-μm resolution. This study paved the way for higher-resolution spatial transcriptomics to single-cell resolution.

    Article  PubMed  CAS  Google Scholar 

  92. Berglund, E. et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat. Commun. 9, 2419 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Thrane, K., Eriksson, H., Maaskola, J., Hansson, J. & Lundeberg, J. Spatially resolved transcriptomics enables dissection of genetic heterogeneity in stage III cutaneous malignant melanoma. Cancer Res. 78, 5970–5979 (2018).

    CAS  PubMed  Google Scholar 

  94. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Chen, A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. Preprint at bioRxiv https://doi.org/10.1101/2021.01.17.427004 (2021).

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  100. Gyllborg, D. et al. Hybridization-based in situ sequencing (HybISS) for spatially resolved transcriptomics in human and mouse brain tissue. Nucleic Acids Res. 48, e112 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. 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  PubMed  PubMed Central  Google Scholar 

  102. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014). A protocol for unbiased gene expression profiling in situ in fixed histological samples. RNA is converted into cross-linked cDNA amplicons and sequenced manually on a confocal microscope.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Massingham, T. & Goldman, N. Error-correcting properties of the SOLiD exact call chemistry. BMC Bioinformatics 13, 145 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Strell, C. et al. Placing RNA in context and space—methods for spatially resolved transcriptomics. FEBS J. 286, 1468–1481 (2019).

    Article  CAS  PubMed  Google Scholar 

  105. Lee, J. H. Quantitative approaches for investigating the spatial context of gene expression. Wiley Interdiscip. Rev. Syst. Biol. Med. 9, e1369 (2017).

    Article  CAS  Google Scholar 

  106. Baharlou, H., Canete, N. P., Cunningham, A. L., Harman, A. N. & Patrick, E. Mass cytometry imaging for the study of human diseases—applications and data analysis strategies. Front. Immunol. 10, 2657 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Efremova, M. & Teichmann, S. A. Computational methods for single-cell omics across modalities. Nat. Methods 17, 14–17 (2020).

    Article  CAS  PubMed  Google Scholar 

  108. Van Valen, D. A. et al. Deep learning automates the quantitative analysis of individual cells in live-cell imaging experiments. PLoS Comput. Biol. 12, e1005177 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Caicedo, J. C. et al. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nat. Methods 16, 1247–1253 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    Article  CAS  PubMed  Google Scholar 

  111. Zhu, Q., Shah, S., Dries, R., Cai, L. & Yuan, G. C. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data. Nat. Biotechnol. 36, 1183–1190 (2018).

    Article  CAS  Google Scholar 

  112. Hollandi, R. et al. nucleAIzer: a parameter-free deep learning framework for nucleus segmentation using image style transfer. Cell Syst. 10, 453–458 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell detection with star-convex polygons. in International Conference on Medical Image Computing and Computer-Assisted Intervention 265–273 (Springer, 2018).

  114. Weigert, M., Schmidt, U., Haase, R., Sugawara, K. & Myers, G. Star-convex polyhedra for 3D object detection and segmentation in microscopy. in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision 3666–3673 (IEEE, 2020).

  115. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).

    Article  CAS  PubMed  Google Scholar 

  116. Palla, G. et al. Squidpy: a scalable framework for spatial single cell 2 analysis. Preprint at bioRxiv https://doi.org/10.1101/2021.02.19.431994 (2021).

  117. Pham, D. et al. stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell–cell interactions and spatial trajectories within undissociated tissues. Preprint at bioRxiv https://doi.org/10.1101/2020.05.31.125658 (2020).

  118. Righelli, D. et al. SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor. Preprint at bioRxiv https://doi.org/10.1101/2021.01.27.428431 (2021).

  119. Dries, R. et al. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. 22, 78 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Bergenstråhle, J., Larsson, L. & Lundeberg, J. Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics 21, 482 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    Article  PubMed  CAS  Google Scholar 

  123. Senabouth, A. et al. Ascend: R package for analysis of single-cell RNA-seq data. Gigascience 8, giz087 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  126. Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. ScPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20, 264 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).

  128. Kleshchevnikov, V. et al. Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2020.11.15.378125 (2020).

  129. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00830-w (2021). RCTD uses cross-platform integration of single-cell sequencing data and spatial transcriptomics to predict cell type composition and location. Technical variation is accounted for between different technologies.

  130. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020). Among the first to use scRNA-seq data as a reference to resolve cell type information in each microarray-based spatial spot, this approach was used to find a spatially restricted distribution of macrophages and fibroblasts in pancreatic ductal adenocarcinoma tissue.

    Article  CAS  PubMed  Google Scholar 

  131. Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514 (2020). scRNA-seq, spatial transcriptomics and spatial proteomic technologies are combined to study the cellular and molecular profiles of ten human skin squamous cell carcinoma and matched normal tissue samples. This multimodal approach and integrative analysis were used to demonstrate the heterogeneity of tumor cells at tumor edges, as well as their spatial relationships and communication networks with immune and stromal cells.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Lopez, R. et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. Preprint at https://arxiv.org/abs/1905.02269 (2019).

  134. Edsgärd, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  135. Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Bergenstråhle, J., Bergenstråhle, L. & Lundeberg, J. SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation. BMC Bioinformatics 21, 161 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-00935-2 (2021).

  138. Biancalani, T. et al. Deep learning and alignment of spatially-resolved whole transcriptomes of single cells in the mouse brain with Tangram. Preprint at bioRxiv https://doi.org/10.1101/2020.08.29.272831 (2020).

  139. Hu, J. et al. Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Preprint at bioRxiv https://doi.org/10.1101/2020.11.30.405118 (2020).

  140. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  143. Kueckelhaus, J. et al. Inferring spatially transient gene expression pattern from spatial transcriptomic studies. Preprint at bioRxiv https://doi.org/10.1101/2020.10.20.346544 (2020).

  144. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  145. Tran, M. et al. Spatial analysis of ligand–receptor interactions in skin cancer at genome-wide and single-cell resolution. Preprint at bioRxiv https://doi.org/10.1101/2020.09.10.290833 (2020).

  146. Schapiro, D. et al. HistoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Elyanow, R., Zeira, R., Land, M. & Raphael, B. J. STARCH: copy number and clone inference from spatial transcriptomics data. Phys. Biol. 18, 035001 (2021).

    Article  CAS  PubMed  Google Scholar 

  148. Kiemen, A. et al. In situ characterization of the 3D microanatomy of the pancreas and pancreatic cancer at single cell resolution. Preprint at bioRxiv https://doi.org/10.1101/2020.12.08.416909 (2020).

  149. Su, A. et al. A deep learning model for molecular label transfer that enables cancer cell identification from histopathology images. Preprint at bioRxiv https://doi.org/10.1101/2021.03.18.436004 (2021).

  150. Tan, X., Su, A., Tran, M. & Nguyen, Q. SpaCell: integrating tissue morphology and spatial gene expression to predict disease cells. Bioinformatics 36, 2293–2294 (2020).

    Article  CAS  PubMed  Google Scholar 

  151. Stoltzfus, C. R. et al. CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues. Cell Rep. 31, 107523 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).

    Article  CAS  PubMed  Google Scholar 

  153. He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020). stNet demonstrates successful application of deep learning to predict gene expression using H&E images, creating the potential for predicting breast cancer gene markers using histological images.

    Article  CAS  PubMed  Google Scholar 

  154. Schmauch, B. et al. A deep learning model to predict RNA-seq expression of tumours from whole slide images. Nat. Commun. 11, 3877 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Preprint at bioRxiv https://doi.org/10.1101/2020.02.28.963413 (2020).

  156. Hu, K. H. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17, 833–843 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

For Fig. 2, we thank L. Cai for contributing seqFISH data; S. Lakhani and P. Kalita de Croft for clinical annotation of breast cancer tissue; and Ionpath for assisting with tissue staining and generation of MIBI data. We thank D. Brown and T. Weber for critical feedback. M.-L.A.-L. is supported by funding from the Viertel Foundation Senior Medical Research Fellowship, NHMRC grant GNT1182155 and the Harry Secomb Foundation, managed by Perpetual. Q.N. is supported by Australian Research Council DECRA fellowship DE190100116 and NHMRC grant GNT2001514. D.M. is supported by Susan G. Komen and Cancer Australia, CCR19606878, a grant from the National Breast Cancer Foundation, Australia, IIRS-19-082 and the Grant-in-Aid Scheme administered by Cancer Council Victoria. The Olivia Newton-John Cancer Research Institute gratefully acknowledges the generous support of the Love Your Sister Foundation. S.H.N. is supported by NHMRC grants GNT1062820, GNT1100033, GNT1101378, GNT1124812 and GNT1145184. We also acknowledge support from the ACRF Centre for Imaging the Tumour Environment at the Olivia Newton-John Cancer Research Institute, the ACRF Program for Resolving Cancer Complexity and Therapeutic Resistance at WEHI and the Operational Infrastructure Support Program provided by the Victorian government and the NHMRC Independent Research Institutes Infrastructure Support Scheme (IRIISS) grant. The contents of the published material are solely the responsibility of the individual authors and do not reflect the views of Cancer Australia, NHMRC or other funding agencies.

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Author notes

  1. These authors contributed equally: Verena C. Wimmer, Delphine Merino, Kelly L. Rogers, Shalin H. Naik.

Authors and Affiliations

  1. Advanced Technology and Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Sabrina M. Lewis, Verena C. Wimmer & Kelly L. Rogers

  2. Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Sabrina M. Lewis, Delphine Merino & Shalin H. Naik

  3. Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, Melbourne, Victoria, Australia

    Sabrina M. Lewis, Marie-Liesse Asselin-Labat, Verena C. Wimmer, Delphine Merino, Kelly L. Rogers & Shalin H. Naik

  4. Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Marie-Liesse Asselin-Labat

  5. Division of Genetics and Genomics, Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia

    Quan Nguyen & Xiao Tan

  6. Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia

    Jean Berthelet & Delphine Merino

  7. School of Cancer Medicine, La Trobe University, Bundoora, Victoria, Australia

    Jean Berthelet & Delphine Merino

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Contributions

All authors contributed to the writing of the manuscript, with all figures generated by S.M.L. along with X.T. for Fig. 6b. Primary data for Fig. 2 were contributed by V.C.W., J.B., K.L.R. and D.M. (Fig. 2a), M.-L.A.-L. (Fig. 2b), and X.T. and Q.N. (Fig. 2d).

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Correspondence to Kelly L. Rogers or Shalin H. Naik.

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Lewis, S.M., Asselin-Labat, ML., Nguyen, Q. et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat Methods 18, 997–1012 (2021). https://doi.org/10.1038/s41592-021-01203-6

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