Molecules and Cells

Korean Society for Molecular and Cellular Biology (한국분자세포생물학회)
권호 표지
DOI QR코드

DOI QR Code

Mapping Cellular Coordinates through Advances in Spatial Transcriptomics Technology

  • Teves, Joji Marie (Biotech Research and Innovation Centre (BRIC), University of Copenhagen) ;
  • Won, Kyoung Jae (Biotech Research and Innovation Centre (BRIC), University of Copenhagen)
  • Received : 2020.01.14
  • Accepted : 2020.05.10
  • Published : 2020.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

Complex cell-to-cell communication underlies the basic processes essential for homeostasis in the given tissue architecture. Obtaining quantitative gene-expression of cells in their native context has significantly advanced through single-cell RNA sequencing technologies along with mechanical and enzymatic tissue manipulation. This approach, however, is largely reliant on the physical dissociation of individual cells from the tissue, thus, resulting in a library with unaccounted positional information. To overcome this, positional information can be obtained by integrating imaging and positional barcoding. Collectively, spatial transcriptomics strategies provide tissue architecture-dependent as well as position-dependent cellular functions. This review discusses the current technologies for spatial transcriptomics ranging from the methods combining mechanical dissociation and single-cell RNA sequencing to computational spatial re-mapping.

Keywords

References

  1. Achim, K., Pettit, J.B., Saraiva, L.R., Gavriouchkina, D., Larsson, T., Arendt, D., and Marioni, J.C. (2015). High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503-509. https://doi.org/10.1038/nbt.3209
  2. Asp, M., Giacomello, S., Larsson, L., Wu, C., Furth, D., Qian, X., Wardell, E., Custodio, J., Reimegard, J., Salmen, F., et al. (2019). A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell 179, 1647-1660.e19. https://doi.org/10.1016/j.cell.2019.11.025
  3. Avital, G., Avraham, R., Fan, A., Hashimshony, T., Hung, D.T., and Yanai, I. (2017). scDual-Seq: mapping the gene regulatory program of Salmonella infection by host and pathogen single-cell RNA-sequencing. Genome Biol. 18, 200. https://doi.org/10.1186/s13059-017-1340-x
  4. Boisset, J.C., Vivie, J., Grun, D., Muraro, M.J., Lyubimova, A., and van Oudenaarden, A. (2018). Mapping the physical network of cellular interactions. Nat. Methods 15, 547-553. https://doi.org/10.1038/s41592-018-0009-z
  5. Browaeys, R., Saelens, W., and Saeys, Y. (2020). NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159-162. https://doi.org/10.1038/s41592-019-0667-5
  6. Burgess, D.J. (2019). Spatial transcriptomics coming of age. Nat. Rev. Genet. 20, 317. https://doi.org/10.1038/s41576-019-0129-z
  7. Carow, B., Hauling, T., Qian, X., Kramnik, I., Nilsson, M., and Rottenberg, M.E. (2019). Spatial and temporal localization of immune transcripts defines hallmarks and diversity in the tuberculosis granuloma. Nat. Commun. 10, 1823. https://doi.org/10.1038/s41467-019-09816-4
  8. Chen, J., Suo, S., Tam, P.P., Han, J.J., Peng, G., and Jing, N. (2017). Spatial transcriptomic analysis of cryosectioned tissue samples with Geo-seq. Nat. Protoc. 12, 566-580. https://doi.org/10.1038/nprot.2017.003
  9. Chen, W.T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Mancuso, R., Qian, X., Lalakova, J., Kuhnemund, M., Voytyuk, I., et al. (2019). Spatial and temporal transcriptomics reveal microglia-astroglia crosstalk in the amyloid-${\beta}$ plaque cell niche of Alzheimer's disease. BioRxiv, https://doi. org/10.1101/719930
  10. Chen, X., Teichmann, S.A., and Meyer, K.B. (2018). From tissues to cell types and back: single-cell gene expression analysis of tissue architecture. Annu. Rev. Biomed. Data Sci. 1, 29-51. https://doi.org/10.1146/annurev-biodatasci-080917-013452
  11. Chung, S.H. and Shen, W. (2015). Laser capture microdissection: from its principle to applications in research on neurodegeneration. Neural Regen. Res. 10, 897-898. https://doi.org/10.4103/1673-5374.158346
  12. Codeluppi, S., Borm, L.E., Zeisel, A., La Manno, G., van Lunteren, J.A., Svensson, C.I., and Linnarsson, S. (2018). Spatial organization of the somatosensory cortex revealed by osmFISH. Nat. Methods 15, 932-935. https://doi.org/10.1038/s41592-018-0175-z
  13. Combs, P.A. and Eisen, M.B. (2013). Sequencing mRNA from cryo-sliced Drosophila embryos to determine genome-wide spatial patterns of gene expression. PLoS One 8, e71820. https://doi.org/10.1371/journal.pone.0071820
  14. Datta, S., Malhotra, L., Dickerson, R., Chaffee, S., Sen, C.K., and Roy, S. (2015). Laser capture microdissection: big data from small samples. Histol. Histopathol. 30, 1255-1269.
  15. Eng, C.L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.C., et al. (2019). Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235-239. https://doi.org/10.1038/s41586-019-1049-y
  16. Giladi, A., Cohen, M., Medaglia, C., Baran, Y., Li, B., Zada, M., Bost, P., Blecher-Gonen, R., Salame, T.M., Mayer, J.U., et al. (2020). Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629-637. https://doi.org/10.1038/s41587-020-0442-2
  17. Grun, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., and van Oudenaarden, A. (2015). Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251-255. https://doi.org/10.1038/nature14966
  18. Halpern, K.B., Shenhav, R., Massalha, H., Toth, B., Egozi, A., Massasa, E.E., Medgalia, C., David, E., Giladi, A., Moor, A.E., et al. (2018). Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36, 962-970. https://doi.org/10.1038/nbt.4231
  19. Halpern, K.B., Shenhav, R., Matcovitch-Natan, O., Toth, B., Lemze, D., Golan, M., Massasa, E.E., Baydatch, S., Landen, S., Moor, A.E., et al. (2017). Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352-356. https://doi.org/10.1038/nature21065
  20. Hernandez, I., Qian, X.Y., Lalakova, J., Verheyen, T., Hilscher, M., and Kuhnemund, M. (2019). Mapping brain cell types with CARTANA in situ sequencing on the Nikon Ti2-E microscope. Nat. Methods 16.
  21. Jakt, L.M., Moriwaki, S., and Nishikawa, S. (2013). A continuum of transcriptional identities visualized by combinatorial fluorescent in situ hybridization. Development 140, 216-225. https://doi.org/10.1242/dev.086975
  22. Junker, J.P., Noel, E.S., Guryev, V., Peterson, K.A., Shah, G., Huisken, J., McMahon, A.P., Berezikov, E., Bakkers, J., and van Oudenaarden, A. (2014). Genome-wide RNA tomography in the zebrafish embryo. Cell 159, 662-675. https://doi.org/10.1016/j.cell.2014.09.038
  23. Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., and Zinzen, R.P. (2017). The Drosophila embryo at singlecell transcriptome resolution. Science 358, 194-199. https://doi.org/10.1126/science.aan3235
  24. Ke, R., Mignardi, M., Pacureanu, A., Svedlund, J., Botling, J., Wahlby, C., and Nilsson, M. (2013). In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857-860. https://doi.org/10.1038/nmeth.2563
  25. Kolodziejczyk, A.A., Kim, J.K., Svensson, V., Marioni, J.C., and Teichmann, S.A. (2015). The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610-620. https://doi.org/10.1016/j.molcel.2015.04.005
  26. Kumar, M.P., Du, J., Lagoudas, G., Jiao, Y., Sawyer, A., Drummond, D.C., Lauffenburger, D.A., and Raue, A. (2018). Analysis of single-cell RNA-seq identifies cell-cell communication associated with tumor characteristics. Cell Rep. 25, 1458-1468.e4. https://doi.org/10.1016/j.celrep.2018.10.047
  27. Lee, J.H., Daugharthy, E.R., Scheiman, J., Kalhor, R., Ferrante, T.C., Terry, R., Turczyk, B.M., Yang, J.L., Lee, H.S., Aach, J., et al. (2015). Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat. Protoc. 10, 442-458. https://doi.org/10.1038/nprot.2014.191
  28. Lee, J.H., Daugharthy, E.R., Scheiman, J., Kalhor, R., Yang, J.L., Ferrante, T.C., Terry, R., Jeanty, S.S., Li, C., Amamoto, R., et al. (2014). Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360-1363. https://doi.org/10.1126/science.1250212
  29. Levsky, J.M. and Singer, R.H. (2003). Fluorescence in situ hybridization: past, present and future. J. Cell Sci. 116, 2833-2838. https://doi.org/10.1242/jcs.00633
  30. Lubeck, E., Coskun, A.F., Zhiyentayev, T., Ahmad, M., and Cai, L. (2014). Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360-361. https://doi.org/10.1038/nmeth.2892
  31. Mathot, L., Kundu, S., Ljungstrom, V., Svedlund, J., Moens, L., Adlerteg, T., Falk-Sorqvist, E., Rendo, V., Bellomo, C., Mayrhofer, M., et al. (2017). Somatic ephrin receptor mutations are associated with metastasis in primary colorectal cancer. Cancer Res. 77, 1730-1740. https://doi.org/10.1158/0008-5472.CAN-16-1921
  32. McFaline-Figueroa, J.L., Hill, A.J., Qiu, X., Jackson, D., Shendure, J., and Trapnell, C. (2019). A pooled single-cell genetic screen identifies regulatory checkpoints in the continuum of the epithelial-to-mesenchymal transition. Nat. Genet. 51, 1389-1398. https://doi.org/10.1038/s41588-019-0489-5
  33. Moffitt, J.R. and Zhuang, X. (2016). RNA imaging with multiplexed errorrobust fluorescence in situ hybridization (MERFISH). Methods Enzymol. 572, 1-49. https://doi.org/10.1016/bs.mie.2016.03.020
  34. Moncada, R., Chiodin, M., Devlin, J.C., Baron, M., Hajdu, C.H., Simeone, D., and Yanai, I. (2018). Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis. BioRxiv, https://doi.org/10.1101/254375
  35. Moor, A.E., Harnik, Y., Ben-Moshe, S., Massasa, E.E., Rozenberg, M., Eilam, R., Bahar Halpern, K., and Itzkovitz, S. (2018). Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156-1167.e15. https://doi.org/10.1016/j.cell.2018.08.063
  36. Nichterwitz, S., Chen, G., Aguila Benitez, J., Yilmaz, M., Storvall, H., Cao, M., Sandberg, R., Deng, Q., and Hedlund, E. (2016). Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat. Commun. 7, 12139. https://doi.org/10.1038/ncomms12139
  37. Nitzan, M., Karaiskos, N., Friedman, N., and Rajewsky, N. (2019). Gene expression cartography. Nature 576, 132-137. https://doi.org/10.1038/s41586-019-1773-3
  38. Noel, F., Massenet-Regad, L., Carmi-Levy, I., Cappuccio, A., Grandclaudon, M., Trichot, C., Kieffer, Y., Mechta-Grigoriou, F., and Soumelis, V. (2020). ICELLNET: a transcriptome-based framework to dissect intercellular communication. BioRxiv, https://doi.org/10.1101/2020.03.05.976878
  39. Okamura-Oho, Y., Shimokawa, K., Takemoto, S., Hirakiyama, A., Nakamura, S., Tsujimura, Y., Nishimura, M., Kasukawa, T., Masumoto, K.H., Nikaido, I., et al. (2012). Transcriptome tomography for brain analysis in the webaccessible anatomical space. PLoS One 7, e45373. https://doi.org/10.1371/journal.pone.0045373
  40. Oktay, M.H., Lee, Y.F., Harney, A., Farrell, D., Kuhn, N.Z., Morris, S.A., Greenspan, E., Mohla, S., Grodzinski, P., and Norton, L. (2015). Cell-to-cell communication in cancer: workshop report. NPJ Breast Cancer 1, 15022. https://doi.org/10.1038/npjbcancer.2015.22
  41. Patel, A.P., Tirosh, I., Trombetta, J.J., Shalek, A.K., Gillespie, S.M., Wakimoto, H., Cahill, D.P., Nahed, B.V., Curry, W.T., Martuza, R.L., et al. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396-1401. https://doi.org/10.1126/science.1254257
  42. Pollen, A.A., Nowakowski, T.J., Shuga, J., Wang, X., Leyrat, A.A., Lui, J.H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014). Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053-1058. https://doi.org/10.1038/nbt.2967
  43. Proserpio, V., Piccolo, A., Haim-Vilmovsky, L., Kar, G., Lonnberg, T., Svensson, V., Pramanik, J., Natarajan, K.N., Zhai, W., Zhang, X., et al. (2016). Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation. Genome Biol. 17, 103. https://doi.org/10.1186/s13059-016-0957-5
  44. Raj, A., Rifkin, S.A., Andersen, E., and van Oudenaarden, A. (2010). Variability in gene expression underlies incomplete penetrance. Nature 463, 913-918. https://doi.org/10.1038/nature08781
  45. Raj, A., van den Bogaard, P., Rifkin, S.A., van Oudenaarden, A., and Tyagi, S. (2008). Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877-879. https://doi.org/10.1038/nmeth.1253
  46. Rodriques, S.G., Stickels, R.R., Goeva, A., Martin, C.A., Murray, E., Vanderburg, C.R., Welch, J., Chen, L.M., Chen, F., and Macosko, E.Z. (2019). Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463-1467. https://doi.org/10.1126/science.aaw1219
  47. Rouhanifard, S.H., Mellis, I.A., Dunagin, M., Bayatpour, S., Jiang, C.L., Dardani, I., Symmons, O., Emert, B., Torre, E., Cote, A., et al. (2018). ClampFISH detects individual nucleic acid molecules using click chemistrybased amplification. Nat. Biotechnol. 2018 Nov 12 [Epub]. https://doi.org/10.1038/nbt.4286
  48. Roy, S. and Kornberg, T.B. (2015). Paracrine signaling mediated at cell-cell contacts. Bioessays 37, 25-33. https://doi.org/10.1002/bies.201400122
  49. Salmen, F., Vickovic, S., Larsson, L., Stenbeck, L., Vallon-Christersson, J., Ehinger, A., Hakkinen, J., Borg, Å., Frisen, J., Stahl, P.L., et al. (2018). Multidimensional transcriptomics provides detailed information about immune cell distribution and identity in HER2+ breast tumors. BioRxiv, https://doi.org/10.1101/358937
  50. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F., and Regev, A. (2015). Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495-502. https://doi.org/10.1038/nbt.3192
  51. Shah, S., Lubeck, E., Zhou, W., and Cai, L. (2016). In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342-357. https://doi.org/10.1016/j.neuron.2016.10.001
  52. Shalek, A.K., Satija, R., Shuga, J., Trombetta, J.J., Gennert, D., Lu, D., Chen, P., Gertner, R.S., Gaublomme, J.T., Yosef, N., et al. (2014). Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363-369. https://doi.org/10.1038/nature13437
  53. Silberstein, L., Goncalves, K.A., Kharchenko, P.V., Turcotte, R., Kfoury, Y., Mercier, F., Baryawno, N., Severe, N., Bachand, J., Spencer, J.A., et al. (2016). Proximity-based differential single-cell analysis of the niche to identify stem/progenitor cell regulators. Cell Stem Cell 19, 530-543. https://doi.org/10.1016/j.stem.2016.07.004
  54. Skelly, D.A., Squiers, G.T., McLellan, M.A., Bolisetty, M.T., Robson, P., Rosenthal, N.A., and Pinto, A.R. (2018). Single-cell transcriptional profiling reveals cellular diversity and intercommunication in the mouse heart. Cell Rep. 22, 600-610. https://doi.org/10.1016/j.celrep.2017.12.072
  55. Soldatov, R., Kaucka, M., Kastriti, M.E., Petersen, J., Chontorotzea, T., Englmaier, L., Akkuratova, N., Yang, Y., Haring, M., Dyachuk, V., et al. (2019). Spatiotemporal structure of cell fate decisions in murine neural crest. Science 364, eaas9536. https://doi.org/10.1126/science.aas9536
  56. Stahl, P.L., Salmen, F., Vickovic, S., Lundmark, A., Navarro, J.F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J.O., Huss, M., et al. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78-82. https://doi.org/10.1126/science.aaf2403
  57. Tiklova, K., Bjorklund, A.K., Lahti, L., Fiorenzano, A., Nolbrant, S., Gillberg, L., Volakakis, N., Yokota, C., Hilscher, M.M., Hauling, T., et al. (2019). Single-cell RNA sequencing reveals midbrain dopamine neuron diversity emerging during mouse brain development. Nat. Commun. 10, 581. https://doi.org/10.1038/s41467-019-08453-1
  58. Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N.J., Livak, K.J., Mikkelsen, T.S., and Rinn, J.L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381-386. https://doi.org/10.1038/nbt.2859
  59. van den Brink, S.C., Alemany, A., van Batenburg, V., Moris, N., Blotenburg, M., Vivie, J., Baillie-Johnson, P., Nichols, J., Sonnen, K.F., Martinez Arias, A., et al. (2020). Single-cell and spatial transcriptomics reveal somitogenesis in gastruloids. Nature 2020 Feb 19 [Epub]. https://doi.org/10.1038/s41586-020-2024-3
  60. Vickovic, S., Eraslan, G., Salmen, F., Klughammer, J., Stenbeck, L., Aijo, T., Bonneau, R., Bergenstrahle, L., Fernandez Navarro, J., Gould, J., et al. (2019). High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987-990. https://doi.org/10.1038/s41592-019-0548-y
  61. Wang, G., Moffitt, J.R., and Zhuang, X. (2018a). Multiplexed imaging of high-density libraries of RNAs with MERFISH and expansion microscopy. Sci. Rep. 8, 4847. https://doi.org/10.1038/s41598-018-22297-7
  62. Wang, X., Allen, W.E., Wright, M.A., Sylwestrak, E.L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018b). Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691. https://doi.org/10.1126/science.aat5691
  63. Xia, C., Fan, J., Emanuel, G., Hao, J., and Zhuang, X. (2019). Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. Proc. Natl. Acad. Sci. U. S. A. 116, 19490-19499. https://doi.org/10.1073/pnas.1912459116
  64. Xue, Y., Liu, D., Cui, G., Ding, Y., Ai, D., Gao, S., Zhang, Y., Suo, S., Wang, X., Lv, P., et al. (2019). A 3D atlas of hematopoietic stem and progenitor cell expansion by multi-dimensional RNA-seq analysis. Cell Rep. 27, 1567- 1578.e5. https://doi.org/10.1016/j.celrep.2019.04.030

Cited by

  1. Emerging Technologies to Study the Glomerular Filtration Barrier vol.8, 2020, https://doi.org/10.3389/fmed.2021.772883
  2. Significance of single-cell and spatial transcriptomes in cell biology and toxicology vol.37, pp.1, 2020, https://doi.org/10.1007/s10565-020-09576-8
  3. A Palette of Cytokines to Measure Anti-Tumor Efficacy of T Cell-Based Therapeutics vol.13, pp.4, 2020, https://doi.org/10.3390/cancers13040821
  4. Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes vol.44, pp.7, 2020, https://doi.org/10.14348/molcells.2021.0042
  5. Exploring tissue architecture using spatial transcriptomics vol.596, pp.7871, 2021, https://doi.org/10.1038/s41586-021-03634-9
  6. Current tools to interrogate microglial biology vol.109, pp.18, 2020, https://doi.org/10.1016/j.neuron.2021.07.004
  7. NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport vol.16, pp.9, 2021, https://doi.org/10.1038/s41596-021-00573-7