Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells (original) (raw)

References

1. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).
   Google Scholar
2. Tabula Muris Consortium, Quake, S.R., Wys-Coray, T. & Darmanis, S. Transcriptomic characterization of 20 organs and tissues from mouse at single cell resolution creates a Tabula Muris. Preprint at https://www.biorxiv.org/content/early/2017/12/20/237446 (2017).
3. Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e17 (2018).
   Google Scholar
4. Crosetto, N., Bienko, M. & van Oudenaarden, A. Spatially resolved transcriptomics and beyond. Nat. Rev. Genet. 16, 57–66 (2015).
   Google Scholar
5. Kolodziejczyk, A.A., Kim, J.K., Svensson, V., Marioni, J.C. & Teichmann, S.A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).
   Google Scholar
6. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
   Google Scholar
7. Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
   Google Scholar
8. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).
   Google Scholar
9. Halpern, K.B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).
   Google Scholar
10. Ståhl, P.L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
    Google Scholar
11. Lein, E., Borm, L.E. & Linnarsson, S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358, 64–69 (2017).
    Google Scholar
12. Lee, J.H. De Novo Gene Expression Reconstruction in Space. Trends Mol. Med. 23, 583–593 (2017).
    Google Scholar
13. Moor, A.E. & Itzkovitz, S. Spatial transcriptomics: paving the way for tissue-level systems biology. Curr. Opin. Biotechnol. 46, 126–133 (2017).
    Google Scholar
14. Jungermann, K. & Kietzmann, T. Zonation of parenchymal and nonparenchymal metabolism in liver. Annu. Rev. Nutr. 16, 179–203 (1996).
    Google Scholar
15. Gebhardt, R. Metabolic zonation of the liver: regulation and implications for liver function. Pharmacol. Ther. 53, 275–354 (1992).
    Google Scholar
16. Wang, B., Zhao, L., Fish, M., Logan, C.Y. & Nusse, R. Self-renewing diploid Axin2(+) cells fuel homeostatic renewal of the liver. Nature 524, 180–185 (2015).
    Google Scholar
17. Planas-Paz, L. et al. The RSPO-LGR4/5-ZNRF3/RNF43 module controls liver zonation and size. Nat. Cell Biol. 18, 467–479 (2016).
    Google Scholar
18. Rocha, A.S. et al. The angiocrine factor rspondin3 is a key determinant of liver zonation. Cell Reports 13, 1757–1764 (2015).
    Google Scholar
19. Braeuning, A. et al. Differential gene expression in periportal and perivenous mouse hepatocytes. FEBS J. 273, 5051–5061 (2006).
    Google Scholar
20. Gebhardt, R. & Matz-Soja, M. Liver zonation: novel aspects of its regulation and its impact on homeostasis. World J. Gastroenterol. 20, 8491–8504 (2014).
    Google Scholar
21. Colnot, S. & Perret, C. Liver Zonation. in Molecular Pathology of Liver Diseases (ed. Monga, S.P.S.) 7–16 (Springer US, 2011).
22. Aird, W.C. Phenotypic heterogeneity of the endothelium. II. Representative vascular beds. Circ. Res. 100, 174–190 (2007).
    Google Scholar
23. Strauss, O., Phillips, A., Ruggiero, K., Bartlett, A. & Dunbar, P.R. Immunofluorescence identifies distinct subsets of endothelial cells in the human liver. Sci. Rep. 7, 44356 (2017).
    Google Scholar
24. Rafii, S., Butler, J.M. & Ding, B.-S. Angiocrine functions of organ-specific endothelial cells. Nature 529, 316–325 (2016).
    Google Scholar
25. Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
    Google Scholar
26. Chistiakov, D.A., Orekhov, A.N., Sobenin, I.A. & Bobryshev, Y.V. Plasmacytoid dendritic cells: development, functions, and role in atherosclerotic inflammation. Front. Physiol. 5, 279 (2014).
    Google Scholar
27. Sierro, F. et al. A liver capsular network of monocyte-derived macrophages restricts hepatic dissemination of intraperitoneal bacteria by neutrophil recruitment. Immunity 47, 374–388.e6 (2017).
    Google Scholar
28. Graeber, T.G. & Eisenberg, D. Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles. Nat. Genet. 29, 295–300 (2001).
    Google Scholar
29. Zhou, J.X., Taramelli, R., Pedrini, E., Knijnenburg, T. & Huang, S. Extracting intercellular signaling network of cancer tissues using ligand-receptor expression patterns from whole-tumor and single-cell transcriptomes. Sci Rep. 7, 8815 (2017).
    Google Scholar
30. Shutter, J.R. et al. Dll4, a novel Notch ligand expressed in arterial endothelium. Genes Dev. 14, 1313–1318 (2000).
    Google Scholar
31. Hellström, M. et al. Dll4 signalling through Notch1 regulates formation of tip cells during angiogenesis. Nature 445, 776–780 (2007).
    Google Scholar
32. Adams, R.H. & Klein, R. Eph receptors and ephrin ligands. essential mediators of vascular development. Trends Cardiovasc. Med. 10, 183–188 (2000).
    Google Scholar
33. Rahner, C., Mitic, L.L. & Anderson, J.M. Heterogeneity in expression and subcellular localization of claudins 2, 3, 4, and 5 in the rat liver, pancreas, and gut. Gastroenterology 120, 411–422 (2001).
    Google Scholar
34. FANTOM Consortium and the RIKEN PMI and CLST (DGT) & Forrest, A.R. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
35. Moore, K.A. & Lemischka, I.R. Stem cells and their niches. Science 311, 1880–1885 (2006).
    Google Scholar
36. Gregorieff, A. et al. Expression pattern of Wnt signaling components in the adult intestine. Gastroenterology 129, 626–638 (2005).
    Google Scholar
37. Sick, S., Reinker, S., Timmer, J. & Schlake, T. WNT and DKK determine hair follicle spacing through a reaction-diffusion mechanism. Science 314, 1447–1450 (2006).
    Google Scholar
38. Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
    Google Scholar
39. Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).
    Google Scholar
40. Bagnoli, J.W. et al. mcSCRB-seq: sensitive and powerful single-cell RNA sequencing. Preprint at https://www.biorxiv.org/content/early/2017/10/18/188367 (2017).
41. Vanlandewijck, M. et al. A molecular atlas of cell types and zonation in the brain vasculature. Nature 554, 475–480 (2018).
    Google Scholar
42. Godoy, P. et al. Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch. Toxicol. 87, 1315–1530 (2013).
    Google Scholar
43. Hernandez-Gea, V. & Friedman, S.L. Pathogenesis of liver fibrosis. Annu. Rev. Pathol. 6, 425–456 (2011).
    Google Scholar
44. Moor, A.E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Preprint at https://www.biorxiv.org/content/early/2018/02/07/261529 (2018).
45. Olivares-Villagómez, D. & Van Kaer, L. Intestinal intraepithelial lymphocytes: sentinels of the mucosal barrier. Trends Immunol. 39, 264–275 (2018).
    Google Scholar
46. Boisset, J.-C. et al. Mapping the physical network of cellular interactions. Nat. Methods 15, 547–553 (2018).
    Google Scholar
47. Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758.e1 (2017).
    Google Scholar
48. Codeluppi, S. et al. Spatial organization of the somatosensory cortex revealed by cyclic smFISH. Preprint at https://www.biorxiv.org/content/early/2018/03/04/276097 (2018).
49. Zeng, W. et al. Sympathetic neuro-adipose connections mediate leptin-driven lipolysis. Cell 163, 84–94 (2015).
    Google Scholar
50. Bhowmick, N.A. & Moses, H.L. Tumor-stroma interactions. Curr. Opin. Genet. Dev. 15, 97–101 (2005).
    Google Scholar
51. Okabe, M., Ikawa, M., Kominami, K., Nakanishi, T. & Nishimune, Y. 'Green mice' as a source of ubiquitous green cells. FEBS Lett. 407, 313–319 (1997).
    Google Scholar
52. Vintersten, K. et al. Mouse in red: red fluorescent protein expression in mouse ES cells, embryos, and adult animals. Genesis 40, 241–246 (2004).
    Google Scholar
53. Bahar Halpern, K. et al. Bursty gene expression in the intact mammalian liver. Mol. Cell 58, 147–156 (2015).
    Google Scholar
54. Lyubimova, A. et al. Single-molecule mRNA detection and counting in mammalian tissue. Nat. Protoc. 8, 1743–1758 (2013).
    Google Scholar
55. Seglen, P.O. Preparation of rat liver cells. 3. Enzymatic requirements for tissue dispersion. Exp. Cell Res. 82, 391–398 (1973).
    Google Scholar
56. George, T.C. et al. Quantitative measurement of nuclear translocation events using similarity analysis of multispectral cellular images obtained in flow. J. Immunol. Methods 311, 117–129 (2006).
    Google Scholar
57. Butler, A. & Satija, R. Integrated analysis of single cell transcriptomic data across conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2017).
    Google Scholar
58. Shay, T. & Kang, J. Immunological Genome Project and systems immunology. Trends Immunol. 34, 602–609 (2013).
    Google Scholar
59. Hughes, M.E., Hogenesch, J.B. & Kornacker, K. JTK_CYCLE: an efficient nonparametric algorithm for detecting rhythmic components in genome-scale data sets. J. Biol. Rhythms 25, 372–380 (2010).
    Google Scholar

Download references