Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Spatial genomics: mapping human steatotic liver disease

Nature Reviews Gastroenterology & Hepatology (2024)Cite this article

Subjects

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic broadly outlining the micro-architectural changes occurring in the liver during progression from steatosis to MASLD to cirrhosis.
Fig. 2: Schematic overview of the liver lobule and the hepatic sinusoid.
Fig. 3: Schematic overview of the main spatial transcriptomics approaches currently used.

Similar content being viewed by others

References

  1. Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J. Hepatol. 79, 1542–1556 (2023).

    Article  CAS  PubMed  Google Scholar 

  2. Paik, J. M., Golabi, P., Younossi, Y., Mishra, A. & Younossi, Z. M. Changes in the global burden of chronic liver diseases from 2012 to 2017: the growing impact of NAFLD. Hepatology 72, 1605–1616 (2020).

    Article  PubMed  Google Scholar 

  3. Paik, J. M. et al. The growing burden of disability related to chronic liver disease in the United States: data from the Global Burden of Disease Study 2007-2017. Hepatol. Commun. 5, 749–759 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Loomba, R., Friedman, S. L. & Shulman, G. I. Mechanisms and disease consequences of nonalcoholic fatty liver disease. Cell 184, 2537–2564 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Younossi, Z. M. et al. Current and future therapeutic regimens for nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 68, 361–371 (2018).

    Article  PubMed  Google Scholar 

  6. Pais, R. et al. Persistence of severe liver fibrosis despite substantial weight loss with bariatric surgery. Hepatology 76, 456–468 (2022).

    Article  CAS  PubMed  Google Scholar 

  7. Singh, S. et al. Fibrosis progression in nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and meta-analysis of paired-biopsy studies. Clin. Gastroenterol. Hepatol. 13, 643–654.e9 (2015).

    Article  PubMed  Google Scholar 

  8. Younossi, Z. M. et al. Nonalcoholic steatohepatitis is the most rapidly increasing indication for liver transplantation in the United States. Clin. Gastroenterol. Hepatol. 19, 580–589.e5 (2021).

    Article  PubMed  Google Scholar 

  9. MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396.e38 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Peiseler, M. et al. Immune mechanisms linking metabolic injury to inflammation and fibrosis in fatty liver disease–novel insights into cellular communication circuits. J. Hepatol. 77, 1136–1160 (2022).

    Article  CAS  PubMed  Google Scholar 

  13. Tilg, H., Adolph, T. E., Dudek, M. & Knolle, P. Non-alcoholic fatty liver disease: the interplay between metabolism, microbes and immunity. Nat. Metab. 3, 1596–1607 (2021).

    Article  CAS  PubMed  Google Scholar 

  14. Gallage, S. et al. A researcher’s guide to preclinical mouse NASH models. Nat. Metab. 4, 1632–1649 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. Paris, J. & Henderson, N. C. Liver zonation, revisited. Hepatology 76, 1219–1230 (2022).

    Article  PubMed  Google Scholar 

  16. Fang, Z.-Q. et al. Notch-triggered maladaptation of liver sinusoidal endothelium aggravates nonalcoholic steatohepatitis through endothelial nitric oxide synthase. Hepatology 76, 742–758 (2022).

    Article  CAS  PubMed  Google Scholar 

  17. Xiong, X. et al. Landscape of intercellular crosstalk in healthy and NASH liver revealed by single-cell secretome gene analysis. Mol. Cell 75, 644–660.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Duan, J.-L. et al. Age-related liver endothelial zonation triggers steatohepatitis by inactivating pericentral endothelium-derived C-kit. Nat. Aging 3, 258–274 (2023).

    Article  CAS  PubMed  Google Scholar 

  19. Mederacke, I. et al. Fate tracing reveals hepatic stellate cells as dominant contributors to liver fibrosis independent of its aetiology. Nat. Commun. 4, 2823 (2013).

    Article  PubMed  Google Scholar 

  20. Lei, L. et al. Portal fibroblasts with mesenchymal stem cell features form a reservoir of proliferative myofibroblasts in liver fibrosis. Hepatology 76, 1360–1375 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Chang, J. et al. Activation of Slit2-Robo1 signaling promotes liver fibrosis. J. Hepatol. 63, 1413–1420 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Wang, Z.-Y. et al. Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis. Sci. Rep. 11, 19396 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wang, S. et al. An autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in nonalcoholic steatohepatitis. Sci. Transl. Med. 15, eadd3949 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Guillot, A. et al. Mapping the hepatic immune landscape identifies monocytic macrophages as key drivers of steatohepatitis and cholangiopathy progression. Hepatology 78, 150–166 (2023).

    Article  PubMed  Google Scholar 

  25. De Muynck, K. et al. Osteopontin characterizes bile duct associated macrophages and correlates with liver fibrosis severity in primary sclerosing cholangitis. Hepatology 79, 269–288 (2023).

    Article  PubMed  Google Scholar 

  26. Jenne, C. N. & Kubes, P. Immune surveillance by the liver. Nat. Immunol. 14, 996–1006 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Iwakiri, Y., Shah, V. & Rockey, D. C. Vascular pathobiology in chronic liver disease and cirrhosis – current status and future directions. J. Hepatol. 61, 912–924 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Peiseler, M. et al. Kupffer cell-like syncytia replenish resident macrophage function in the fibrotic liver. Science 381, eabq5202 (2023).

    Article  CAS  PubMed  Google Scholar 

  29. Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698.e14 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hendrikx, T. et al. Soluble TREM2 levels reflect the recruitment and expansion of TREM2+ macrophages that localize to fibrotic areas and limit NASH. J. Hepatol. 77, 1373–1385 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Deczkowska, A. et al. XCR1+ type 1 conventional dendritic cells drive liver pathology in non-alcoholic steatohepatitis. Nat. Med. 27, 1043–1054 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Dudek, M. et al. Auto-aggressive CXCR6+ CD8 T cells cause liver immune pathology in NASH. Nature 592, 444–449 (2021).

    Article  CAS  PubMed  Google Scholar 

  33. Breuer, D. A. et al. CD8+ T cells regulate liver injury in obesity-related nonalcoholic fatty liver disease. Am. J. Physiol. Gastrointest. Liver Physiol. 318, G211–G224 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Bhattacharjee, J. et al. Hepatic natural killer T-cell and CD8+ T-cell signatures in mice with nonalcoholic steatohepatitis. Hepatol. Commun. 1, 299–310 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Van Herck, M. A. et al. Diet reversal and immune modulation show key role for liver and adipose tissue T cells in murine nonalcoholic steatohepatitis. Cell Mol. Gastroenterol. Hepatol. 10, 467–490 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kotsiliti, E. et al. Intestinal B cells license metabolic T-cell activation in NASH microbiota/antigen-independently and contribute to fibrosis by IgA-FcR signalling. J. Hepatol. 79, 296–313 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Koda, Y. et al. CD8+ tissue-resident memory T cells promote liver fibrosis resolution by inducing apoptosis of hepatic stellate cells. Nat. Commun. 12, 4474 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Miele, L. et al. Increased intestinal permeability and tight junction alterations in nonalcoholic fatty liver disease. Hepatology 49, 1877–1887 (2009).

    Article  CAS  PubMed  Google Scholar 

  39. Roehlen, N. et al. A monoclonal antibody targeting nonjunctional claudin-1 inhibits fibrosis in patient-derived models by modulating cell plasticity. Sci. Transl. Med. 14, eabj4221 (2022).

    Article  CAS  PubMed  Google Scholar 

  40. Zhu, C. et al. Hepatocyte Notch activation induces liver fibrosis in nonalcoholic steatohepatitis. Sci. Transl. Med. 10, eaat0344 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yu, J. et al. Hepatocyte TLR4 triggers inter-hepatocyte Jagged1/Notch signaling to determine NASH-induced fibrosis. Sci. Transl. Med. 13, eabe1692 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Xiao, Y. et al. Hepatocytes demarcated by EphB2 contribute to the progression of nonalcoholic steatohepatitis. Sci. Transl. Med. 15, eadc9653 (2023).

    Article  CAS  PubMed  Google Scholar 

  43. Engelmann, C. & Tacke, F. The potential role of cellular senescence in non-alcoholic fatty liver disease. Int. J. Mol. Sci. 23, 652 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Baboota, R. K. et al. BMP4 and Gremlin 1 regulate hepatic cell senescence during clinical progression of NAFLD/NASH. Nat. Metab. 4, 1007–1021 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kong, X. et al. Interleukin-22 induces hepatic stellate cell senescence and restricts liver fibrosis in mice. Hepatology 56, 1150–1159 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Doshida, Y. et al. Single-cell RNA sequencing to detect age-associated genes that identify senescent cells in the liver of aged mice. Sci. Rep. 13, 14186 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Medina, C. B. et al. Metabolites released from apoptotic cells act as tissue messengers. Nature 580, 130–135 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Mederacke, I. et al. The purinergic P2Y14 receptor links hepatocyte death to hepatic stellate cell activation and fibrogenesis in the liver. Sci. Transl. Med. 14, eabe5795 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Donne, R. et al. Replication stress triggered by nucleotide pool imbalance drives DNA damage and cGAS-STING pathway activation in NAFLD. Dev. Cell 57, 1728–1741.e6 (2022).

    Article  CAS  PubMed  Google Scholar 

  50. Ragu, S., Matos-Rodrigues, G. & Lopez, B. S. Replication stress, DNA damage, inflammatory cytokines and innate immune response. Genes 11, 409 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ferri-Borgogno, S. et al. Spatial transcriptomics depict ligand–receptor cross-talk heterogeneity at the tumor-stroma interface in long-term ovarian cancer survivors. Cancer Res. 83, 1503–1516 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Pérez-Schindler, J. et al. Characterization of regulatory transcriptional mechanisms in hepatocyte lipotoxicity. Sci. Rep. 12, 11477 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Schwabe, R. F. & Luedde, T. Apoptosis and necroptosis in the liver: a matter of life and death. Nat. Rev. Gastroenterol. Hepatol. 15, 738–752 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Shi, H. et al. CD47-SIRPα axis blockade in NASH promotes necroptotic hepatocyte clearance by liver macrophages and decreases hepatic fibrosis. Sci. Transl. Med. 14, eabp8309 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Butler, P. et al. RNA disruption is a widespread phenomenon associated with stress-induced cell death in tumour cells. Sci. Rep. 13, 1711 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science 381, eabq4964 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).

    Article  CAS  PubMed  Google Scholar 

  58. Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 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 

  60. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  61. 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 

  62. Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2022).

    Article  CAS  PubMed  Google Scholar 

  63. 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 

  64. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).

    Article  CAS  PubMed  Google Scholar 

  65. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).

    Article  CAS  PubMed  Google Scholar 

  66. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Loft, A. et al. Liver-fibrosis-activated transcriptional networks govern hepatocyte reprogramming and intra-hepatic communication. Cell Metab. 33, 1685–1700.e9 (2021).

    Article  CAS  PubMed  Google Scholar 

  68. Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).

    Article  CAS  PubMed  Google Scholar 

  69. 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 

  70. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).

    Article  CAS  PubMed  Google Scholar 

  72. Matchett, K. et al. Multimodal decoding of human liver regeneration. Preprint at bioRxiv https://doi.org/10.1101/2023.02.24.529873 (2023).

    Article  Google Scholar 

  73. McCowan, J. et al. The transcription factor EGR2 is indispensable for tissue-specific imprinting of alveolar macrophages in health and tissue repair. Sci. Immunol. 6, eabj2132 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Fabre, T. et al. Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation. Sci. Immunol. 8, eadd8945 (2023).

    Article  CAS  PubMed  Google Scholar 

  75. Remmerie, A. et al. Osteopontin expression identifies a subset of recruited macrophages distinct from Kupffer cells in the fatty liver. Immunity 53, 641–657.e14 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Niccoli, G. et al. Optimized treatment of ST-elevation myocardial infarction. Circ. Res. 125, 245–258 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Chung, B. K., Øgaard, J., Reims, H. M., Karlsen, T. H. & Melum, E. Spatial transcriptomics identifies enriched gene expression and cell types in human liver fibrosis. Hepatol. Commun. 6, 2538–2550 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Andrews, T. S. et al. Single-cell, single-nucleus, and spatial RNA sequencing of the human liver identifies cholangiocyte and mesenchymal heterogeneity. Hepatol. Commun. 6, 821–840 (2022).

    Article  CAS  PubMed  Google Scholar 

  80. Yu, S. et al. Spatial transcriptome profiling of normal human liver. Sci. Data 9, 633 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Aron-Wisnewsky, J., Warmbrunn, M. V., Nieuwdorp, M. & Clément, K. Nonalcoholic fatty liver disease: modulating gut microbiota to improve severity? Gastroenterology 158, 1881–1898 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. Albhaisi, S. A. M. & Bajaj, J. S. The influence of the microbiome on NAFLD and NASH. Clin. Liver Dis. 17, 15–18 (2021).

    Article  Google Scholar 

  83. Brandl, K. & Schnabl, B. Intestinal microbiota and nonalcoholic steatohepatitis. Curr. Opin. Gastroenterol. 33, 128–133 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Manfredo Vieira, S. et al. Translocation of a gut pathobiont drives autoimmunity in mice and humans. Science 359, 1156–1161 (2018).

    Article  CAS  PubMed  Google Scholar 

  85. Iebba, V. et al. Combining amplicon sequencing and metabolomics in cirrhotic patients highlights distinctive microbiota features involved in bacterial translocation, systemic inflammation and hepatic encephalopathy. Sci. Rep. 8, 8210 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Nakamoto, N. et al. Gut pathobionts underlie intestinal barrier dysfunction and liver T helper 17 cell immune response in primary sclerosing cholangitis. Nat. Microbiol. 4, 492–503 (2019).

    Article  CAS  PubMed  Google Scholar 

  87. Galeano Niño, J. L. et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611, 810–817 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Anstee, Q. M., Reeves, H. L., Kotsiliti, E., Govaere, O. & Heikenwalder, M. From NASH to HCC: current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 16, 411–428 (2019).

    Article  PubMed  Google Scholar 

  89. Namjou, B. et al. GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE network. BMC Med. 17, 135 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Sveinbjornsson, G. et al. Multiomics study of nonalcoholic fatty liver disease. Nat. Genet. 54, 1652–1663 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Vázquez-García, I. et al. Ovarian cancer mutational processes drive site-specific immune evasion. Nature 612, 778–786 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608, 360–367 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Zhu, A. X. et al. Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat. Med. 28, 1599–1611 (2022).

    Article  CAS  PubMed  Google Scholar 

  96. Zhang, S. et al. Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence. Genome Med. 15, 72 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Hwang, W. L. et al. Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment. Nat. Genet. 54, 1178–1191 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514.e22 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Hunter, M. V., Moncada, R., Weiss, J. M., Yanai, I. & White, R. M. Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface. Nat. Commun. 12, 6278 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Peng, H. et al. Multiplex immunofluorescence and single-cell transcriptomic profiling reveal the spatial cell interaction networks in the non-small cell lung cancer microenvironment. Clin. Transl. Med. 13, e1155 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Zhang, Q. et al. The spatial transcriptomic landscape of non-small cell lung cancer brain metastasis. Nat. Commun. 13, 5983 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Arnold, M. et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut 66, 683–691 (2017).

    Article  PubMed  Google Scholar 

  103. Lv, Y., Patel, N. & Zhang, H.-J. The progress of non-alcoholic fatty liver disease as the risk of liver metastasis in colorectal cancer. Expert. Rev. Gastroenterol. Hepatol. 13, 1169–1180 (2019).

    Article  CAS  PubMed  Google Scholar 

  104. Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single cell, spatial and in situ analysis. Nat. Commun. 14, 8353 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. 41, 788–793 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. 41, 1405–1409 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. 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.e26 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. 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 

  112. Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Llorens-Bobadilla, E. et al. Solid-phase capture and profiling of open chromatin by spatial ATAC. Nat. Biotechnol. 41, 1085–1088 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Mahpour, A. & Mullen, A. C. Our emerging understanding of the roles of long non-coding RNAs in normal liver function, disease, and malignancy. JHEP Rep. 3, 100177 (2021).

    Article  PubMed  Google Scholar 

  116. Webster, N. J. G. Alternative RNA splicing in the pathogenesis of liver disease. Front. Endocrinol. 8, 133 (2017).

    Article  Google Scholar 

  117. Isakova, A., Neff, N. & Quake, S. R. Single-cell quantification of a broad RNA spectrum reveals unique noncoding patterns associated with cell types and states. Proc. Natl Acad. Sci. USA 118, e2113568118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40, 1780–1793 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. McKellar, D. W. et al. Spatial mapping of the total transcriptome by in situ polyadenylation. Nat. Biotechnol. 41, 513–520 (2023).

    Article  CAS  PubMed  Google Scholar 

  120. 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 

  121. Chen, W. et al. Live-seq enables temporal transcriptomic recording of single cells. Nature 608, 733–740 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. 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 

  123. Lu, Q. et al. Metabolic changes of hepatocytes in NAFLD. Front. Physiol. 12, 710420 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  124. He, M. J. et al. Comparing DESI-MSI and MALDI-MSI mediated spatial metabolomics and their applications in cancer studies. Front. Oncol. 12, 891018 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Hall, Z. et al. Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease. Hepatology 65, 1165–1180 (2017).

    Article  CAS  PubMed  Google Scholar 

  126. Rappez, L. et al. SpaceM reveals metabolic states of single cells. Nat. Methods 18, 799–805 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Hu, H. & Laskin, J. Emerging computational methods in mass spectrometry imaging. Adv. Sci. 9, 2203339 (2022).

    Article  Google Scholar 

  128. Naoumov, N. V. et al. Digital pathology with artificial intelligence analyses provides greater insights into treatment-induced fibrosis regression in NASH. J. Hepatol. 77, 1399–1409 (2022).

    Article  CAS  PubMed  Google Scholar 

  129. Conway, J. et al. Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH. Cell Rep. Med. 4, 101016 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Nakamura, Y. et al. Automated fibrosis phenotyping of liver tissue from non-tumor lesions of patients with and without hepatocellular carcinoma after liver transplantation for non-alcoholic fatty liver disease. Hepatol. Int. 16, 555–561 (2022).

    Article  PubMed  Google Scholar 

  131. He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020).

    Article  CAS  PubMed  Google Scholar 

  132. Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022).

    Article  PubMed  Google Scholar 

  133. Krassowski, M., Das, V., Sahu, S. K. & Misra, B. B. State of the field in multi-omics research: from computational needs to data mining and sharing. Front. Genet. 11, 610798 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017).

    Article  CAS  PubMed  Google Scholar 

  135. Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e16 (2017).

    Article  CAS  PubMed  Google Scholar 

  136. Aizarani, N. et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572, 199–204 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Ho, D. W.-H. et al. Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and stemness-related subpopulations in liver cancer. Cancer Lett. 459, 176–185 (2019).

    Article  CAS  PubMed  Google Scholar 

  138. Cavalli, M. et al. A multi-omics approach to liver diseases: integration of single nuclei transcriptomics with proteomics and HiCap bulk data in human liver. OMICS 24, 180–194 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Massalha, H. et al. A single cell atlas of the human liver tumor microenvironment. Mol. Syst. Biol. 16, e9682 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Zhao, J. et al. Single-cell RNA sequencing reveals the heterogeneity of liver-resident immune cells in human. Cell Discov. 6, 22 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Diamanti, K. et al. Single nucleus transcriptomics data integration recapitulates the major cell types in human liver. Hepatol. Res. 51, 233–238 (2021).

    Article  CAS  PubMed  Google Scholar 

  142. Hou, X. et al. Integrating spatial transcriptomics and single-cell RNA-seq reveals the gene expression profling of the human embryonic liver. Front. Cell Dev. Biol. 9, 652408 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Payen, V. L. et al. Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity. JHEP Rep. 3, 100278 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Wu, R. et al. Comprehensive analysis of spatial architecture in primary liver cancer. Sci. Adv. 7, eabg3750 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Filliol, A. et al. Opposing roles of hepatic stellate cell subpopulations in hepatocarcinogenesis. Nature 610, 356–365 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Meng, Y. et al. Single cell transcriptional diversity and intercellular crosstalk of human liver cancer. Cell Death Dis. 13, 261 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Wen, F., Tang, X., Xu, L. & Qu, H. Comparison of single-nucleus and single-cell transcriptomes in hepatocellular carcinoma tissue. Mol. Med. Rep. 26, 339 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Ye, C. et al. Single‐cell and spatial transcriptomics reveal the fibrosis‐related immune landscape of biliary atresia. Clin. Transl. Med. 12, e1070 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Zhang, P. et al. Single-cell RNA transcriptomics reveals the state of hepatic lymphatic endothelial cells in hepatitis B virus-related acute-on-chronic liver failure. J. Clin. Med. 11, 2910 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Andrews, T. S. et al. Single-cell, single nucleus and spatial transcriptomics characterization of the immunological landscape in healthy and PSC human liver. J. Hepatol. https://doi.org/10.1016/j.jhep.2023.12.023 (2024).

    Article  PubMed  Google Scholar 

  151. Li, M. et al. Spatial and single-cell transcriptomics reveal the regional division of the spatial structure of NASH fibrosis. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-2958625/v1 (2023).

  152. Starlinger, P. et al. Transcriptomic landscapes of effective and failed liver regeneration in humans. JHEP Rep. 5, 100683 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  153. Yu, X. et al. Spatial transcriptomics reveals a low extent of transcriptionally active hepatitis B virus integration in patients with HBsAg loss. Gut https://doi.org/10.1136/gutjnl-2023-330577 (2023).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  Google Scholar 

  156. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  157. 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 

  158. Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572.e22 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).

    Article  CAS  PubMed  Google Scholar 

  160. Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7, eabg4755 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

N.C.H. is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science (ref. 219542/Z/19/Z), the Medical Research Council, and a Chan Zuckerberg Initiative Seed Network grant.

Author information

Authors and Affiliations

  1. Centre for Inflammation Research, Institute for Regeneration and Repair, Edinburgh BioQuarter, University of Edinburgh, Edinburgh, UK

    Kylie P. Matchett, Jasmin Paris & Neil C. Henderson

  2. Wellcome Sanger Institute, Cambridge, UK

    Sarah A. Teichmann

  3. Department of Physics, Cavendish Laboratory, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

  4. MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK

    Neil C. Henderson

Authors
  1. Kylie P. Matchett
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jasmin Paris
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sarah A. Teichmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Neil C. Henderson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.P.M., J.P., S.A.T., and N.C.H. researched data for the article. All authors contributed substantially to discussion of the content. All authors wrote the article. All authors reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Neil C. Henderson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Gastroenterology & Hepatology thanks Frank Tacke, Thomas Baumert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Related links

NCT00063622: https://clinicaltrials.gov/study/NCT00063622

NCT02855164: https://clinicaltrials.gov/study/NCT02855164

NCT03299946: https://clinicaltrials.gov/study/NCT03299946

Rights and permissions

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Matchett, K.P., Paris, J., Teichmann, S.A. et al. Spatial genomics: mapping human steatotic liver disease. Nat Rev Gastroenterol Hepatol (2024). https://doi.org/10.1038/s41575-024-00915-2

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41575-024-00915-2

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research