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.
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References
Rinella, M. E. et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. J. Hepatol. 79, 1542–1556 (2023).
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).
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).
Loomba, R., Friedman, S. L. & Shulman, G. I. Mechanisms and disease consequences of nonalcoholic fatty liver disease. Cell 184, 2537–2564 (2021).
Younossi, Z. M. et al. Current and future therapeutic regimens for nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 68, 361–371 (2018).
Pais, R. et al. Persistence of severe liver fibrosis despite substantial weight loss with bariatric surgery. Hepatology 76, 456–468 (2022).
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).
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).
MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).
Guilliams, M. et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185, 379–396.e38 (2022).
Ramachandran, P. et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575, 512–518 (2019).
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).
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).
Gallage, S. et al. A researcher’s guide to preclinical mouse NASH models. Nat. Metab. 4, 1632–1649 (2022).
Paris, J. & Henderson, N. C. Liver zonation, revisited. Hepatology 76, 1219–1230 (2022).
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).
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).
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).
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).
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).
Chang, J. et al. Activation of Slit2-Robo1 signaling promotes liver fibrosis. J. Hepatol. 63, 1413–1420 (2015).
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).
Wang, S. et al. An autocrine signaling circuit in hepatic stellate cells underlies advanced fibrosis in nonalcoholic steatohepatitis. Sci. Transl. Med. 15, eadd3949 (2023).
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).
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).
Jenne, C. N. & Kubes, P. Immune surveillance by the liver. Nat. Immunol. 14, 996–1006 (2013).
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).
Peiseler, M. et al. Kupffer cell-like syncytia replenish resident macrophage function in the fibrotic liver. Science 381, eabq5202 (2023).
Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698.e14 (2019).
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).
Deczkowska, A. et al. XCR1+ type 1 conventional dendritic cells drive liver pathology in non-alcoholic steatohepatitis. Nat. Med. 27, 1043–1054 (2021).
Dudek, M. et al. Auto-aggressive CXCR6+ CD8 T cells cause liver immune pathology in NASH. Nature 592, 444–449 (2021).
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).
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).
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).
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).
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).
Miele, L. et al. Increased intestinal permeability and tight junction alterations in nonalcoholic fatty liver disease. Hepatology 49, 1877–1887 (2009).
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).
Zhu, C. et al. Hepatocyte Notch activation induces liver fibrosis in nonalcoholic steatohepatitis. Sci. Transl. Med. 10, eaat0344 (2018).
Yu, J. et al. Hepatocyte TLR4 triggers inter-hepatocyte Jagged1/Notch signaling to determine NASH-induced fibrosis. Sci. Transl. Med. 13, eabe1692 (2021).
Xiao, Y. et al. Hepatocytes demarcated by EphB2 contribute to the progression of nonalcoholic steatohepatitis. Sci. Transl. Med. 15, eadc9653 (2023).
Engelmann, C. & Tacke, F. The potential role of cellular senescence in non-alcoholic fatty liver disease. Int. J. Mol. Sci. 23, 652 (2022).
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).
Kong, X. et al. Interleukin-22 induces hepatic stellate cell senescence and restricts liver fibrosis in mice. Hepatology 56, 1150–1159 (2012).
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).
Medina, C. B. et al. Metabolites released from apoptotic cells act as tissue messengers. Nature 580, 130–135 (2020).
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).
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).
Ragu, S., Matos-Rodrigues, G. & Lopez, B. S. Replication stress, DNA damage, inflammatory cytokines and innate immune response. Genes 11, 409 (2020).
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).
Pérez-Schindler, J. et al. Characterization of regulatory transcriptional mechanisms in hepatocyte lipotoxicity. Sci. Rep. 12, 11477 (2022).
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).
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).
Butler, P. et al. RNA disruption is a widespread phenomenon associated with stress-induced cell death in tumour cells. Sci. Rep. 13, 1711 (2023).
Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science 381, eabq4964 (2023).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
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).
Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).
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).
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature 568, 235–239 (2019).
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).
Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat. Methods 18, 100–106 (2021).
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).
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).
Loft, A. et al. Liver-fibrosis-activated transcriptional networks govern hepatocyte reprogramming and intra-hepatic communication. Cell Metab. 33, 1685–1700.e9 (2021).
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).
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
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).
Matchett, K. et al. Multimodal decoding of human liver regeneration. Preprint at bioRxiv https://doi.org/10.1101/2023.02.24.529873 (2023).
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).
Fabre, T. et al. Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation. Sci. Immunol. 8, eadd8945 (2023).
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).
Niccoli, G. et al. Optimized treatment of ST-elevation myocardial infarction. Circ. Res. 125, 245–258 (2019).
Kuppe, C. et al. Spatial multi-omic map of human myocardial infarction. Nature 608, 766–777 (2022).
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).
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).
Yu, S. et al. Spatial transcriptome profiling of normal human liver. Sci. Data 9, 633 (2022).
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).
Albhaisi, S. A. M. & Bajaj, J. S. The influence of the microbiome on NAFLD and NASH. Clin. Liver Dis. 17, 15–18 (2021).
Brandl, K. & Schnabl, B. Intestinal microbiota and nonalcoholic steatohepatitis. Curr. Opin. Gastroenterol. 33, 128–133 (2017).
Manfredo Vieira, S. et al. Translocation of a gut pathobiont drives autoimmunity in mice and humans. Science 359, 1156–1161 (2018).
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).
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).
Galeano Niño, J. L. et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611, 810–817 (2022).
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).
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).
Sveinbjornsson, G. et al. Multiomics study of nonalcoholic fatty liver disease. Nat. Genet. 54, 1652–1663 (2022).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020).
Vázquez-García, I. et al. Ovarian cancer mutational processes drive site-specific immune evasion. Nature 612, 778–786 (2022).
Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608, 360–367 (2022).
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).
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).
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).
Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514.e22 (2020).
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).
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).
Zhang, Q. et al. The spatial transcriptomic landscape of non-small cell lung cancer brain metastasis. Nat. Commun. 13, 5983 (2022).
Arnold, M. et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut 66, 683–691 (2017).
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).
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).
Ortiz, C. et al. Molecular atlas of the adult mouse brain. Sci. Adv. 6, eabb3446 (2020).
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681.e18 (2020).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Ben-Chetrit, N. et al. Integration of whole transcriptome spatial profiling with protein markers. Nat. Biotechnol. 41, 788–793 (2023).
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).
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).
Takei, Y. et al. Integrated spatial genomics reveals global architecture of single nuclei. Nature 590, 344–350 (2021).
Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).
Llorens-Bobadilla, E. et al. Solid-phase capture and profiling of open chromatin by spatial ATAC. Nat. Biotechnol. 41, 1085–1088 (2023).
Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).
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).
Webster, N. J. G. Alternative RNA splicing in the pathogenesis of liver disease. Front. Endocrinol. 8, 133 (2017).
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).
Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40, 1780–1793 (2022).
McKellar, D. W. et al. Spatial mapping of the total transcriptome by in situ polyadenylation. Nat. Biotechnol. 41, 513–520 (2023).
Hu, K. H. et al. ZipSeq: barcoding for real-time mapping of single cell transcriptomes. Nat. Methods 17, 833–843 (2020).
Chen, W. et al. Live-seq enables temporal transcriptomic recording of single cells. Nature 608, 733–740 (2022).
Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).
Lu, Q. et al. Metabolic changes of hepatocytes in NAFLD. Front. Physiol. 12, 710420 (2021).
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).
Hall, Z. et al. Lipid zonation and phospholipid remodeling in nonalcoholic fatty liver disease. Hepatology 65, 1165–1180 (2017).
Rappez, L. et al. SpaceM reveals metabolic states of single cells. Nat. Methods 18, 799–805 (2021).
Hu, H. & Laskin, J. Emerging computational methods in mass spectrometry imaging. Adv. Sci. 9, 2203339 (2022).
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).
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).
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).
He, B. et al. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat. Biomed. Eng. 4, 827–834 (2020).
Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022).
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).
Camp, J. G. et al. Multilineage communication regulates human liver bud development from pluripotency. Nature 546, 533–538 (2017).
Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e16 (2017).
Aizarani, N. et al. A human liver cell atlas reveals heterogeneity and epithelial progenitors. Nature 572, 199–204 (2019).
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).
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).
Massalha, H. et al. A single cell atlas of the human liver tumor microenvironment. Mol. Syst. Biol. 16, e9682 (2020).
Zhao, J. et al. Single-cell RNA sequencing reveals the heterogeneity of liver-resident immune cells in human. Cell Discov. 6, 22 (2020).
Diamanti, K. et al. Single nucleus transcriptomics data integration recapitulates the major cell types in human liver. Hepatol. Res. 51, 233–238 (2021).
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).
Payen, V. L. et al. Single-cell RNA sequencing of human liver reveals hepatic stellate cell heterogeneity. JHEP Rep. 3, 100278 (2021).
Wu, R. et al. Comprehensive analysis of spatial architecture in primary liver cancer. Sci. Adv. 7, eabg3750 (2021).
Filliol, A. et al. Opposing roles of hepatic stellate cell subpopulations in hepatocarcinogenesis. Nature 610, 356–365 (2022).
Meng, Y. et al. Single cell transcriptional diversity and intercellular crosstalk of human liver cancer. Cell Death Dis. 13, 261 (2022).
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).
Ye, C. et al. Single‐cell and spatial transcriptomics reveal the fibrosis‐related immune landscape of biliary atresia. Clin. Transl. Med. 12, e1070 (2022).
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).
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).
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).
Starlinger, P. et al. Transcriptomic landscapes of effective and failed liver regeneration in humans. JHEP Rep. 5, 100683 (2023).
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).
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).
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Vickovic, S. et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat. Methods 16, 987–990 (2019).
Cho, C.-S. et al. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184, 3559–3572.e22 (2021).
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).
Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7, eabg4755 (2021).
Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).
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.
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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
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DOI: https://doi.org/10.1038/s41575-024-00915-2
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