[1]

Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, et al. 2024. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 74:229−263

doi: 10.3322/caac.21834
[2]

Kalasekar SM, VanSant-Webb CH, Evason KJ. 2021. Intratumor heterogeneity in hepatocellular carcinoma: challenges and opportunities. Cancers 13:5524

doi: 10.3390/cancers13215524
[3]

Halpern KB, Shenhav R, Matcovitch-Natan O, Tóth B, Lemze D, et al. 2017. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542:352−356

doi: 10.1038/nature21065
[4]

Yu Y, Li Y, Zhou L, Cheng X, Gong Z. 2024. Hepatic stellate cells promote hepatocellular carcinoma development by regulating histone lactylation: Novel insights from single-cell RNA sequencing and spatial transcriptomics analyses. Cancer Letters 604:217243

doi: 10.1016/j.canlet.2024.217243
[5]

Saviano A, Roehlen N, Virzì A, Roca Suarez AA, Hoshida Y, et al. 2019. Stromal and immune drivers of hepatocarcinogenesis. In Hepatocellular Carcinoma: Translational Precision Medicine Approaches, ed. Hoshida Y. Cham: Springer International Publishing. pp. 317–331 doi: 10.1007/978-3-030-21540-8_15
[6]

Traum D, Wang YJ, Schwarz KB, Schug J, Wong DKH, et al. 2021. Highly multiplexed 2-dimensional imaging mass cytometry analysis of HBV-infected liver. JCI Insight 6:e146883

doi: 10.1172/jci.insight.146883
[7]

Dolina JS, Van Braeckel-Budimir N, Thomas GD, Salek-Ardakani S. 2021. CD8+ T cell exhaustion in cancer. Frontiers in Immunology 12:715234

doi: 10.3389/fimmu.2021.715234
[8]

Myojin Y, Babaei S, Trehan R, Hoffman C, Kedei N, et al. 2025. Multiomics analysis of immune correlatives in hepatocellular carcinoma patients treated with tremelimumab plus durvalumab. Gut 74:983−995

doi: 10.1136/gutjnl-2024-334026
[9]

Liu S, Iorgulescu JB, Li S, Borji M, Barrera-Lopez IA, et al. 2022. Spatial maps of t cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity 55:1940−1952.e5

doi: 10.1016/j.immuni.2022.09.002
[10]

Rao A, Barkley D, França GS, Yanai I. 2021. Exploring tissue architecture using spatial transcriptomics. Nature 596:211−220

doi: 10.1038/s41586-021-03634-9
[11]

Janesick A, Shelansky R, Gottscho AD, Wagner F, Williams SR, et al. 2023. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nature Communications 14:8353

doi: 10.1038/s41467-023-43458-x
[12]

Langer-Safer PR, Levine M, Ward DC. 1982. Immunological method for mapping genes on Drosophila polytene chromosomes. Proceedings of the National Academy of Sciences of the United States of America 79:4381−4385

doi: 10.1073/pnas.79.14.4381
[13]

Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, et al. 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78−82

doi: 10.1126/science.aaf2403
[14]

Moses L, Pachter L. 2022. Museum of spatial transcriptomics. Nature Methods 19:534−546

doi: 10.1038/s41592-022-01409-2
[15]

de Oliveira MF, Romero JP, Chung M, Williams SR, Gottscho AD, et al. 2025. High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer. Nature Genetics 57:1512−1523

doi: 10.1038/s41588-025-02193-3
[16]

Stickels RR, Murray E, Kumar P, Li J, Marshall JL, et al. 2021. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nature Biotechnology 39:313−319

doi: 10.1038/s41587-020-0739-1
[17]

You Y, Fu Y, Li L, Zhang Z, Jia S, et al. 2024. Systematic comparison of sequencing-based spatial transcriptomic methods. Nature Methods 21:1743−1754

doi: 10.1038/s41592-024-02325-3
[18]

Cho CS, Xi J, Si Y, Park SR, Hsu JE, et al. 2021. Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184:3559−3572.e22

doi: 10.1016/j.cell.2021.05.010
[19]

Zhao Y, Li Y, He Y, Wu J, Liu Y, et al. 2025. Stereo-seq V2: spatial mapping of total RNA on FFPE sections with high resolution. Cell 188:6554−6571.e21

doi: 10.1016/j.cell.2025.08.008
[20]

Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. 2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090

doi: 10.1126/science.aaa6090
[21]

Liu M, Hernandez MO, Castven D, Lee HP, Wu W, et al. 2026. Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer. Nature Communications 17:1986

doi: 10.1038/s41467-026-69797-z
[22]

Liu B, Hu X, Feng K, Gao R, Xue Z, et al. 2022. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nature Cancer 3:108−121

doi: 10.1038/s43018-021-00292-8
[23]

Lim CJ, Lee YH, Pan L, Lai L, Chua C, et al. 2019. Multidimensional analyses reveal distinct immune microenvironment in hepatitis B virus-related hepatocellular carcinoma. Gut 68(5):916−927

doi: 10.1136/gutjnl-2018-316510
[24]

Ramachandran P, Dobie R, Wilson-Kanamori JR, Dora EF, Henderson BEP, et al. 2019. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575:512−518

doi: 10.1038/s41586-019-1631-3
[25]

Ruf B, Bruhns M, Babaei S, Kedei N, Ma L, et al. 2023. Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin. Cell 186:3686−3705.e32

doi: 10.1016/j.cell.2023.07.026
[26]

Liang Y, Qiao L, Qian Q, Zhang R, Li Y, et al. 2025. Integrated single-cell and spatial transcriptomic profiling reveals that CD177+ tregs enhance immunosuppression through apoptosis and resistance to immunotherapy in hepatocellular carcinoma. Oncogene 44:1578−1591

doi: 10.1038/s41388-025-03330-2
[27]

Cable DM, Murray E, Zou LS, Goeva A, Macosko EZ, et al. 2022. Robust decomposition of cell type mixtures in spatial transcriptomics. Nature Biotechnology 40:517−526

doi: 10.1038/s41587-021-00830-w
[28]

Biancalani T, Scalia G, Buffoni L, Avasthi R, Lu Z, et al. 2021. Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram. Nature Methods 18:1352−1362

doi: 10.1038/s41592-021-01264-7
[29]

Jing SY, Liu D, Feng N, Dong H, Wang HQ, et al. 2024. Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma. Genome Medicine 16:98

doi: 10.1186/s13073-024-01367-8
[30]

Stringer C, Wang T, Michaelos M, Pachitariu M. 2021. Cellpose: a generalist algorithm for cellular segmentation. Nature Methods 18:100−106

doi: 10.1038/s41592-020-01018-x
[31]

Bannon D, Moen E, Schwartz M, Borba E, Kudo T, et al. 2021. DeepCell Kiosk: scaling deep learning – enabled cellular image analysis with kubernetes. Nature methods 18:43−45

doi: 10.1038/s41592-020-01023-0
[32]

Yang M, Song X, Zhang F, Li M, Chang W, et al. 2025. Spatial proteomic landscape of primary and relapsed hepatocellular carcinoma reveals immune escape characteristics in early relapse. Hepatology 81:1452−1467

doi: 10.1097/HEP.0000000000000979
[33]

Liu Y, Xun Z, Ma K, Liang S, Li X, et al. 2023. Identification of a tumour immune barrier in the HCC microenvironment that determines the efficacy of immunotherapy. Journal of Hepatology 78:770−782

doi: 10.1016/j.jhep.2023.01.011
[34]

Meroueh C, Warasnhe K, Tizhoosh HR, Shah VH, Ibrahim SH. 2025. Digital pathology and spatial omics in steatohepatitis: clinical applications and discovery potentials. Hepatology 82:1619−1644

doi: 10.1097/HEP.0000000000000866
[35]

Hong L, Mei J, Sun X, Wu Y, Dong Z, et al. 2026. Spatial single-cell proteomics landscape decodes the tumor microenvironmental ecosystem of intrahepatic cholangiocarcinoma. Hepatology 83(1):57−74

doi: 10.1097/hep.0000000000001283
[36]

Mund A, Brunner AD, Mann M. 2022. Unbiased spatial proteomics with single-cell resolution in tissues. Molecular Cell 82:2335−2349

doi: 10.1016/j.molcel.2022.05.022
[37]

Rosenberger FA, Mädler SC, Thorhauge KH, Steigerwald S, Fromme M, et al. 2025. Deep visual proteomics maps proteotoxicity in a genetic liver disease. Nature 642:484−491

doi: 10.1038/s41586-025-08885-4
[38]

Andersson A, Bergenstråhle J, Asp M, Bergenstråhle L, Jurek A, et al. 2020. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Communications Biology 3:565

doi: 10.1038/s42003-020-01247-y
[39]

Editorial Team. 2024. Method of the year 2024: spatial proteomics. Nature Methods 21:2195−2196

doi: 10.1038/s41592-024-02565-3
[40]

Lin JR, Izar B, Wang S, Yapp C, Mei S, et al. 2018. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7:e31657

doi: 10.7554/eLife.31657
[41]

Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A, et al. 2014. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods 11:417−422

doi: 10.1038/nmeth.2869
[42]

Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C, et al. 2014. Multiplexed ion beam imaging (MIBI) of human breast tumors. Nature Medicine 20:436−442

doi: 10.1038/nm.3488
[43]

Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M, et al. 2018. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174:968−981.e15

doi: 10.1016/j.cell.2018.07.010
[44]

Bao F, Deng Y, Wan S, Shen SQ, Wang B, et al. 2022. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature Biotechnology 40:1200−1209

doi: 10.1038/s41587-022-01251-z
[45]

Santos AA, Delgado TC, Marques V, Ramirez-Moncayo C, Alonso C, et al. 2024. Spatial metabolomics and its application in the liver. Hepatology 79:1158−1179

doi: 10.1097/HEP.0000000000000341
[46]

Wu L, Jin Y, Zhao X, Tang K, Zhao Y, et al. 2023. Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α. Cell Metabolism 35:1580−1596.e9

doi: 10.1016/j.cmet.2023.07.001
[47]

Zhao M, Yuan H, Yang G, Wang Y, Bu Y, et al. 2024. Tumour cell-expressed PD-L1 reprograms lipid metabolism via EGFR/ITGB4/SREBP1c signalling in liver cancer. JHEP Reports 6:101009

doi: 10.1016/j.jhepr.2024.101009
[48]

Miyamoto S, Hsu CC, Hamm G, Darshi M, Diamond-Stanic M, et al. 2016. Mass spectrometry imaging reveals elevated glomerular ATP/AMP in diabetes/obesity and identifies sphingomyelin as a possible mediator. eBioMedicine 7:121−134

doi: 10.1016/j.ebiom.2016.03.033
[49]

Morato NM, Cooks RG. 2023. Desorption electrospray ionization mass spectrometry: 20 years. Accounts of Chemical Research 56:2526−2536

doi: 10.1021/acs.accounts.3c00382
[50]

Lockyer NP, Aoyagi S, Fletcher JS, Gilmore IS, van der Heide PAW, et al. 2024. Secondary ion mass spectrometry. Nature Reviews Methods Primers 4:32

doi: 10.1038/s43586-024-00311-9
[51]

Rae Buchberger A, DeLaney K, Johnson J, Li L. 2018. Mass spectrometry imaging: a review of emerging advancements and future insights. Analytical chemistry 90:240−265

doi: 10.1021/acs.analchem.7b04733
[52]

Lv Y, Li T, Guo C, Sun C, Tang F, et al. 2019. A high-performance bio-tissue imaging method using air flow-assisted desorption electrospray ionization coupled with a high-resolution mass spectrometer. Chinese Chemical Letters 30:461−464

doi: 10.1016/j.cclet.2018.06.006
[53]

Rappez L, Stadler M, Triana S, Gathungu RM, Ovchinnikova K, et al. 2021. SpaceM reveals metabolic states of single cells. Nature Methods 18:799−805

doi: 10.1038/s41592-021-01198-0
[54]

Pachuta SJ and Cooks RG. 1987. Mechanisms in molecular SIMS. Chemical Reviews 87:647−669

doi: 10.1021/cr00079a009
[55]

Liu ZY, Wu CY, Wu RQ, Wang JC, Huang CX, et al. 2025. Efflux of N1-acetylspermidine from hepatoma fosters macrophage-mediated immune suppression to dampen immunotherapeutic efficacy. Immunity 58:1572−1585.e10

doi: 10.1016/j.immuni.2025.05.006
[56]

Martinez-Outschoorn UE, Peiris-Pagés M, Pestell RG, Sotgia F, Lisanti MP. 2017. Cancer metabolism: a therapeutic perspective. Nature Reviews Clinical Oncology 14:11−31

doi: 10.1038/nrclinonc.2016.60
[57]

Martínez-Reyes I, Chandel NS. 2021. Cancer metabolism: looking forward. Nature Reviews Cancer 21:669−680

doi: 10.1038/s41568-021-00378-6
[58]

Pavlova NN, Thompson CB. 2016. The emerging hallmarks of cancer metabolism. Cell Metabolism 23:27−47

doi: 10.1016/j.cmet.2015.12.006
[59]

Vander Heiden MG. 2011. Targeting cancer metabolism: a therapeutic window opens. Nature Reviews Drug Discovery 10:671−684

doi: 10.1038/nrd3504
[60]

Finley LWS. 2023. What is cancer metabolism? Cell 186:1670−1688

doi: 10.1016/j.cell.2023.01.038
[61]

Schumacher TN, Thommen DS. 2022. Tertiary lymphoid structures in cancer. Science 375:eabf9419

doi: 10.1126/science.abf9419
[62]

Zhao L, Jin S, Wang S, Zhang Z, Wang X, et al. 2024. Tertiary lymphoid structures in diseases: immune mechanisms and therapeutic advances. Signal Transduction and Targeted Therapy 9:225

doi: 10.1038/s41392-024-01947-5
[63]

Chen S, Huang C, Liao G, Sun H, Xie Y, et al. 2023. Distinct single-cell immune ecosystems distinguish true and de Novo HBV-related hepatocellular carcinoma recurrences. Gut 72:1196−1210

doi: 10.1136/gutjnl-2022-328428
[64]

Engblom C, Thrane K, Lin Q, Andersson A, Toosi H, et al. 2023. Spatial transcriptomics of B cell and T cell receptors reveals lymphocyte clonal dynamics. Science 382:eadf8486

doi: 10.1126/science.adf8486
[65]

Benotmane JK, Kueckelhaus J, Will P, Zhang J, Ravi VM, et al. 2023. High-sensitive spatially resolved T cell receptor sequencing with SPTCR-Seq. Nature Communications 14:7432

doi: 10.1038/s41467-023-43201-6
[66]

Jia G, He P, Dai T, Goh D, Wang J, et al. 2025. Spatial immune scoring system predicts hepatocellular carcinoma recurrence. Nature 640:1031−1041

doi: 10.1038/s41586-025-08668-x
[67]

Wu L, Yan J, Bai Y, Chen F, Zou X, et al. 2023. An invasive zone in human liver cancer identified by stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor progression. Cell Research 33:585−603

doi: 10.1038/s41422-023-00831-1
[68]

Zhao E, Stone MR, Ren X, Guenthoer J, Smythe KS, et al. 2021. Spatial transcriptomics at subspot resolution with BayesSpace. Nature Biotechnology 39:1375−1384

doi: 10.1038/s41587-021-00935-2
[69]

Dries R, Zhu Q, Dong R, Eng CHL, Li H, et al. 2021. Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biology 22:78

doi: 10.1186/s13059-021-02286-2
[70]

Hu J, Li X, Coleman K, Schroeder A, Ma N, et al. 2021. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nature Methods 18:1342−1351

doi: 10.1038/s41592-021-01255-8
[71]

Petukhov V, Xu RJ, Soldatov RA, Cadinu P, Khodosevich K, et al. 2022. Cell segmentation in imaging-based spatial transcriptomics. Nature Biotechnology 40:345−354

doi: 10.1038/s41587-021-01044-w
[72]

Jones DC, Elz AE, Hadadianpour A, Ryu H, Glass DR, et al. 2025. Cell simulation as cell segmentation. Nature Methods 22:1331−1342

doi: 10.1038/s41592-025-02697-0
[73]

Chen H, Li D, Bar-Joseph Z. 2023. SCS: cell segmentation for high-resolution spatial transcriptomics. Nature Methods 20:1237−1243

doi: 10.1038/s41592-023-01939-3
[74]

Wang Y, Wang W, Liu D, Hou W, Zhou T, et al. 2023. GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging. Genome Biology 24:235

doi: 10.1186/s13059-023-03054-0
[75]

Kleshchevnikov V, Shmatko A, Dann E, Aivazidis A, King HW, et al. 2022. Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology 40:661−671

doi: 10.1038/s41587-021-01139-4
[76]

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

doi: 10.1093/nar/gkab043
[77]

Liu X, Peng T, Xu M, Lin S, Hu B, et al. 2024. Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications. Journal of Hematology & Oncology 17:72

doi: 10.1186/s13045-024-01596-9
[78]

Enninful A, Zhang Z, Klymyshyn D, Ingalls M, Yang M, et al. 2026. Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus. Nature Methods 2026:1−13

doi: 10.1038/s41592-025-02948-0
[79]

Vicari M, Mirzazadeh R, Nilsson A, Shariatgorji R, Bjärterot P, et al. 2024. Spatial multimodal analysis of transcriptomes and metabolomes in tissues. Nature Biotechnology 42:1046−1050

doi: 10.1038/s41587-023-01937-y
[80]

Miao J, Li J, Xin J, Tu J, Ge M, et al. 2025. MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning. Nature Communications 16:9403

doi: 10.1038/s41467-025-63418-x
[81]

Bienroth D, Charitakis N, Wong D, Zhang YC, Jaeger-Honz S, et al. 2025. Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics. Genome Biology 26:182

doi: 10.1186/s13059-025-03630-6
[82]

Si Y, Lee JS, Jun G, Kang HM, Lee JH. 2025. Spatial omics enters the microscopic realm: opportunities and challenges. Trends in Genetics 41:774−787

doi: 10.1016/j.tig.2025.05.002
[83]

Reynolds DE, Roh YH, Oh D, Vallapureddy P, Fan R, et al. 2025. Temporal and spatial omics technologies for 4D profiling. Nature Methods 22:1408−1419

doi: 10.1038/s41592-025-02683-6
[84]

Bressan D, Battistoni G, and Hannon GJ. 2023. The dawn of spatial omics. Science 381:eabq4964

doi: 10.1126/science.abq4964