AI-driven deciphering of cell niches with single-cell and spatial omics: a new perspective for traditional Chinese medicine research

Jingyang Qian , Hudong Bao , Haolong Yang , Jiatian Zhang , Xin Shao , Xiaohui Fan

Targetome ›› 2026, Vol. 2 ›› Issue (2) : e009

PDF (3737KB)
Targetome ›› 2026, Vol. 2 ›› Issue (2) :e009 DOI: 10.48130/targetome-0026-0009
INVITED REVIEW
research-article
AI-driven deciphering of cell niches with single-cell and spatial omics: a new perspective for traditional Chinese medicine research
Author information +
History +
PDF (3737KB)

Abstract

The therapeutic effects of traditional Chinese medicine (TCM) are characterized by holistic and systematic regulation of the organism, typically achieved through coordinating tissue microenvironments and multicellular interactions. However, conventional molecular biology approaches struggle to precisely characterize cellular composition, spatial architecture, and associated microenvironment (cell niche) at the tissue level, constraining a comprehensive understanding of the mechanism of action of TCM. In recent years, the rapid advancement of single-cell and spatial omics technologies, together with artificial intelligence (AI)-driven computational methods, has enabled systematic deciphering of cell niches within complex tissues, offering new opportunities for TCM research. Centered on the cell niche, this review outlines its conceptual development and research progress, with a particular focus on recent advances in AI-assisted cell niche analysis based on single-cell and spatial omics data. We further summarize representative scenarios of cell niche analysis in TCM research, while discussing current challenges and future directions, highlighting its potential to provide a new perspective and analytical paradigm in TCM.

Cite this article

Download citation ▾
Jingyang Qian, Hudong Bao, Haolong Yang, Jiatian Zhang, Xin Shao, Xiaohui Fan. AI-driven deciphering of cell niches with single-cell and spatial omics: a new perspective for traditional Chinese medicine research. Targetome, 2026, 2 (2) : e009 DOI:10.48130/targetome-0026-0009

登录浏览全文

4963

注册一个新账户 忘记密码

Acknowledgments

This work is supported by New Generation Artificial Intelligence-National Science and Technology Major Project (2025ZD0122805), and the National Natural Science Foundation of China (U23A20513).

Ethical statements

Not applicable.

Author contributions

The authors confirm their contributions to the review as follows: conceptualization: Fan X, Shao X; manuscript writing: Qian J, Bao H, Yang H, Zhang J. All authors reviewed the results and approved the final version of the manuscript.

Data availability

Data sharing is not applicable to this review as no datasets were generated or analyzed.

Conflict of interest

The authors declare that there is no conflict of interest.

References

[1]

Li X, Liu Z, Liao J, Chen Q, Lu X, et al. 2023. Network pharmacology approaches for research of Traditional Chinese Medicines. Chinese Journal of Natural Medicines 21: 323-332

[2]

Su X, Yan X, Zhang H. 2025. The tumor microenvironment in hepatocellular carcinoma: mechanistic insights and therapeutic potential of traditional Chinese medicine. Molecular Cancer 24: 173

[3]

Wu L, Wu Q, Du X, Ling M, Tong H. 2025. Revolutionizing pharmacological research of traditional Chinese medicine with single-cell omics technologies. Fitoterapia 186: 106846

[4]

Wang Z, Zhang T, Wang B, Li S. 2025. TCM network pharmacology: new perspective integrating network target with artificial intelligence and multi-modal multi-omics technologies. Chinese Journal of Natural Medicines 23: 1425-1434

[5]

Zhai Y, Liu L, Zhang F, Chen X, Wang H, et al. 2025. Network pharmacology: a crucial approach in traditional Chinese medicine research. Chinese Medicine 20: 8

[6]

Wang X, Wang ZY, Zheng JH, Li S. 2021. TCM network pharmacology: a new trend towards combining computational, experimental and clinical approaches. Chinese Journal of Natural Medicines 19: 1-11

[7]

Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161: 1187-1201

[8]

Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161: 1202-1214

[9]

Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, et al. 2013. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature Methods 10: 1096-1098

[10]

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

[11]

Moffitt JR, Bambah-Mukku D, Eichhorn SW, Vaughn E, Shekhar K, et al. 2018. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362: eaau5324

[12]

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

[13]

Chen A, Liao S, Cheng M, Ma K, Wu L, et al. 2022. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185: 1777-1792.e21

[14]

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

[15]

Keren L, Bosse M, Marquez D, Angoshtari R, Jain S, et al. 2018. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174: 1373-1387.e19

[16]

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

[17]

Shao X, Yang H, Zhuang X, Liao J, Yang P, et al. 2021. scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Research 49: e122

[18]

Qian J, Liao J, Liu Z, Chi Y, Fang Y, et al. 2023. Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace. Nature Communications 14: 2484

[19]

Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nature Methods 15: 1053-1058

[20]

Qian J, Bao H, Shao X, Fang Y, Liao J, et al. 2024. Simulating multiple variability in spatially resolved transcriptomics with scCube. Nature Communications 15: 5021

[21]

Levine D, Rizvi SA, Lévy S, Pallikkavaliyaveetil N, Zhang D, et al. 2023. Cell2Sentence: teaching large language models the language of biology. bioRxiv 00:Preprint

[22]

Cui H, Wang C, Maan H, Pang K, Luo F, et al. 2024. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nature Methods 21: 1470-1480

[23]

Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, et al. 2023. Transfer learning enables predictions in network biology. Nature 618: 616-624

[24]

He S, Zhu Y, Tavakol DN, Ye H, Lao YH, et al. 2026. Squidiff: predicting cellular development and responses to perturbations using a diffusion model. Nature Methods 23: 65-77

[25]

Aivazidis A, Memi F, Kleshchevnikov V, Er S, Clarke B, et al. 2025. Cell2fate infers RNA velocity modules to improve cell fate prediction. Nature Methods 22: 698-707

[26]

Mizukoshi C, Kojima Y, Nomura S, Hayashi S, Abe K, et al. 2024. DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates. Genome Biology 25: 229

[27]

Fan X, Liu J, Yang Y, Gu C, Han Y, et al. 2024. scGraphformer: unveiling cellular heterogeneity and interactions in scRNA-seq data using a scalable graph transformer network. Communications Biology 7: 1463

[28]

Raghavan V, Zheng Y, Li Y, Ding J. 2025. Harnessing agent-based frameworks in CellAgentChat to unravel cell-cell interactions from single-cell and spatial transcriptomics. Genome Research 35: 1646-1663

[29]

Velikic G, Maric DM, Maric DL, Supic G, Puletic M, et al. 2024. Harnessing the stem cell niche in regenerative medicine: innovative avenue to combat neurodegenerative diseases. International Journal of Molecular Sciences 25: 993

[30]

Lázár E, Lundeberg J. 2026. Spatial architecture of development and disease. Nature Reviews Genetics 27: 118-136

[31]

Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. 2025. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nature Reviews Molecular Cell Biology 26: 11-31

[32]

Varrone M, Tavernari D, Santamaria-Martínez A, Walsh LA, Ciriello G. 2024. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nature Genetics 56: 74-84

[33]

Rajachandran S, Zhang X, Cao Q, Caldeira-Brant AL, Zhang X, et al. 2023. Dissecting the spermatogonial stem cell niche using spatial transcriptomics. Cell Reports 42: 112737

[34]

Adams GB, Martin RP, Alley IR, Chabner KT, Cohen KS, et al. 2007. Therapeutic targeting of a stem cell niche. Nature Biotechnology 25: 238-243

[35]

Lee CYC, McCaffrey J, McGovern D, Clatworthy MR. 2025. Profiling immune cell tissue niches in the spatial-omics era. The Journal of Allergy and Clinical Immunology 155: 663-677

[36]

Qian J, Shao X, Bao H, Fang Y, Guo W, et al. 2025. Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution. Nature Communications 16: 1693

[37]

Rojas-Ríos P, González-Reyes A. 2014. Concise review: The plasticity of stem cell niches: a general property behind tissue homeostasis and repair. Stem Cells 32: 852-859

[38]

Mendelson A, Frenette PS. 2014. Hematopoietic stem cell niche maintenance during homeostasis and regeneration. Nature Medicine 20: 833-846

[39]

Kanemaru K, Cranley J, Muraro D, Miranda AMA, Ho SY, et al. 2023. Spatially resolved multiomics of human cardiac niches. Nature 619: 801-810

[40]

Ren Y, Huang Z, Zhou L, Xiao P, Song J, et al. 2023. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. Nature Communications 14: 1028

[41]

Guilliams M, Bonnardel J, Haest B, Vanderborght B, Wagner C, et al. 2022. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell 185: 379-396.e38

[42]

Lake BB, Menon R, Winfree S, Hu Q, Melo Ferreira R, et al. 2023. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619: 585-594

[43]

Pascual-Reguant A, Kroh S, Hauser AE. 2024. Tissue niches and immunopathology through the lens of spatial tissue profiling techniques. European Journal of Immunology 54: e2350484

[44]

Hicks MR, Pyle AD. 2023. The emergence of the stem cell niche. Trends in Cell Biology 33: 112-123

[45]

Park SY, Matte A, Jung Y, Ryu J, Anand WB, et al. 2020. Pathologic angiogenesis in the bone marrow of humanized sickle cell mice is reversed by blood transfusion. Blood 135: 2071-2084

[46]

Celià-Terrassa T, Kang Y. 2018. Metastatic niche functions and therapeutic opportunities. Nature Cell Biology 20: 868-877

[47]

Chen Q, Boire A, Jin X, Valiente M, Er EE, et al. 2016. Carcinoma-astrocyte gap junctions promote brain metastasis by cGAMP transfer. Nature 533: 493-498

[48]

Ma Y, Bao Y, Wang H, Jiang H, Zhou L, et al. 2024. 1H-NMR-based metabolomics to dissect the traditional Chinese medicine promotes mesenchymal stem cell homing as intervention in liver fibrosis in mouse model of Wilson's disease. The Journal of Pharmacy and Pharmacology 76: 656-671

[49]

Voog J, D'Alterio C, Jones DL. 2008. Multipotent somatic stem cells contribute to the stem cell niche in the Drosophila testis . Nature 454: 1132-1136

[50]

Kiel MJ, Yilmaz ÖH, Iwashita T, Yilmaz OH, Terhorst C, et al. 2005. SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 121: 1109-1121

[51]

Ali Abid Al-Juboori A, Ghosh A, Bin Jamaluddin MF, Kumar M, Sahoo SS, et al. 2019. Proteomic analysis of stromal and epithelial cell communications in human endometrial cancer using a unique 3D co-culture model. Proteomics 19: e1800448

[52]

Bloise N, Giannaccari M, Guagliano G, Peluso E, Restivo E, et al. 2024. Growing role of 3D in vitro cell cultures in the study of cellular and molecular mechanisms: short focus on breast cancer, endometriosis, liver and infectious diseases . Cells 13: 1054

[53]

Katt ME, Placone AL, Wong AD, Xu ZS, Searson PC. 2016. In vitro tumor models: advantages, disadvantages, variables, and selecting the right platform . Frontiers in Bioengineering and Biotechnology 4: 12

[54]

Shah S, D'Souza GGM. 2025. Modeling tumor microenvironment complexity in vitro: spheroids as physiologically relevant tumor models and strategies for their analysis . Cells 14: 732

[55]

van Dam S, Baars MJD, Vercoulen Y. 2022. Multiplex tissue imaging: spatial Revelations in the tumor microenvironment. Cancers 14: 3170

[56]

Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6: 377-382

[57]

Qadir MMF, Álvarez-Cubela S, Klein D, van Dijk J, Muñiz-Anquela R, et al. 2020. Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche. Proceedings of the National Academy of Sciences of the United States of America 117: 10876-10887

[58]

Cheng HW, Mörbe U, Lütge M, Engetschwiler C, Onder L, et al. 2022. Intestinal fibroblastic reticular cell niches control innate lymphoid cell homeostasis and function. Nature Communications 13: 2027

[59]

Shao X, Wang Z, Wang K, Lu X, Zhang P, et al. 2024. A single-cell landscape of human liver transplantation reveals a pathogenic immune niche associated with early allograft dysfunction. Engineering 36: 193-208

[60]

Xie H, Lu Y, Zhang A, Zheng A, Rao B, et al. 2025. Single-cell transcriptome analyses reveal disturbed decidual microenvironment in women of advanced maternal age. Clinical and Translational Medicine 15: e70541

[61]

Luca BA, Steen CB, Matusiak M, Azizi A, Varma S, et al. 2021. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 184: 5482-5496.e28

[62]

Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, et al. 2019. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nature Biotechnology 37: 773-782

[63]

Shi Q, Chen Y, Li Y, Qin S, Yang Y, et al. 2025. Cross-tissue multicellular coordination and its rewiring in cancer. Nature 643: 529-538

[64]

Jerby-Arnon L, Regev A. 2022. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nature Biotechnology 40(10): 1467-1477

[65]

Tian L, Chen F, Macosko EZ. 2023. The expanding vistas of spatial transcriptomics. Nature Biotechnology 41: 773-782

[66]

Liao J, Lu X, Shao X, Zhu L, Fan X. 2021. Uncovering an organ's molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends in Biotechnology 39: 43-58

[67]

Moffitt JR, Lundberg E, Heyn H. 2022. The emerging landscape of spatial profiling technologies. Nature Reviews Genetics 23: 741-759

[68]

Close JL, Long BR, Zeng H. 2021. Spatially resolved transcriptomics in neuroscience. Nature Methods 18: 23-25

[69]

Liu L, Chen A, Li Y, Mulder J, Heyn H, et al. 2024. Spatiotemporal omics for biology and medicine. Cell 187: 4488-4519

[70]

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

[71]

Liu J, Tran V, Vemuri VNP, Byrne A, Borja M, et al. 2022. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Science Alliance 6: e202201701

[72]

He S, Bhatt R, Brown C, Brown EA, Buhr DL, et al. 2022. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nature Biotechnology 40: 1794-1806

[73]

Milosevic V. 2023. Different approaches to Imaging Mass Cytometry data analysis. Bioinformatics Advances 3: vbad046

[74]

Passarelli MK, Pirkl A, Moellers R, Grinfeld D, Kollmer F, et al. 2017. The 3D OrbiSIMS-label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power. Nature Methods 14: 1175-1183

[75]

Capolupo L, Khven I, Lederer AR, Mazzeo L, Glousker G, et al. 2022. Sphingolipids control dermal fibroblast heterogeneity. Science 376: eabh1623

[76]

Seydel C. 2021. Single-cell metabolomics hits its stride. Nature Methods 18: 1452-1456

[77]

Yuan Z, Zhou Q, Cai L, Pan L, Sun W, et al. 2021. SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment. Nature Methods 18: 1223-1232

[78]

Llorens-Bobadilla E, Zamboni M, Marklund M, Bhalla N, Chen X, et al. 2023. Solid-phase capture and profiling of open chromatin by spatial ATAC. Nature Biotechnology 41: 1085-1088

[79]

Deng Y, Bartosovic M, Ma S, Zhang D, Kukanja P, et al. 2022. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609: 375-383

[80]

Deng Y, Bartosovic M, Kukanja P, Zhang D, Liu Y, et al. 2022. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375: 681-686

[81]

Ma Y, Guo W, Mou Q, Shao X, Lyu M, et al. 2024. Spatial imaging of glycoRNA in single cells with ARPLA. Nature Biotechnology 42: 608-616

[82]

Kuppe C, Ramirez Flores RO, Li Z, Hayat S, Levinson RT, et al. 2022. Spatial multi-omic map of human myocardial infarction. Nature 608: 766-777

[83]

Avila Cobos F, Vandesompele J, Mestdagh P, De Preter K. 2018. Computational deconvolution of transcriptomics data from mixed cell populations. Bioinformatics 34: 1969-1979

[84]

Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, et al. 2016. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Systems 3: 346-360.e4

[85]

Wang X, Park J, Susztak K, Zhang NR, Li M. 2019. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nature Communications 10: 380

[86]

Dong M, Thennavan A, Urrutia E, Li Y, Perou CM, et al. 2021. SCDC bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Briefings in Bioinformatics 22: 416-427

[87]

Gong T, Szustakowski JD. 2013. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics 29: 1083-1085

[88]

Du R, Carey V, Weiss ST. 2019. deconvSeq: deconvolution of cell mixture distribution in sequencing data. Bioinformatics 35: 5095-5102

[89]

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, et al. 2015. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods 12: 453-457

[90]

Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, et al. 2019. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363: 1463-1467

[91]

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

[92]

Zhou Z, Zhong Y, Zhang Z, Ren X. 2023. Spatial transcriptomics deconvolution at single-cell resolution using Redeconve. Nature Communications 14: 7930

[93]

Dong R, Yuan GC. 2021. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biology 22: 145

[94]

Danaher P, Kim Y, Nelson B, Griswold M, Yang Z, et al. 2022. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nature Communications 13: 385

[95]

Liu Z, Wu D, Zhai W, Ma L. 2023. SONAR enables cell type deconvolution with spatially weighted Poisson-Gamma model for spatial transcriptomics. Nature Communications 14: 4727

[96]

Xun Z, Ding X, Zhang Y, Zhang B, Lai S, et al. 2023. Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis. Nature Communications 14: 933

[97]

Ru B, Huang J, Zhang Y, Aldape K, Jiang P. 2023. Estimation of cell lineages in tumors from spatial transcriptomics data. Nature Communications 14: 568

[98]

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

[99]

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

[100]

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

[101]

He S, Jin Y, Nazaret A, Shi L, Chen X, et al. 2025. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nature Biotechnology 43: 223-235

[102]

Miller BF, Huang F, Atta L, Sahoo A, Fan J. 2022. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nature Communications 13: 2339

[103]

Sun D, Liu Z, Li T, Wu Q, Wang C. 2022. STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing. Nucleic Acids Research 50: e42

[104]

Liao J, Qian J, Fang Y, Chen Z, Zhuang X, et al. 2022. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution . Nature Communications 13: 6498

[105]

Lopez R, Li B, Keren-Shaul H, Boyeau P, Kedmi M, et al. 2022. DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology 40: 1360-1369

[106]

Xu H, Wang S, Fang M, Luo S, Chen C, et al. 2023. SPACEL: deep learning-based characterization of spatial transcriptome architectures. Nature Communications 14: 7603

[107]

Song Q, Su J. 2021. DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence. Briefings in Bioinformatics 22: bbaa414

[108]

Li Y, Luo Y. 2024. STdGCN: spatial transcriptomic cell-type deconvolution using graph convolutional networks. Genome Biology 25: 206

[109]

Li H, Li H, Zhou J, Gao X. 2022. SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information. Bioinformatics 38: 4878-4884

[110]

Bae S, Na KJ, Koh J, Lee DS, Choi H, et al. 2022. CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data. Nucleic Acids Research 50: e57

[111]

Bae S, Choi H, Lee DS. 2023. spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data. Genome Medicine 15: 19

[112]

Zhao C, Xu Z, Wang X, Tao S, MacDonald WA, et al. 2024. Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression. Briefings in Bioinformatics 25: bbae052

[113]

Lund JB, Lindberg EL, Maatz H, Pottbaecker F, Hübner N, et al. 2022. AntiSplodge: a neural-network-based RNA-profile deconvolution pipeline designed for spatial transcriptomics. NAR Genomics and Bioinformatics 4: lqac073

[114]

Charytonowicz D, Brody R, Sebra R. 2023. Interpretable and context-free deconvolution of multi-scale whole transcriptomic data with UniCell deconvolve. Nature Communications 14: 1350

[115]

Zhan Y, Zhang Y, Hu Z, Wang Y, Zhu Z, et al. 2025. LETSmix: a spatially informed and learning-based domain adaptation method for cell-type deconvolution in spatial transcriptomics. Genome Medicine 17: 16

[116]

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

[117]

Ma Y, Zhou X. 2022. Spatially informed cell-type deconvolution for spatial transcriptomics. Nature Biotechnology 40: 1349-1359

[118]

Georgaka S, Morgans WG, Zhao Q, Martinez DS, Ali A, et al. 2025. CellPie: a scalable spatial transcriptomics factor discovery method via joint non-negative matrix factorization . Nucleic Acids Research 53: gkaf251

[119]

Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology 36: 411-420

[120]

Wei R, He S, Bai S, Sei E, Hu M, et al. 2022. Spatial charting of single-cell transcriptomes in tissues. Nature Biotechnology 40: 1190-1199

[121]

Cang Z, Nie Q. 2020. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nature Communications 11: 2084

[122]

Nitzan M, Karaiskos N, Friedman N, Rajewsky N. 2019. Gene expression cartography. Nature 576: 132-137

[123]

Mages S, Moriel N, Avraham-Davidi I, Murray E, Watter J, et al. 2023. TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics. Nature Biotechnology 41: 1465-1473

[124]

Rahimi A, Vale-Silva LA, Fälth Savitski M, Tanevski J, Saez-Rodriguez J. 2024. DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics. Nature Communications 15: 4994

[125]

Shah S, Lubeck E, Zhou W, Cai L. 2016. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus . Neuron 92: 342-357

[126]

Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N, et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361: eaat5691

[127]

Fan J, Lu F, Qin T, Peng W, Zhuang X, et al. 2023. Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nature Genetics 55: 2175-2188

[128]

Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, et al. 2019. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177: 1873-1887.e17

[129]

Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, et al. 2019. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods 16: 1289-1296

[130]

Abdelaal T, Mourragui S, Mahfouz A, Reinders MJT. 2020. SpaGE: spatial gene enhancement using scRNA-seq. Nucleic Acids Research 48: e107

[131]

Lopez R, Nazaret A, Langevin M, Samaran J, Regier J, et al. 2019. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. arXiv 00:1905.02269

[132]

Chen S, Zhang B, Chen X, Zhang X, Jiang R. 2021. stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics. Bioinformatics 37: i299-i307

[133]

Wan X, Xiao J, Tam SST, Cai M, Sugimura R, et al. 2023. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nature Communications 14: 7848

[134]

Yang P, Jin L, Liao J, Jin K, Shao X, et al. 2023. Revealing spatial multimodal heterogeneity in tissues with SpaTrio. Cell Genomics 3: 100446

[135]

Yang P, Jin K, Yao Y, Jin L, Shao X, et al. 2025. Spatial integration of multi-omics single-cell data with SIMO . Nature Communications 16: 1265

[136]

Vahid MR, Brown EL, Steen CB, Zhang W, Jeon HS, et al. 2023. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nature Biotechnology 41: 1543-1548

[137]

Zhu Q, Shah S, Dries R, Cai L, Yuan GC. 2018. Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data . Nature Biotechnology 36: 1183-1190

[138]

Liu W, Liao X, Yang Y, Lin H, Yeong J, et al. 2022. Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data. Nucleic Acids Research 50: e72

[139]

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

[140]

Li Z, Zhou X. 2022. BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome Biology 23: 168

[141]

Liu W, Liao X, Luo Z, Yang Y, Lau MC, et al. 2023. Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature Communications 14: 296

[142]

Chidester B, Zhou T, Alam S, Ma J. 2023. SpiceMix enables integrative single-cell spatial modeling of cell identity. Nature Genetics 55: 78-88

[143]

Cable DM, Murray E, Shanmugam V, Zhang S, Zou LS, et al. 2022. Cell type-specific inference of differential expression in spatial transcriptomics. Nature Methods 19: 1076-1087

[144]

Mason K, Sathe A, Hess PR, Rong J, Wu CY, et al. 2024. Niche- DE niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions . Genome Biology 25: 14

[145]

Kim J, Rustam S, Mosquera JM, Randell SH, Shaykhiev R, et al. 2022. Unsupervised discovery of tissue architecture in multiplexed imaging. Nature Methods 19: 1653-1661

[146]

He Y, Tang X, Huang J, Ren J, Zhou H, et al. 2021. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nature Communications 12: 5909

[147]

Singhal V, Chou N, Lee J, Yue Y, Liu J, et al. 2024. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nature Genetics 56: 431-441

[148]

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

[149]

Wang B, Luo J, Liu Y, Shi W, Xiong Z, et al. 2023. Spatial-MGCN: a novel multi-view graph convolutional network for identifying spatial domains with attention mechanism. Briefings in Bioinformatics 24: bbad262

[150]

Shi X, Zhu J, Long Y, Liang C. 2023. Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks . Briefings in Bioinformatics 24: bbad278

[151]

Dong K, Zhang S. 2022. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention autoencoder. Nature Communications 13: 1739

[152]

Zhou X, Dong K, Zhang S. 2023. Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. Nature Computational Science 3: 894-906

[153]

Lin X, Gao L, Whitener N, Ahmed A, Wei Z. 2022. A model-based constrained deep learning clustering approach for spatially resolved single-cell data. Genome Research 32: 1906-1917

[154]

Li J, Chen S, Pan X, Yuan Y, Shen HB. 2022. Cell clustering for spatial transcriptomics data with graph neural networks. Nature Computational Science 2: 399-408

[155]

Ren H, Walker BL, Cang Z, Nie Q. 2022. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nature Communications 13: 4076

[156]

Guo T, Yuan Z, Pan Y, Wang J, Chen F, et al. 2023. SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies. Genome Biology 24: 241

[157]

Xu H, Fu H, Long Y, Ang KS, Sethi R, et al. 2024. Unsupervised spatially embedded deep representation of spatial transcriptomics. Genome Medicine 16: 12

[158]

Xu C, Jin X, Wei S, Wang P, Luo M, et al. 2022. DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic Acids Research 50: e131

[159]

Huo Y, Guo Y, Wang J, Xue H, Feng Y, et al. 2023. Integrating multimodal information to detect spatial domains of spatial transcriptomics by graph attention network. Journal of Genetics and Genomics 50: 720-733

[160]

Wang L, Hu Y, Xiao K, Zhang C, Shi Q, et al. 2024. Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics. Briefings in Bioinformatics 25: bbae257

[161]

Long Y, Ang KS, Li M, Chong KLK, Sethi R, et al. 2023. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nature Communications 14: 1155

[162]

Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008: P10008

[163]

Traag VA, Waltman L, van Eck NJ. 2019. From Louvain to leiden: guaranteeing well-connected communities. Scientific Reports 9: 5233

[164]

Miller BF, Bambah-Mukku D, Dulac C, Zhuang X, Fan J. 2021. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities. Genome Research 31: 1843-1855

[165]

Wu Z, Kondo A, McGrady M, Baker EAG, Chidester B, et al. 2024. Discovery and generalization of tissue structures from spatial omics data. Cell Reports Methods 4: 100838

[166]

Müller-Bötticher N, Sahay S, Eils R, Ishaque N. 2025. SpatialLeiden: spatially aware Leiden clustering. Genome Biology 26: 24

[167]

Schürch CM, Bhate SS, Barlow GL, Phillips DJ, Noti L, et al. 2020. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 183: 838

[168]

Chen Z, Soifer I, Hilton H, Keren L, Jojic V. 2020. Modeling multiplexed images with spatial-LDA reveals novel tissue microenvironments. Journal of Computational Biology 27: 1204-1218

[169]

Hu Y, Rong J, Xu Y, Xie R, Peng J, et al. 2024. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nature Methods 21: 267-278

[170]

Yuan Z, Zhao F, Lin S, Zhao Y, Yao J, et al. 2024. Benchmarking spatial clustering methods with spatially resolved transcriptomics data. Nature Methods 21: 712-722

[171]

Chen R, Yao Y, Qian J, Peng X, Shao X, et al. 2025. A comprehensive benchmarking for spatially resolved transcriptomics clustering methods across variable technologies, organs, and replicates. iMeta 4: e70084

[172]

Asiry S, Kim G, Filippou PS, Sanchez LR, Entenberg D, et al. 2021. The cancer cell dissemination machinery as an immunosuppressive niche: a new obstacle towards the era of cancer immunotherapy. Frontiers in Immunology 12: 654877

[173]

Gatenbee CD, Baker AM, Schenck RO, Strobl M, West J, et al. 2022. Immunosuppressive niche engineering at the onset of human colorectal cancer. Nature Communications 13: 1798

[174]

Zou H, Xiong W, He Y. 2025. Single-cell and spatial transcriptome-based metabolism-immunity interaction network and therapeutic target discovery of matrine in cervical cancer. Naunyn-Schmiedeberg's Archives of Pharmacology

[175]

Yu D, Yang P, Lu X, Huang S, Liu L, et al. 2023. Single-cell RNA sequencing reveals enhanced antitumor immunity after combined application of PD-1 inhibitor and Shenmai injection in non-small cell lung cancer. Cell Communication and Signaling 21: 169

[176]

Lin W, Chen X, Wang D, Lu R, Zhang C, et al. 2023. Single-nucleus ribonucleic acid-sequencing and spatial transcriptomics reveal the cardioprotection of Shexiang Baoxin Pill (SBP) in mice with myocardial ischemia-reperfusion injury. Frontiers in Pharmacology 14: 1173649

[177]

Liu J, Zhang Q, Wong YK, Luo P, Chen J, et al. 2024. Single-cell transcriptomics reveals the ameliorative effect of oridonin on septic liver injury. Advanced Biology 8: 2300542

[178]

Jin K, Gao S, Yang P, Guo R, Li D, et al. 2022. Single-cell RNA sequencing reveals the temporal diversity and dynamics of cardiac immunity after myocardial infarction. Small Methods 6: 2100752

[179]

Shu J, Qin D, Tu W, Zhao D, Yang Q, et al. 2026. Single-cell and spatially resolved transcriptomics elucidate the therapeutic mechanism of Tripterygium wilfordii Polyglycosidium in ulcerative colitis . Phytomedicine 150: 157569

[180]

Zhu Y, Zhao L, Yan W, Ma H, Zhao W, et al. 2025. Celastrol directly targets LRP1 to inhibit fibroblast-macrophage crosstalk and ameliorates psoriasis progression. Acta Pharmaceutica Sinica B 15: 876-891

[181]

Xin J, Yang T, Wu X, Wu Y, Liu Y, et al. 2023. Spatial transcriptomics analysis of zone-dependent hepatic ischemia-reperfusion injury murine model. Communications Biology 6: 194

[182]

Lu R, Sun W, Lin W, Zhu S, Wang D, et al. 2025. Fufang Danshen Pill improves mitochondrial homeostasis by regulating the S100a9/TLR4 axis to alleviate myocardial ischemia-reperfusion injury. Phytomedicine 148: 157433

[183]

Subramanian A, Nemat-Gorgani N, Ellis-Caleo TJ, van IJzendoorn DGP, Sears TJ, et al. 2024. Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy. Nature Cancer 5: 642-658

[184]

Xiao M, Hong S, Peng P, Cai S, Huang Y, et al. 2024. Co-delivery of protopanaxatriol/icariin into niche cells restores bone marrow niches to rejuvenate HSCs for chemotherapy-induced myelosuppression. Phytomedicine 134: 155978

[185]

Du J, Yang YC, An ZJ, Zhang MH, Fu XH, et al. 2023. Advances in spatial transcriptomics and related data analysis strategies. Journal of Translational Medicine 21: 330

[186]

De Jonghe J, Opzoomer JW, Vilas-Zornoza A, Nilges BS, Crane P, et al. 2024. scTrends: a living review of commercial single-cell and spatial 'omic technologies. Cell Genomics 4: 100723

[187]

Zhang D, Schroeder A, Yan H, Yang H, Hu J, et al. 2024. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nature Biotechnology 42: 1372-1377

[188]

Yang J, Zheng Z, Jiao Y, Yu K, Bhatara S, et al. 2025. Spotiphy enables single-cell spatial whole transcriptomics across an entire section. Nature Methods 22: 724-736

[189]

He HF, Peng P, Yang ST, Wang MG, Zhang XF, et al. 2026. Unlocking single-cell level and continuous whole-slide insights in spatial transcriptomics with PanoSpace. Nature Computational Science 00: 1-14

[190]

Ge S, Sun S, Xu H, Cheng Q, Ren Z. 2025. Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective. Briefings in Bioinformatics 26: bbaf136

[191]

Velten B, Stegle O. 2023. Principles and challenges of modeling temporal and spatial omics data. Nature Methods 20: 1462-1474

[192]

Hou ZK, Hu W, Liu FB, Liang YY, Huang ZY, et al. 2020. Reviews and thoughts on the relevance between qualitative Chinese medicinal properties and quantitative material components. Evidence-Based Complementary and Alternative Medicine 2020: 8643746

[193]

Zhou J, Zhou T, Chen M, Jiang M, Wang X, et al. 2014. Research progress on synergistic anti-tumor mechanisms of compounds in Traditional Chinese Medicine. Journal of Traditional Chinese Medicine 34: 100-105

[194]

Zhang L, Yan J, Liu X, Ye Z, Yang X, et al. 2012. Pharmacovigilance practice and risk control of Traditional Chinese Medicine drugs in China: current status and future perspective. Journal of Ethnopharmacology 140: 519-525

[195]

Miao Z, Humphreys BD, McMahon AP, Kim J. 2021. Multi-omics integration in the age of million single-cell data. Nature Reviews Nephrology 17: 710-724

[196]

Vandereyken K, Sifrim A, Thienpont B, Voet T. 2023. Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics 24: 494-515

[197]

Shao X, Lu X, Liao J, Chen H, Fan X. 2020. New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein & Cell 11: 866-880

[198]

Liberali P, Snijder B, Pelkmans L. 2015. Single-cell and multivariate approaches in genetic perturbation screens. Nature Reviews Genetics 16: 18-32

[199]

Bunne C, Roohani Y, Rosen Y, Gupta A, Zhang X, et al. 2024. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187: 7045-7063

[200]

Ling S, Xu JW. 2013. Model organisms and traditional Chinese medicine syndrome models. Evidence-Based Complementary and Alternative Medicine 2013: 761987

PDF (3737KB)

0

Accesses

0

Citation

Detail

Sections
Recommended

/