Molekulyarnaya Meditsina (Molecular medicine)Molekulyarnaya Meditsina (Molecular medicine)Молекулярная медицина1728-29182499-9490Russkiy Vrach Publishing House71723610.29296/24999490-2026-04-09xklnkmReviewsОбзорыReview ArticleSpatial analysis of tissue microenvironment functional morphology: multidimensional visualization techniques and artificial intelligenceПространственный анализ функциональной морфологии тканевого микроокружения: многомерные методы визуализации и искусственный интеллектhttps://orcid.org/0000-0002-8347-4556AtiakshinDmitrii A.АтякшинДмитрий Андреевич
Russian Federation

Director of the Research and Educational Resource Center “Innovative technologies of immunophenotyping, digital spatial profiling and ultrastructural analysis”, Peoples’ Friendship University of Russia named after Patrice Lumumba; Head of the Laboratory of Molecular Morphology and Immunohistochemistry, Research Institute of Experimental Biology and Medicine, Burdenko Voronezh State Medical University. Doctor of Medical Sciences

директор научно-образовательного ресурсного центра «Инновационные технологии иммунофенотипирования, цифрового пространственного профилирования и ультраструктурного анализа, ФГАОУ ВО «Российский университет дружбы народов имени Патриса Лумумбы». Доктор медицинских наук

atyakshin-da@rudn.ruhttps://orcid.org/0000-0001-9796-2924PoddubskyAnton A.ПоддубскийАнтон Александрович
Russian Federation

Associate Professor, Dean of the Faculty of Artificial Intelligence; Candidate of Technical Sciences

доцент, декан факультета искусственного интеллекта; Кандидат технических наук

ai@rudn.ruhttps://orcid.org/0000-0001-8447-2600DemyashkinGrigory A.ДемяшкинГригорий Александрович
Russian Federation

Leading Researcher at the Scientific and Educational Resource Center “Innovative Technologies of Immunophenotyping, Digital Spatial Profiling and Ultrastructural Analysis”; Doctor of Medical Sciences, Professor

профессор, ведущий научный сотрудник научно-образовательного ресурсного центра «Инновационные технологии иммунофенотипирования, цифрового пространственного профилирования и ультраструктурного анализа»; Доктор медицинских наук

demyashkin-ga@rudn.ruhttps://orcid.org/0000-0003-4068-6986RezaianNaimРезаианНаим
Russian Federation

Head of the Laboratory of Artificial Intelligence, Faculty of Artificial Intelligence

заведующий лабораторией искусственного интеллекта факультета искусственного интеллекта

ai@rudn.ruhttps://orcid.org/0000-0002-3394-4812SaridisMikis R.СаридисМикис Ремаевич
Russian Federation

Research Intern at the Scientific and Educational Resource Center “Innovative Technologies of Immunophenotyping, Digital Spatial Profiling and Ultrastructural Analysis”

стажер-исследователь Научно-образовательного ресурсного центра «Инновационные технологии иммунофенотипирования, цифрового пространственного профилирования и ультраструктурного анализа»

saridis-mr@rudn.ruhttps://orcid.org/0009-0004-8127-7251PrikhodkoAleksandra T.ПриходькоАлександра Тимуровна
Russian Federation

Research Intern at the Scientific and Educational Resource Center “Innovative Technologies of Immunophenotyping, Digital Spatial Profiling and Ultrastructural Analysis”

стажер-исследователь Научно-образовательного ресурсного центра «Инновационные технологии иммунофенотипирования, цифрового пространственного профилирования и ультраструктурного анализа»

prikhodko-at@rudn.ruhttps://orcid.org/0000-0002-8942-2984AlekhnovichAlexander V.АлехновичАлександр Владимирович
Russian Federation

Head of the Directorate (project office) of the program “Scientific and Educational Biomedical Cluster “Translational Medicine” of the Scientific and Educational Resource Center “Innovative Technologies of Immunophenotyping, Digital Spatial Profiling and Ultrastructural Analysis”; Doctor of Medical Sciences, Professor, Corresponding Member of the Russian Academy of Sciences

руководитель Дирекции (проектный офис) программы «Научно-образовательный биомедицинский кластер “Трансляционная медицина”»; Доктор медицинских наук, профессор, член-корреспондент Российской академии наук

alekhnovich-av@rudn.ruhttps://orcid.org/0000-0002-0792-6012KostinAndrey A.КостинАндрей Александрович
Russian Federation

Vice-Rector for Research, Scientific and Educational Resource Center “Innovative Technologies of Immunophenotyping, Digital Spatial Profiling and Ultrastructural Analysis”; Doctor of Medical Sciences, Professor, Corresponding Member of the Russian Academy of Sciences

первый проректор-проректор по научной работе; Доктор медицинских наук, профессор, член-корреспондент Российской академии наук

kostin-aa@rudn.ruhttps://orcid.org/0000-0002-1921-25439619-9301PaltsevaEkaterina М.ПальцеваЕкатерина Михайловна
Russian Federation

Professor; Doctor of Medical Sciences

профессор; Доктор медицинских наук

paltseva-k@rusvrach.ruPeoples’ Friendship University of Russia named after Patrice LumumbaФГАОУ ВО «Российский университет дружбы народов им. Патриса Лумумбы»Burdenko Voronezh State Medical UniversityФГБОУ ВО «Воронежский государственный медицинский университет им. Н.Н. Бурденко» Минздрава РоссииRussian Academy of SciencesФГБУ «Российская академия наук»1308202624460701208202612082026Copyright ©; 2026, Russkiy Vrach Publishing HouseCopyright ©; 2026, ИД "Русский врач"2026Russkiy Vrach Publishing HouseИД "Русский врач"https://journals.eco-vector.com/1728-2918/article/view/717236

Objective. To systematically categorize contemporary approaches and technologies for visualizing structural components of the tissue microenvironment (TME), with a focus on the advantages, limitations, and data analysis strategies of multiplex immunohistochemistry.

Material and Methods. A comprehensive literature analysis was conducted using the PubMed, Scopus, and Web of Science databases. The search was restricted to publications from the last 5–7 years.

Results. The integration of multidimensional histology with spatial context and AI-driven analysis establishes a new paradigm of informativeness in functional morphology. Current spatial profiling techniques – including macro- and microdissection, barcoding, optical imaging, and mass spectrometry imaging - differ substantially in resolution, multiplexing capacity, and the types of molecular markers they can analyze. Among optical imaging methods, multiplex immunofluorescence (mIF) offers distinct advantages: high sensitivity, preservation of tissue structural and biological integrity, reliable objectivity in cell detection within the histoarchitectural context, and robust validation of results through accessible quality control of immunolabeling at each stage of the staining design. mIF-based spatial proteomics is technologically accessible and cost-effective compared to other omics technologies, positioning it as a promising tool of choice for functional morphology in both normal and pathological states. The integration of artificial intelligence into whole-slide image analysis enables automated cell segmentation, classification, and subsequent statistical evaluation.

Conclusion. The convergence of visualization technologies and spatial analysis of the tissue microenvironment provides unprecedented opportunities to uncover fundamental principles of biological organization at the tissue, cellular, and molecular levels. This creates a novel informational framework for the development of translational biomarkers, enhances the informativeness of diagnostic and prognostic algorithms, and improves therapeutic efficacy within the paradigm of personalized medicine.

Цель обзора. Систематизация современных подходов и технологий визуализации структурных компонентов тканевого микроокружения (ТМО) с акцентом на преимущества, ограничения и стратегии анализа данных мультиплексной иммуногистохимии.

Материал и методы. Анализ литературы проведен с использованием баз данных PubMed, Scopus, Web of Science. Глубина поиска охватывала публикации за последние 5-7 лет.

Основные положения. Анализ ТМО методами многомерной гистологии с пространственным контекстом и применением ИИ создает новый уровень информативности в функциональной морфологии. Современные методы пространственного профилирования (макро-/микродиссекция, баркодирование, оптическая визуализация, масс-спектрометрическая визуализация) различаются по разрешению, мультиплексности и типу анализируемых маркеров. Мультиплексная иммунофлуоресценция (мИФ) среди других методов оптической визуализации обладает рядом преимуществ, включая высокую чувствительность, сохранение структурно-биологических параметров тканевой организации, достоверную объективность при детекции клеток в контексте гистоархитектоники и надежную верификацию полученных результатов благодаря доступной оценке качества иммуномечения на каждом этапе дизайна окрашивания. Пространственная протеомика на основе мИФ технологически доступна и имеет низкую себестоимость по сравнению с другими «омиксными» технологиями, что делает её перспективным инструментом выбора. Интеграция искусственного интеллекта в анализ гистосканов позволяет автоматизировать сегментацию, классификацию клеток и последующий статистический анализ.

Заключение. Совокупность технологий визуализации и пространственного анализа ТМО открывает возможности для выявления фундаментальных закономерностей организации живой материи на тканевом, клеточном и молекулярном уровнях. Это создает новый информационный базис для разработки трансляционных маркеров, повышения информативности диагностических и прогностических алгоритмов, а также эффективности терапии в персонализированной медицине.

tissue microenvironmentartificial intelligenceomics technologiesmultiplex immunofluorescenceimmunofluorescence image analysismultidimensional morphologyspatial phenotypingprofiling and mappingтканевое микроокружениеискусственный интеллектомиксные технологиипространственная протеомикамультиплексная иммуногистохимиямногомерная морфологияпространственное фенотипированиепрофилирование и картированиеGerlinger M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New England journal of medicine. 2012; 366 (10): 883–92. https://doi.org/10.1056/NEJMoa1113205Laberiano-Fernández C. et al. Best practices for technical reproducibility assessment of multiplex immunofluorescence. Frontiers in Molecular Biosciences. 2021; 8: 660202. https://doi.org/10.3389/fmolb.2021.660202Rodriques S.G. et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019; 363 (6434): 1463–7. https://doi.org/10.1126/science.aaw1219Lin J.R. et al. Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. Еlife. 2018; 7: e31657. https://doi.org/10.7554/eLife.31657Hoyt C.C. Multiplex immunofluorescence and multispectral imaging: forming the basis of a clinical test platform for immuno-oncology. Frontiers in molecular biosciences. 2021; 8: 674747. https://doi.org/10.3389/fmolb.2021.674747Giesen C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature methods. 2014; 11 (4): 417–22. https://doi.org/10.1038/nmeth.2869Porta Siegel T. et al. Mass spectrometry imaging and integration with other imaging modalities for greater molecular understanding of biological tissues. Molecular imaging and biology. 2018; 20 (6): 888–901. https://doi.org/10.1007/s11307-018-1267-yWilson C.M. et al. Challenges and opportunities in the statistical analysis of multiplex immunofluorescence data. Cancers. 2021; 13 (12): 3031. https://doi.org/10.3390/cancers13123031Hwang K. et al. High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research. J. of Visualized Experiments (JoVE). 2025; 220: e67584. https://doi.org/10.3791/67584Teplitz K. et al. 56 Quantitative evaluation of the tissue micro-environment by high-resolution 17-plex immunofluorescence reveals distinct cell populations. J. for ImmunoTherapy of Cancer. 2021; 9 (2): A64-A64. https://doi.org/10.1136/jitc-2021-sitc2021.056Datta S. et al. Laser capture microdissection: Big data from small samples. Histology and histopathology. 2015; 30 (11): 1255. https://doi.org/10.14670/HH-11-622Butler A.E. et al. Recovery of high-quality RNA from laser capture microdissected human and rodent pancreas. J. of histotechnology. 2016; 39 (2): 59–65. https://doi.org/10.1080/01478885.2015.1106073Miller B.F. et al. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nature communications. 2022; 13 (1): 2339. https://doi.org/10.1038/s41467-022-30033-zStickels R.R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nature biotechnology. 2021; 39 (3): 313–9. https://doi.org/10.1038/s41587-020-0739-1Wang G. et al. Tyramide signal amplification method in multiple-label immunofluorescence confocal microscopy. Methods. 1999; 18 (4): 459–64. https://doi.org/10.1006/METH.1999.0813Chen K.H. et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015; 348 (6233): aaa6090. https://doi.org/10.1126/science.aaa6090Gonzalez A.D. et al. Digital pathology in tau research: A comparison of QuPath and HALO. J. of Neuropathology & Experimental Neurology. 2025; nlaf026. https://doi.org/10.1093/jnen/nlaf026Merritt C.R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nature biotechnology. 2020; 38 (5): 586–99. https://doi.org/10.1038/s41587-020-0472-9Hwang K. et al. High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research. J. of Visualized Experiments (JoVE). 2025; 220: e67584. https://doi.org/10.3791/67584Angelo M. et al. Multiplexed ion beam imaging of human breast tumors. Nature medicine. 2014; 20 (4): 436–42. https://doi.org/10.1038/nm.3488Pennie R.L. et al. PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images. bioRxiv. 2025; 2025.01. 08.632002. https://doi.org/10.1101/2025.01.08.632002Espina V. et al. Laser capture microdissection technology. Expert review of molecular diagnostics. 2007; 7 (5): 647–57. https://doi.org/10.1586/14737159.7.5.647Kwon Y. et al. Spatial Proteomics towards cellular Resolution. Expert Review of Proteomics. 2024; 21 (12): 505–14. https://doi.org/10.1080/14789450.2024.2445809Chen A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. BioRxiv. 2021; 2021.Oliveira M.F. et al. High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer. Nature Genetics. 2025; 1–12. https://doi.org/10.1038/s41588-025-02193-3Ståhl P.L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016; 353 (6294): 78–82. https://doi.org/10.1126/science.aaf2403Wang N. et al. Spatial transcriptomics and proteomics technologies for deconvoluting the tumor microenvironment. Biotechnology J. 2021; 16 (9): 2100041. https://doi.org/10.1002/biot.202100041Dezem F.S. et al. Spatially resolved single-cell omics: Methods, challenges, and future perspectives. Annual Review of Biomedical Data Science. 2024; 7 (2024): 131–53. https://doi.org/10.1146/annurev-biodatasci-102523-103640Eng C.H.L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. Nature. 2019; 568 (7751): 235–9. https://doi.org/10.1038/s41586-019-1049-yHu B. et al. A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology. Frontiers in Immunology. 2022; 13: 996721. https://doi.org/10.3389/fimmu.2022.996721Basu S.S. et al. Rapid MALDI mass spectrometry imaging for surgical pathology. NPJ precision oncology. 2019; 3 (1): 17. https://doi.org/10.1038/s41698-019-0089-yYun S. et al. Mew: Multiplexed immunofluorescence image analysis through an efficient multiplex network. Eur. Conference on Computer Vision. Cham: Springer Nature Switzerland. 2024; 127–44. https://doi.org/10.48550/arXiv.2407.17857Tahir W. et al. Development of a high-throughput image processing pipeline for multiplex immunofluorescence whole slide images at scale. Cancer Research. 2023; 83 (7): 6616. https://doi.org/10.1158/1538-7445.am2023-6616He K. et al. Mask r-cnn. Proceedings of the IEEE international conference on computer vision. 2017; 2961–9.Amitay Y. et al. CellSighter: a neural network to classify cells in highly multiplexed images. Nature communications. 2023; 14 (1): 4302. https://doi.org/10.1038/s41467-023-40066-7Xiong J. et al. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. Bioinformatics. 2024; 40 (6): btae356. https://doi.org/10.1093/bioinformatics/btae356Yosofvand M. et al. Spatial immunophenotyping from whole-slide multiplexed tissue imaging using convolutional neural networks. bioRxiv. 2024; 2024.08. 16.608247. https://doi.org/10.1101/2024.08.16.608247Parra E.R. Methods to determine and analyze the cellular spatial distribution extracted from multiplex immunofluorescence data to understand the tumor microenvironment. Frontiers in Molecular Biosciences. 2021; 8: 668340. https://doi.org/10.3389/fmolb.2021.668340Kostin A. et al. Mast Cell Association with the Microenvironment of a Phosphaturic Mesenchymal Tumour Secreting Fibroblast Growth Factor 23. Medical Sciences. 2025; 13 (3): 195. https://doi.org/10.3390/medsci13030195Schmidt U. et al. Cell detection with star-convex polygons. International conference on medical image computing and computer-assisted intervention. Cham: Springer International Publishing. 2018; 265–73.Stringer C. et al. Cellpose: a generalist algorithm for cellular segmentation. Nature methods. 2021; 18 (1): 100–6. https://doi.org/10.1038/s41592-020-01018-xGoldsborough T. et al. A novel channel invariant architecture for the segmentation of cells and nuclei in multiplexed images using InstanSeg. bioRxiv. 2024; 2024.09. 04.611150.Greenwald N.F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature biotechnology. 2022; 40 (4): 555–65. https://doi.org/10.1038/s41587-021-01094-0Marks M. et al. CellSAM: a foundation model for cell segmentation. Nature Methods. 2025; 1–9. https://doi.org/10.1038/s41592-025-02879-wMikhalev S. et al. Spatial Phenotype of the Mast Cell Population in Endometritis of Various Severities. Cells. 2025; 15 (1): 38. https://doi.org/10.3390/cells15010038Zhang W. et al. Comparison of QuPath and HALO platforms for analysis of the tumor microenvironment in prostate cancer. bioRxiv. 2025; 2025.05. 16.654590. https://doi.org/10.1101/2025.05.16.654590Laberiano C. et al. 670 Develop a multiplex immunofluorescence panel to identification of distinct complex immune landscapes in pleural effusion liquids from patients with metastatic lung adenocarcinoma. 2020. https://doi.org/10.1136/jitc-2020-sitc2020.0670Manikis G. et al. Abstract P1-02-28: PDGFRa+/FAP-fibroblasts at multiplex in situ resolution are associated with resistance to HER2 blockade in neoadjuvant treated HER2+ breast cancer: results from the Swedish PREDIX HER2 trial. Clinical Cancer Research. 2025; 31 (12) P1-02-28-P1-02-28. https://doi.org/10.1158/1557-3265.sabcs24-p1-02-28Nikitin B.M. et al. QuPath versus InForm: Digital image analysis of multiplex immunofluorescence (mIF) tumor-infiltrating lymphocyte (TIL) outcomes in an immunotherapy clinical trial. Cancer Research. 2021; 81 (13): 1665. https://doi.org/10.1158/1538-7445.AM2021-1665McKendell A.K. et al. Basic Science and Pathogenesis. Alzheimer’s & dementia: the J. of the Alzheimer’s Association. 2024; 20: e093006. https://doi.org/10.1002/alz.093006Sankaranarayanan A. et al. Quantitative benchmarking of nuclear segmentation algorithms in multiplexed immunofluorescence imaging for translational studies. Communications Biology. 2025; 8 (1): 836. https://doi.org/10.1038/s42003-025-08184-8Taube J.M. et al. Multi-institutional TSA-amplified multiplexed immunofluorescence reproducibility evaluation (MITRE) study. J. for immunotherapy of cancer. 2021; 9 (7): e002197. https://doi.org/10.1136/jitc-2020-002197