Abstract
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.
Keywords
Authors and affiliations
https://elibrary.ru/xklnkm D.A. Atiakshin(1, 2), A.A. Poddubsky(1), G.A. Demyashkin(1), N. Rezaian(1),
M.R. Saridis(1), A.T. Prikhodko(1), A.V. Alekhnovich(1), A.A. Kostin(1),Е.М. Paltseva(3)
1-Peoples’ Friendship University of Russia named after Patrice Lumumba, Miklukho-Maklaya St., 6, Moscow, 117198,
Russian Federation; 2-Burdenko Voronezh State Medical University, Studencheskaya Str. 10, Voronezh, 394036,
Russian Federation; 3-Russian Academy of Sciences, Leninsky prospekt, 32, Moscow, 119991, Russian Federation
References
1. Gerlinger 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/NEJMoa1113205
2. Laberiano-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.2021.660202
3. Rodriques 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.aaw1219
4. Lin 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.31657
5. Hoyt 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.2021.674747
6. Giesen 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.2869
7. Porta 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-y
8. Wilson 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/cancers13123031
9. Hwang K. et al. High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research. J. of Visualized Experiments (JoVE). 2025; 220: e67584. https://doi.org/10.3791/67584
10. Teplitz 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-sitc2021.056
11. Datta S. et al. Laser capture microdissection: Big data from small samples. Histology and histopathology. 2015; 30 (11): 1255. https://doi.org/10.14670/HH-11-622
12. Butler 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/10.1080/01478885.2015.1106073
13. Miller 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-z
14. Stickels 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/s41587-020-0739-1
15. Wang 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.1999.0813
16. Chen K.H. et al. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015; 348 (6233): aaa6090. https://doi.org/10.1126/science.aaa6090
17. Gonzalez 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/nlaf026
18. Merritt 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/s41587-020-0472-9
19. Hwang K. et al. High-Throughput Automated Multiplex Immunofluorescence Assays for Translational Research. J. of Visualized Experiments (JoVE). 2025; 220: e67584. https://doi.org/10.3791/67584
20. Angelo M. et al. Multiplexed ion beam imaging of human breast tumors. Nature medicine. 2014; 20 (4): 436–42. https://doi.org/10.1038/nm.3488
21. Pennie 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/10.1101/2025.01.08.632002
22. Espina V. et al. Laser capture microdissection technology. Expert review of molecular diagnostics. 2007; 7 (5): 647–57. https://doi.org/10.1586/14737159.7.5.647
23. Kwon Y. et al. Spatial Proteomics towards cellular Resolution. Expert Review of Proteomics. 2024; 21 (12): 505–14. https://doi.org/10.1080/14789450.2024.2445809
24. Chen A. et al. Large field of view-spatially resolved transcriptomics at nanoscale resolution. BioRxiv. 2021; 2021.
25. 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/s41588-025-02193-3
26. Ståhl 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.aaf2403
27. Wang 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.202100041
28. Dezem 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-102523-103640
29. Eng 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-y
30. Hu 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.2022.996721
31. Basu 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-y
32. Yun 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.2407.17857
33. Tahir 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.am2023-6616
34. He K. et al. Mask r-cnn. Proceedings of the IEEE international conference on computer vision. 2017; 2961–9.
35. 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/s41467-023-40066-7
36. Xiong J. et al. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. Bioinformatics. 2024; 40 (6): btae356. https://doi.org/10.1093/bioinformatics/btae356
37. Yosofvand M. et al. Spatial immunophenotyping from whole-slide multiplexed tissue imaging using convolutional neural networks. bioRxiv. 2024; 2024.08. 16.608247. https://doi.org/10.1101/2024.08.16.608247
38. Parra 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.2021.668340
39. Kostin 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/medsci13030195
40. Schmidt 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.
41. 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-x
42. Goldsborough 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.
43. 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/s41587-021-01094-0
44. Marks M. et al. CellSAM: a foundation model for cell segmentation. Nature Methods. 2025; 1–9. https://doi.org/10.1038/s41592-025-02879-w
45. Mikhalev 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/cells15010038
46. Zhang 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/10.1101/2025.05.16.654590
47. Laberiano 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-sitc2020.0670
48. Manikis 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-p1-02-28
49. Nikitin 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.AM2021-1665
50. McKendell 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.093006
51. Sankaranarayanan 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/s42003-025-08184-8
52. Taube 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