Spatial analysis of tissue microenvironment functional morphology: multidimensional visualization techniques and artificial intelligence

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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.

About the authors

Dmitrii A. Atiakshin

Peoples’ Friendship University of Russia named after Patrice Lumumba; Burdenko Voronezh State Medical University

Author for correspondence.
Email: atyakshin-da@rudn.ru
ORCID iD: 0000-0002-8347-4556

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198; Studencheskaya Str. 10, Voronezh, 394036

Anton A. Poddubsky

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: ai@rudn.ru
ORCID iD: 0000-0001-9796-2924

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Grigory A. Demyashkin

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: demyashkin-ga@rudn.ru
ORCID iD: 0000-0001-8447-2600

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Naim Rezaian

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: ai@rudn.ru
ORCID iD: 0000-0003-4068-6986

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Mikis R. Saridis

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: saridis-mr@rudn.ru
ORCID iD: 0000-0002-3394-4812

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Aleksandra T. Prikhodko

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: prikhodko-at@rudn.ru
ORCID iD: 0009-0004-8127-7251

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Alexander V. Alekhnovich

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: alekhnovich-av@rudn.ru
ORCID iD: 0000-0002-8942-2984

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Andrey A. Kostin

Peoples’ Friendship University of Russia named after Patrice Lumumba

Email: kostin-aa@rudn.ru
ORCID iD: 0000-0002-0792-6012

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

Russian Federation, Miklukho-Maklaya St., 6, Moscow, 117198

Ekaterina М. Paltseva

Russian Academy of Sciences

Email: paltseva-k@rusvrach.ru
ORCID iD: 0000-0002-1921-2543
SPIN-code: 9619-9301

Professor; Doctor of Medical Sciences

Russian Federation, Leninsky prospekt, 32, Moscow, 119991

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