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Mapping Immunity With Cutting‐Edge Spatial Biology and Tissue Cytometry Innovations.

Title: Mapping Immunity With Cutting‐Edge Spatial Biology and Tissue Cytometry Innovations.
Authors: Cárdenas‐Piedra, Lilibeth; Samuel, Selwin G.; Mungenast, Felicitas; Yasothkumar, Dinesh; Ecker, Rupert C.; Batra, Jyotsna
Source: MedComm; Jun2026, Vol. 7 Issue 6, p1-26, 26p
Subject Terms: Cytometry; Cell imaging; Tissue analysis; Neuroimmunology; Autoimmune diseases; Tumor microenvironment; Immunology; Quantitative research
Abstract: The immune system operates within organized tissue landscapes, where spatial relationships between cells shape the nature and outcome of immune responses. Over the past decade, protein imaging‐based spatial technologies have transformed how immune responses are studied within their native tissue context, enabling simultaneous high‐plex detection of proteins and transcripts at single‐cell resolution. Driven by advances in computational methods such as tissue image cytometry, these approaches now allow quantitative extraction of spatial metrics such as cellular neighborhoods, cell–cell distances, coexpression patterns, and tissue architecture that are emerging as next‐generation spatial biomarkers of immune function and disease diagnosis and/or prognosis. This review provides a consolidated overview of immunology through the lens of imaging‐based spatial proteomics and tissue image cytometry, discussing the multiplex imaging platforms, staining strategies, and computational tools that enabled this shift. We survey applications spanning tumor immunology, autoimmune and infectious diseases, immunometabolism, neuroimmunology, and transplant immunology, revealing that immune organization is not random but spatially determined and directly linked to disease outcome. We further evaluate the strengths and limitations of current approaches and explore future directions including 3D imaging, artificial intelligence‐driven spatial analysis, and the clinical translation of spatial biomarkers. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index