Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment

Area of Science:

• Immunology
• Oncology
• Computational Pathology

Background:

• The tumor microenvironment (TME) immune landscape critically influences cancer progression and therapeutic outcomes.
• Immune cell density and location within the TME hold significant diagnostic and prognostic value.
• Current multiomic profiling advances TME understanding but lacks spatial resolution of cell interactions.

Purpose of the Study:

• To develop an accessible, spatial multiplexing technique for immune cell profiling in tissue sections.
• To complement single-cell technologies by providing whole-tissue spatial context.
• To enable automated identification, quantification, and mapping of immune cells within the TME.

Main Methods:

• Integration of serial imaging, sequential labeling, and image alignment to create virtual multiparameter slides.
• Automated analysis of virtual slides using user-defined protocols.
• Application of specific analysis modules: Tissuealign, Author, and HISTOmap.

Main Results:

• Successful generation of virtual multiparameter slides from whole tissue sections.
• Demonstrated automated identification, quantification, and spatial mapping of immune cell populations.
• Maximized information retrieval from limited clinical tissue samples.

Conclusions:

• The described strategy offers an affordable and accessible method for high-resolution TME spatial analysis.
• This approach provides an unbiased, comprehensive view of the immune landscape across entire tissue sections.
• The technique enhances the utility of limited tissue samples for cancer research and clinical applications.