Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.

Area of Science:

• Spatial biology
• Proteomics
• Biotechnology

Background:

• Tissue microenvironment heterogeneity influences biological processes and disease.
• Highly multiplexed tissue imaging (HMTI) offers single-cell spatial proteomics.
• Expanding HMTI landscape presents challenges in platform selection and data analysis.

Purpose of the Study:

• To provide a comprehensive comparison of HMTI platforms.
• To highlight analytical bottlenecks in HMTI data analysis.
• To discuss challenges and future directions for HMTI.

Main Methods:

• Review of HMTI platforms using DNA, fluorophore, or metal-tagged reagents.
• Analysis of HMTI data preprocessing, normalization, denoising, and cell segmentation.
• Discussion of spatial multi-omics integration and 3D spatial proteomics.

Main Results:

• HMTI enables quantification of tissue microenvironment heterogeneity at single-cell resolution.
• Identified key analytical bottlenecks including preprocessing, cell annotation, and microarchitecture analysis.
• Highlighted limitations, scalability, and performance under challenging conditions.

Conclusions:

• HMTI-driven spatial proteomics is powerful for tissue analysis.
• Standardized computational pipelines and robust datasets are crucial for progress.
• Advances will enhance reproducibility and accelerate clinical translation for precision medicine.