An integrated approach for analyzing spatially resolved multi-omics datasets from the same tissue section.

Area of Science:

• Biotechnology
• Molecular Biology
• Cancer Research

Background:

• Spatial transcriptomics (ST) and spatial proteomics (SP) offer high-dimensional molecular profiling at single-cell resolution.
• Current ST and SP methods analyze separate tissue sections, hindering direct cross-modal comparisons.
• Understanding the tumor-immune microenvironment requires integrated molecular data.

Purpose of the Study:

• To develop and validate a framework for performing and integrating ST and SP from the same tissue section.
• To enable direct, single-cell level comparisons of RNA and protein expression in human lung cancer.
• To assess the feasibility and utility of co-localized multi-omics analysis for disease research.

Main Methods:

• Developed a wet-lab and computational workflow for co-analysis of ST and SP on identical tissue sections.
• Utilized hematoxylin and eosin (H&E) staining for morphological consistency.
• Employed Weave software for computational registration and annotation transfer across modalities.
• Performed single-cell level comparisons of RNA and protein expression, including correlation analyses.

Main Results:

• Successfully integrated ST and SP data from the same human lung cancer tissue sections.
• Achieved accurate alignment and annotation transfer between molecular layers using computational registration.
• Enabled direct, single-cell resolution comparisons of transcript and protein expression.
• Observed and resolved systematic low correlations between RNA and protein levels at the cellular level.

Conclusions:

• The developed framework allows high-quality, spatially-resolved multi-omics analysis on a single tissue section.
• This approach facilitates concordance studies and region-specific analyses of molecular markers.
• Advances understanding of disease heterogeneity by enabling integrated molecular profiling.