Spatial Multi-Omics Workflow and Analytical Guidelines for Alzheimer's Neuropathology.

Area of Science:

• Spatial biology
• Genomics
• Proteomics
• Neuroscience

Background:

• Spatial biology technologies offer high-dimensional tissue profiling.
• Standardized analytical frameworks are crucial for data quality and reproducibility.
• Existing platforms require robust methods for multi-omic data interpretation.

Purpose of the Study:

• To present an end-to-end pipeline for the GeoMx Digital Spatial Profiler.
• To enable simultaneous whole-transcriptome and protein measurements from user-defined tissue regions.
• To establish guidelines for reproducible spatial multi-omic analyses.

Main Methods:

• Developed an integrated workflow for morphology-guided region selection, quality control, and normalization.
• Applied the pipeline to formalin-fixed cortical tissues from Alzheimer's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, and controls.
• Utilized multi-modal data interpretation for transcript and protein analysis.

Main Results:

• The framework resolved spatially distinct molecular domains within complex tissues.
• Transcript and protein signals showed divergence across pathological regions (e.g., amyloid plaques).
• RNA-protein concordance varied by disease condition, and single-neuron profiling demonstrated protein assay sensitivity.

Conclusions:

• The presented dataset is a validated resource for spatial multi-omic analyses.
• The pipeline provides broadly applicable guidelines for reliable and reproducible tissue profiling.
• This approach enhances understanding of molecular and cellular organization in disease.