Spatial proteomics and transcriptomics characterization of tissue and multiple cancer types including decalcified marrow.

Abstract

BackgroundRecent technologies enabling the study of spatial biology include multiple high-dimensional spatial imaging methods that have rapidly emerged with different capabilities evaluating tissues at different resolutions for different sample formats. Platforms like Xenium (10x Genomics) and PhenoCycler-Fusion (Akoya Biosciences) enable single-cell resolution analysis of gene and protein expression in archival FFPE tissue slides. However, a key limitation is the absence of systematic methods to ensure tissue quality, marker integrity, and data reproducibility.ObjectiveWe seek to optimize the technical methods for spatial work by addressing preanalytical challenges with various tissue and tumor types, including a decalcification protocol for processing FFPE bone marrow core specimens to preserve nucleic acids for effective spatial proteomics and transcriptomics. This study characterizes a multicancer tissue microarray (TMA) and a molecular- and protein-friendly decalcification protocol that supports downstream spatial biology investigations.MethodsWe developed a multi-cancer tissue microarray (TMA) and processed bone marrow core samples using a molecular- and protein-friendly decalcification protocol. PhenoCycler high-plex immunohistochemistry (IHC) generated spatial proteomics data, analyzed with QuPath and single-cell analysis. Xenium provided spatial transcriptomics data, analyzed via Xenium Explorer and custom pipelines.ResultsResults showed that PhenoCycler and Xenium platforms applied to TMA sections of tonsil and various tumor types achieved good marker concordance. Bone marrow decalcification with our optimized protocol preserved mRNA and protein markers, allowing Xenium analysis to resolve all major cell types while maintaining tissue morphology.ConclusionsWe have shared our preanalytical verification of tissues and demonstrate that both the PhenoCycler-Fusion high-plex spatial proteomics and Xenium spatial transcriptomics platforms work well on various tumor types, including marrow core biopsies decalcified using a molecular- and protein-friendly decalcificationprotocol. We also demonstrate our laboratory's methods for systematic quality assessment of the spatial proteomic and transcriptomic data from these platforms, such that either platform can provide orthogonal confirmation for the other.