Ultra High-Plex Spatial Proteogenomic Investigation of Giant Cell Glioblastoma Multiforme Immune Infiltrates Reveals Distinct Protein and RNA Expression Profiles

Abstract

A deeper understanding of complex biological processes, including tumor development and immune response, requires ultra high-plex, spatial interrogation of multiple 'omes'. Here we present the development and implementation of a novel spatial proteogenomic (SPG) assay on the GeoMx® Digital Spatial Profiler platform with NGS readout that enables ultra high-plex digital quantitation of proteins (> 100-plex) and RNA (whole transcriptome, > 18,000-plex) from a single FFPE sample. This study highlighted the high concordance, R > 0.85, and <11% change in sensitivity between SPG assay and the single analyte assays on various cell lines and tissues from human and mouse. Furthermore, we demonstrate that the SPG assay was reproducible across multiple users. When used in conjunction with advanced cellular neighborhood segmentation, distinct immune or tumor RNA and protein targets were spatially resolved within individual cell subpopulations in human colorectal cancer and non-small cell lung cancer. We used the SPG assay to interrogate 23 different glioblastoma multiforme samples across 4 pathologies. The study revealed distinct clustering of both RNA and protein based on pathology and anatomic location. The in-depth investigation of giant cell glioblastoma multiforme revealed distinct protein and RNA expression profiles compared to that of the more common glioblastoma multiforme. More importantly, the use of spatial proteogenomics allowed simultaneous interrogation of critical protein post-translational modifications alongside whole transcriptomic profiles within the same distinct cellular neighborhoods.

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“…Protein expression counts from the nCounter run were retrieved and uploaded into the GeoMx DSP analysis suite (Nanostring, version 3.0.109), and the nCounter read counts were normalized using a geometric mean of the signal-to-noise ratio (SNR) of IgG negative controls as described previously (98,102). The normalized read counts are presented as Log 2 of SNR, and the difference was presented with false discovery rate (FDR).…”

“…In addition, the mouse immune profile (ImmuneAtlas_ImmGene) was used as the cell matrix for estimating immune cell abundance in the pancreas, spleen, and PLN. The cell matrices were downloaded from (101,102). Basic deconvolution was performed following the workflow provided in the R package.…”