Tumor Microenvironment Characterization using a Spatial Multiomic Assay to Simultaneously Profile Protein and Gene Expression in FFPE Tumors

Abstract

The tumor microenvironment (TME) is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. The constant interaction between a tumor and its microenvironment plays a critical role in how the cancer develops, progresses, and responds to therapies. Traditionally, Hematoxylin and Eosin (H&E) and immunohistochemistry staining have been used to annotate and characterize tissues and associated pathologies. Recent single analyte approaches spatially interrogate targeted or transcriptome‐wide expression of RNA in tissue sections, while others capture phenotypes using a limited number of protein markers. However, for a more comprehensive understanding of the unique characteristics of cell types, cell states, and cell‐cell interactions within the TME, analysis of multiple analytes is necessary.Here we demonstrate a novel, streamlined multiomic spatial assay that integrates histological staining and imaging with simultaneous transcriptome‐wide gene expression and highly multiplexed protein expression profiling from the same formalin‐fixed paraffin embedded (FFPE) tissue section. In short, tissue sections from archived FFPE samples were placed on slides containing arrayed capture oligos with unique positional barcodes. The H&E stained tissues were then imaged, followed by incubation with transcriptome‐wide probes and a high‐plex DNA‐barcoded antibody panel containing intra‐ and extracellular markers. Transcriptome probes and antibody‐barcodes were then spatially captured on the slide and converted into sequencing‐ready libraries. Our data analysis and interactive visualization software enable interrogation of all data layers (H&E morphology, RNA, protein) from the same tissue section.We apply this method to simultaneously measure gene and protein expression within the TME of human breast cancer and melanoma FFPE samples using whole transcriptome probes and an immune‐oncology antibody panel. The data enables comparison and correlation of multiple analytes and their patterns within the same sample section. In addition, this simultaneous detection enables marker‐guided regional selection and differential gene expression analysis on the defined areas. Taken together, our data demonstrates that a spatially resolved, multiomic approach provides a more comprehensive understanding of cellular behavior in and around tumors, yielding new insights into disease progression, predictive biomarkers, drug response and resistance, and therapeutic development.