Abstract 6764: Multi-parametric comparison of scRNA-seq with CITE-seq and ultrahigh-plex spatial phenotyping of proteins in FFPE head and neck tumor biopsies: An opportunity to generate uniquely comprehensive multi-omic single cell datasets to investigate the tumor microenvironment

Abstract

Abstract Single cell RNA-seq (scRNA-seq) performed on cell suspensions isolated from tumor biopsies provides substantial insights into the transcriptional state of the cells in the tumor microenvironment (TME). Complementing this work with multi-omic Cellular Indexing of Transcriptomes and Epitopes (CITE-seq), which adds barcoded antibodies to label the surface of isolated cells, allows us to additionally characterize surface proteins. While information gleaned from these studies is dramatically expanding our understanding of the evolving immune response and tumor-immune interactions, it lacks an appreciation of the many spatial relationships that are present within the TME. Spatial phenotyping with the power of single cell resolution and whole slide imaging provides a valuable tool for identifying the cellular architecture and organization of spatial neighborhoods that perform critical roles in tumor progression, resistance, and clinical responses. Here, we explore bioinformatic strategies aimed at combining scRNA-seq and CITE-seq data with single-cell spatial protein data obtained with the PhenoCycler-Fusion (PCF) imaging platform. To this end, we performed whole-slide spatial phenotyping with an ultrahigh-plex PCF panel on FFPE human head and neck tumors. The same biopsies were also enzymatically digested and subjected to CITE-seq and scRNA-seq analysis. The combined multi-omic, single cell dataset provides a uniquely comprehensive account of the cellular states, functional significance and spatial neighborhoods that govern the TME. This study represents a novel approach to integrate high dimensional data from bulk tissue and single-cell spatial technologies to provide deeper insights into tumor biology and, in turn, to develop more informed strategies for combination immunotherapy trials and improved patient outcomes. Citation Format: Aditya Pratapa, Shawn M. Jensen, Niyati Jhaveri, Yoshinobu Koguchi, Venkatesh Rajamanickam, Brady Bernard, Tanisha Christie, Brian Piening, Rom S. Leidner, Oliver Braubach, Bernard A. Fox. Multi-parametric comparison of scRNA-seq with CITE-seq and ultrahigh-plex spatial phenotyping of proteins in FFPE head and neck tumor biopsies: An opportunity to generate uniquely comprehensive multi-omic single cell datasets to investigate the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6764.