Abstract 4308: Same-section spatial multiomic analyses using MICS technology for investigating the dynamics of the tumor microenvironment

• Published in: Cancer research (Chicago, Ill.) Vol. 84; no. 6_Supplement; p. 4308
• Main Authors: Park, Dongju, Neil, Emily, Hennessey, Rebecca C., DiBuono, Michael, Lafayette, Hanna, Lloyd, Erica, Lo, Hsinyi, Femel, Julia, Makrigiorgos, Alex, Lu, Shaina, Lee, John, Soliman, Sameh, Mangiardi, Dominic, Praveen, Paurush, Staubach, Fabian, Hindman, Ryan, Rothmann, Thomas, Santos, Telma, Borbe, Stefan, Meyer, Hansueli, Wantenaar, Tanya, Wang, Jinling, Müller, Werner, Pinard, Robert, Bosio, Andreas
• Format: Journal Article
• Language: English
• Published: 22.03.2024
• ISSN: 1538-7445; 1538-7445
• DOI: 10.1158/1538-7445.AM2024-4308

Abstract The recent increase in image-based, spatially-resolved technologies enables researchers to profile the tumor microenvironment (TME) by capturing gene expression profiles within tissue sections. However, a significant limitation of these technologies is the lack of ability to resolve protein and RNA information in the same section, as well as conveniently analyze multimodal data sets. Here, we report a spatial RNA detection method, RNAsky, using Miltenyi Biotec’s MACSima™ Platform as an automated, multiomic approach. Our method integrates spatial proteomics and transcriptomics data to provide in-depth profiling with single-cell resolution on the same tissue section. We demonstrate these capabilities by characterizing key immune-oncology markers across normal and diseased tissues using our specialized MACS® iQ View analysis software. MACS iQ View provides fast segmentation and intuitive gating and clustering strategies to simultaneously assess protein and RNA data. We investigated the impact of clustering using protein, RNA, or the combination of both to evaluate the contribution of different information modalities on TME spatial dynamics. This cutting-edge approach will enable the identification of valuable parameters and new cell types, furthering the discovery and development of predictive and prognostic biomarkers. Citation Format: Dongju Park, Emily Neil, Rebecca C. Hennessey, Michael DiBuono, Hanna Lafayette, Erica Lloyd, Hsinyi Lo, Julia Femel, Alex Makrigiorgos, Shaina Lu, John Lee, Sameh Soliman, Dominic Mangiardi, Paurush Praveen, Fabian Staubach, Ryan Hindman, Thomas Rothmann, Telma Santos, Stefan Borbe, Hansueli Meyer, Tanya Wantenaar, Jinling Wang, Werner Müller, Robert Pinard, Andreas Bosio. Same-section spatial multiomic analyses using MICS technology for investigating the dynamics of the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4308.