Abstract 6612: Deep and scalable spatial transcriptomics for oncology research

The emerging field of spatial transcriptomics is revolutionizing histopathology by simultaneously quantifying numerous RNA transcripts in situ across a tissue section. However, current technologies provide a broad survey of the spatial environment at the expense of low-complexity genomic data. We present the Picoception RNA platform, which produces deep genomic data from spatially targeted regions of a tissue section. This platform combines laser-capture microdissection (LCM) with comprehensive mRNA-sequencing, which reads all polyadenylated transcripts including pathogens and model organisms. From a single <10 µm section of formalin-fixed paraffin-embedded (FFPE) tissue, Picoception RNA can profile an unlimited number of target regions smaller than 0.1 mm2, typically reading 200,000-1,000,000 unique molecules each with a dynamic range of more than 100:1. By isolating small areas of a tissue section, Picoception RNA allows deep molecular profiling of pure, distinct cellular regions such as tumor epithelial vs. normal or immune microenvironment. Combined with the depth of the gene-expression data, the cost-effectiveness of this technique enables statistically powerful analyses from large clinical cohorts. Furthermore, Picoception RNA’s compatibility with archived FFPE samples unlocks genomic signals from patients with known clinical outcomes, allowing the process of biomarker and pathway discovery to begin immediately without prospectively recruiting new subjects. We have applied this technique successfully in a wide range of applications, including oncology clinical trials for targeted therapy and immunotherapy, rare cancers, and cancers with limited biopsy material (including punch biopsies and core-needle biopsies), as well as rodent models. We have had consistent success profiling FFPE samples more than 20 years old. Picoception RNA complements previous spatial transcriptomics techniques, filling the need for deep genomic analysis at microscopic resolution using large-scale cohorts of FFPE samples. Citation Format: Joseph W. Foley, Wei K. Teo, Joshua K. Tay. Deep and scalable spatial transcriptomics for oncology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6612.