Abstract 2079: Spatially guided single-cell analysis integrating spatial transcriptomics and spatial cell sorting for in-depth profiling

Spatial transcriptomics technologies, such as Xenium, provide detailed spatial mapping of gene expression at the single-cell level, revealing intricate tissue organization and distinct cell populations. However, translating these spatial insights into actionable molecular information often requires further downstream analysis of specific cells of interest. To address this, we introduce an integrated workflow combining SLACS (Spatially-resolved Laser-Activated Cell Sorting) with Xenium-derived spatial data, enabling targeted isolation and in-depth transcriptomic analysis of defined cell populations. SLACS technology is a novel cell sorting method that utilizes laser-based activation for precise, spatially guided isolation of individual cells. In this study, we applied SLACS to samples previously analyzed using Xenium spatial transcriptomics, focusing on cell populations identified as functionally relevant or rare based on their spatial gene expression profiles. The targeted cells were isolated using SLACS, followed by high-resolution RNA-seq analysis to further characterize their unique transcriptomic signatures. Utilizing two distinct breast cancer samples analyzed via Xenium, we demonstrate the effective identification of regions of interest (ROIs) for SLACS-based isolation and subsequent high-resolution transcriptomic analysis. The integration of SLACS with Xenium spatial data significantly enhanced the resolution of single-cell transcriptomic analysis. In the luminal A sample, SLACS successfully isolated rare cell populations, including luminal B stem cells and CAFs, which exhibited distinct gene expression profiles. RNA-seq analysis revealed differential expression of key markers, such as ESR1 and PGR, which are crucial for understanding hormone receptor signaling pathways in luminal A breast cancer. In contrast, the TNBC sample displayed higher heterogeneity, with a prominent presence of EMT-transitioning cells and cancer cycling cells. These findings suggest active paracrine signaling between cancer cells and the surrounding stromal cells, contributing to the aggressive phenotype observed in TNBC. Our study demonstrates the powerful synergy of combining SLACS with Xenium spatial transcriptomics for targeted single-cell analysis. By leveraging spatial information to guide cell sorting, we achieved high-resolution characterization of rare and functionally distinct cell populations. This integrated approach provides new insights into the cellular and molecular mechanisms underlying breast cancer, highlighting key differences between luminal A and triple-negative subtypes. Citation Format: Sumin Lee, Amos Lee. Spatially guided single-cell analysis integrating spatial transcriptomics and spatial cell sorting for in-depth profiling [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 2079.