Understanding cellular heterogeneity within complex tissue environments is critical for advancing our knowledge of biological processes and disease mechanisms. 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. We applied Xenium spatial transcriptomics analysis of two breast cancer samples: one luminal A subtype and one triple-negative breast cancer. The Xenium platform provided detailed maps of gene expression, highlighting specific cell populations and spatially distinct ROIs. Using custom cell type annotations generated with the spacexr R package (RCTD), we identified key cell types, such as luminal progenitor cells, cancer-associated fibroblasts (CAFs), and cells undergoing epithelial-to-mesenchymal transition (EMT).
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