87
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      scite_
       
      • Record: found
      • Abstract: found
      • Conference Proceedings: found
      Is Open Access

      Spatially Guided Single-Cell Analysis Integrating Spatial Transcriptomics and Spatial Cell Sorting for In-Depth Profiling

      Published
      conference-abstract
      1 , 1 ,
      13th Asia Pacific Microscopy Congress 2025 (APMC13)
      2-7 Febuary 2025
      Spatial transcriptomics, Biomarker discovery, In situ sequencing, Region of Interest profiling, Spatial cell sorting

            Abstract

            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).

            Content

            Author and article information

            Conference
            21 January 2025
            : e332
            Affiliations
            [1 ]Meteor Biotech, Seoul, Republic of Korea
            Author notes
            Article
            10.14293/APMC13-2025-0332
            e5be97fc-a07c-42d7-b6fc-6c8e32a49243
            2025 The Authors.

            Published under Creative Commons Attribution 4.0 International ( CC BY 4.0). Users are allowed to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), as long as the authors and the publisher are explicitly identified and properly acknowledged as the original source.

            13th Asia Pacific Microscopy Congress 2025
            APMC13
            13
            Brisbane, Australia
            2-7 Febuary 2025
            History
            Categories
            ID11 - Emerging New Technologies & Techniques Development

            Spatial transcriptomics,Spatial cell sorting,Region of Interest profiling,In situ sequencing,Biomarker discovery

            References

            1. Lee et al., Nat Comm, 13, 2540 (2022). https://doi.org/10.1038/s41467-022-30299-3.

            2. Ke et al., Nat Methods 10, 857–860 (2013). https://doi.org/10.1038/nmeth.2563.

            Comments

            Comment on this article