Introduction to Spatial Single-Cell Transcriptomics Technology and Analytics

Spatial transcriptomics is revolutionizing the study of tissue architecture, cellular states, and tumor-immune interactions in clinical specimens. This presentation introduces the principles and workflows of spatial single-cell transcriptomics using NanoString’s GeoMx Digital Spatial Profiler and CosMx Spatial Molecular Imaging platforms. The talk contrasts pseudo-spatial region-of-interest profiling with true single-cell, in situ measurement, then follows the analytical pipeline from assay design and field-of-view selection through segmentation, cell typing, and downstream spatial analyses. Using a non-small cell lung cancer case study, the session illustrates how spatial data can identify cell types, define cellular neighborhoods and tumor microenvironment niches, and quantify tumor-immune ligand-receptor interactions, including PD-L1/PD-1, across samples. Particular emphasis is placed on cell segmentation as the primary determinant of data quality and on the computational scale of image-rich datasets. The session concludes by highlighting the translational and clinical relevance of spatial transcriptomics for tumor immune phenotyping and the potential assessment of immunotherapy response.

Learning Objectives:

1. Compare the operating principles, spatial resolution, and analytical outputs of GeoMx DSP and CosMx SMI for spatial transcriptomic profiling.

2.  Explain how cell segmentation, cell typing, neighborhood and niche analysis, and ligand-receptor modeling influence the interpretation of spatial single-cell data in a case study using data from non-small cell lung cancer (NSCLC)

3. Describe how spatial transcriptomics can support tumor immune phenotyping and the translational assessment of immunotherapy response in clinical research.