M. T. Machado, M. He, L. Alonso Galicia, Z. Andrusivova, E. Perisynaki, M. W. Myers, S. Giatrellis, S. O'Toole, B. Kiedik, R. Mauron, S. van der Leij, K. Harvey, J. Reeves, J. Escudero Morlanes, T. Hu, M. Long, M. Nilsson, T. Li, X. Chen, J. Hartman, M. Mihalffy, T. Wang, M. Vicari, L. Savolainen, A. Erickson, S. Figiel, A. Lamb, A. Swarbrick, J. Lundeberg, R. Mirzazadeh
Here we introduce SIMPlex, a method that generates matched spatial and single-nucleus gene-expression profiles from the same 5 um FFPE section.
Spatial transcriptomics often relies on reference-based deconvolution to infer cell types in a tissue context; however, public single-cell datasets can miss patient-specific biology. Here we introduce SIMPlex, a method that generates matched spatial and single-nucleus gene-expression profiles from the same 5 um FFPE section. We demonstrate context-matched profiles across mouse brain, breast cancer and prostate cancer tissues, resolving fine-grained cell-states with distinct spatial signatures. By extracting both spatial and nuclear layers, SIMPlex maximises the information recovered from a single tissue section, an advantage for scarce archival and clinical specimens.