PreprintCC BYbioRxiv
Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section
- 1Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
Abstract
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.
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BibTeX
@misc{machado2026highly,
title = {{Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section}},
author = {M. T. Machado and M. He and L. Alonso Galicia and Z. Andrusivova and E. Perisynaki and M. W. Myers and S. Giatrellis and S. O'Toole and B. Kiedik and R. Mauron and S. van der Leij and K. Harvey and J. Reeves and J. Escudero Morlanes and T. Hu and M. Long and M. Nilsson and T. Li and X. Chen and J. Hartman and M. Mihalffy and T. Wang and M. Vicari and L. Savolainen and A. Erickson and S. Figiel and A. Lamb and A. Swarbrick and J. Lundeberg and R. Mirzazadeh},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.07.749790},
url = {https://www.biorxiv.org/content/10.64898/2026.09.07.749790v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Highly resolved tumor architecture via matched spatial and nucleus transcriptomics from a single tissue section
AU - M. T. Machado
AU - M. He
AU - L. Alonso Galicia
AU - Z. Andrusivova
AU - E. Perisynaki
AU - M. W. Myers
AU - S. Giatrellis
AU - S. O'Toole
AU - B. Kiedik
AU - R. Mauron
AU - S. van der Leij
AU - K. Harvey
AU - J. Reeves
AU - J. Escudero Morlanes
AU - T. Hu
AU - M. Long
AU - M. Nilsson
AU - T. Li
AU - X. Chen
AU - J. Hartman
AU - M. Mihalffy
AU - T. Wang
AU - M. Vicari
AU - L. Savolainen
AU - A. Erickson
AU - S. Figiel
AU - A. Lamb
AU - A. Swarbrick
AU - J. Lundeberg
AU - R. Mirzazadeh
PY - 2026
DA - 2026/09/07
PB - bioRxiv
DO - 10.64898/2026.09.07.749790
UR - https://www.biorxiv.org/content/10.64898/2026.09.07.749790v1
AB - 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.
ER -