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Inferring histology-associated gene expression gradients in spatial transcriptomic studies

Author

Listed:
  • Jan Kueckelhaus

    (Freiburg University
    Erlangen University)

  • Simon Frerich

    (University Hospital, LMU Munich
    LMU Munich)

  • Jasim Kada-Benotmane

    (Freiburg University
    Freiburg University)

  • Christina Koupourtidou

    (LMU Munich
    Munich Cluster for Systems Neurology (SyNergy))

  • Jovica Ninkovic

    (LMU Munich
    Munich Cluster for Systems Neurology (SyNergy))

  • Martin Dichgans

    (University Hospital, LMU Munich
    Munich Cluster for Systems Neurology (SyNergy)
    German Center for Neurodegenerative Diseases (DZNE))

  • Juergen Beck

    (Freiburg University)

  • Oliver Schnell

    (Erlangen University)

  • Dieter Henrik Heiland

    (Freiburg University
    Erlangen University
    University of Freiburg
    German Cancer Consortium (DKTK) partner site Freiburg)

Abstract

Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.

Suggested Citation

  • Jan Kueckelhaus & Simon Frerich & Jasim Kada-Benotmane & Christina Koupourtidou & Jovica Ninkovic & Martin Dichgans & Juergen Beck & Oliver Schnell & Dieter Henrik Heiland, 2024. "Inferring histology-associated gene expression gradients in spatial transcriptomic studies," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50904-x
    DOI: 10.1038/s41467-024-50904-x
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    References listed on IDEAS

    as
    1. Jasim Kada Benotmane & Jan Kueckelhaus & Paulina Will & Junyi Zhang & Vidhya M. Ravi & Kevin Joseph & Roman Sankowski & Jürgen Beck & Catalina Lee-Chang & Oliver Schnell & Dieter Henrik Heiland, 2023. "High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Koen Van den Berge & Hector Roux de Bézieux & Kelly Street & Wouter Saelens & Robrecht Cannoodt & Yvan Saeys & Sandrine Dudoit & Lieven Clement, 2020. "Trajectory-based differential expression analysis for single-cell sequencing data," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    Full references (including those not matched with items on IDEAS)

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