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Related Experiment Video

Updated: Jun 23, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

STITCH links cellular morphology and gene expression in spatial transcriptomics.

Shashwat Kumar, Yingxiao Shi, Tuulia Vallius

    Biorxiv : the Preprint Server for Biology
    |June 22, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    We developed STITCH, a new method using tangent principal component analysis (TPCA) to analyze cell shape and gene expression in spatial sequencing data. STITCH reveals interpretable morphology-transcriptome links, outperforming deep learning approaches.

    Area of Science:

    • Computational Biology
    • Spatial Transcriptomics
    • Bioinformatics

    Background:

    • In situ spatial sequencing (ISS) links cellular morphology and gene expression in vivo.
    • Existing methods, particularly deep learning, confound cell shape and size.
    • A principled, interpretable mathematical representation of cell morphology is needed for ISS.

    Purpose of the Study:

    • To present an interpretable, size-independent representation of cellular boundary contours.
    • To develop STITCH (Shape-TranscriptomIc Correlation and Harmonization) for morphology-transcriptome analysis in ISS datasets.
    • To demonstrate STITCH's ability to recover known and discover novel morphology-gene expression relationships.

    Main Methods:

    • Tangent Principal Component Analysis (TPCA) on a Kendall shape manifold for size-independent contour features.

    Related Experiment Videos

    Last Updated: Jun 23, 2026

    Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
    10:22

    Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

    Published on: October 31, 2025

  • Development of the STITCH algorithm integrating TPCA with gene expression data.
  • Application of STITCH to Xenium and CosMx spatial transcriptomics datasets.

    • Main Results:

    • TPCA successfully recovers shape-perturbing genes, outperforming previous methods.
    • STITCH outperforms deep learning in identifying keratinocyte organization and nuclear eccentricity gradients in Xenium data.
    • STITCH links fibroblast morphology to malignant cell proximity and myofibroblast programs in CosMx melanoma data.
    • STITCH independently confirms links between mesenchymal cell states and cell area in melanoma.

    Conclusions:

    • STITCH provides an interpretable mathematical framework for morphology-transcriptome relationships in spatial biology.
    • The method accurately captures size-independent cell shape features for biological interpretation.
    • STITCH advances the analysis of complex spatial transcriptomics data across various cell types and platforms.