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<p>Solid tumors are spatially heterogeneous in their genetic, molecular, and cellular composition, but recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we develop SlideCNA, a computational tool to extract CNA signals from sparse&#xa0;spatial transcriptomics data with near single cellular resolution. SlideCNA uses expression-aware spatial binning to overcome sparsity limitations while maintaining spatial signal to recover CNA patterns. We test SlideCNA on simulated and real Slide-seq data of (metastatic) breast cancer and demonstrate its potential for spatial subclone detection.</p>

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SlideCNA: spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月2日

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  • Diane Zhang,
  • Åsa Segerstolpe,
  • Michal Slyper,
  • Julia Waldman,
  • Evan Murray,
  • Robert Strasser,
  • Jan Watter,
  • Ofir Cohen,
  • Orr Ashenberg,
  • Daniel Abravanel,
  • Judit Jané-Valbuena,
  • Simon Mages,
  • Ana Lako,
  • Karla Helvie,
  • Orit Rozenblatt-Rosen,
  • Scott Rodig,
  • Fei Chen,
  • Nikhil Wagle,
  • Aviv Regev,
  • Johanna Klughammer

摘要

Solid tumors are spatially heterogeneous in their genetic, molecular, and cellular composition, but recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we develop SlideCNA, a computational tool to extract CNA signals from sparse spatial transcriptomics data with near single cellular resolution. SlideCNA uses expression-aware spatial binning to overcome sparsity limitations while maintaining spatial signal to recover CNA patterns. We test SlideCNA on simulated and real Slide-seq data of (metastatic) breast cancer and demonstrate its potential for spatial subclone detection.