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

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.

Authors

Institutions

Publication Details

Journal
Genome biology
Published
2025-05-02
DOI
https://doi.org/10.1186/s13059-025-03573-y
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Aviv Regev, Jan Watter, Julia Waldman, Judit Jané‐Valbuena et al.
Genome biology
Single-cell and spatial transcriptomics
article

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

Aviv Regev, Jan Watter, Julia Waldman, Judit Jané‐Valbuena, Simon Mages, Åsa Segerstolpe, Orr Ashenberg, Ana Lako, Scott J. Rodig, Nikhil Wagle, Orit Rozenblatt–Rosen, Michal Slyper, Ofir Cohen, Karla Helvie, Ralf Strasser, Diane Zhang, Daniel L. Abravanel, Johanna Klughammer, Fei Chen, Evan Murray
article en

Abstract

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.

Genome biologyVol. 26(1)
Broad Institute (US), Ben-Gurion University of the Negev (IL), Brigham and Women's Hospital (US), HES-SO Genève (CH), Gene Therapy Laboratory (FR), Dana-Farber Cancer Institute (US), Massachusetts Institute of Technology (US), Ludwig-Maximilians-Universität München (DE)
Human Frontier Science Program, Deutsche Forschungsgemeinschaft, Else Kröner-Fresenius-Stiftung, Israel Science Foundation, Ludwig-Maximilians-Universität München
Good health and well-being
Openalex Percentile: Top 92%
Single-cell and spatial transcriptomics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.