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Related Experiment Video

Updated: May 2, 2026

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

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

Published on: October 31, 2025

Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.

Mariia Bilous¹, Daria Buszta^1,2, Jonathan Bac¹

¹Biomedical Data Science Center, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.

|April 30, 2026

Summary

This summary is machine-generated.

This study benchmarks Xenium spatial transcriptomics data quality, revealing technical noise. A new method, SPLIT, refines Xenium data by correcting transcript contamination, improving cell-type resolution and uncovering hidden T-cell exhaustion signatures.

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Last Updated: May 2, 2026

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

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Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

Spatial transcriptomics offers high-resolution gene expression mapping in tissues.
Xenium is a popular platform, but its data properties and limitations require thorough characterization.
Existing datasets lack comprehensive analysis of Xenium-derived data quality and technical noise.

Purpose of the Study:

To comprehensively evaluate Xenium spatial transcriptomics data quality and limitations.
To identify and quantify sources of technical noise, such as transcript spillover.
To introduce a novel method for refining spatial transcriptomic data purity and resolution.

Main Methods:

Profiling over 40 breast and lung tumor sections using Xenium spatial transcriptomics with diverse gene panels.
Systematic dissection of technical noise, including transcript spillover, assay specificity, and panel performance.
Utilizing single-nucleus RNA sequencing for precise quantification of transcript contamination.
Development and application of the SPLIT (Spatial Purification of Layered Intracellular Transcripts) method.

Main Results:

Characterization of one of the most comprehensive Xenium datasets to date.
Demonstration of transcript contamination quantification using single-nucleus RNA sequencing.
SPLIT method successfully improved signal purity, background correction, and cell-type resolution.
Revelation of T-cell exhaustion signatures linked to malignant cell colocalization, previously obscured by noise.

Conclusions:

Established a critical benchmark for Xenium spatial transcriptomics performance.
Introduced SPLIT as a scalable strategy for refining spatial transcriptomic data.
Highlighted the importance of addressing technical noise for accurate biological interpretation in spatial transcriptomics.