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Related Experiment Video

Updated: May 7, 2026

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.

Sergio Marco Salas^1,2, Louis B Kuemmerle^3,4, Christoffer Mattsson-Langseth⁵

¹Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden. sergiomarco.salas@scilifelab.se.

|March 14, 2025

Summary

This summary is machine-generated.

This study independently analyzes the Xenium In Situ platform for spatial transcriptomics, comparing its performance and providing analysis recommendations for researchers using this technology.

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Area of Science:

Spatial transcriptomics
Genomics
Bioinformatics

Background:

The Xenium In Situ platform offers in situ gene mapping at subcellular resolution.
Choosing among diverse spatial transcriptomics technologies requires clear guidance.
Independent performance evaluation is crucial for platform selection and data analysis.

Purpose of the Study:

To independently evaluate the Xenium In Situ platform's performance across multiple datasets.
To compare Xenium with other spatial transcriptomics technologies regarding scalability, resolution, and data quality.
To benchmark open-source computational tools for Xenium data analysis and provide best practices.

Main Methods:

Analysis of 25 Xenium In Situ datasets from various tissues and species.
Comparative assessment against eight other spatial transcriptomics platforms.
Benchmarking of open-source bioinformatics tools for preprocessing, cell segmentation, and feature selection.

Main Results:

Detailed comparison of Xenium's scalability, resolution, data quality, and limitations.
Performance metrics for various computational tools applied to Xenium data.
Identification of optimal analysis workflows for Xenium datasets.

Conclusions:

Xenium In Situ platform demonstrates specific capabilities and limitations.
Recommendations for selecting spatial transcriptomics platforms and analysis strategies are provided.
Best practices for analyzing Xenium data are established for the research community.