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

Sergio Marco Salas; Louis B. Kuemmerle; Christoffer Mattsson-Langseth; Sebastian Tismeyer; Christophe Avenel; Taobo Hu; Habib Rehman; Marco Grillo; Paulo Czarnewski; Saga Helgadottir; Katarina Tiklova; Axel Andersson; Nima Rafati; Maria Chatzinikolaou; Fabian J. Theis; Malte D. Luecken; Carolina Wählby; Naveed Ishaque; Mats Nilsson

doi:10.1038/s41592-025-02617-2

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

Sergio Marco Salas, Louis B. Kuemmerle, Christoffer Mattsson-Langseth, Sebastian Tismeyer, Christophe Avenel, Taobo Hu, Habib Rehman, Marco Grillo, Paulo Czarnewski, Saga Helgadottir, Katarina Tiklova, Axel Andersson, Nima Rafati, Maria Chatzinikolaou, Fabian J. Theis, Malte D. Luecken, Carolina Wählby, Naveed Ishaque, Mats Nilsson

Research output: Contribution to journal › Article › peer-review

Abstract

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

Original language	English
Article number	aaa6090
Journal	Nature Methods
DOIs	https://doi.org/10.1038/s41592-025-02617-2
State	Accepted/In press - 2025
Externally published	Yes

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Marco Salas, S., Kuemmerle, L. B., Mattsson-Langseth, C., Tismeyer, S., Avenel, C., Hu, T., Rehman, H., Grillo, M., Czarnewski, P., Helgadottir, S., Tiklova, K., Andersson, A., Rafati, N., Chatzinikolaou, M., Theis, F. J., Luecken, M. D., Wählby, C., Ishaque, N., & Nilsson, M. (Accepted/In press). Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows. Nature Methods, Article aaa6090. https://doi.org/10.1038/s41592-025-02617-2

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title = "Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows",

abstract = "The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.",

author = "{Marco Salas}, Sergio and Kuemmerle, {Louis B.} and Christoffer Mattsson-Langseth and Sebastian Tismeyer and Christophe Avenel and Taobo Hu and Habib Rehman and Marco Grillo and Paulo Czarnewski and Saga Helgadottir and Katarina Tiklova and Axel Andersson and Nima Rafati and Maria Chatzinikolaou and Theis, {Fabian J.} and Luecken, {Malte D.} and Carolina W{\"a}hlby and Naveed Ishaque and Mats Nilsson",

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doi = "10.1038/s41592-025-02617-2",

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journal = "Nature Methods",

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Marco Salas, S, Kuemmerle, LB, Mattsson-Langseth, C, Tismeyer, S, Avenel, C, Hu, T, Rehman, H, Grillo, M, Czarnewski, P, Helgadottir, S, Tiklova, K, Andersson, A, Rafati, N, Chatzinikolaou, M, Theis, FJ, Luecken, MD, Wählby, C, Ishaque, N & Nilsson, M 2025, 'Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows', Nature Methods. https://doi.org/10.1038/s41592-025-02617-2

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T1 - Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows

AU - Marco Salas, Sergio

AU - Kuemmerle, Louis B.

AU - Mattsson-Langseth, Christoffer

AU - Tismeyer, Sebastian

AU - Avenel, Christophe

AU - Hu, Taobo

AU - Rehman, Habib

AU - Grillo, Marco

AU - Czarnewski, Paulo

AU - Helgadottir, Saga

AU - Tiklova, Katarina

AU - Andersson, Axel

AU - Rafati, Nima

AU - Chatzinikolaou, Maria

AU - Theis, Fabian J.

AU - Luecken, Malte D.

AU - Wählby, Carolina

AU - Ishaque, Naveed

AU - Nilsson, Mats

PY - 2025

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AB - The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

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JO - Nature Methods

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

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