Evidence of off-target probe binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel compromises accuracy of spatial transcriptomic profiling
Curation statements for this article:-
Curated by eLife
eLife Assessment
This valuable study identifies and characterizes probe binding errors in a widely used commercial platform for visualizing gene activity in tissue samples, discovering that at least 21 out of 280 genes in a human breast cancer panel are not accurately detected. The authors provide convincing evidence for their findings validated against multiple independent sequencing technologies and reference datasets. Given the broad adoption of this spatial gene detection platform in biomedical research, this work provides an essential quality control resource that will improve data interpretation across numerous studies.
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
Abstract
Abstract
The accuracy of spatial gene expression profiles generated by probe-based in situ spatially-resolved transcriptomic technologies depends on the specificity with which probes bind to their intended target gene. Off-target binding, defined as a probe binding to something other than the target gene, can distort a gene’s true expression profile, making probe specificity essential for reliable transcriptomics. Here, we investigate off-target binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel. We developed a software tool, Off-target Probe Tracker (OPT), to identify putative off-target binding via alignment of probe sequences and found at least 21 out of the 280 genes in the panel impacted by off-target binding to protein-coding genes. To substantiate our predictions, we leveraged a previously published Xenium breast cancer dataset generated using this gene panel and compared results to orthogonal spatial and single-cell transcriptomic profiles from Visium CytAssist and 3ʹ single-cell RNA-seq derived from the same tumor block. Our findings indicate that for some genes, the expression patterns detected by Xenium demonstrably reflect the aggregate expression of the target and predicted off-target genes based on Visium and single-cell RNA-seq rather than the target gene alone. Overall, this work enhances the biological interpretability of spatial transcriptomics data and improves reproducibility in spatial transcriptomics research.
Article activity feed
-
eLife Assessment
This valuable study identifies and characterizes probe binding errors in a widely used commercial platform for visualizing gene activity in tissue samples, discovering that at least 21 out of 280 genes in a human breast cancer panel are not accurately detected. The authors provide convincing evidence for their findings validated against multiple independent sequencing technologies and reference datasets. Given the broad adoption of this spatial gene detection platform in biomedical research, this work provides an essential quality control resource that will improve data interpretation across numerous studies.
-
Reviewer #1 (Public review):
Summary:
In the manuscript, Hallinan et al. describe off-target probe binding in the 10x Genomics Xenium platform, which results in invalid profiling of some genes in a spatial context. This was validated by comparing the Xenium results with Visium and scRNA-seq using human breast tissue, which are comprehensive and convincing. The authors also provide a dedicated tool to predict such off-target binding, Off-target Probe Tracker (OPT), which could be widely adopted in the field by researchers who are interested in validating the previously published results.
Strengths:
(1) This is the first study to suggest off-target binding of probes in the gene panels of the Xenium platform, which could be easily overlooked.
(2) The results were rigorously validated with two different methods.
(3) This paper will be a …
Reviewer #1 (Public review):
Summary:
In the manuscript, Hallinan et al. describe off-target probe binding in the 10x Genomics Xenium platform, which results in invalid profiling of some genes in a spatial context. This was validated by comparing the Xenium results with Visium and scRNA-seq using human breast tissue, which are comprehensive and convincing. The authors also provide a dedicated tool to predict such off-target binding, Off-target Probe Tracker (OPT), which could be widely adopted in the field by researchers who are interested in validating the previously published results.
Strengths:
(1) This is the first study to suggest off-target binding of probes in the gene panels of the Xenium platform, which could be easily overlooked.
(2) The results were rigorously validated with two different methods.
(3) This paper will be a helpful resource for properly interpreting the results of previously published papers based on the Xenium platform (the signals could be mixed).
Weaknesses:
(1) The results were only tested with one tissue (human breast). However, this is not a major weakness, as one can easily extrapolate that this should be the case for any other tissue.
(2) Once the 10X Genomics corrects their gene panels according to this finding, the tool (OPT) will not be useful for most people. Still, it can be used by those who want to design de novo probes from scratch.
-
Reviewer #2 (Public review):
This paper describes an analysis of a commercially available panel for a spatial transcriptomic approach and introduces a computational tool to predict potential off-target binding sites for the type of probe used in the aforementioned panel. The performance of the prediction tool was validated by examining a dataset that profiled the same cancer tissue with multiple modalities. Finally, a detailed analysis of the potential pitfalls in a published study communicated by the company that commercialized the spatial transcriptomic platform in question is provided, along with best practice guidelines for future studies to follow.
Strengths:
The manuscript is clearly written and easy to follow.
The authors provide clean, organized, and well-documented code in the associated GitHub repository.
Weaknesses:
The …
Reviewer #2 (Public review):
This paper describes an analysis of a commercially available panel for a spatial transcriptomic approach and introduces a computational tool to predict potential off-target binding sites for the type of probe used in the aforementioned panel. The performance of the prediction tool was validated by examining a dataset that profiled the same cancer tissue with multiple modalities. Finally, a detailed analysis of the potential pitfalls in a published study communicated by the company that commercialized the spatial transcriptomic platform in question is provided, along with best practice guidelines for future studies to follow.
Strengths:
The manuscript is clearly written and easy to follow.
The authors provide clean, organized, and well-documented code in the associated GitHub repository.
Weaknesses:
The manuscript section on the software tool feels underdeveloped.
-
Reviewer #3 (Public review):
Summary:
The authors present a new computational method (OPT) for predicting off-target probe binding in the commercial 10X Xenium spatial transcriptomics platform. They identified 28 genes in the 10x xenium human breast cancer gene panel (280 genes) that are not accurately detected at the single-molecule level. They validated the predicted off-target binding using reference data from single-cell RNA-seq and 3'-sequencing-based Visium RNA-seq. This work provides a practical resource and will serve as a valuable reference for future data interpretation.
Strengths:
(1) Provides a toolbox for the community to identify off-target probes.
(2) Validates the predictions using single-cell RNA-seq and sequencing-based Visium RNA-seq datasets.
Weaknesses:
(1) Does not apply the OPT method to the most widely used …
Reviewer #3 (Public review):
Summary:
The authors present a new computational method (OPT) for predicting off-target probe binding in the commercial 10X Xenium spatial transcriptomics platform. They identified 28 genes in the 10x xenium human breast cancer gene panel (280 genes) that are not accurately detected at the single-molecule level. They validated the predicted off-target binding using reference data from single-cell RNA-seq and 3'-sequencing-based Visium RNA-seq. This work provides a practical resource and will serve as a valuable reference for future data interpretation.
Strengths:
(1) Provides a toolbox for the community to identify off-target probes.
(2) Validates the predictions using single-cell RNA-seq and sequencing-based Visium RNA-seq datasets.
Weaknesses:
(1) Does not apply the OPT method to the most widely used Xenium gene panels (e.g., pan-Human, pan-Mouse panels with ~5,000 genes each).
(2) Lacks clarity on how the confidence level of off-target predictions is calculated.
-
Author response:
We sincerely thank the editors and the reviewers for their feedback in helping us improve this manuscript. During the time this work has been under review, 10x Genomics has updated the probe sequences of their gene panels. We therefore plan to update these findings as well as further expand to incorporate reviewer recommendations.
-
-
-
