MerQuaCo: a computational tool for quality control in image-based spatial transcriptomics
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Curated by eLife
eLife Assessment
This valuable study describes MerQuaCo, a computational and automatic quality control tool for spatial transcriptomics datasets. The authors have collected a remarkable number of tissues to construct the main algorithm. The exceptional strength of the evidence is demonstrated through a combination of empirical observations, automated computational approaches, and validation against existing software packages. MerQuaCo will interest researchers who routinely perform spatial transcriptomic imaging (especially MERSCOPE), as it provides an imperfection detector and quality control measures for reliable and reproducible downstream analysis.
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Abstract
Image-based spatial transcriptomics platforms are powerful tools often used to identify cell populations and describe gene expression in intact tissue. Spatial experiments return large, high-dimension datasets and several open-source software packages are available to facilitate analysis and visualization. Spatial results are typically imperfect. For example, local variations in transcript detection probability are common. Software tools to characterize imperfections and their impact on downstream analyses are lacking so the data quality is assessed manually, a laborious and often a subjective process. Here we describe imperfections in a dataset of 641 fresh-frozen adult mouse brain sections collected using the Vizgen MERSCOPE. Common imperfections included the local loss of tissue from the section, tissue outside the imaging volume due to detachment from the coverslip, transcripts missing due to dropped images, varying detection probability through space, and differences in transcript detection probability between experiments. We describe the incidence of each imperfection and the likely impact on the accuracy of cell type labels. We develop MerQuaCo, open-source code that detects and quantifies imperfections without user input, facilitating the selection of sections for further analysis with existing packages. Together, our results and MerQuaCo facilitate rigorous, objective assessment of the quality of spatial transcriptomics results.
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eLife Assessment
This valuable study describes MerQuaCo, a computational and automatic quality control tool for spatial transcriptomics datasets. The authors have collected a remarkable number of tissues to construct the main algorithm. The exceptional strength of the evidence is demonstrated through a combination of empirical observations, automated computational approaches, and validation against existing software packages. MerQuaCo will interest researchers who routinely perform spatial transcriptomic imaging (especially MERSCOPE), as it provides an imperfection detector and quality control measures for reliable and reproducible downstream analysis.
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Reviewer #1 (Public review):
Summary:
The authors present MerQuaCo, a computational tool that fills a critical gap in the field of spatial transcriptomics: the absence of standardized quality control (QC) tools for image-based datasets. Spatial transcriptomics is an emerging field where datasets are often imperfect, and current practices lack systematic methods to quantify and address these imperfections. MerQuaCo offers an objective and reproducible framework to evaluate issues like data loss, transcript detection variability, and efficiency differences across imaging planes.
Strengths:
(1) The study draws on an impressive dataset comprising 641 mouse brain sections collected on the Vizgen MERSCOPE platform over two years. This scale ensures that the documented imperfections are not isolated or anecdotal but represent systemic …
Reviewer #1 (Public review):
Summary:
The authors present MerQuaCo, a computational tool that fills a critical gap in the field of spatial transcriptomics: the absence of standardized quality control (QC) tools for image-based datasets. Spatial transcriptomics is an emerging field where datasets are often imperfect, and current practices lack systematic methods to quantify and address these imperfections. MerQuaCo offers an objective and reproducible framework to evaluate issues like data loss, transcript detection variability, and efficiency differences across imaging planes.
Strengths:
(1) The study draws on an impressive dataset comprising 641 mouse brain sections collected on the Vizgen MERSCOPE platform over two years. This scale ensures that the documented imperfections are not isolated or anecdotal but represent systemic challenges in spatial transcriptomics. The variability observed across this large dataset underscores the importance of using sufficiently large sample sizes when benchmarking different image-based spatial technologies. Smaller datasets risk producing misleading results by over-representing unusually successful or unsuccessful experiments. This comprehensive dataset not only highlights systemic challenges in spatial transcriptomics but also provides a robust foundation for evaluating MerQuaCo's metrics. The study sets a valuable precedent for future quality assessment and benchmarking efforts as the field continues to evolve.
(2) MerQuaCo introduces thoughtful metrics and filters that address a wide range of quality control needs. These include pixel classification, transcript density, and detection efficiency across both x-y axes (periodicity) and z-planes (p6/p0 ratio). The tool also effectively quantifies data loss due to dropped images, providing tangible metrics for researchers to evaluate and standardize their data. Additionally, the authors' decision to include examples of imperfections detectable by visual inspection but not flagged by MerQuaCo reflects a transparent and balanced assessment of the tool's current capabilities.
Weaknesses:
(1) The study focuses on cell-type label changes as the main downstream impact of imperfections. Broadening the scope to explore expression response changes of downstream analyses would offer a more complete picture of the biological consequences of these imperfections and enhance the utility of the tool.
(2) While the manuscript identifies and quantifies imperfections effectively, it does not propose post-imaging data processing solutions to correct these issues, aside from the exclusion of problematic sections or transcript species. While this is understandable given the study is aimed at the highest quality atlas effort, many researchers don't need that level of quality to compare groups. It would be important to include discussion points as to how those cut-offs should be decided for a specific study.
(3) Although the authors demonstrate the applicability of MerQuaCo on a large MERFISH dataset, and the limited number of sections from other platforms, it would be helpful to describe its limitations in its generalizability.
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Reviewer #2 (Public review):
Summary:
The authors present MerQuaCo, a computational tool for quality control in image-based spatial transcriptomic, especially MERSCOPE. They assessed MerQuaCo on 641 slides that are produced in their institute in terms of the ratio of imperfection, transcript density, and variations of quality by different planes (x-axis).
Strengths:
This looks to be a valuable work that can be a good guideline of quality control in future spatial transcriptomics. A well-controlled spatial transcriptomics dataset is also important for the downstream analysis.
Weaknesses:
The results section needs to be more structured.
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Reviewer #3 (Public review):
Summary:
MerQuaCo is an open-source computational tool developed for quality control in image-based spatial transcriptomics data, with a primary focus on data generated by the Vizgen MERSCOPE platform. The authors analyzed a substantial dataset of 641 fresh-frozen adult mouse brain sections to identify and quantify common imperfections, aiming to replace manual quality assessment with an automated, objective approach, providing standardized data integrity measures for spatial transcriptomics experiments.
Strengths:
The manuscript's strengths lie in its timely utility, rigorous empirical validation, and practical contributions to methodology and biological discovery in spatial transcriptomics.
Weaknesses:
While MerQuaCo demonstrates utility in large datasets and cross-platform potential, its generalizability …
Reviewer #3 (Public review):
Summary:
MerQuaCo is an open-source computational tool developed for quality control in image-based spatial transcriptomics data, with a primary focus on data generated by the Vizgen MERSCOPE platform. The authors analyzed a substantial dataset of 641 fresh-frozen adult mouse brain sections to identify and quantify common imperfections, aiming to replace manual quality assessment with an automated, objective approach, providing standardized data integrity measures for spatial transcriptomics experiments.
Strengths:
The manuscript's strengths lie in its timely utility, rigorous empirical validation, and practical contributions to methodology and biological discovery in spatial transcriptomics.
Weaknesses:
While MerQuaCo demonstrates utility in large datasets and cross-platform potential, its generalizability and validation require expansion, particularly for non-MERSCOPE platforms and real-world biological impact.
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