SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis

  1. Xiao Li
  2. Ximo Pechuan-Jorge
  3. Tyler Risom
  4. Conrad Foo
  5. Alexander Prilipko
  6. Artem Zubkov
  7. Caleb Chan
  8. Patrick Chang
  9. Frank Peale
  10. James Ziai
  11. Sandra Rost
  12. Derrek Hibar
  13. Lisa McGinnis
  14. Evgeniy Tabatsky
  15. Xin Ye
  16. Hector Corrada Bravo
  17. Zhen Shi
  18. Malgorzata Nowicka
  19. Jon Scherdin
  20. James Cowan
  21. Jennifer Giltnane
  22. Darya Orlova
  23. Rajiv Jesudason

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Abstract

Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEX’s infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEX’s ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

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  1. GigaScience

    Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEX’s infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEX’s ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf090), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 3: Hongyoon Choi

    The manuscript introduces SPEX, a web-based platform designed for spatial omics data analysis. The authors highlight its user-friendly UI, modular analysis pipelines, and integration with open-source image data management systems. The platform supports image processing such as cell/nucleus segmentation, clustering, and spatial analysis. GUI-based approaches as well as python script-based modules increase usability for the broader research community. While the goals of the platform are commendable, and the integration of multiple analysis modules is a valuable contribution, there are critical shortcomings in the manuscript that must be addressed before publication. Several key weaknesses significantly limit the scientific rigor and impact of this work.

    • One of the critical omissions in this manuscript is the lack of rigorous benchmarking against established tools. Though it demonstrated the comparison with other tools such as Squidpy, Giotto, and MC Micro, but there is no quantitative comparison to demonstrate its advantages over existing methodologies. In particular, spatial analysis such as CLQ is introduced as a different approach within the spatial biology analytics framework, but how does it compare to existing co-occurrence analysis methods? Additionally, similar analyses have been conducted using other tools (e.g., Mah, C.K., et al., Genome Biol 25, 82 (2024)), including in 'subcellular' colocalization. In this regard, concerns about its novelty arise. Moreover, as mentioned in relation to Bento, CLQ could also be applied to subcellular analysis?

    • In this regard, for spatial co-occurence or other algorithms in SPEX, the authors should run identical datasets through both SPEX and existing tools to compare performance and biological insights. it is impossible to assess whether SPEX provides any meaningful improvement over existing platforms.

    • The cell typing process is one of the most fundamental steps in spatial omics analysis. However, SPEX does not integrate a dedicated cell typing module, forcing users to use another tool or define cell types manually. The accuracy of all downstream analyses (clustering, spatial interaction, pathway analysis) depends on robust and reliable cell typing. It would be better to integrate with automated cell typing solutions to increase usability.

    • The manuscript focuses almost exclusively on single-cell resolution data and high-dimensional imaging-based methods (e.g., IMC, MIBI, MERFISH). However, spot-based transcriptomics platforms such as Visium are widely used in the field. In this regard, SPEX does not provide modules tailored methodology for spot-based spatial analysis (such as deconvolution) or super-resolution or transforming cell-based analysis from spots (e.g. bin2cell in VisiumHD). Neighborhood analyses or spatially variable gene detection, etc. are specialized in whole-gene covered, spot-based methods, as well, for example.

    • The manuscript does not clarify whether users can modify or extend the pipeline with custom Python scripts. Describing further this point, customization in this ecosystem with python script, for 'power-users' of this system could be helpful.

    • The biological relevance of the SPEX platform remains unclear, as the case studies presented are not sufficiently rigorous. As mentioned above, comparisons with other tools based on quantification can clarify why SPEX is better than other published tools/ecosystems in certain aspect. Or meaningful biological findings and explanations based on this tool as a case study could be helpful. While the results demonstrate technical capabilities, the manuscript does not show how SPEX enables novel biological discoveries compared to existing tools.

  2. GigaScience

    Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEX’s infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEX’s ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf090), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 2: Qianqian Song

    The manuscript presents an advancement in spatial omics analysis but needs improvements in Quantitative benchmarking, Computational scalability assessment, etc. With these revisions, SPEX has the potential to become a widely adopted platform in the spatial omics community. I have specific comments as below:

    1. While the manuscript provides a qualitative comparison of SPEX with other spatial omics tools (e.g., Squidpy, Giotto, Aquilla), quantitative benchmarking is missing. It is needed to include a performance benchmark comparing runtime efficiency, segmentation accuracy, and clustering resolution against existing tools. Also, it is necessary to show computational efficiency metrics (e.g., memory usage, execution time, scalability across datasets of varying sizes).

    2. The study presents compelling results, but there is no independent validation or interpretation of computational outputs using experimental methods.

    3. The manuscript does not discuss hardware requirements, processing speed, or computational limitations. It is needed to provide an assessment of SPEX's performance on different computing environments (e.g., local workstations vs. cloud computing vs. high-performance clusters).

    4. The Colocation Quotient (CLQ) method is well described, but the manuscript does not provide statistical validation (e.g., p-values, confidence intervals) for detected spatial relationships.

  3. GigaScience

    Recent advancements in transcriptomics and proteomics have opened the possibility for spatially resolved molecular characterization of tissue architecture with the promise of enabling a deeper understanding of tissue biology in either homeostasis or disease. The wealth of data generated by these technologies has recently driven the development of a wide range of computational methods. These methods have the requirement of advanced coding fluency to be applied and integrated across the full spatial omics analysis process thus presenting a hurdle for widespread adoption by the biology research community. To address this, we introduce SPEX (Spatial Expression Explorer), a web-based analysis platform that employs modular analysis pipeline design, accessible through a user-friendly interface. SPEX’s infrastructure allows for streamlined access to open source image data management systems,analysis modules, and fully integrated data visualization solutions. Analysis modules include essential steps covering image processing, single-cell and spatial analysis. We demonstrate SPEX’s ability to facilitate the discovery of biological insights in spatially resolved omics datasets from healthy tissue to tumor samples.

    This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giaf090), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

    Reviewer 1: Ka Yee Yeung

    Li et al. presented SPEX (Spatial Expression Explorer), a web-based open-source end-to-end analysis platform offering modular design and a user accessible interface. The users demonstrated use cases in spatial transcriptomics (MERFISH lung cancer) and spatial proteomics datasets (tonsil, public multiplex ion beam imaging data). SPEX includes the following analytical modules

    1. image processing modules includes a 4-step sequence (image pre-processing, single-cell segmentation, post-processing, feature selection). Image loading supports OMERO integration. Output is a cell by expression matrix in Anndata format.
    2. clustering modules for both spatial transcriptomic and proteomic data.
    3. spatial analysis module implements the CLQ (Colocation Quotient) method.
    4. spatial expression analysis module includes differential expression and pathway analysis. SPEX supports visualization via Vitessce.

    The paper is well written, addresses a rising interest and critical need in the biomedical community. The reviewer would like to request clarifications on how extensible the modules are. The author mentioned a SPEX pipeline builder in which "modules are selected from a library and dragged into a visual pipeline map", and also mentioend the support for "flexible plug-in analysis modules". What are the packages available from the library? Can users import their own code or script or package? How to create new plug-in's?

    The reviewer is also wondering how do the users interact with the results? Can the user click on the resulting image and select regions of interest to zoom in?

  4. Version published to 10.1101/2022.08.22.504841 on bioRxiv