SPEX: A modular end-to-end platform for high-plex tissue spatial omics analysis
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 analysis, 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.
Authors
- James Ziai (ORCID: https://orcid.org/0000-0002-2617-2279)
- James D. Cowan
- Rajiv Jesudason (ORCID: https://orcid.org/0000-0003-2359-4481)
- Ximo Pechuan-Jorge (ORCID: https://orcid.org/0000-0001-8938-7931)
- Evgeniy Tabatsky
- Lisa McGinnis (ORCID: https://orcid.org/0000-0002-8532-9264)
- Héctor Corrada Bravo (ORCID: https://orcid.org/0000-0002-1255-4444)
- Frank Peale
- Jennifer M. Giltnane (ORCID: https://orcid.org/0000-0002-8333-9995)
- Caleb K. Chan (ORCID: https://orcid.org/0000-0002-6367-5617)
- Artem Zubkov
- A. Ya. Prilipko
- Sandra Rost
- Xiao Li (ORCID: https://orcid.org/0000-0001-5600-2871)
- Patrick Chang (ORCID: https://orcid.org/0000-0001-5737-1011)
- Tyler Risom (ORCID: https://orcid.org/0000-0003-1089-9542)
- Małgorzata Nowicka (ORCID: https://orcid.org/0000-0002-5918-5981)
- Jon Scherdin (ORCID: https://orcid.org/0000-0003-4795-4090)
- Xin Ye (ORCID: https://orcid.org/0000-0002-2026-9280)
- Zhen Shi (ORCID: https://orcid.org/0009-0005-8502-7978)
- Derrek P. Hibar (ORCID: https://orcid.org/0000-0001-9771-8614)
- Darya Orlova
- Conrad Foo (ORCID: https://orcid.org/0000-0002-1333-3678)
Institutions
- Roche (Switzerland) (CH)
- Amazon (United States) (US)
- Revolution Medicines (United States) (US)
- GlaxoSmithKline (United States) (US)
- Sunesis (United States) (US)
Publication Details
- Journal
- GigaScience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1093/gigascience/giaf090
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Genentech