SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data

• Verfasst von: Tiesmeyer, Sebastian [VerfasserIn]
• Verfasst von: Sahay, Shashwat [VerfasserIn]
• Verfasst von: Müller-Bötticher, Niklas [VerfasserIn]
• Verfasst von: Eils, Roland [VerfasserIn]
• Verfasst von: Mackowiak, Sebastian D. [VerfasserIn]
• Verfasst von: Ishaque, Naveed [VerfasserIn]
• Titel: SSAM-lite: a light-weight web app for rapid analysis of spatially resolved transcriptomics data
• Verf.angabe: Sebastian Tiesmeyer, Shashwat Sahay, Niklas Müller-Bötticher, Roland Eils, Sebastian D. Mackowiak and Naveed Ishaque
• E-Jahr: 2022
• Jahr: 28 February 2022
• Umfang: 7 S.
• Fussnoten: Gesehen am 12.05.2022
• Titel Quelle: Enthalten in: Frontiers in genetics
• Ort Quelle: Lausanne : Frontiers Media, 2010
• Jahr Quelle: 2022
• Band/Heft Quelle: 13(2022) vom: Feb., Artikel-ID 785877, Seite 1-7
• ISSN Quelle: 1664-8021
• DOI: doi:10.3389/fgene.2022.785877
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1801688656

Abstract

The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data.