Sainsc

• Verfasst von: Müller-Bötticher, Niklas [VerfasserIn]
• Verfasst von: Tiesmeyer, Sebastian [VerfasserIn]
• Verfasst von: Eils, Roland [VerfasserIn]
• Verfasst von: Ishaque, Naveed [VerfasserIn]
• Titel: Sainsc
• Titelzusatz: a computational tool for segmentation-free analysis of in situ capture data
• Verf.angabe: Niklas Müller-Bötticher, Sebastian Tiesmeyer, Roland Eils, and Naveed Ishaque
• Ausgabe: Online version of record before inclusion in an issue
• E-Jahr: 2024
• Jahr: 12 November 2024
• Umfang: 11 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 20.05.2025
• Titel Quelle: Enthalten in: Small Methods
• Ort Quelle: Weinheim : WILEY-VCH Verlag GmbH & Co. KGaA, 2017
• Jahr Quelle: 2024
• Band/Heft Quelle: (2024), Artikel-ID 2401123, Seite 1-11
• ISSN Quelle: 2366-9608
• DOI: doi:10.1002/smtd.202401123
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: bioinformatics
• Sach-SW: cell type annotation
• Sach-SW: in situ capture spatial transcriptomics
• Sach-SW: segmentation-free
• Sach-SW: spatial biology
• Sach-SW: spatial omics
• K10plus-PPN: 1926095022

Abstract

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.