A novel Slide-seq based image processing software to identify gene expression at the single cell level

Götz T, Cong X, Rauber S, Angeli MR, Lang EW, Ramming A, Schmidkonz C (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Journal of Pathology Informatics Medknow Publications

Book Volume: 15

Article Number: 100384

DOI: 10.1016/j.jpi.2024.100384

Abstract

Analysis of gene expression at the single-cell level could help predict the effectiveness of therapies in the field of chronic inflammatory diseases such as arthritis. Here, we demonstrate an adopted approach for processing images from the Slide-seq method. Using a puck, which consists of about 50,000 DNA barcode beads, an RNA sequence of a cell is to be read. The pucks are repeatedly brought into contact with liquids and then recorded with a conventional epifluorescence microscope. The image analysis initially consists of stitching the partial images of a sequence recording, registering images from different sequences, and finally reading out the bases. The new method enables the use of an inexpensive epifluorescence microscope instead of a confocal microscope.

Authors with CRIS profile

Theresa Götz Max Schaldach-Stiftungsprofessur für Biomedizinische Technik Simon Rauber Department of Medicine 3 – Rheumatology and Immunology Mario Raphael Angeli Department of Medicine 3 – Rheumatology and Immunology Alina Ramming Department of Medicine 3 – Rheumatology and Immunology Christian Schmidkonz Lehrstuhl für Klinische Nuklearmedizin

Involved external institutions

Universität Regensburg DE Germany (DE)

How to cite

APA:

Götz, T., Cong, X., Rauber, S., Angeli, M.R., Lang, E.W., Ramming, A., & Schmidkonz, C. (2024). A novel Slide-seq based image processing software to identify gene expression at the single cell level. Journal of Pathology Informatics, 15. https://doi.org/10.1016/j.jpi.2024.100384

MLA:

Götz, Theresa, et al. "A novel Slide-seq based image processing software to identify gene expression at the single cell level." Journal of Pathology Informatics 15 (2024).

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