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Inferring histology-associated gene expression gradients in spatial transcriptomic studies

Authors :
Jan Kueckelhaus
Simon Frerich
Jasim Kada-Benotmane
Christina Koupourtidou
Jovica Ninkovic
Martin Dichgans
Juergen Beck
Oliver Schnell
Dieter Henrik Heiland
Source :
Nature Communications, Vol 15, Iss 1, Pp 1-19 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
15
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.5ddb7137c89d4ed887c861114fc3f3b6
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-024-50904-x