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Kuemmerle, L. ; Luecken, M. ; Firsova, A.B.* ; Barros De Andrade E Sousa, L. ; Straßer, L. ; Mekki, I.I ; Campi, F. ; Heumos, L. ; Shulman, M. ; Beliaeva, V. ; Hediyeh-Zadeh, S. ; Schaar, A. ; Mahbubani, K.T.* ; Sountoulidis, A.* ; Balassa, T.* ; Kovács, F.* ; Horvath, P. ; Piraud, M. ; Ertürk, A. ; Samakovlis, C.* ; Theis, F.J.

Probe set selection for targeted spatial transcriptomics.

Nat. Methods 21, 2260–2270 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
Targeted spatial transcriptomic methods capture the topology of cell types and states in tissues at single-cell and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing the spatial signals present in a tissue. This requires selecting the most informative, yet minimal, set of genes to profile (gene set selection) for which it is possible to build probes (probe design). However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or new states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both gene set specificity for cell type identification and within-cell type expression variation to resolve spatially distinct populations while considering prior knowledge as well as probe design and expression constraints. We evaluated Spapros and show that it outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Cell Atlas; Gene-expression; Tissue
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Journal Nature Methods
Quellenangaben Volume: 21, Issue: 12, Pages: 2260–2270 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of AI for Health (AIH)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Grants European Union
Helmholtz Association's Initiative and Networking Fund
Virological and immunological determinants of COVID-19 pathogenesis - lessons to get prepared for future pandemics