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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
Published on: July 6, 2022
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Probe set selection for targeted spatial transcriptomics
Louis B Kuemmerle1,2,3, Malte D Luecken1,4,5, Alexandra B Firsova6
1Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
Nature Methods
|November 18, 2024
Summary
Spapros optimizes gene selection for spatial transcriptomics, improving cell type identification and revealing subtle expression variations within tissues. This method enhances spatial profiling by moving beyond traditional marker genes.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Targeted spatial transcriptomic methods analyze tissue topology at single-cell resolution.
- Optimal gene set selection is critical for accurate spatial signal capture.
- Current methods often rely on marker genes, limiting detection of continuous signals or novel cell states.
Purpose of the Study:
- To present Spapros, an end-to-end pipeline for selecting optimal gene sets for spatial transcriptomics.
- To optimize gene selection for both cell type identification and within-cell type expression variation.
- To consider probe design and expression constraints in gene set selection.
Main Methods:
- Spapros pipeline optimizes gene set specificity and expression variation.
- Considers prior biological knowledge, probe design, and expression constraints.
- Evaluated against existing gene selection approaches.
Main Results:
- Spapros outperforms other methods in cell type recovery.
- Spapros effectively recovers expression variation beyond cell types.
- Demonstrated utility in a single-cell resolution in situ hybridization on tissues (SCRINSHOT) experiment on adult lung tissue.
Conclusions:
- Spapros enables more comprehensive spatial transcriptomic analysis.
- The method identifies cell types and detects spatial variation within cell types.
- Spapros advances the capability to resolve complex tissue architectures and cellular heterogeneity.
