ArticleNature methods2024
Probe set selection for targeted spatial transcriptomics.
Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- RKMR: A Rapid Kernel Machine Regression Framework for Optimal Marker Detection in Spatial Omics Data.bioRxiv : the preprint server for biology · 2026Article
- Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Cerebrovascular vulnerability and fibrosis in human brain aneurysms.Nature neuroscience · 2026Article
- The lung tissue environment inbioRxiv : the preprint server for biology · 2026Article
- ProbeST: a custom probe design pipeline for dual host-pathogen Spatial Transcriptomics.BMC genomics · 2026Article
- Optimal gene panel selection for targeted spatial transcriptomics experiments.Nucleic acids research · 2026Article
- Chromogenic in situ HCR as a targeted translational interface for routine molecular histology.Histochemistry and cell biology · 2026Article
- RECOMBINE identifies recurrent composite markers of cell types and states.Genome research · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- Article
- MISSTE: a multiscale integrative spatial simulator for understanding the mechanisms underlying tissue ecosystems.bioRxiv : the preprint server for biology · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- BEASTsim-a benchmarking and analysis platform for spatial transcriptomics simulations.Briefings in bioinformatics · 2026Article
- A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia.bioRxiv : the preprint server for biology · 2026Article
- CiCLoDS: Joint cell clustering and gene selection for single-cell spatial transcriptomics.Scientific reports · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- PeakPrime: a peak-guided primer design pipeline for target enrichment in 3'-end RNA-seq.Bioinformatics advances · 2026Article
- Optimal marker genes forGenome research · 2025Article
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21 authors.
Funding
Abstract
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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