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| Item Type: | Preprint |
|---|---|
| Title: | pyTrance finds co-localizing RNAs in subcellular spatial transcriptomics data |
| Creators Name: | Strenger, Leon, Cerda-Jara, Cledi Alicia, Karaiskos, Nikos and Rajewsky, Nikolaus |
| Abstract: | Regulation of RNA subcellular localization is crucial for cellular functions in health and disease. For example, local translation of co-localized RNAs is crucial for neural biology. However, it is challenging to identify RNA co-localization events. Here, we present pyTrance, a computational framework that predicts and quantifies subcellular RNA co-localization from spatial transcriptomics data, leveraging latent embeddings learned by a graph neural network. Based on extensive benchmarking, detection of co-localizing RNAs was more accurate and robust compared to existing methods. In mouse brain tissue, pyTrance found several RNA co-localization patterns. Co-localized RNAs were often functionally related and validated by biological knowledge. Interestingly, among novel patterns, pyTrance identified co-localization of GABAergic markers, including Gad1, in neuronal projections. Experimental validation led to the discovery of a spatial overlap between Gad1 mRNA/protein, strongly suggesting local translation. Our results establish pyTrance as a state-of-the-art method to discover biologically important RNA co-localization at subcellular resolution. |
| Source: | bioRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2026.05.07.723470 |
| Date: | 11 May 2026 |
| Official Publication: | https://doi.org/10.64898/2026.05.07.723470 |
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