Uncovering functional lncRNAs by scRNA-seq with ELATUS

Enrique Goñi, Aina Maria Mas, Jovanna Gonzalez, Amaya Abad, Marta Santisteban, Puri Fortes, Maite Huarte () and Mikel Hernaez ()
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Enrique Goñi: University of Navarra
Aina Maria Mas: University of Navarra
Jovanna Gonzalez: University of Navarra
Amaya Abad: University of Navarra
Marta Santisteban: Institute of Health Research of Navarra (IdiSNA)
Puri Fortes: University of Navarra
Maite Huarte: University of Navarra
Mikel Hernaez: University of Navarra

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: Abstract Long non-coding RNAs (lncRNAs) play fundamental roles in cellular processes and pathologies, regulating gene expression at multiple levels. Despite being highly cell type-specific, their study at single-cell (sc) level is challenging due to their less accurate annotation and low expression compared to protein-coding genes. Here, we systematically benchmark different preprocessing methods and develop a computational framework, named ELATUS, based on the combination of the pseudoaligner Kallisto with selective functional filtering. ELATUS enhances the detection of functional lncRNAs from scRNA-seq data, detecting their expression with higher concordance than standard methods with the ATAC-seq profiles in single-cell multiome data. Interestingly, the better results of ELATUS are due to its advanced performance with an inaccurate reference annotation such as that of lncRNAs. We independently confirm the expression patterns of cell type-specific lncRNAs exclusively detected with ELATUS and unveil biologically important lncRNAs, such as AL121895.1, a previously undocumented cis-repressor lncRNA, whose role in breast cancer progression is unnoticed by traditional methodologies. Our results emphasize the necessity for an alternative scRNA-seq workflow tailored to lncRNAs that sheds light on the multifaceted roles of lncRNAs.

Date: 2024
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DOI: 10.1038/s41467-024-54005-7

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