Uncovering functional lncRNAs by scRNA-seq with ELATUS

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Long non-coding RNAs (lncRNAs) are crucial regulators of gene expression in cellular functions and diseases.
• Studying cell type-specific lncRNAs using single-cell RNA sequencing (scRNA-seq) is challenging due to annotation inaccuracies and low expression levels.

Purpose of the Study:

• To benchmark preprocessing methods for lncRNA detection in scRNA-seq data.
• To develop and validate a computational framework, ELATUS, for enhanced lncRNA identification.
• To uncover novel, biologically significant lncRNAs and their roles in pathologies.

Main Methods:

• Systematic benchmarking of scRNA-seq preprocessing techniques.
• Development of the ELATUS computational framework combining Kallisto pseudoalignment with selective functional filtering.
• Validation using single-cell multiome ATAC-seq data and independent confirmation of detected lncRNA expression patterns.

Main Results:

• ELATUS significantly enhances the detection of functional lncRNAs from scRNA-seq data.
• ELATUS demonstrates higher concordance with ATAC-seq profiles compared to standard methods, especially with imperfect reference annotations.
• The framework identified previously undocumented lncRNAs, including AL121895.1, a novel cis-repressor lncRNA implicated in breast cancer progression.

Conclusions:

• A specialized scRNA-seq workflow tailored for lncRNAs is essential for comprehensive analysis.
• ELATUS provides a robust method for uncovering the multifaceted roles of lncRNAs in cellular processes and disease.
• The study highlights the potential of ELATUS to reveal novel biomarkers and therapeutic targets, such as AL121895.1 in breast cancer.