PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell/nuclei RNA sequencing (scRNA-seq) enables large-scale gene expression quantification but struggles to profile noncoding RNAs like microRNAs (miRNAs) at the same resolution.
• Primary microRNAs (pri-miRNAs) are precursors to mature miRNAs and possess independent functions, including acting as long noncoding RNAs or enhancers, highlighting the need for their scRNA-seq profiling.
• Current computational methods lack the capability to profile and quantify pri-miRNAs at single-cell-type resolution.

Purpose of the Study:

• To develop a computational framework for profiling pri-miRNAs from scRNA-seq datasets at single-cell-type resolution.
• To enable the investigation of pri-miRNA distribution across cell types and states.
• To establish relationships between sequencing depth and pri-miRNA detection.

Main Methods:

• Development of a computational framework named PPMS (Profiling of pri-MiRNAs from single-cell RNA-sequencing datasets).
• PPMS is designed to process both new and existing scRNA-seq data.
• Application of PPMS to human heart tissues, differentiating pluripotent stem cells, and SARS-CoV-2 infected cardiomyocytes.

Main Results:

• PPMS successfully profiles pri-miRNAs at cell-type resolution from scRNA-seq data.
• The framework allows for the analysis of pri-miRNA distribution in various cell types and states.
• Demonstrated efficacy of PPMS on human heart, cardiomyocyte differentiation, and viral infection datasets.

Conclusions:

• PPMS provides a novel computational solution for pri-miRNA profiling in scRNA-seq data.
• The tool expands the scope of scRNA-seq analysis beyond protein-coding genes to include functional noncoding RNAs.
• This advancement facilitates deeper understanding of gene regulation and cellular functions at single-cell resolution.