Inferring cell-specific lncRNA regulation with single-cell RNA-sequencing data in the developing human neocortex

TL;DR

This paper introduces CSlncR, a novel computational method that leverages single-cell RNA-sequencing data to identify cell-specific lncRNA-mRNA regulatory networks in the developing human neocortex, revealing unique regulation patterns across cells and stages.

Contribution

The paper presents CSlncR, a new approach integrating binding data with scRNA-seq to analyze cell-specific lncRNA regulation at single-cell resolution.

Findings

lncRNA regulation varies across individual cells and developmental stages

CSlncR effectively predicts cell-specific lncRNA targets

The method aids in clustering cells and understanding cell communication

Abstract

Long non-coding RNAs (lncRNAs) are important regulators to modulate gene expression and cell proliferation in the developing human brain. Previous methods mainly use bulk lncRNA and mRNA expression data to study lncRNA regulation. However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data. Recent advance in scRNA-seq has provided a way to investigate lncRNA regulation at single-cell level. We will propose a novel computational method, CSlncR (cell-specific lncRNA regulation), which combines putative lncRNA-mRNA binding information with scRNA-seq data including lncRNAs and mRNAs to identify cell-specific lncRNA-mRNA regulation networks at individual cells. To understand lncRNA regulation at different development stages, we apply CSlncR to the scRNA-seq data of human neocortex. Network analysis shows that the lncRNA regulation is unique in each cell from the different human neocortex development stages. The comparison results indicate that CSlncR is also an effective tool for predicting cell-specific lncRNA targets and clustering single cells, which helps understand cell-cell communication.