Inferring cell developmental stage-specific lncRNA regulation in the developing human neocortex with CDSlncR

Area of Science:

• Genomics
• Computational Biology
• Developmental Biology

Background:

• Noncoding RNAs (ncRNAs), particularly long non-coding RNAs (lncRNAs), are crucial gene expression regulators in biological processes.
• Existing computational methods using bulk transcriptomics struggle to capture cell developmental stage-specific lncRNA regulation.
• Single-cell sequencing offers a powerful approach to investigate dynamic lncRNA functions across cellular differentiation.

Purpose of the Study:

• To introduce CDSlncR, a novel computational method for inferring cell developmental stage-specific lncRNA regulation.
• To integrate putative lncRNA-target binding information with single-cell transcriptomics data.
• To construct cell developmental stage-specific lncRNA regulatory networks.

Main Methods:

• Developed CDSlncR, a computational tool combining lncRNA-target binding data and single-cell transcriptomics.
• Applied CDSlncR to single-cell data from the developing human neocortex.
• Performed network analysis to identify stage-specific lncRNA regulatory patterns.

Main Results:

• CDSlncR successfully inferred cell developmental stage-specific lncRNA regulatory networks.
• Analysis of human neocortex development revealed unique lncRNA regulation at each stage.
• Case study on autism spectrum disorder biomarkers demonstrated CDSlncR's predictive power for stage-specific lncRNA targets.

Conclusions:

• CDSlncR is an effective computational method for predicting cell developmental stage-specific lncRNA targets.
• The study highlights the dynamic and stage-unique nature of lncRNA regulation during human neocortex development.
• CDSlncR provides a valuable tool for dissecting lncRNA roles in developmental processes and disease.