Mosaic autosomal aneuploidies are detectable from single-cell RNAseq data

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Aneuploidies, or variations in chromosome number, are prevalent in diseases like cancer and in embryonic development.
• Mosaic aneuploidy, where only a subset of cells are affected, presents detection challenges.
• Current single-cell DNA sequencing methods for aneuploidy detection are time-consuming and costly.

Purpose of the Study:

• To develop a rapid and cost-effective method for identifying aneuploidies from single-cell RNA-seq data.
• To leverage chromosome-wide expression imbalances as a marker for aneuploidy detection.
• To provide a user-friendly R software package for implementing the developed method.

Main Methods:

• Utilized chromosome-wide expression imbalances in single-cell RNA sequencing (scRNA-seq) data to infer aneuploidies.
• Developed a quantitative calling method for aneuploidies.
• Integrated the method into an R software package for accessibility.

Main Results:

• Validated the method using datasets with known copy numbers, achieving high accuracy in aneuploidy detection with a low false discovery rate.
• Confirmed method efficacy through analyses of allele-specific gene expression and differential expression.
• Demonstrated the method's ability to accurately identify the majority of aneuploidies.

Conclusions:

• The developed method offers a quick, interpretable, and cost-saving alternative to single-cell genome sequencing for aneuploidy detection.
• The approach is suitable for investigating the functional consequences of aneuploidy or for filtering aneuploid cells in scRNA-seq analyses.
• Limitations include reduced suitability for datasets with highly variable gene expression.