Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) provides valuable gene expression profiles but often yields sparse data, limiting comprehensive transcriptome analysis.
• The complexity of scRNA-seq libraries can hinder the full characterization of individual cell transcriptomes.
• Existing methods may not offer sufficient depth for detailed analysis of specific cell populations or gene expression.

Purpose of the Study:

• To develop a method for recovering targeted gene expression information from scRNA-seq libraries.
• To enhance the depth of sequencing and gene detection for individual cells.
• To improve the characterization of rare cell types and specific molecular features within scRNA-seq data.

Main Methods:

• Developed a transcriptome resampling strategy to physically recover DNA molecules from scRNA-seq libraries.
• Applied cell-centric mode to isolate and re-sequence transcriptomes of rare megakaryocytes.
• Applied gene-centric mode to isolate specific mRNA fragments (e.g., CD3D) across multiple cells.

Main Results:

• Achieved up to 20-fold greater sequencing depth per cell for rare megakaryocytes.
• Increased the median number of detected genes per cell from 1313 to 2002.
• Improved the detection rate of CD3D mRNA expression from 59.7% to 100% in a T cell line.

Conclusions:

• Transcriptome resampling is a versatile approach to extract focused gene expression data from scRNA-seq libraries.
• This method significantly enhances the utility and information yield of scRNA-seq experiments.
• The technique holds potential for broader applications in single-cell molecular profiling and analysis.