Gene length is a pivotal feature to explain disparities in transcript capture between single transcriptome...

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are powerful tools for cell mapping.
• Direct comparisons of technical differences between scRNA-seq and snRNA-seq are limited.
• Understanding these differences is crucial for accurate biological interpretation.

Purpose of the Study:

• To compare the transcriptome output of whole cells versus nuclei using paired scRNA-seq and snRNA-seq.
• To investigate disparities in gene recovery and structural characteristics between the two techniques.
• To assess the impact of these technical differences on downstream analyses.

Main Methods:

• Paired scRNA-seq and snRNA-seq were performed on matched samples from three organs: heart, lung, and kidney.
• Transcriptome data were analyzed to compare cell cluster recovery, gene recovery rates, and genomic/gene structural features.
• Differential gene expression analysis and Gene Ontology (GO) term enrichment were conducted to evaluate downstream impacts.

Main Results:

• Major cell clusters were recovered by both techniques, but in different proportions.
• snRNA-seq uniquely detected some cell clusters in kidney and lung samples.
• Significant differences were observed in gene length, exon content, and transcript length between scRNA-seq and snRNA-seq.
• Both techniques showed biases compared to the whole genome in coding sequence length, transcript length, genomic span, and exon count distribution.
• Top differentially expressed genes between techniques revealed distinct GO terms.

Conclusions:

• scRNA-seq and snRNA-seq exhibit disparities in RNA capture efficiency and gene recovery.
• These technical differences introduce biases that affect the calculation of basic cellular parameters and downstream analysis outcomes.
• The choice of single-cell or single-nucleus RNA sequencing technique has significant implications for data interpretation and biological conclusions.