Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.

Area of Science:

• Neuroscience
• Genomics
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is a powerful tool for transcriptomic profiling.
• Single-nucleus RNA sequencing (snRNA-seq) offers potential advantages over scRNA-seq for complex tissues.
• snRNA-seq can be applied to archived frozen specimens and may reduce transcriptional artifacts.

Purpose of the Study:

• To compare cell type detection between snRNA-seq and scRNA-seq using matched datasets from mouse visual cortex.
• To evaluate the utility of nuclear RNA for characterizing cellular diversity.
• To assess the impact of including intronic sequences in snRNA-seq analysis.

Main Methods:

• Utilized well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex.
• Compared transcript detection and cell type discrimination between the two methods.
• Analyzed the proportion of intronic sequences in nuclear RNA.

Main Results:

• While whole cells detected more transcripts (~11,000 genes) than nuclei (~7,000 genes), snRNA-seq could discriminate closely related neuronal types when intronic sequences were included.
• Estimated nuclear mRNA proportion varied from 20% to over 50% for different pyramidal neuron types.
• Demonstrated high information content of nuclear RNA for brain tissue cellular diversity.

Conclusions:

• snRNA-seq provides less biased cellular coverage and avoids cell isolation artifacts compared to scRNA-seq.
• Nuclear RNA is a valuable resource for transcriptomic profiling and characterizing cellular diversity, particularly in brain tissues.
• Including intronic sequences in snRNA-seq analysis is crucial for accurate cell type discrimination.