A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type...

Area of Science:

• Neuroscience
• Genomics
• Molecular Biology

Background:

• Single whole cell approaches face challenges in complex tissues like the brain.
• Single-nucleus RNA sequencing (snRNA-seq) offers an alternative for transcriptome analysis using isolated nuclei.
• Direct comparisons between nuclear and whole cell transcriptomes are scarce.

Purpose of the Study:

• To characterize and compare nuclear and whole cell transcriptomes in mouse single neurons.
• To develop a normalization strategy for single-nucleus RNA sequencing data.
• To assess the concordance of snRNA-seq with whole cell sequencing for various gene sets.

Main Methods:

• Single-nucleus RNA sequencing (snRNA-seq) and single whole cell RNA sequencing were performed on mouse neurons.
• A normalization strategy was developed to account for differences in genic region lengths.
• Transcriptome data from nuclei and whole cells were directly compared.

Main Results:

• High concordance was observed between nuclear and whole cell transcriptomes for cell type and metabolic markers.
• A subset of mitochondrial respiration genes showed lower concordance between the two methods.
• The developed normalization strategy effectively reduced method-specific differences.

Conclusions:

• Single-nucleus RNA sequencing is an effective method for profiling cell type expression dynamics.
• snRNA-seq enables the study of gene expression in tissues previously inaccessible to whole cell methods.
• This study provides a valuable resource and methodology for researchers using snRNA-seq.