From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
• Distinguishing biological variation from technical noise in scRNA-seq data remains a challenge.
• Current methods need refinement for analyzing gene expression in rare cell types and states.

Purpose of the Study:

• To apply the SMART-seq protocol to a reference lymphoblastoid cell line (GM12878).
• To quantify absolute RNA molecules per cell and assess cell-to-cell expression variability.
• To investigate cell-specific gene coexpression, alternative splicing, and allelic bias.

Main Methods:

• Utilized the SMART-seq single-cell RNA sequencing protocol.
• Employed spike-in standards for absolute RNA quantification.
• Implemented a pool/split design to measure technical stochasticity.
• Analyzed gene coexpression modules, alternative splicing, and allelic bias.

Main Results:

• Significant variation in total mRNA content (50,000–300,000 transcripts/cell) was observed.
• Biological differences in gene expression between cells exceeded technical variation.
• Specific coexpression modules, including mRNA processing factors, showed preferential expression in cell subsets.
• Cell-to-cell variation in alternative splicing and splice site usage was significant.

Conclusions:

• scRNA-seq can reveal substantial biological variation beyond technical noise.
• Small pools of 30-100 cells yield transcriptomic data comparable to bulk RNA-seq.
• This study provides a framework for analyzing gene expression in rare cell populations.