Scientists from the University of New South Wales, Australia, have developed a new statistical approach called ASPEN to better understand how individual copies of genes behave inside single cells using RNA sequencing data.

Cells contain two copies (or alleles) of most genes, one inherited from each parent. In certain biological situations, one copy can be more active than the other. Detecting these differences in tiny amounts of data from individual cells has been hard because of technical noise and low counts in standard single-cell RNA sequencing. ASPEN helps researchers more accurately measure both the average activity of each allele and how much that activity varies from cell to cell.

Overview of ASPEN LHS: Raw sequencing reads are mapped to a
combined genome created by concatenating the two parental genomes

The C57BL/6J (GRCm38) genome serves as the reference, while strain-specific genomes are produced by integrating variant information (SNPs and indels) into the C57BL/6J genome. RHS: ASPEN employs the Bayesian shrinkage method to stabilize dispersion estimates across genes with varying expression levels. Using weighted log-likelihood, original dispersion estimates are shrunk toward the common trend dispersion — the expected level of variation for genes exhibiting similar expression levels. The posterior (shrunken) dispersion estimates facilitate the detection of state-specific and differential changes in the allelic ratio distribution. 

By combining careful mapping of sequencing reads with a statistical strategy that stabilizes noisy measurements, ASPEN can pick up subtle allele-specific activity patterns that older methods often miss. When applied to single cells from mouse brain organoids and immune T cells, it not only found genes where one allele is more active than the other, but also genes with unusual variability in allele activity. Some of these genes are involved in brain development or immune responses, showing that this method can highlight biologically meaningful regulation that would otherwise remain hidden.

This progress gives scientists a clearer view of how gene regulation differs between alleles in individual cells and opens doors to new insights into development, immune function, and disease mechanisms using RNA sequencing technologies.

Availability – ASPEN is available as an R package at https://github.com/ewonglab/ASPEN