ASPEN: Tracking gene variations in individual cells using RNA sequencing

Jenn Hoskins
22nd December, 2025

ASPEN: Tracking gene variations in individual cells using RNA sequencing

The ASPEN method is validated as a robust and sensitive tool for detecting allelic imbalance, outperforming existing methods in simulations (b–d), accurately identifying monoallelic expression in known genes like Tmsb4x and Meg3 (e), and producing results consistent with bulk-level data (f), all supported by its adaptive shrinkage strategy (a).

Image adapted from: Petrova et al. / CC BY (Source)

Key Findings

  • Researchers developed ASPEN, a new method to analyze gene activity in individual cells from hybrid mouse strains, improving detection of subtle differences in gene expression
  • ASPEN revealed that essential genes, crucial for basic cell function, show remarkably stable activity levels across cells, suggesting tight regulation
  • The study identified genes with variable activity, particularly those involved in brain development and immune responses, indicating a potential for adaptability and context-dependent expression
Our bodies are built from cells, each containing genetic information inherited from both parents. While we typically think of genes as being expressed identically from both copies, this isn’t always the case. Sometimes, only the gene copy from one parent is active, a phenomenon known as allele-specific expression. This is particularly evident in processes like genomic imprinting, where certain genes are exclusively expressed from either the mother or father’s allele[2]. Understanding how this happens, and the extent to which it varies between cells, is crucial for comprehending normal development and disease. However, measuring which parent’s gene copy is active within individual cells has been technically challenging. Researchers at the Victor Chang Cardiac Research Institute, University of New South Wales, Sloan Kettering Institute, and CANADA have developed a new method, called ASPEN (Allelic Specific Profiling ENhancer), to overcome these challenges[1]. ASPEN analyzes single-cell RNA sequencing (single-cell RNA-seq) data – a technique that measures the activity of all genes within individual cells – from hybrid offspring (F1 hybrids). These hybrids inherit a mix of genes from both parents, providing a natural system to study allele-specific expression. The core problem ASPEN addresses is the ‘noise’ in single-cell RNA-seq data; because cells contain relatively small amounts of RNA, it’s difficult to reliably determine which parent’s gene copy is being expressed. The method works by statistically modelling both the average expression level and the variability of each gene allele within single cells. Previous approaches often struggled to differentiate between genuine differences in allele expression and random fluctuations. ASPEN uses a ‘sensitive mapping pipeline’ to accurately identify which gene allele is being read and then employs ‘adaptive shrinkage’ – a statistical technique that reduces noise by adjusting estimates based on the data’s reliability. This combination significantly improves the ability to detect subtle differences in allele expression. In tests using both simulated data and real biological samples, ASPEN demonstrated a 30% increase in sensitivity compared to existing methods. To demonstrate ASPEN’s capabilities, the researchers applied it to mouse brain organoids (3D structures grown in a lab that mimic brain development) and T cells (immune cells). They discovered genes with incomplete X chromosome inactivation. X chromosome inactivation is a process where one of the two X chromosomes in female cells is randomly silenced to equalize gene dosage between males (XY) and females (XX). ASPEN revealed that some genes on the inactivated X chromosome were still being expressed, indicating incomplete silencing. Furthermore, ASPEN identified genes exhibiting ‘stochastic monoallelic expression’ – meaning that in some cells, only one parent’s gene copy was active, while in others, it was the other. This randomness wasn’t previously detectable with older methods. Importantly, the study revealed that the variability in allele expression wasn’t uniform across all genes. Essential genes – those critical for basic cell function – showed reduced variation in allele expression, suggesting tight regulation. Conversely, genes involved in neurodevelopment and immune responses displayed increased variability. These findings build on earlier work that identified hundreds of imprinted genes in mice[2] and suggested that the number of imprinted genes in typical tissues is relatively small. ASPEN’s ability to analyze allele-specific expression at a single-cell level allows for a more nuanced understanding of imprinting and related phenomena. For example, the discovery of novel non-coding RNAs within known imprinted loci[2] can now be investigated in detail regarding their cell-to-cell expression patterns. The study also complements research highlighting the importance of epigenetic mechanisms – modifications to DNA that affect gene expression without altering the underlying DNA sequence – in regulating allele-specific expression[3]. The researchers found that DNA methylation and Polycomb group repressors, both epigenetic factors, are essential for imprinting, but operate on different sets of genes. Interestingly, the study also supports the idea that gene expression levels are fine-tuned by a combination of cis and trans regulatory elements[4]. Cis elements are DNA sequences near a gene that directly influence its expression, while trans elements are factors that act from a distance. The observation of increased allelic variance in neurodevelopmental and immune genes suggests that these genes are more susceptible to regulatory changes, potentially allowing for greater adaptability. The finding that essential genes exhibit reduced variance aligns with the observation that genes with expression divergence driven by trans variants showed higher sequence constraint[4], implying a stronger selective pressure to maintain stable expression levels. Ultimately, ASPEN provides a powerful tool for dissecting the complex regulatory landscape of allele-specific expression, offering new insights into development, immunity, and disease.

GeneticsBiochemPlant Science

References

Main Study

1) ASPEN: Robust detection of allelic dynamics in single cell RNA-seq

Published 19th December, 2025

https://doi.org/10.1371/journal.pcbi.1013837

Related Studies

2) Global survey of genomic imprinting by transcriptome sequencing.

https://doi.org/10.1016/j.cub.2008.09.044

3) Diverse epigenetic mechanisms maintain parental imprints within the embryonic and extraembryonic lineages.

https://doi.org/10.1016/j.devcel.2021.10.010

4) Extensive compensatory cis-trans regulation in the evolution of mouse gene expression.

https://doi.org/10.1101/gr.142281.112


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