Comparison of single-cell sequencing technologies for allele-specific expression analysis in rabbit spermatids.

Combining allele-specific expression (ASE) analysis with single-cell RNA-seq can elucidate how genomic variation affects RNA expression at the single-cell level. We explore how experimental and computational choices impact the power of ASE-based methods and develop a suite of single-cell ASE computational tools. With single-nucleus RNA-Seq, we extract more ASE information from reads in intronic than exonic regions. We show how read length can increase power and that hybrid selection improves power to detect ASE in targeted genes. We apply our methods to a Parkinson's disease cohort and show that ASE analysis has more power than eQTL analysis.

Canker disease caused by Pseudomonas syringae pv. actinidiae (Psa) poses a major threat to cultivated kiwifruit, and utilization of wild relatives are key to improve resistance. However, comprehensive comparative genomic analyses between cultivated kiwifruit and their wild relatives with enhanced resistance to Psa remain limited. Here we generate chromosome-scale genome assemblies for eleven wild Actinidia eriantha accessions and one interspecific hybrid between Actinidia eriantha and cultivated Actinidia chinensis var. chinensis. Integrating these with twelve previously released genomes including three Actinidia eriantha and nine Actinidia chinensis var. chinensis, we construct a reference-unbiased graph-based pangenome. These datasets reveal extensive genomic variation, including 31,790,044 SNPs, 13,512,079 InDels and 623,478 structural variations, and provide a landscape of structural variations within and between the two species. Leveraging these datasets, we identify a wild allele showing allele-specific expression, AeNLR25-1, which enhances Psa resistance in cultivated kiwifruit. Genetic and molecular analyses demonstrate that a transposable element-induced structural variation in the AeNLR25-1 promoter introduces a species-specific WRKY binding site, conferring enhanced defense against Psa. Pangenome across cultivated species and wild relatives provides a theoretical framework for accelerating kiwifruit genetic improvement through pangenome-enabled identification of favorable wild alleles.

BackgroundAllele Specific Expression analysis is an important tool for integrating genome and transcriptome data. It quantifies expression variation between the two haplotypes of a diploid individual distinguished by heterozygous sites, and is a powerful tool to estimate cis-regulatory diversity of alleles. Clustering algorithms can be used to identify patterns or groups of genes/samples based on their expression profiles. Depending on the structure of the data, different existing clustering algorithm can be adapted to allele specific expression data. However, no ad-hoc procedure has been developed.ResultsIn this work, we begin defining an expression matrix capturing allele expressions from an RNA-sequencing experiment. On this matrix, we develop a novel two-phase unsupervised clustering procedure, built on top of a spectral clustering algorithm, whose aim is to partition the population into groups of similar individuals, according to their allelic expression. As case-studies, the approach is used to cluster 98 cultivars representative of the variability observed in Vitis vinifera, starting from read counts of genes of chromosome 1 of leaves, and to analyze allele-specific count data from a CASTxMRL F1 hybrid mice dataset.ConclusionUsing the above mentioned real case-studies as well as generated synthetic data, we see that our algorithm shows significant robustness and outperforms other standard clustering techniques.

Single-cell and haplotype-resolved transcriptomics reveal molecular signatures of orchid floral structures.

Interpreting the effects of novel mutations on phenotypic traits remains challenging, particularly for cis -regulatory variants. For rare variants, individuals typically possess at most one affected copy of the causal allele, leading to allelic imbalance, and thus the ability to infer inheritance of allelic imbalance can inform genetic studies of phenotypic traits. While many methods for detection of allele-specific expression (ASE) exist, they largely focus on ASE in one individual. We show that performing joint inference across multiple individuals in a trio allows for simultaneously improving estimates of ASE and identifying its likely mode of inheritance. Our Bayesian approach has the benefit of being able to (1) aggregate information across individuals so as to improve statistical power, (2) estimate uncertainty in estimates, and (3) rank modes of inheritance by posterior probability. We demonstrate that this model is also applicable to other forms of imbalance such as allele-specific chromatin accessibility. Applying the model to ATAC-seq and RNA-seq from several trios, we uncover examples in which ASE can be linked to imbalance in chromatin state of cis -regulatory elements and to potential causal variants. As the cost of sequencing continues to decrease, we expect that powerful methodologies such as the one presented here will promote more routine collection of samples from related individuals and improve our understanding of genetic effects on gene regulation and their contribution to phenotypic traits.

Structural variants (SVs) are a major source of genetic diversity, yet how they impact cell types in complex brain diseases remains largely unexplored, partially due to limitations of short-read sequencing. Here, we addressed this fundamental question in Parkinson's disease (PD). generating long-read whole-genome sequencing (WGS) data for 100 post-mortem brain samples from a PD cohort, constructing a high-confidence catalog of 74,552 SVs. To resolve their functional impact, we integrated single-nucleus RNA-sequencing data from two brain regions from the same samples and focused functional analyses on SVs proximal to genes previously nominated as cis-regulated, potential causal targets of PD-associated GWAS loci. Using expression quantitative trait locus and allele-specific expression analyses, we uncovered SVs significantly associated with expression in specific cell types as well as effects shared across cell types. This study demonstrates the power of uniting long-read WGS with transcriptomics to uncover SVs underlying complex disease architecture with cell type resolution.

Allele-specific expression (ASE) of somatic mutations can be caused by cis-activation of the mutant allele or silencing of the wild-type allele and has been investigated by examining the enrichment of mutant allele in RNA relative to DNA. Here we show that this mutation-based approach can be confounded by gene expression differences in tumor and normal cells that coexist in most bulk tumor samples. We model mutant allele expression by incorporating tumor/normal expression difference, mutant allele dosage, tumor purity, and nonsense-mediated decay (NMD) efficiency, projecting that such enrichments can occur without ASE. This confounding effect is exacerbated with low tumor purity and is dependent on mutant allele dosage for NMD-triggering mutations. The model predictions are validated by a pancancer bulk tumor analysis with somatic insertions/deletions (indels) from 9101 The Cancer Genome Atlas (TCGA) samples. A single-cell analysis in five cutaneous squamous cell carcinomas demonstrates the robustness of this model to intratumor heterogeneity. As a byproduct of this confounding effect, we evaluate whether the inverse relationship between mutant allele enrichment in RNA and tumor purity could be leveraged to complement DNA-based somatic mutation detection in low purity samples. Indeed, our de novo somatic indel calling from TCGA RNA-seq increases the TCGA driver indel repertoire by ∼14%, especially in samples with purity less than 0.4, including actionable EGFR indels in lung adenocarcinoma and FLT3 in acute myeloid leukemia. Our study not only reveals confounders in somatic mutant ASE analysis but also demonstrates their utility in RNA-based mutation calling.

Since DNA sequencing alone faces challenges in variant interpretation during genetic diagnosis, RNA sequencing has recently gained attention in resolving these diagnostic gaps. This study aimed to evaluate the advantages of liver tissue RNA sequencing in the diagnosis of genetic liver diseases. Liver tissue RNA sequencing was performed on 147 patients with prior DNA sequencing. We evaluated the role of RNA sequencing by analyzing aberrant gene expression, splicing, allele-specific expression, transcript-level similarity, and mosaic variants. Liver RNA-seq supported the molecular diagnoses in 56 patients diagnosed by DNA sequencing alone. Among 91 previously undiagnosed patients, incorporating RNA sequencing established a diagnosis in 17 (18.68%) patients. Among the 33 patients with indicative clinical phenotypes or prioritized variants, diagnosis was established in 15 (45.45%) patients with the help of RNA sequencing. This improvement was primarily (16/17) driven by the detection of aberrant splicing and allele-specific expression, instead of aberrant expression. RNA sequencing revealed ±50bp of cryptic splicing sites as hotspot regions, characterized allele-specific expression at both the gene and variant levels, and revealed shared transcriptomic features in low-GGT cholestasis. While DNA sequencing demonstrates superior sensitivity in detecting clinically relevant variants, liver RNA sequencing significantly enhances genetic diagnosis, mainly by revealing aberrant splicing and allele-specific expression. These findings suggest that RNA sequencing is an essential complement to DNA sequencing.

Single-cell allele-specific expression (ASE) provides valuable insights into gene regulatory mechanisms. However, its utility is limited by the lack of dedicated computational tools. We present DAESC + , a dual-module end-to-end software package for the processing and analysis of single-cell ASE. The preprocessing module, DAESC-P, is a user-friendly bioinformatics pipeline to obtain ASE counts from multiplexed scRNA-seq data. The analysis module, DAESC-GPU, is a scalable tool for differential ASE analysis powered by graphics processing units (GPUs). We demonstrated that DAESC-P is more accurate than the existing SALSA pipeline. DAESC-GPU is dozens of times faster than our previous method (DAESC) and scalable to over a million cells. Applying DAESC + to a subset of the OneK1K cohort, we identified 15 genes exhibiting differential regulatory patterns between naïve and central memory CD4 + T cells, and 2 genes between naïve and memory B cells.

Deserts are among the most extreme environments on Earth. High temperatures and a lack of water impose powerful selective pressures on desert species, offering an opportunity to investigate the genetic basis of local adaptation. Despite the unique challenges of desert living, house mice (Mus musculus domesticus), a species native to Western Europe, have recently colonized the Sonoran Desert in North America within the last 400-600 generations. House mice from the Sonoran Desert show phenotypic differences consistent with adaptation to water scarcity, including maintaining weight better under water stress than non-desert mice. To investigate the genetic basis of the physiological responses to water deprivation, we compared gene expression responses of desert house mice and an interfertile non-desert dwelling subspecies (M. m. musculus) and their F1 hybrids after 72 hours without water access. First, we show that desert and non-desert mice exhibit highly divergent transcriptional responses to water deprivation across three tissues (hypothalamus, liver, and kidney). Then, by surveying allele-specific expression in intersubspecific hybrids between desert and non-desert mice, we uncover cis-regulatory differences driving changes in the transcriptional response to dehydration (e.g., cis-by-environment interactions). These cis-regulatory changes were highly tissue-specific, consistent with modular regulatory changes shaping expression divergence. Intriguingly, we find that genes with cis-regulatory differences induced by water access were involved in the arachidonic acid pathway, a primary adaptation pathway across many desert species, and lipid metabolism. Finally, our results highlight several candidate genes of interest for understanding rapid adaptation to desert living. Together, our results identify context-dependent cis-regulatory evolution as a key contributor to variation in dehydration response and a potential mechanism facilitating rapid adaptation to extreme environments.

Closely related species often exhibit distinct morphologies that can contribute to species-specific adaptations and reproductive isolation. One example is Lepidopteran caterpillar appendages, such as the “caudal horn” of Bombycoidea moths, which have evolved substantial morphological diversity among species in this group. Using interspecific crosses, we identify the genetic basis of the caudal horn size difference between Bombyx mori and its closest relative Bombyx mandarina. The three largest of eight QTL account for one third the mean horn length difference between the species. The largest of these, on chromosome 4, encompasses a conserved Wnt family gene cluster, key upstream regulators that are well-known for their roles in morphological diversification in animals. Using allele-specific expression analysis and CRISPR/Cas9 knockouts, we show that tissue-specific cis-regulatory changes to Wnt1 and Wnt6 contribute to the species difference in caudal horn size. This kind of modularity enables highly pleiotropic genes, including key upstream growth regulators, to contribute to the evolution of morphological traits without causing widespread deleterious effects.

BackgroundGenomic imprinting, a mechanism resulting in parent-of-origin expression of genes through epigenetic regulation, intersects with a broad range of biological fields, including evolution, molecular genetics and epigenetics, and determinism of complex traits. Although next generation sequencing technologies nowadays enable imprinted genes to be detected in a genome-wide manner, a wide spectrum of this phenomena is evaluated only in humans and rodents.ResultsHere, we propose to map genes showing a parental expression imbalance in hypothalamus, muscle and placenta in piglets around birth using an extensive strategy that minimized biases and relied on reciprocal crosses, reconstruction of parental phases after imputation, and statistical analyses discriminating parent-of-origin from allele-specific expression. We detected 141 genes with strong to exclusive parental expression imbalance (ratio > 25:75). A large proportion (80%) of these genes have never been shown to exhibit parent-of-origin expression and a small proportion (15%) are shared by at least two tissues, suggesting an overall weak conservation landscape of genomic imprinting. Interestingly, we identified novel parent-of-origin expressed genes involved in neurodevelopmental (PREPL, Prolyl Endopeptidase Like) and fetal growth (FAM20B, Glycosaminoglycan Xylosylkinase, and POU6F2, POU Class 6 Homeobox 2) functions. In-depth analyses of specific loci highlighted specific imprinted isoforms of COPG2 (COPI Coat Complex Subunit Gamma 2) and confirmed livestock-specific imprinted genes such as the Zinc Finger Protein 300-like gene.ConclusionsAltogether, our results provide an atlas of parent-of-origin expressed genes in the pig, making it the most documented species for genomic imprinting after humans and rodents. Our findings indicate weak conservation of this mechanism across species and tissues, suggesting a small number of core imprinted genes shared across eutherians and another imprinted genes that seem specific to species or tissues. These latter parent-of-origin expressed genes may have been subjected to evolutionary forces that have determine their imprinting status in either a livestock-specific or a tissue-specific manner.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12711-026-01031-2.

Allele-specific expression (ASE) of long non-coding RNAs (lncRNAs) links genetic variation to synaptic dysfunction in psychiatric disorders. We previously identified a disease-associated ASE shift in LINC02449 at rs149707223 (C/G), with preferential expression of the alternative G allele in bipolar disorder (BD) and schizophrenia (SZ). Overexpression of LINC02449-G induced social deficits, repetitive behaviors, and enhanced excitatory synaptic transmission in the medial prefrontal cortex-nucleus accumbens (mPFC-NAc) circuit via upregulation of CPLX1. We hypothesized that these abnormalities are mediated by glutamatergic hyperexcitability and are pharmacologically reversible. Adult C57BL/6 mice received mPFC-targeted AAV-mediated LINC02449-G overexpression followed by systemic administration of Riluzole. Molecular, behavioral, and electrophysiological analyses were performed. Riluzole dose-dependently normalized elevated Cplx1 expression induced by LINC02449-G in mice. Behavioral assays showed significant rescue of social interaction deficits and reductions in excessive grooming and marble-burying behavior. Whole-cell recordings revealed that Riluzole restored increased mEPSC frequency and amplitude in NAc neurons to control levels, indicating correction of mPFC-NAc circuit hyperexcitability. These results demonstrate that synaptic and behavioral abnormalities caused by ASE-driven lncRNA dysregulation are pharmacologically reversible. Riluzole represents a promising candidate for repurposing in neuropsychiatric disorders characterized by glutamatergic imbalance and frontostriatal circuit dysfunction, and highlights ASE-regulated lncRNAs as actionable therapeutic targets.

Quantitative trait loci as indicators of potential susceptibility to allele-specific dropout for forensic RNA markers.

SUMMARYHuman induced pluripotent stem cells (hiPSC) and iPSC-differentiated neural cells, in combination with CRISPR editing, are commonly used for studying neurodevelopmental and other brain disorders. Female iPSCs undergo random X-chromosome inactivation (XCI) via epigenetic silencing by noncoding X inactive specific transcript (XIST). It is known that female iPSCs may lose XIST expression, leading to XCI erosion that affects both X-linked and autosomal gene expression. However, the effects of CRSIPR editing and neural differentiation on XCI erosion in iPSC-derived neurons and how this may confound a real-world transcriptomic analysis of differentially expressed genes (DEGs) are poorly understood. Here, leveraging bulk RNA-seq of hundreds of CRISPR-edited female iPSC lines from four donor lines for 66 genes and single-cell RNA-seq of iPSC-derived neurons of a subset of 42 edited genes, we investigated the effects of XCI erosion during CRISPR editing and in iPSC-derived neurons. We found that XCI erosion was variable in CRISPR-edited female iPSCs and largely preserved in iPSC-derived neurons. Like in iPSCs, XIST in neurons predominately influenced the expression of X-linked genes; however, its effect on autosomal genes was more pronounced in single neurons. Mechanistically, XIST epigenetically causes allelic imbalance of both X-linked and autosomal genes, with the former showing stronger allele-specific expression (ASE) bias. Notably, XIST-induced ASE bias exhibited a conserved positional pattern at loci affecting neurodevelopmental genes across different female lines and cell types. Finally, we demonstrated a confounding effect of XCI erosion on DEG analyses in iPSC-derived neurons. These results have significant implications in hiPSC modeling of neurodevelopmental and other brain disorders.

Studying the molecular mechanisms underlying autism spectrum disorders (ASD) requires cellular models capable of capturing cis-regulatory effects and allele-specific gene expression. In this study, we present an approach for generating induced pluripotent stem cells (iPSCs) modified using an adenine base editor (ABE) to introduce synonymous single-nucleotide substitutions in the AUTS2 gene - a candidate involved in ASD pathogenesis. These substitutions serve as allele-specific markers, enabling the tracking of expression differences between normal and rearranged alleles in a cis-regulatory context. We developed a high-efficiency strategy for genotyping clones using amplicon-based next-generation sequencing (NGS). Analysis of over 100 subclones demonstrated that this approach surpasses Sanger sequencing in scalability, sensitivity, and cost-effectiveness. We selected clones with targeted heterozygous substitutions, assessed mosaicism levels, and performed phasing with germline heterozygous variants to confirm monoclonal origin and identify the allele carrying the chromosomal rearrangement. The resulting iPSC lines mark distinct AUTS2 alleles, providing a foundation for analyzing the impact of cis-regulatory elements on gene expression across different cell types. Our findings highlight the practical value of base editors and targeted NGS genotyping in creating cellular models with single-nucleotide substitutions for both basic and applied research.

Closely related species often exhibit distinct morphologies that can contribute to species-specific adaptations and reproductive isolation. One example is Lepidopteran caterpillar appendages, such as the "caudal horn" of Bombycoidea moths, which have evolved substantial morphological diversity among species in this group. Using interspecific crosses, we identify the genetic basis of the caudal horn size difference between Bombyx mori and its closest relative Bombyx mandarina. The three largest of eight QTL account for one third the mean horn length difference between the species. The largest of these, on chromosome 4, encompasses a conserved Wnt family gene cluster, key upstream regulators that are well-known for their roles in morphological diversification in animals. Using allele-specific expression analysis and CRISPR/Cas9 knockouts, we show that tissue-specific cis-regulatory changes to Wnt1 and Wnt6 contribute to the species difference in caudal horn size. This kind of modularity enables highly pleiotropic genes, including key upstream growth regulators, to contribute to the evolution of morphological traits without causing widespread deleterious effects.

INFO GRAB: An interactive shiny application for gene expression analysis and visualization.

Individuals with monogenic diseases, even those with identical disease-causing mutations, exhibit considerable clinical heterogeneity in severity and outcomes. In this study, we quantified total and allele-specific transcript levels in nasal brushings from patients diagnosed with cystic fibrosis (CF) who are homozygous or compound heterozygous for variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Among CF patients, total CFTR transcript levels showed greater variability than in non-CF individuals, although both groups exhibited a broad range of transcript expression. Compound heterozygous study subjects also displayed notable differences in allelic dosage, known as allelic skewing. The expression from the non-F508del allele predominated in the total transcript pool. For a small group of patients with nasal brushings available before and after treatment with FDA/EMA-approved drugs Trikafta/Kaftrio (a combination of elexacaftor-tezacaftor-ivacaftor) or Symkevi (a combination of tezacaftor-ivacaftor), we observed no effect on total CFTR transcript abundance but detected changes in allelic expression patterns. These findings reveal important aspects to be considered for personalized therapeutic approaches to CF and other monogenic diseases and emphasize previously unappreciated aspects of mRNA expression during pathogenesis.