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Meiotic crossovers promote correct chromosome segregation and the shuffling of genetic diversity. However, the measurement of crossovers remains challenging, impeding our ability to decipher the molecular mechanisms that are necessary for their formation and regulation. Here we demonstrate a novel repurposing of the single-nucleus Assay for Transposase Accessible Chromatin with sequencing (snATAC-seq) as a simple and high-throughput method to identify and characterize meiotic crossovers from haploid testis nuclei. We first validate the feasibility of obtaining genome-wide coverage from snATAC-seq by using ATAC-seq on bulk haploid mouse testis nuclei, ensuring adequate variant detection for haplotyping. Subsequently, we adapt droplet-based snATAC-seq for crossover detection, revealing >25 000 crossovers in F hybrid mice. Comparison between the wild type and a hyper-recombinogenic -deficient mutant mouse model confirmed an increase in crossover rates in this genotype, however with a distribution which was unchanged. We also find that regions with the highest rate of crossover formation are enriched for PRDM9. Our findings demonstrate the utility of snATAC-seq as a robust and scalable tool for high-throughput crossover detection, offering insights into meiotic crossover dynamics and elucidating the underlying molecular mechanisms. It is possible that the research presented here with snATAC-seq of haploid post-meiotic nuclei could be extended into fertility-related diagnostics.
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http://dx.doi.org/10.1093/nargab/lqaf122 | DOI Listing |
NAR Genom Bioinform
September 2025
DNA Repair and Recombination Laboratory, St Vincent's Institute of Medical Research, Fitzroy VIC 3065, Australia.
Meiotic crossovers promote correct chromosome segregation and the shuffling of genetic diversity. However, the measurement of crossovers remains challenging, impeding our ability to decipher the molecular mechanisms that are necessary for their formation and regulation. Here we demonstrate a novel repurposing of the single-nucleus Assay for Transposase Accessible Chromatin with sequencing (snATAC-seq) as a simple and high-throughput method to identify and characterize meiotic crossovers from haploid testis nuclei.
View Article and Find Full Text PDFUnlabelled: Meiotic crossovers are needed to produce genetically balanced gametes. In mammals, crossover formation is mediated by a conserved set of pro-crossover proteins via mechanisms that remain unclear. Here, we characterize a mammalian pro-crossover factor HEIP1.
View Article and Find Full Text PDFNat Plants
September 2025
Laboratory of Genome Biology, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland.
Meiotic crossovers, which exchange DNA between homologous chromosomes, are vital for accurate segregation and generate genetic diversity. In plant breeding, they help create new haplotypes by combining beneficial alleles. In Arabidopsis, heterozygous regions in an otherwise homozygous background attract more crossovers than in full F hybrids-a phenomenon so far observed only in this self-fertilizing species.
View Article and Find Full Text PDFbioRxiv
August 2025
Department of Medicine, University of North Carolina, Chapel Hill, NC 27514, USA.
Meiotic recombination ensures the fidelity of chromosome segregation in most organisms with sexual reproduction. The distribution of crossovers along chromosomes is governed in part by interference, which prevents multiple crossovers from occurring in close proximity, though not all crossovers are subject to interference. Neither the factors that control strength of interference, nor the extent to which they vary within and between species, are well understood.
View Article and Find Full Text PDFMol Biol Evol
September 2025
Department of Statistics, University of Oxford, Oxford, UK.
Recent breakthroughs have enabled the accurate inference of large-scale genealogies. Through modelling the impact of recombination on the correlation structure between genealogical local trees, we evaluate how this structure is reconstructed by leading approaches. Despite identifying pervasive biases, we show that applying a simple correction recovers the desired distributions for one algorithm, Relate.
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