Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility

Queitsch, Konstantin; Moore, Travis W.; O’Connell, Brendan L.; Nichols, Ruth V.; Muschler, John L.; Keith, Dove; Lopez, Charles; Sears, Rosalie C.; Mills, Gordon B.; Yardımcı, Galip Gürkan; Adey, Andrew C.

doi:10.1016/j.crmeth.2023.100625

Cell Reports Methods

2023

DOI: 10.1016/j.crmeth.2023.100625

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Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility

Konstantin Queitsch,

Travis W. Moore,

Brendan L. O’Connell

et al.

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2024

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Cited by 2 publications

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Expression quantitative trait locus mapping in recombinant gametes using single nucleus RNA sequencing

Parker,

Amar,

Campoy

et al. 2024

Preprint

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Phenotypic differences between individuals of a species are often caused by differences in gene expression, which are in turn caused by genetic variation. Expression quantitative trait locus (eQTL) analysis is a methodology by which we can identify such causal variants. Scaling eQTL analysis is costly due to the expense of generating mapping populations, and the collection of matched transcriptomic and genomic information. We developed a rapid eQTL analysis approach using single-cell/nucleus RNA sequencing of gametes from a small number of heterozygous individuals. Patterns of inherited polymorphisms are used to infer the recombinant genomes of thousands of individual gametes and identify how different haplotypes correlate with variation in gene expression. Applied to Arabidopsis pollen nuclei, our approach uncovers bothcis- andtrans-eQTLs, ultimately mapping variation in a master regulator of sperm cell development that affects the expression of hundreds of genes. This establishes snRNA-sequencing as a powerful, cost-effective method for the mapping of meiotic recombination, addressing the scalability challenges of eQTL analysis and enabling eQTL mapping in specific cell-types.

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Expression quantitative trait locus mapping in recombinant gametes using single nucleus RNA sequencing

Parker,

Amar,

Campoy

et al. 2024

Preprint

View full text Add to dashboard Cite

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Single cell genome and epigenome co-profiling reveals hardwiring and plasticity in breast cancer

Wang,

Yan,

Elgamal

et al. 2024

Preprint

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Understanding the impact of genetic alterations on epigenomic phenotypes during breast cancer progression is challenging with unimodal measurements. Here, we report wellDA-seq, the first high-genomic resolution, high-throughput method that can simultaneously measure the whole genome and chromatin accessibility profiles of thousands of single cells. Using wellDA-seq, we profiled 22,123 single cells from 2 normal and 9 tumors breast tissues. By directly mapping the epigenomic phenotypes to genetic lineages across cancer subclones, we found evidence of both genetic hardwiring and epigenetic plasticity. In 6 estrogen-receptor positive breast cancers, we directly identified the ancestral cancer cells, and found that their epithelial cell-of-origin was Luminal Hormone Responsive cells. We also identified cell types with copy number aberrations (CNA) in normal breast tissues and discovered non-epithelial cell types in the microenvironment with CNAs in breast cancers. These data provide insights into the complex relationship between genetic alterations and epigenomic phenotypes during breast tumor evolution.

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Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility

Cited by 2 publications

References 23 publications

Expression quantitative trait locus mapping in recombinant gametes using single nucleus RNA sequencing

Expression quantitative trait locus mapping in recombinant gametes using single nucleus RNA sequencing

Single cell genome and epigenome co-profiling reveals hardwiring and plasticity in breast cancer

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