Identify Regulatory eQTLs by Multiome Sequencing in Prostate Single Cells.

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Molecular Biology

Background:

• Genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) analysis identify prostate cancer risk variants but struggle to pinpoint regulatory effects.
• Single-cell sequencing technologies enable simultaneous profiling of chromatin accessibility and gene expression, offering a powerful tool to link regulatory elements to gene activity.
• Understanding the functional impact of noncoding variants is crucial for deciphering prostate cancer etiology and developing targeted therapies.

Purpose of the Study:

• To test the hypothesis that multiome single-cell sequencing can map regulatory elements and their target genes at prostate cancer risk loci.
• To identify and functionally validate novel regulatory single nucleotide polymorphisms (SNPs) and their target genes associated with prostate cancer.

Main Methods:

• Applied a 10X Multiome ATAC + Gene Expression platform to profile 65,501 single cells from multiple prostate cell lines.
• Utilized targeted sequencing to enrich data at prostate cancer risk loci, focusing on 2,730 candidate germline variants and 273 associated genes.
• Integrated single-cell multiomic data with bulk eQTL data from the GTEx prostate cohort and performed reporter assays and SILAC proteomics for functional validation.

Main Results:

• Targeted multiome data improved gene expression and chromatin accessibility abundance by approximately 20% and 5%, respectively.
• Demonstrated significant overlap between single-cell multiome associations and bulk eQTL findings, with ~20% of GTEx eQTLs covered and ~10% of multiome associations identified by GTEx eQTLs.
• Identified previously reported regulatory variants (e.g., rs60464856-RUVBL1, rs7247241-SPINT2) and functionally validated a novel regulatory SNP (rs2474694-VPS53).

Conclusions:

• The multiome single-cell approach is feasible and effective for identifying regulatory SNPs and their regulated genes in prostate cancer.
• This methodology provides a high-resolution map of regulatory elements and their target genes at the single-cell level, crucial for understanding noncoding variant function.
• The findings pave the way for a deeper understanding of the genetic architecture of prostate cancer and the development of precision medicine strategies.