虽然全基因组关联研究和表达数量性状位点（eQTL）分析在识别与前列腺癌风险和大量组织转录组变化相关的非编码变异方面取得了重大进展，但这些遗传元件对基因表达的调节作用仍然很大程度上未知。单细胞测序的最新发展使得同时进行 ATAC-seq 和 RNA-seq 分析以捕获染色质可及性和基因表达之间的功能关联成为可能。在这项研究中，我们测试了我们的假设，即这种多组单细胞方法可以在前列腺癌风险位点绘制调控元件及其靶基因。我们应用 10X 多组 ATAC 基因表达平台封装来自多个前列腺细胞系的 Tn5 转座酶标记的细胞核，以获得来自 RWPE1、RWPE2、PrEC、BPH1、DU145、PC3、22Rv1 和 LNCaP 细胞系的总共 65,501 个高质量单细胞。为了解决单细胞测序中常见的数据稀疏问题，我们进行了靶向测序，以丰富前列腺癌风险位点的测序数据，涉及 2,730 个候选种系变异和 273 个相关基因。尽管没有增加捕获的细胞数量，但目标多组数据确实将 eQTL 基因表达丰度提高了约 20%，将染色质可及性丰度提高了约 5%。基于这种多组学分析，我们进一步将 RNA 表达变化与单细胞水平的种系变异的染色质可及性联系起来。交叉验证分析显示多组关联与 GTEx 前列腺队列的大量 eQTL 结果之间存在高度重叠。我们发现大约 20% 的 GTEx eQTL 被显着的多组关联覆盖（p 值≤ 0.05，基因丰度百分比≥ 5%），并且大约 10% 的多组关联可以被显着的 GTEx eQTL 识别。我们还分析了具有可用杂合 SNP 读数的可访问区域，并观察到基因组区域与等位基因可访问变体更频繁地关联 (p = 0.0055)。这些发现包括先前报道的调节变异，包括 rs60464856-RUVBL1（BPH1 中多组 p 值 = 0.0099）和 rs7247241-SPINT2（22Rv1 中多组 p 值 = 0.0002-0.0004）。我们还通过报告基因测定和 SILAC 蛋白质组测序对新的调控 SNP 及其靶基因 rs2474694-VPS53（BPH1 中的多组 p 值 = 0.00956，DU145 中的多组 p 值 = 0.00625）进行了功能验证。总而言之，我们的数据证明了多组单细胞方法用于识别调控 SNP 及其调控基因的可行性。

While genome-wide association studies and expression quantitative trait loci (eQTL) analysis have made significant progress in identifying noncoding variants associated with prostate cancer risk and bulk tissue transcriptome changes, the regulatory effect of these genetic elements on gene expression remains largely unknown. Recent developments in single-cell sequencing have made it possible to perform ATAC-seq and RNA-seq profiling simultaneously to capture functional associations between chromatin accessibility and gene expression. In this study, we tested our hypothesis that this multiome single-cell approach allows for mapping regulatory elements and their target genes at prostate cancer risk loci. We applied a 10X Multiome ATAC + Gene Expression platform to encapsulate Tn5 transposase-tagged nuclei from multiple prostate cell lines for a total of 65,501 high quality single cells from RWPE1, RWPE2, PrEC, BPH1, DU145, PC3, 22Rv1 and LNCaP cell lines. To address data sparsity commonly seen in the single-cell sequencing, we performed targeted sequencing to enrich sequencing data at prostate cancer risk loci involving 2,730 candidate germline variants and 273 associated genes. Although not increasing the number of captured cells, the targeted multiome data did improve eQTL gene expression abundance by about 20% and chromatin accessibility abundance by about 5%. Based on this multiomic profiling, we further associated RNA expression alterations with chromatin accessibility of germline variants at single cell levels. Cross validation analysis showed high overlaps between the multiome associations and the bulk eQTL findings from GTEx prostate cohort. We found that about 20% of GTEx eQTLs were covered within the significant multiome associations ( p -value ≤ 0.05, gene abundance percentage ≥ 5%), and roughly 10% of the multiome associations could be identified by significant GTEx eQTLs. We also analyzed accessible regions with available heterozygous SNP reads and observed more frequent association in genomic regions with allelically accessible variants ( p = 0.0055). Among these findings were previously reported regulatory variants including rs60464856- RUVBL1 ( multiome p -value = 0.0099 in BPH1 ) and rs7247241- SPINT2 ( multiome p -value = 0.0002- 0.0004 in 22Rv1 ) . We also functionally validated a new regulatory SNP and its target gene rs2474694- VPS53 ( multiome p -value = 0.00956 in BPH1 and 0.00625 in DU145) by reporter assay and SILAC proteomics sequencing. Taken together, our data demonstrated the feasibility of the multiome single-cell approach for identifying regulatory SNPs and their regulated genes.