Objective

Type 2 diabetes (T2D) is a complex disease characterized by pancreatic islet dysfunction, insulin resistance, and disruption of blood glucose levels. Genome-wide association studies (GWAS) have identified > 400 independent signals that encode genetic predisposition. More than 90% of associated single-nucleotide polymorphisms (SNPs) localize to non-coding regions and are enriched in chromatin-defined islet enhancer elements, indicating a strong transcriptional regulatory component to disease susceptibility. Pancreatic islets are a mixture of cell types that express distinct hormonal programs, so each cell type may contribute differentially to the underlying regulatory processes that modulate T2D-associated transcriptional circuits. Existing chromatin profiling methods such as ATAC-seq and DNase-seq, applied to islets in bulk, produce aggregate profiles that mask important cellular and regulatory heterogeneity.

Methods

We present genome-wide single-cell chromatin accessibility profiles in >1,600 cells derived from a human pancreatic islet sample using single-cell combinatorial indexing ATAC-seq (sci-ATAC-seq). We also developed a deep learning model based on U-Net architecture to accurately predict open chromatin peak calls in rare cell populations.

Results

We show that sci-ATAC-seq profiles allow us to deconvolve alpha, beta, and delta cell populations and identify cell-type-specific regulatory signatures underlying T2D. Particularly, T2D GWAS SNPs are significantly enriched in beta cell-specific and across cell-type shared islet open chromatin, but not in alpha or delta cell-specific open chromatin. We also demonstrate, using less abundant delta cells, that deep learning models can improve signal recovery and feature reconstruction of rarer cell populations. Finally, we use co-accessibility measures to nominate the cell-specific target genes at 104 non-coding T2D GWAS signals.

Conclusions

Collectively, we identify the islet cell type of action across genetic signals of T2D predisposition and provide higher-resolution mechanistic insights into genetically encoded risk pathways.

中文翻译：

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人类胰岛中的单细胞 ATAC-Seq 和稀有细胞的深度学习升级揭示了细胞特异性 2 型糖尿病监管特征。

 客观的

2 型糖尿病 (T2D) 是一种复杂的疾病，其特征是胰岛功能障碍、胰岛素抵抗和血糖水平紊乱。全基因组关联研究 (GWAS) 已识别出 > 400 个编码遗传倾向的独立信号。超过 90% 的相关单核苷酸多态性 (SNP) 定位于非编码区，并且富含染色质定义的胰岛增强子元件，表明对疾病易感性具有强大的转录调节成分。胰岛是表达不同激素程序的细胞类型的混合物，因此每种细胞类型可能对调节 T2D 相关转录回路的潜在调节过程有不同的贡献。现有的染色质分析方法（例如 ATAC-seq 和 DNase-seq）应用于批量胰岛，产生的聚集图谱掩盖了重要的细胞和调控异质性。

 方法

我们使用单细胞组合索引 ATAC-seq (sci-ATAC-seq) 展示了源自人胰岛样本的 >1,600 细胞的全基因组单细胞染色质可及性概况。我们还开发了基于 U-Net 架构的深度学习模型，以准确预测稀有细胞群中的开放染色质峰调用。

 结果

我们表明，sci-ATAC-seq 配置文件使我们能够对 α、β 和 δ 细胞群进行去卷积，并识别 T2D 背后的细胞类型特异性调控特征。特别是，T2D GWAS SNP 在 β 细胞特异性和跨细胞类型共享的胰岛开放染色质中显着富集，但在 α 或 δ 细胞特异性开放染色质中不显着富集。我们还证明，使用不太丰富的 delta 细胞，深度学习模型可以改善稀有细胞群的信号恢复和特征重建。最后，我们使用共同可及性措施来提名 104 个非编码 T2D GWAS 信号的细胞特异性靶基因。

 结论

总的来说，我们确定了胰岛细胞在 T2D 易感基因信号中的作用类型，并为基因编码的风险途径提供了更高分辨率的机制见解。