Single-nucleus multiomic mapping of m6A methylome and transcriptome in native populations of cells with sn-m6A-CT [scRNA-seq, scCUT&Tag]. Single-nucleus multiomic mapping of m6A methylome and transcriptome in native populations of cells with sn-m6A-CT [scRNA-seq, scCUT&Tag]

N6-methyladenosine (m6A) RNA modification plays important roles in the governance of gene expression and is temporally regulated in different cell states. In contrast to global m6A profiling in bulk sequencing, technologies for analyzing m6A heterogeneity across single-cells are not extensively established. Here, we report single-nucleus m6A CUT-Tag (sn-m6A-CT) for simultaneous profiling of m6A methylome and transcriptome within a single nucleus. m6A-CT is capable of enriching m6A-marked RNA molecules in situ, without isolating RNAs from cells, in a highly efficient manner. We adapted m6A-CT to the droplet-based single-cell omics platform and demonstrated high throughput performance in analyzing nuclei isolated from thousands of cells from various cell types. We show that sn-m6A-CT profiling is sufficient to determine cell identity and allows the generation of cell-type-specific m6A methylome landscapes from heterogeneous populations. Further, we exemplified the robust deconvolution capabilities in m6A heterogeneities with stem cells in multiple distinct pluripotency states. These indicate that sn-m6A-CT provides additional dimensions to multimodal datasets and new insights into epitranscriptomic landscape in defining cell fate identity and states. Overall design: Bulk CUT&Tag or single-nucleus CUT&Tag followed by Illumina sequencingN6-甲基腺嘌呤（N6-methyladenosine，m6A）RNA修饰在基因表达调控中发挥重要作用，且在不同细胞状态下呈现时序调控特性。与基于批量测序的全局m6A图谱分析不同，用于解析单细胞水平m6A异质性的技术尚未得到广泛建立。本研究报道了单细胞核m6A CUT-Tag技术（sn-m6A-CT），可同时对单个细胞核内的m6A甲基化组与转录组进行图谱分析。m6A-CT可在无需从细胞中分离RNA的情况下，以高效方式原位富集带有m6A修饰的RNA分子。我们将m6A-CT技术适配至基于微滴的单细胞多组学平台，并证明其可对从多种细胞类型的数千个细胞中分离得到的细胞核实现高通量分析。研究表明，sn-m6A-CT图谱分析足以确定细胞身份，并可从异质性细胞群体中生成细胞类型特异性的m6A甲基化组图谱。进一步，我们以处于多种不同多能性状态的干细胞为例，展示了该技术在解析m6A异质性方面的强大解卷积能力。上述结果表明，sn-m6A-CT技术为多模态数据集提供了额外的分析维度，并为解析细胞命运身份与状态的表观转录组景观提供了新的见解。整体实验设计：先开展批量CUT&Tag或单细胞核CUT&Tag实验，随后进行Illumina测序。