Noncoding Mendelian epigenomics - single cell multiome mouse cMN data

Although Mendelian disorders are overwhelmingly attributed to protein-coding pathogenic variants, a majority of unsolved cases do not harbor obvious causal pathogenic variants in the coding sequence, suggesting a potential non-coding etiology. However, classification of pathogenicity in non-coding sequence remains prohibitive due to a vastly increased search space and the lack of a standardized rubric for interpretation. Here, we present an integrated single cell multiomic framework to nominate pathogenic non-coding variants for the congenital cranial dysinnervation disorders (CCDDs). The CCDDs are Mendelian neurodevelopmental disorders that result from aberrant development of cranial motor neurons in the embryonic brainstem. We created a non-coding reference atlas of single cell chromatin accessibility profiles for 86,089 embryonic mouse cranial motor neurons (cMNs). We found that high-quality single cell ATAC-seq (scATAC) profiles alone were a strong predictor of enhancement (64% in vivo validation rate). To further aid in interpretation, we integrated single cell histone modification and gene expression information to distinguish individual enhancers and their cognate genes. Relatively subtle differences in cellular composition of input data often led to substantial differences in predicted enhancer strength, cognate gene, and tissue of activity. Next, we mapped candidate non-coding variants from 899 whole genome sequences from 270 CCDD pedigrees to the murine cMN-specific regulatory elements and trained a machine learning classifier to accurately predict the functional effects of patient variants within these elements. We then performed high coverage scATACseq and site-specific footprinting analysis on an allelic series of CRISPR-humanised mice to validate our machine learning predictions and render important clues to the mode of pathogenicity. Finally, we performed peak- and gene-centric allelic aggregation to nominate non-coding variants, including those regulating MN1 and EBF3, respectively. Altogether this work extends non-coding variant analysis to Mendelian disease and presents a generalizable framework for nominating novel non-coding variants in other rare disorders. Single cell multiome of mouse developing cranial motor neurons and spinal motor neurons at e11.5尽管孟德尔疾病绝大多数由蛋白质编码区的致病变异引发，但多数未确诊病例的编码序列中并未携带明确的致病致病变异，提示存在非编码区致病机制的可能性。然而，由于非编码序列的搜索空间大幅扩增，且缺乏标准化的解读评判框架，非编码序列的致病性分类仍难以开展。本研究构建了一套整合型单细胞多组学框架，用于为先天性颅神经支配障碍（congenital cranial dysinnervation disorders, CCDDs）筛选致病性非编码变异。该疾病属于孟德尔式神经发育障碍，由胚胎脑干内颅运动神经元发育异常所致。我们针对86089个小鼠胚胎颅运动神经元（cranial motor neurons, cMNs）构建了单细胞染色质可及性谱的非编码参考图谱。研究发现，仅依靠高质量的单细胞转座酶可及性测序（single cell ATAC-seq, scATAC）谱即可有效预测增强子活性，体内验证率达64%。为进一步辅助变异解读，我们整合了单细胞组蛋白修饰与基因表达信息，以区分单个增强子及其靶基因。输入数据的细胞组成仅存在细微差异，往往会导致预测的增强子强度、靶基因及活性组织出现显著差异。随后，我们将270个CCDD家系的899份全基因组测序数据中的候选非编码变异，比对至小鼠cMN特异性调控元件，并训练了机器学习分类器，以精准预测这些元件内患者变异的功能效应。随后，我们针对一系列CRISPR人源化小鼠的等位基因系列，开展了高覆盖度scATAC测序与位点特异性足迹分析，以验证机器学习预测结果，并为致病性作用模式提供重要线索。最后，我们通过以峰区和基因为中心的等位基因聚集分析，筛选出分别调控MN1与EBF3的非编码变异。综上，本研究将非编码变异分析拓展至孟德尔疾病领域，并为其他罕见疾病中新的致病性非编码变异的筛选提供了可推广的分析框架。本数据集包含小鼠胚胎发育至e11.5时期的颅运动神经元与脊髓运动神经元的单细胞多组学数据。