MultiomicsTracks96: A high throughput PIXUL-Matrix-based toolbox to profile frozen and FFPE tissues multiomes

Background: The multiome is an integrated assembly of distinct classes of molecules and molecular properties, or “omes,” measured in the same biospecimen. Freezing and formalin-fixed paraffin-embedding (FFPE) are two common ways to store tissues, and these practices have generated vast biospecimen repositories. However, these biospecimens have been underutilized for multi-omic analysis due to the low throughput of current analytical technologies that impede large-scale studies. Methods: Tissue sampling, preparation, and downstream analysis were integrated into a 96-well format multi-omics workflow, MultiomicsTracks96. Frozen mouse organs were sampled using the CryoGrid system, and matched FFPE samples were processed using a microtome. The 96-well format sonicator, PIXUL, was adapted to extract DNA, RNA, chromatin, and protein from tissues. The 96-well format analytical platform, Matrix, was used for chromatin immunoprecipitation (ChIP), methylated DNA immunoprecipitation (MeDIP), methylated RNA immunoprecipitation (MeRIP), and RNA reverse transcription (RT) assays followed by qPCR and sequencing. LCMS/ MS was used for protein analysis. The Segway genome segmentation algorithm was used to identify functional genomic regions, and linear regressors based on the multi-omics data were trained to predict protein expression. Results: MultiomicsTracks96 was used to generate 8-dimensional datasets including RNA-seq measurements of mRNA expression; MeRIP-seq measurements of m6A and m5C; ChIP-seq measurements of H3K27Ac, H3K4m3, and Pol II; MeDIP-seq measurements of 5mC; and LCMS/ MS measurements of proteins. We observed high correlation between data from matched frozen and FFPE organs. The Segway genome segmentation algorithm applied to epigenomic profiles (ChIP-seq: H3K27Ac, H3K4m3, Pol II; MeDIP-seq: 5mC) was able to recapitulate and predict organ-specific super-enhancers in both FFPE and frozen samples. Linear regression analysis showed that proteomic expression profiles can be more accurately predicted by the full suite of multi-omics data, compared to using epigenomic, transcriptomic, or epitranscriptomic measurements individually. Conclusions: The MultiomicsTracks96 workflow is well suited for high dimensional multi-omics studies – for instance, multiorgan animal models of disease, drug toxicities, environmental exposure, and aging as well as large-scale clinical investigations involving the use of biospecimens from existing tissue repositories.

背景：多组学（"multiome"）是在同一生物样本中测得的不同类别分子、分子特性或各类‘组学’的整合集合。冷冻保存与福尔马林固定石蜡包埋（"FFPE"）是两种常见的组织储存方式，相关操作已积累了海量的生物样本库。然而，由于当前分析技术通量较低，阻碍了大规模研究的开展，这类生物样本在多组学分析中的应用仍未得到充分开发。
方法：本研究将组织取样、制备及下游分析整合为96孔板格式的多组学工作流程MultiomicsTracks96。使用CryoGrid系统对冷冻小鼠器官进行取样，并使用切片机处理配对的FFPE样本。适配96孔板格式的超声破碎仪PIXUL可用于从组织中提取DNA、RNA、染色质与蛋白质。采用96孔板格式的分析平台Matrix开展染色质免疫沉淀（"ChIP"）、甲基化DNA免疫沉淀（"MeDIP"）、甲基化RNA免疫沉淀（"MeRIP"）以及RNA反转录（"RT"）实验，后续结合定量聚合酶链反应（"qPCR"）与测序技术进行检测；采用液相色谱-串联质谱（"LCMS/MS"）开展蛋白质组分析。使用Segway基因组分段算法识别功能基因组区域，并基于多组学数据训练线性回归模型以预测蛋白质表达水平。
结果：通过MultiomicsTracks96工作流程可生成8维数据集，涵盖：mRNA表达的RNA测序（"RNA-seq"）检测、m6A与m5C甲基化的甲基化RNA免疫沉淀测序（"MeRIP-seq"）检测、H3K27Ac、H3K4m3及RNA聚合酶II（"Pol II"）的染色质免疫沉淀测序（"ChIP-seq"）检测、5mC甲基化的甲基化DNA免疫沉淀测序（"MeDIP-seq"）检测，以及蛋白质的液相色谱-串联质谱（"LCMS/MS"）检测。我们观察到配对的冷冻与FFPE组织样本的数据之间具有较高的相关性。将Segway基因组分段算法应用于表观基因组图谱（ChIP-seq：H3K27Ac、H3K4m3、Pol II；MeDIP-seq：5mC）后，可在FFPE与冷冻样本中重现并预测器官特异性超级增强子。线性回归分析结果显示，相较于单独使用表观组、转录组或表观转录组检测数据，利用完整的多组学数据集可更精准地预测蛋白质组表达谱。
结论：MultiomicsTracks96工作流程非常适用于高维度多组学研究，例如疾病、药物毒性、环境暴露与衰老的多器官动物模型研究，以及利用现有组织样本库生物样本开展的大规模临床研究。