Seq-Scope: Microscopic Examination of Spatial Single Cell Transcriptome in Cell and Tissue Senescence

总结 标准免疫染色或RNA原位杂交只能检测一个或少数靶分子， 因此，从单个实验会话中获得的信息量是有限的。 为了克服这一点，新兴的空间转录组学（ST）技术旨在检查从基因组中表达的所有基因。 从一张组织学切片中提取基因组。有三种主要的实验方法 实施ST：顺序原位杂交法、原位测序法和空间条形码 法在这些方法中，空间条形码方法是最直接、最全面的，并且到目前为止， 唯一可扩展用于大量样品的方法。空间条形码方法揭示了RNA 通过使用空间条形码化的寡核苷酸阵列捕获组织RNA，来确定序列及其空间位置。 然而，目前的空间条形码方法本质上受到其低分辨率和低RNA的限制 捕获效率;相应地，所有目前可用的技术都未能揭示微观 空间转录组的细节我们最近开发了一种名为Seq-Scope的技术，它克服了 所有这些限制。Seq-Scope具有0.5-1 μm的有效分辨率，每个分辨率显示超过20个转录本。 μm2面积（文库饱和时估计约为50个转录物/μm2）。分辨率和转录组捕获 Seq-Scope的输出是迄今为止文献中描述的所有可用技术中最好的。与此 前所未有的性能，Seq-Scope可视化空间转录组异质性在多个组织学 尺度，包括根据门中央（肝脏）、隐窝表面（结肠）和炎症的组织分区， 纤维化（损伤的肝）轴，细胞成分，包括单细胞类型和亚型，和亚细胞 细胞核、细胞质和线粒体的结构。Seq-Scope也有改进的潜力， 补充目前的scRNA-seq方法。针对SenNet的公告，我们建议调整 并利用Seq-Scope提供正常人类衰老过程中多种组织中细胞衰老的图谱， （1）鉴定和表征肝衰老细胞群体， 肝病。(2)表征肝脏空间转录组的肝脏相关变化。(3)联合收割机序列 衰老蛋白和细胞类型标记蛋白的检测范围。 对于所有的目标，我们将开始与分析小鼠组织建立监测细胞和组织的可行性 衰老（UG 3），然后使用从不同组织中获得的组织扩展人体组织中的工作。 包括SenNet组织映射中心（TMC）（UH 3）在内的资源。Seq-Scope是一种通用技术， 这是快速的、直接的、可扩展的和可适应的。一旦优化，一个研究人员可以处理5-10 冷冻组织块每周产生一个高质量的空间单细胞转录组数据。所以我们的 团队相信，我们将能够使我们的技术适用于任何组织系统， UH 3阶段的SenNet TMC。

SUMMARY Standard immunostaining or RNA in situ hybridization can examine only one or a handful of target molecular species at a time; therefore, the amount of information obtained from a single experimental session is limited. To overcome this, emerging Spatial Transcriptomics (ST) techniques aim to examine all genes expressed from the genome from a single histological slide. There are three major methodologies of experimentally implementing ST: the sequential in situ hybridization method, in situ sequencing method and spatial barcoding method. Among these, the spatial barcoding method is the most straightforward, comprehensive and so far the only method scalable for large amount of samples. The spatial barcoding method reveals both the RNA sequence and their spatial locations by capturing tissue RNA using a spatially-barcoded oligonucleotide array. The current spatial barcoding method, however, are intrinsically limited by their low resolution and low RNA capture efficiencies; correspondingly, all currently available technologies failed to reveal the microscopic details of the spatial transcriptome. We recently developed a technology named Seq-Scope, which overcomes all these limitations. Seq-Scope has an effective resolution of 0.5-1 μm, and reveals over 20 transcripts per μm2 area (~50 transcripts/μm2 estimated at library saturation). Both resolution and transcriptome capture output of Seq-Scope are the best among all available technologies described in the literature so far. With this unprecedented performance, Seq-Scope visualized spatial transcriptome heterogeneity at multiple histological scales, including tissue zonation according to the portal-central (liver), crypt-surface (colon) and inflammation- fibrosis (injured liver) axes, cellular components including single cell types and subtypes, and subcellular architectures of nucleus, cytoplasm and mitochondria. Seq-Scope also has a potential to improve and complement current scRNA-seq approaches. In response to the SenNet announcement, we propose to adapt and utilize Seq-Scope to provide atlases of cellular senescence in multiple tissues during normal human aging, focusing on the following three aims: (1) Identify and Characterize Hepatic Senescent Cell Population during Liver Disease. (2) Characterize Age-Associated Changes of Hepatic Spatial Transcriptome. (3) Combine Seq- Scope with Detection of Senescence Protein and Cell-type Marker Proteins. For all aims, we will begin with analyzing mouse tissue to establish the feasibility of monitoring cell and tissue senescence (UG3), and then expand the work in human tissues using the tissue obtained from diverse resources including the SenNet tissue mapping center (TMC) (UH3). Seq-Scope is a versatile technology, which is quick, straightforward, scalable and adaptable. Once optimized, a single researcher can process 5-10 frozen tissue blocks every week to generate a high-quality spatial single cell transcriptome data. Therefore, our team is confident that we will be able to adapt our technology to any of the tissue systems that are provided by the SenNet TMC in the UH3 phase.