Single-cell Cyclic Multiplex in Situ Tagging to Advance Kidney Research

由于肾脏由许多功能独特的细胞类型组成，迫切需要工具来研究疾病机制并在单细胞水平上确定临床治疗的靶点。最新的基于测序的转录切割技术通过彻底分类细胞亚型及其不同的基因表达，使肾脏研究发生了革命性的变化。 然而，弥合基因表达和临床诊断之间差距的全基因组蛋白质信息一直缺乏，特别是在单细胞分辨率上。功能蛋白(迄今已知的数百种)代表细胞的表型、生理活性、药物靶点、信号通路和调节。目前的功能蛋白质组工具要么只分析单个细胞中的几十个蛋白质，要么缺乏空间背景。 在这个项目中，我们将开发一种单细胞空间多重原位标记(MIST)技术，并将其应用于肾活检样本的功能蛋白质组研究。我们之前的研究表明，循环雾技术测量T细胞的450个蛋白质和其他组织样本的200个蛋白质。这项技术具有前所未有的高重复性，经过充分的验证和优化，将进一步发展到应用于肾脏疾病的数百种功能蛋白，包括几乎所有已知的生物标志物、重要的信号蛋白和转录因子。该项目的完成将产生一种在肾脏研究界广泛使用的使能技术和方法，从一个新的、临床相关的角度研究肾脏疾病。这项技术将为未来驱动肾脏疾病发病机制的研究和确定潜在的治疗靶点奠定基础。

Since the kidney is composed of many functionally unique cell types, there is a dire need for tools to investigate the disease mechanisms and identify targets for clinical treatment at the single cell level. Recent sequencing-based transcriptomic technologies have revolutionized kidney research by their capability of thorough classification of cell subtypes and their varied gene expression. However, genome-wide protein information that bridges the gap between gene expression and clinical diagnosis has been lacking particularly at the single-cell resolution. Functional proteins (hundreds as known so far) have been well known for representing phenotypes, physiological activities, drug targets, signaling pathways and regulations for cells. The current functional proteome tools either only analyze dozens of proteins in single cells or lack spatial context. In this project, we will develop a single-cell spatial multiplex in situ tagging (MIST) technology and apply it to kidney biopsy samples for functional proteome studies. Our prior studies show the cyclic MIST technology measures >450 proteins for T cells and ~200 proteins for other tissue samples. With the unprecedented high multiplexity, this technology will be further developed to be applied to kidney diseases by measuring hundreds of functional proteins that include almost all known biomarkers, important signaling proteins and transcription factors after thorough validation and optimization. The completion of this project will generate an enabling technology and method widely accessible in the kidney research community to investigate kidney diseases from a new, clinically relevant perspective. This technology will lay the foundation for future mechanistic studies driving kidney disease pathogenesis and identification of potential therapeutic targets.