Link phenotypic characteristics with gene expression profile of single cells at high throughput for drug discovery and cell therapy development

项目摘要/摘要 研究人员对表型组学和转录组数据的整合表现出了浓厚的兴趣 生物学洞察力，因为表型特征与基因表达模式高度相关。然而， 缺乏将基因表达谱与细胞表型联系起来的有效方法(例如， 蛋白质丰度和定位、细胞形态、酶和代谢活性)在单细胞水平上 一种高吞吐量方式。为了弥合这一差距，WellSIM建议开发一个可以提取 通过高含量成像从数千个单个细胞获得表型特征，并将这些特征联系起来 以及他们的转录特征。这项技术不仅将实现多维的单细胞研究，而且 还可以通过自动准备样本来提高单细胞RNA测序的性能 无需使用条形码微珠的微流控装置。

PROJECT SUMMARY / ABSTRACT Researchers have shown substantial interest in the integration of phenomics and transcriptomics data for novel biological insights since phenotypic characteristics are highly correlated with gene expression patterns. However, there is a lack of an efficient approach that could link gene expression profiles to cellular phenotypes (e.g., protein abundance and localization, cellular morphology, enzymatic and metabolic activity) at single-cell level in a high-throughput manner. To bridge this gap, WellSIM proposes to develop a platform which can extract phenotypic characteristics from thousands of individual cells via high-content imaging and link these features with their transcriptomic profiles. This technology will not only achieve multi-dimensional single cell studies, but also improve the performance of single-cell RNA sequencing through automatic sample preparation on a microfluidic device without using barcoded beads.