报告题目：Effective Detection of Variations in Single Cell Transcriptome

报告人：Prof. Chenghang Zong

        Department of Molecular and Human Genetics

        Baylor College of Medicine

报告时间：2016年11月18日上午10:00

报告地点：北京大学综合科研楼302会议室

摘要：The development of single-cell RNA-seq methods has allowed the detection of gene expression at the microscopic scale that is not accessible by bulk RNA-seq approaches. While single-cell RNA-seq has been successfully used for the identification of new cell types in complex tissues, recent analyses have also indicated that technical noise still exists in single-cell RNA-seq assays. In contrast to the gene expression differences between different cell types, single cells of the same type possess dynamic transcriptional variations due to intrinsic and extrinsic noises. The successful detection of these transcriptional variations essentially requires a very sensitive and quantitative single-cell RNA-seq assay.

Here we present a new single-cell RNA-seq assay --- Multiple Annealing and dC-Tailing based Quantitative single-cell RNA-seq (MATQ-seq). This method demonstrates ~90% sensitivity comparing to 50~60% sensitivity of current single-cell RNA-seq assays. More importantly, MATQ-seq provides the highly desired accuracy for detecting transcriptional variations existing in single cells of the same population. The technical noise of MATQ-seq was systematically characterized to warrant the detected variations are biologically genuine. By mapping the reads to the exons and introns respectively, we measured the transcriptional noise in mature RNAs and premature RNAs separately. The observation of large transcriptional noise in premature RNA is consistent with the transcriptional burst dynamics that have been widely observed in biological systems. With the sensitivity and accuracy demonstrated in measuring transcriptional variations among single cells of the same population, we believe that MATQ-seq will have broad applications in biological and clinical research.

联系人：黄岩谊

电话：62744058

Email：yanyi@pku.edu.cn