多路复用
RNA序列
条形码
计算生物学
多路复用
核糖核酸
细胞
寡核苷酸
单细胞分析
生物
计算机科学
生物信息学
转录组
基因
遗传学
基因表达
电信
操作系统
作者
Christopher S. McGinnis,David M. Patterson,Juliane Winkler,Daniel N. Conrad,Marco Y. Hein,Vasudha Srivastava,Jennifer L. Hu,Lyndsay M. Murrow,Jonathan S. Weissman,Zena Werb,Eric D. Chow,Zev J. Gartner
出处
期刊:Nature Methods
[Springer Nature]
日期:2019-06-17
卷期号:16 (7): 619-626
被引量:540
标识
DOI:10.1038/s41592-019-0433-8
摘要
Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer. Tagging live single cells and nuclei with lipid- or cholesterol-modified oligonucleotides enables massive scRNA-seq sample multiplexing, identifies doublets and recovers cells with low RNA content.
科研通智能强力驱动
Strongly Powered by AbleSci AI