Single cell transcriptomics for characterization of complex systems and biomarker detection
Stéphane C. Boutet, Deanna M. Church, Zachary Bent, Sofia Kyriazopoulou-Panagiotopoulou, Josephine Lee, Patrick Marks, Samual Marrs, Elliott Meer, Jeff Mellon, Luz Montesclaros, Daniel Riordan, Paul Ryvkin, Joe Shuga, Jessica Terry, Kevin Wu, Grace X.Y. Zheng, and Tarjei Mikkelsen
10x Genomics, Pleasanton, CA
High throughput, droplet based systems for assaying transcriptomes at single cell resolution have revolutionized our approach to studying complex biological systems. We recently described a droplet based approach, the ChromiumTM single cell solution, that enables 3’ mRNA digital counting of up to millions of single cells. High efficiency cell capture coupled with a low doublet rate (<1%) facilitates the profiling of rare cell populations. We have also developed an open source analysis pipeline, Cell RangerTM, and an interactive analysis and visualization tool, the LoupeTM Cell Browser.
We demonstrate the power of this system to profile single cell gene expression in ~1.3 million brain cells from cortex, hippocampus and ventricular zones of 2 E18 mice. Major neuronal and non-neuronal cell types from different brain layers were identified and rare interneurons were readily detected without FACS enrichment.
Recent system additions allow for the characterization of T-cell receptor alpha and beta chain pairing in 10s of 1000s of T cells. This application enables one to determine which functional subsets of T cells have undergone clonal expansion and is invaluable in areas of infectious diseases and immuno-oncology.
We expanded the use of this technology to the detection of biomarkers to monitor disease state. We illustrate the power of this system to track the progression of diseases through comparative analysis of AML patients undergoing hematopoietic stem cell transplant. Combining single cell transcriptional profiling with genotype analysis at the single cell level, we compared the host and donor cell population changes before and after the transplant and accurately inferred the relapse state of AML patients after transplant.
Credits: None available.
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