Project description:We successfully develop a two-in-one approach to generate non-viral genome specific targeted CAR T cells through CRISPR/Cas9. In the adoptive therapy for relapsed/refractory aggressive B-cell non-Hodgkin lymphoma, we observed durable responses without serious adverse events and complete remission in patients treated with these PD1 knockout CAR T cells. Here we applied single-cell sequencing to analyze the characteristcis of T cells prepared by different methods.
Project description:We performed miRNA microarray profiling on samples prepared from two different cell lines by three widely-used total RNA sample prep methods.
Project description:We successfully develop a two-in-one approach to generate non-viral genome specific targeted CAR T cells through CRISPR/Cas9. In the adoptive therapy for relapsed/refractory aggressive B-cell non-Hodgkin lymphoma, we observed durable responses without serious adverse events and complete remission in patients treated with these PD1 knockout CAR T cells. Here we applied single-cell sequencing to analyze the characteristcis of T cells harvested 4 hours after preparation by different methods.
Project description:A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples – cell lines, peripheral blood mononuclear cells and brain tissue – generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.
Project description:Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods and provides a framework for benchmarking further improvements of scRNA-seq protocols.