Project description:KIR+ CD8 T cells (Live CD3+CD56-TCRab+CD8+KIR+ cells) were sorted from the blood of healthy subjects (N=10) and patients with MS (N=2), SLE (N=6), or CeD (N=5) and subjected to single-cell RNA-seq analysis by Smart-seq2. In parallel, we also analyzed their T-cell receptor (TCR) α and β sequences. Unsupervised clustering of these KIR+CD8+ T cells by Seurat identified six clusters, with Clusters 1 to 3 mostly containing expanded KIR+CD8+ T cells (≥2 cells expressing same TCR) and Clusters 5 and 6 consisting of unexpanded cells expressing unique TCRs. There are common features shared by KIR+CD8+ T cells from healthy subjects and different diseases, yet there is also heterogeneity (i.e., upregulated type I IFN signaling and glycolysis in Clusters 2 and 3) associated with different diseases or treatments.
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we perform a replicate of Smart-Seq2 method on Fluidigm C1 system and generate single-cell libraries using Nextera XT kit
Project description:Four Kcng4-cre;stop-YFP mouse retinas from two mice were dissected, dissociated and FACS sorted, and single cell RNA-seq libraries were generated for 384 single cells using Smart-seq2. Aligned bam files are generated for 383 samples as one failed to align. Four mouse retinas (labeled 1la, 1Ra, and 2la, 2Ra respective from the two mice) were used, and 96 single cells from each were processed using Smart-seq2. Total 384 cells Smart-seq2 analysis of P17 FACS sorted retinal cells from the Kcng4-cre;stop-YFP mice (Kcng4tm1.1(cre)Jrs mice [Duan et al., Cell 158, 793-807, 2015] crossed to the cre-dependent reporter Thy1-stop-YFP Line#1 [Buffelli et al., Nature 424, 430-434, 2003])
Project description:In this study, we assess technical differences between commonly used single-cell RNA-Sequencing (scRNA-Seq) methods. We perform scRNA-seq on a homogenous population of mouse embryonic stem cells along with two kinds of control spike-in molecules to assess sensitivity and accuracy of these specific methods. In this dataset, we assess the RNA-degradation and decay rates by subjecting both spike-in molecules to range of repeated freezing and thawing (freeze-thaw) cycles. We manually add spike-in molecules across a 96-well plate (containing cells and reagents), perform Smart-Seq2 method manually and generate single-cell libraries using Nextera XT kit
Project description:Improved Smart-Seq for sensitive full-length transcriptome profiling in single cells. Cells of four different origins were profiled using commercial SMARTer and compared to five variants of an improved protocol (Smart-Seq2).
Project description:We studied the heterogeneity among human KIR/NKG2A+CD8+ T cells. First, we found that KIRs and NKG2A are expressed on human CD8+ T cells in a mutually exclusive manner. Therefore, we compared KIR+CD8+ and NKG2A+CD8+ T cells in regards to TCR overlap and transcriptomic profiles and demonstrated that KIR+CD8+ and NKG2A+CD8+ T cells are distinct innate-like populations.