ABSTRACT: Murine tumour infiltrating lymphocytes (CD8+ T cells) were index-sorted based on expression of CD8a and CD3e. Subsequently cDNA was generated from these samples using the Smart-seq2 protocol. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
Project description:Systematic interrogation of tumor-infiltrating lymphocytes is key to the development of immunotherapies and the prediction of their clinical responses in cancers. Here, we perform deep single-cell RNA sequencing on single T cells isolated from peripheral blood, tumor and adjacent normal tissues from hepatocellular carcinoma patients. Overall design: T cells from HCC patients were sorted, profiled by Smart-seq2 and Tang2010 protocol (for patient P1202, the suffix "t" indicate cells from this patient were processed using Tang2010 protocol; cells from other patients were processed using SMART-Seq2) and sequenced on Illumina HiSeq2500 and HiSeq4000. Based on FACS analysis, single cells of different subtypes, including CD8+ T cells (CD3+ and CD8+), T helper cells (CD3+, CD4+ and CD25-), and regulatory T cells (CD3+, CD4+ and CD25high) were sorted to perform RNA sequencing. The categories （"sampleType" column in the SAMPLES section） contain PTC(CD8+ T cells from peripheral blood), NTC(CD8+ T cells from adjacent normal liver tissues) ,TTC (CD8+ T cells from tumor), PTH(CD3+, CD4+ and CD25- T cells from peripheral blood), NTH(CD3+, CD4+ and CD25- T cells from adjacent normal liver tissues), TTH(CD3+, CD4+ and CD25- T cells from tumor), PTR(CD3+, CD4+ and CD25high T cells from peripheral blood), NTR(CD3+, CD4+ and CD25high T cells from adjacent normal liver tissues), TTR(CD3+, CD4+ and CD25high T cells from tumor), JTH(CD3+, CD4+ and CD25- T cells from joint area between the tumor and the adjacent normal tissue),JTR(CD3+, CD4+ and CD25high T cells from joint area between the tumor and the adjacent normal tissue), JTC(CD3+CD8+ T cells from joint area between the tumor and the adjacent normal tissue) Raw data access provided at: European Genome-phenome Archive (EGA) under accession EGAS00001002072
Project description:Study was designed to compare the transcriptome of Th2 cells in presence or absence of 4m8c. Murine splenic naive T helper cells were activated in presence of anti-CD3e, anti-CD28 antibodies and IL4 and IL2 cytokines for 3 days. Cells were then transferred from activation plate to resting for 3 days. Cells were reactivated by anti-CD3e antibidy. Similar culture were maintained in presence of 15uM 4m8c.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/
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:T lymphocytes are essential contributors to the adaptive immune system and consist of multiple lineages that serve various effector and regulatory roles. As such, precise control of gene expression is essential to the proper development and function of these cells. Previously, we identified Snai2 and Snai3 as being essential regulators of immune tolerance partly due to the impaired function of CD4+ regulatory T cells in Snai2/3 conditional double knockout mice. Here we extend those previous findings using a bone marrow transplantation model to provide an environmentally unbiased view of the molecular changes imparted onto various T lymphocyte populations once Snai2 and Snai3 are deleted. The data presented here demonstrate that Snai2 and Snai3 transcriptionally regulate the cellular fitness and functionality of not only CD4+ regulatory T cells but effector CD8α+ and CD4+ conventional T cells as well. This is achieved through the modulation of gene sets unique to each cell type and includes transcriptional targets relevant to the survival and function of each T cell lineage. As such, Snai2 and Snai3 are essential regulators of T cell immunobiology. Overall design: GFP- CD3e+ CD8a+ CD4-, GFP- CD3e+ CD8a- CD4+ CD25- and GFP- CD3e+ CD8a- CD4+ CD25+ T cells were isolated from spleens of UBC-GFP mice transplanted with WT or cDKO lineage-depleted donor bone marrow following lethal irradiation of recipient mice. RNA-seq was performed on 3-4 biological replicates from each genotype for all T cell populations analyzed.
Project description:Many library preparation methods are available for gene expression quantification. Here, we sequenced and analysed Universal Human Reference RNA (UHRR) prepared using Smart-Seq2, TruSeq (public data) and a protocol using unique molecular identifiers (UMIs) that all include the ERCC spike-in mRNAs to investigate the effects of amplification bias on expression quantification. UHRR 10 and 12 replicates for Smart-seq2 and UMI-seq library preparation methods, respectively.
Project description:SMART-seq2 was performed on single cells isolated from visually staged zebrafish embryos. Overall design: Samples were all sequenced in one batch. Some were generated with a 5' UMI-tagged method, and others are full-length SMART-seq2.
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: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. Overall design: J1 mESC in two replicates per library preparation method.
Project description:Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with three-fold higher sensitivity, lower costs, and less hands-on time. We also implemented CEL-Seq2 on Fluidigm’s C1 system, thereby providing its first single-cell, on-chip barcoding method, and detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use Overall design: Single mouse dendritic and fibroblast cells were analysed for comaprison of CEL-Seq2 to CEL-Seq and Smart-Seq on C1. C. elegans totat RNA was used to calibrate and optimze the new CEL-Seq2 protocol.
Project description:Three Vsx2-GFP mouse retinas were dissected, dissociated and FACS sorted, and single cell RNA-seq libraries were generated for 288 single cells and 3 bulk libraries using Smart-seq2 (~10,000 cells each) Overall design: Three mouse retinas (labeled 1a, 2a, 2b) were used, and 96 single cells from each were processed using Smart-seq2. A bulk RNA-seq control of 10,000 cells from each retina was also prepared