The effect of anti-HER2/CD3 TDB on transcription in human PBMCs (single-cell)
ABSTRACT: Single-cell RNA-seq libraries were generated from human PBMCs that were incubated with anti-HER2/CD3 TDB in the presence of KPL-4 cells. This dataset only contains the metadata and processed data. Raw data can be accessed via the EGA accession EGAS00001003734
Project description:Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. Using this technique, we analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility and low noise of this high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships. A total of 8 single cell data sets are submitted: 3 for mouse embryonic stem (ES) cells (1 biological replicate, 2 technical replicates); 3 samples following LIF withdrawal (days 2,4, 7); one pure RNA data set (from human lymphoblast K562 cells); and one sample of single K562 cells.
Project description:Differentiation into diverse cell lineages requires orchestration of gene regulatory networks guiding cell fate choices. Genetic factors acting through changes in transcriptional levels can contribute to cardiovascular disease risk by impacting early stages of development and have cell type-specific effects. We set out to characterize lineage trajectory progression of subpopulations and identify potential disease-related genes by examining their expression changes in single cells during early stages of cardiac lineage specification. Using 43,168 single-cell transcriptomes, we developed novel classification and trajectory analysis methods to dissect cellular composition and gene networks across five discrete time points underlying lineage derivation of mesoderm, definitive endoderm, vascular endothelium, cardiac precursors, and definitive cell types that comprise cardiomyocytes and a previously unrecognized cardiac outflow tract population.
Project description:With the advent of cancer immunotherapy, intense investigation has been focused on tumor-infiltrating immune cells. With only a fraction of patients responding to these new therapies, a better understanding of all elements of the tumor microenvironment (TME) that may influence therapeutic outcome is needed. Stromal elements of the TME, chiefly fibroblasts, have emerged as potential contributors to tumor progression and most recently resistance to immunotherapy, but their precise composition and clinical relevance remain incompletely understood. Here we use single-cell transcriptomics to chart the fibroblastic landscape during pancreatic ductal adenocarcinoma (PDAC) progression in animal models, identifying two healthy tissue fibroblast subsets that co-evolve along individual trajectories into four subsets of carcinoma-associated fibroblasts (CAFs).
Project description:Single cell transcriptomics has emerged as a powerful approach to dissecting phenotypic heterogeneity in complex, unsynchronized cellular populations. However, many important biological questions demand quantitative analysis of large numbers of individual cells. Hence, new tools are urgently needed for efficient, inexpensive, and parallel manipulation of RNA from individual cells. We report a simple microfluidic platform for trapping single cell lysates in sealed, picoliter microwells capable of “printing” RNA on glass or capturing RNA on polymer beads. To demonstrate the utility of our system for single cell transcriptomics, we developed a highly scalable technology for genome-wide, single cell RNA-Seq. The current implementation of our device is pipette-operated, profiles hundreds of individual cells in parallel with library preparation costs of ~$0.10-$0.20/cell, and includes five lanes for simultaneous experiments. We anticipate that this system will ultimately serve as a general platform for large-scale single cell transcriptomics, compatible with both imaging and sequencing readouts.!Series_type = Expression profiling by high throughput sequencing A microfluidic device that pairs sequence-barcoded mRNA capture beads with individual cells was used to barcode cDNA from individual cells which was then pre-amplified by in vitro transcription in a pool and converted into an Illumina RNA-Seq library. Libraries were generated from ~600 individual cells in parallel and extensive analysis was done on 396 cells from the U87 and MCF10a cell lines and from ~500 individual cells with extensive analysis on 247 cells from the U87 and WI-38 cell lines. Sequencing was done on the 3'-end of the transcript molecules. The first read contains cell-identifying barcodes that were present on the capture bead and the second read contains a unique molecular identifier (UMI) barcode, a lane-identifying barcode, and then the sequence of the transcript.
Project description:We used the resolving power of single-cell transcriptional profiling to molecularly characterize the mouse adipose stem and progenitor cell-enriched, subcutaneous adipose stromal vascular fraction. We molecularly assessed CD45- CD31- SVF cells using the 10x Genomics Chromium (10x) platform.
Project description:This is a droplet-based single cell transcriptome data set from 13 human fetal livers (6-18 PCW) and 8 skin and kidney samples (6-12 PCW). It includes 199,642 cells with a mean detected gene number of 3000.
Project description:Mucosal-Associated Invariant T cells have a unique specificity for the microbial metabolite 5-OP-RU presented by the non-classical presentation molecule MR1. Upon activation, they release cytotoxic mediators and engage an antimicrobial activity. As a subset of T lymphocytes, MAIT development occurs in the thymus where they acquire their effector phenotype under the control of the key transcription factor ZBTB16. This particular maturation process is in contrast with conventional T cells that egress the thymus with a naive phenotype before populating the secondary lymphoid organs, and the molecular events driving the MAIT lineage decision are poorly known. In the present work, we evaluated the transcriptional events and the role of the slam-SAP pathway on the lineage decision of MR1-restricted T cells by single cell RNAseq. MAIT cells undergoing positive selection were FACS-sorted with a MR1:5-OP-RU labeled tetramer, from thymus of wild-type and sapKO mice. Their transcriptomes were captured using a 10x chromium system.
Project description:Test data used for the evaluation of ESAT performance and results files for data from 3' and 5' end-sequencing RNA-Seq protocols and droplet-based single-cell RNA-Seq. Quantification and analysis of Tophat-aligned (v2.0.9) samples from mouse bone-marrow derived dendritic cells (mBMDC) timecourse (0, 2, 4 and 6 hours) post LPS stimulation and non-diabetic BBDR rat pancreatic islet cells. Since end-sequencing (3' or 5') is used for all samples, alignments are only required for the R2 (sequence-containing) read.
Project description:Barcode swapping results in the mislabeling of sequencing reads between multiplexed samples on the new patterned flow cell Illumina sequencing machines. This may compromise the validity of numerous genomic assays, especially for single-cell studies where many samples are routinely multiplexed together. The severity and consequences of barcode swapping for single-cell transcriptomic studies remain poorly understood. We have used two statistical approaches to robustly quantify the fraction of swapped reads in each of two plate-based single-cell RNA sequencing datasets. We found that approximately 2.5% of reads were mislabeled between samples on the HiSeq 4000 machine, which is lower than previous reports. We observed no correlation between the swapped fraction of reads and the concentration of free barcode across plates. Further- more, we have demonstrated that barcode swapping may generate complex but artefactual cell libraries in droplet-based single-cell RNA sequencing studies. To eliminate these artefacts, we have developed an algorithm to exclude individual molecules that have swapped between samples in 10X Genomics experiments, exploiting the combinatorial complexity present in the data. This permits the continued use of cutting-edge sequencing machines for droplet-based experiments while avoiding the confounding effects of barcode swapping. This data repository contains the sequencing files associated with the droplet based scRNA-seq dataset in Griffiths et al. (2018). The data presented here should purely used for technical analysis, the biological motivation is nonetheless briefly described in the following: The mammary gland is a unique organ as it undergoes most of its development during puberty and adulthood. Characterising the hierarchy of the various mammary epithelial cells and how they are regulated in response to gestation, lactation and involution is important for understanding how breast cancer develops. Recent studies have used numerous markers to enrich, isolate and characterise the different epithelial cell compartments within the adult mammary gland. However, in all of these studies only a handful of markers were used to define and trace cell populations. Therefore, there is a need for an unbiased and comprehensive description of mammary epithelial cells within the gland at different developmental stages. To this end we used single cell RNA sequencing (scRNAseq) to determine the gene expression profile of individual mammary epithelial cells across four adult developmental stages; nulliparous, mid gestation, lactation and post weaning (full natural involution).