Single-cell RNA-sequencing of Platynereis dumerilii
ABSTRACT: For unbiased, whole-organism wide cell type profiling, we randomly sampled cells from dissociated Platynereis larvae. To generate the single-cell mRNA-sequencing data, P. dumerilii larvae were dissociated, followed by cell capture, cDNA synthesis and amplification on the C1 Single-Cell Auto Prep IFCs for 5-10 um or 10-17 um cells (Fluidigm). Sequencing libraries were produced using Nexera XT DNA kit (Illumina). In total, we sequenced 596 samples, of which 373 correspond to single, alive cells that passed the quality check criteria. Part of this dataset was previously published (ArrayExpress accession number E-MTAB-2865). Here, we publish additional 383 sequenced cells.
INSTRUMENT(S): C1 Single-Cell Auto Prep System, Fluidigm, Illumina HiSeq 2000
Project description:Understanding cell type identity in complex tissues or organisms requires integration of each cell's expression profile with its spatial location within the tissue under study. We developed a high-throughput method that combines in vitro single-cell RNA-sequencing with a gene expression atlas to map single cells back to their location within the tissue of interest. We used the developing brain of a marine annelid, Platynereis dumerilii that is an important model system for studying bilaterian brain evolution, to benchmark our approach. To generate the single-cell mRNA-sequencing data, P. dumerilii larval brains were dissociated, followed by cell capture, cDNA synthesis and amplification on the C1 Single-Cell Auto Prep IFC for 10-17 um cells (Fluidigm). Sequencing libraries were produced using Nexera XT DNA kit (Illumina). In total, we sequenced 213 samples, of which 129 correspond to single, alive cells (as judged by visual inspection of the captured cells) with the remainder consisting of a variety of single dead cells (n=18), wells containing extracellular matrix contaminants (n=8) or multiple cells (n=17), as well as a negative controls where no cells were observed (n=41). For this dataset, we achieved ~90% success rate for the spatial mapping of the single-cell RNA-seq data to P. dumerilii brain atlas. NOTE: 72 additional samples were added on 13th December 2014.
Project description:Haematopoietic stem cells can differentiate into all blood cell types. In this process, cells become progressively restricted to a single cell type. The order in which differentiating cells loose lineage potential, and the prospective isolation of cells with a defined potential remains a long-standing question. We performed gene expression analysis of haematopoietic cells from Gata1-EGFP reporter mice, leading to a model for hematopoiesis where the initial lineage decision consists of a seperation of erythroid/megakaryocyte/mast cell/eosinophil potential from lymphopoietic/monocyte/neutrophil potential Find unbiased heterogeneity in the preGM hematopoietic progenitor population
Project description:Neurogenesis in the adult hippocampus contributes to information processing critical for cognition, adaptation, learning and memory, and is implicated in numerous neurological disorders. New neurons are continuously produced from neural stem cells via a well-controlled developmental process. The immature neuron stage defined by doublecortin (DCX) expression is the most sensitive to regulation by extrinsic factors. However, little is known about the dynamic biology within this critical interval that drives maturation and confers susceptibility to regulating signals. This study aims to test the hypothesis that DCX-expressing immature neurons in adult mouse hippocampus progress through developmental stages via activity of specific transcriptional networks. Using single-cell RNA-seq combined with a novel integrative bioinformatics approach, we discovered that individual immature neuron can be classified into distinct developmental subgroups based on characteristic gene expression profiles and subgroup-specific markers. Comparisons between immature and more mature subgroups revealed novel pathways involved in neuronal maturation. Genes enriched in more immature cells shared significant overlap with genes implicated in neurodegenerative diseases, while genes positively associated with neuronal maturation were enriched for autism-related gene sets. Our study thus discovers molecular signatures of individual adult-born immature neurons and unveils potential novel targets for therapeutic approaches to treat neurodevelopmental and neurological diseases. mRNA sequencing and expression estimation in 64 individual DCX-dsRed+ cells isolated from transgenic DCX-dsRed mice by FACS sorting
Project description:Single cell whole transcriptome analysis of young (2-3 months) and old (20-25 months) mouse HSCs, defined as Lin–Sca-1+c-Kit+150+CD48– . Differential gene expression analysis of young and old mouse HSCs (Lin–Sca-1+c-Kit+150+CD48– )
Project description:We explored how aging impacts transcriptional dynamics using single-cell RNA-sequencing to profile hundreds of CD4+ T cells from young and old mice from two divergent species. In young animals, immunological challenge drives a conserved transcriptomic switch from highly variable to tightly regulated gene expression, characterized by a strong up-regulation of a core activation program, coupled with a decrease in cell-to-cell variability. Aging significantly perturbed the activation of this core program, and increased expression heterogeneity across the population of cells in both species.
Project description:Oscillatory gene expression is fundamental to mammalian development, but technologies to monitor expression oscillations are limited. We have developed a statistical approach called Oscope to identify and characterize the transcriptional dynamics of oscillating genes in single-cell RNA-seq data from an unsynchronized cell population. Applications to a number of data sets, include a single-cell RNA-seq data set of human embroyonic stem cells (hESCs), demonstrate advantages of the approach and also identify a potential artifact in the Fluidigm C1 platform. Total 213 H1 single cells and 247 H1-Fucci single cells were sequenced. The 213 H1 cells were used to evaluate Oscope in identifying oscillatory genes. The H1-Fucci cells were used to confirm the cell cycle gene cluster identified by Oscope in the H1 hESCs.
Project description:In this study we performed single-cell transcriptome analysis of THP-1 macrophages, stimulated with high levels of free fatty acids (palmitate, PAL) typical for obese adipose tissue microenvironment or lipopolysaccharide (LPS), representing a classical stimulus activating innate immune response. Analysing full transcriptomes of individual cells, we were able to distinguish 3 macrophage transcriptional states and decipher gene regulatory pathways underlying macrophage state identity in both stimulations.
Project description:We generated the individual transcriptomes of 96 liver cells using the Fluidigm C1 platform. In brief, a suspension of cells was prepared from the liver of a 14-week old B6CastF1 (C57Bl/6J mother x CAST/Ei father) female mouse and loaded onto a 10-17 m C1 Single-Cell Auto Prep IFC (Fluidigm), and cell capture was performed according to the manufacturers instructions.
Project description:We analysed a well-established fraction of mouse subcutaneous adipose-derived SVF cells that is generally considered to harbour adipose stem and progenitor cells (ASPCs). To study the molecular characteristics and the subpopulation structure of ASPCs, we performed three replicate scRNA-seq experiments. We collected Lin- (CD31- CD45- TER119-) CD29+ CD34+ SCA1+ cells from the mouse subcutaneous SVF of transgenic mice, in which red fluorescent protein (RFP) is induced in Dlk1-expressing cells upon feeding with tamoxifen. While CD29, CD34, and SCA1 are generally expected to enrich for stem cells, DLK1 has previously been suggested to specifically mark pre-adipocytes. The submission includes the raw data of all sequenced cells, while we only processed 208 high quality single cells. RFP status is indicated in the processed files.
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