Project description:The human brain has changed dramatically from other primate species, but the genetic and developmental mechanisms behind the differences remains unclear. Here we used single cell RNA sequencing based on 10X technology to explore temporal transcriptomic dynamics and cellular heterogeneity in cerebral organoids derived from human and non-human primates chimpanzee and rhesus macaque stem cells. Using cerebral organoids as a proxy of early brain development, we detect a delayed pace of human brain development relative to the other two primate species. Additional human-specific gene expression patterns resolved to different cell states through progenitors to neurons are also found. Our data provide a transcriptomic cell atlas of primate early brain development, and illustrate features that are unique to humans.
Project description:We used micro-dissection with FACS sorting techniques to isolate single cells from the metanephric mesenchyme of the E11.5 developing kidney. A subset of these single cell populations is analysed individually via Fluidigm single cell analysis. This analysis will determine the transcriptional profile of each cell type, identify compartment specific transcripts, compartment specific transcript isoforms and cell-type specific long-noncoding RNAs. In addition the unbiased nature of RNA-SEQ will potentially identify novel transcripts that have not been annotated in the database. Kidneys are harvested from Tg(Crym-EGFP)GF82Gsat mice. Single cells are extracted from E11.5 metanephric mesenchyme using manual micro-dissection techniques. A subset of these cells is analyzed individually via Fluidigm single cell analysis. The long term goal is to generate a transcriptional atlas of the developing kidney.
Project description:Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and diseasee. LncRNAs exhibit cell type- and tissue-specific expression, but little is known about the expression and function of lncRNAs in the developing human brain. Here, we deeply profiled lncRNAs from polyadenylated and total RNA obtained from human neocortex at different stages of development and integrated this resource to analyze the transcriptomes of single cells. While lncRNAs were generally detected at low levels in whole tissues, single cell transcriptomics revealed that many lncRNAs are abundantly expressed in individual cells and are cell type-specific. Furthermore, we used CRISRPi to show that LOC646329, a lncRNA enriched in radial glia but detected at low abundance in tissues, regulates cell proliferation. The discrete and abundant expression of lncRNAs among individual cells has important implications for both their biological function and utility for distinguishing neural cell types. 16 Bulk Tissue Samples from GW13-23; 226 Single Cells from GW19.5-23.5 ------------------ bulk_tpm.polya.txt: bulk RNA-seq expression; using polyA full reference scell_ncounts.genes.thresh.txt: single cell RNA-seq expression; using polyA stringent reference; includes 50 GW16, GW21, GW21p3 cells previously analyzed (Pollen et. al. 2014) polya_RNA_stringent_ref.gtf: bulk RNA-seq polyA stringent transcriptome reference polya_RNA_full_ref.gtf: bulk RNA-seq polyA full transcriptome reference total_RNA_stringent_ref.gtf: bulk RNA-seq total stringent transcriptome reference total_RNA_full_ref.gtf: bulk RNA-seq total full transcriptome reference GW13_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal
Project description:Variation in gene expression is an important feature of mouse embryonic stem cells (ESCs). However, the mechanisms responsible for global gene expression variation in ESCs are not fully understood. We performed single cell mRNA-seq analysis of mouse ESCs and uncovered significant heterogeneity in ESCs cultured in serum. Using novel computational approaches we define highly variable gene clusters; and reveal their distinct epigenetic characteristics. We show that bivalent genes are prone to expression variation. At the same time, we identify an ESC priming pathway that initiates the exit from the naïve ESC state. Finally, we provide evidence that a large proportion of intracellular network variability is due to the extracellular culture environment. Serum free culture reduces cellular heterogeneity and transcriptome variation in ESCs. Single cell mRNA-seq analysis of ES cells cultured with serum
Project description:In this study, we aimed to study the gene expression patterns at single cell level across the different cell cycle stages in mESC. We performed single cell RNA-Seq experiment on mESC that were stained with Hoechst 33342 and Flow cytometry sorted for G1, S and G2M stages of cell cycle. Single cell RNA-Seq was performed using Fluidigm C1 system and libraries were generated using Nextera XT (Illumina) kit.
Project description:Single-cell expression profiling by RNA-Seq promises to exploit cell-to-cell variation in gene expression to reveal regulatory circuitry governing cell differentiation and other biological processes. Here, we describe Monocle, a novel unsupervised algorithm for ordering cells by progress through differentiation that dramatically increases temporal resolution of expression measurements. This reordering unmasks switch-like changes in expression of key regulatory factors, reveals sequentially organized waves of gene regulation, and exposes regulators of cell differentiation. A functional screen confirms that a number of these regulators dramatically alter the efficiency of myoblast differentiation, demonstrating that single-cell expression analysis with Monocle can uncover new regulators even in well-studied systems. We selected primary human myoblasts as a model system of cell differentiation to investigate whether ordering cells by progress revealed new regulators of the process. We sequenced RNA-Seq libraries from each of several hundred cells taken over a time-course of serum-induced differentiation. Please note that this dataset is a single-cell RNA-Seq data set, and each cell comes from a capture plate. Thus, each well of the plate was scored and flagged with several QC criteria prior to library construction, which are provided as sample characteristics; CONTROL indicates that this library is a off-chip tube control library constructed from RNA of approximately 250 cells and 'DEBRIS' indicates that the well contained visible debris (and may or may not include a cell). Libraries marked DEBRIS thus cannot be confirmed to come from a single cell.
Project description: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.
Project description:We used micro-dissection and trypsinization techniques to isolate single cells from the E12.5 total kidney. A subset of these single cell populations is analysed individually via Fluidigm single cell analysis. This analysis will determine the transcriptional profile of each cell type, identify compartment specific transcripts, compartment specific transcript isoforms and cell-type specific long-noncoding RNAs. In addition the unbiased nature of RNA-SEQ will potentially identify novel transcripts that have not been annotated in the database. E12.5 kidneys are dissected; the kidneys are made into a single cell suspension via trypsinization. A subset of these cells is analysed individually via Fluidigm C1 single cell analysis. The long term goal is to generate a single cell resolution transcriptional atlas of the developing kidney.
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:This study set out to assay the (polyA+) transcriptomes of single mature (MHCII high) mouse medullary thymic epithelial cells (mTEC). Following isolation by FACs, the transcriptomes of single mature mTEC was assayed using the Fluidigm C1 microfluidics platform and Illumina RNA-seq.