HEK293 cells 100 cell RNAseq profiling on Illumina NextSeq 500
ABSTRACT: Three libraries from 100 HEK293 cells each were prepared using a Smartseq based custom library preparation approach with unique molecular identifiers. Libraries were sequenced on a Illumina NextSeq 500 HEK293 cell (100 cells) 5' selective RNAseq profiling, N4H4 unique molecular identifiers, 3 replicates
Project description:Five libraries from 100 HEK293 cells each were prepared using a Smartseq based custom library preparation approach with unique molecular identifiers. One batch of 2 replicates (A) and one batch of 3 replicates (B) were prepared from different cell cultures. Libraries were sequenced on an Ion Proton HEK293 cell (100 cells) 5' selective RNAseq profiling, N4H4 unique molecular identifiers, 2 replicates (A) and 3 replicates (B)
Project description:Purpose: We applied cDNA molecule counting using unique molecular identifiers combined with high-throughput sequencing to study the transcriptome of individual mouse embryonic stem cells, with spike-in controls to monitor technical performance. We further examined transcriptional noise in the embryonic stem cells. One 96-well plate of single-stranded cDNA libraries generated from 96 single R1 mouse embryonic stem cells sequenced on two lanes, and one 96-well plate of the same libraries further amplified by 9 PCR cycles sequenced on one lane.
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:The molecular mechanism regulating phasic corticotropin-releasing hormone (CRH) release from parvocellular neurons (PVN) remains poorly understood. Here, we find a cohort of parvocellular cells interspersed with magnocellular PVN neurons expressing secretagogin. Single-cell transcriptome analysis combined with protein interactome profiling identifies secretagogin neurons as a distinct CRH-releasing neuron population reliant on secretagogin’s Ca2+ sensor properties and protein interactions with the vesicular traffic and exocytosis release machineries to liberate this key hypothalamic releasing hormone. single cells from the PVN region juvenile (21-28 days) mice were dissected and subject to whole transcriptome analysis
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:A single hematopoietic stem cell can give rise to all blood cells with remarkable fidelity. Here, we define the chromatin accessibility and transcriptional landscape controlling this process in thirteen primary cell types that traverse the hematopoietic hierarchy. Exploiting the finding that enhancer landscapes better reflect cell identity than mRNA levels, we enable "enhancer cytometry" for accurate enumeration of pure cell types from complex populations. We further reveal the lineage ontogeny of genetic elements linked to diverse human diseases. In acute myeloid leukemia, chromatin accessibility reveals distinctive regulatory evolution in pre-leukemic HSCs (pHSCs), leukemia stem cells, and leukemic blasts. These leukemic cells demonstrate unique lineage infidelity, confirmed by single cell regulomes. We further show that pHSCs have a competitive advantage that is conferred by reduced chromatin accessibility at HOXA9 targets and is associated with adverse patient outcomes. Thus, regulome dynamics can provide diverse insights into human hematopoietic development and disease. Single-cell ATAC-seq of LMPPs, Monocytes, LSCs and Luekemic blast cells.
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:Cell-to-cell variation is a universal feature of life that impacts a wide range of biological phenomena, from developmental plasticity to tumor heterogeneity. While recent advances have improved our ability to document cellular phenotypic variation the fundamental mechanisms that generate variability from identical DNA sequences remain elusive. Here we reveal the landscape and principles of cellular DNA regulatory variation by developing a robust method for mapping the accessible genome of individual cells via assay of transposase accessible chromatin sequencing (ATAC-seq). Single-cell ATAC-seq (scATAC-seq) maps from hundreds of single-cells in aggregate closely resemble accessibility profiles from tens of millions of cells and provides insights into cell-to-cell variation. Accessibility variance is systematically associated with specific trans-factors and cis-elements, and we discover combinations of trans-factors associated with either induction or suppression of cell-to-cell variability. We further identify sets of trans-factors associated with cell-type specific accessibility variance across 6 cell types. Targeted perturbations of cell cycle or transcription factor signaling evoke stimulus-specific changes in this observed variability. The pattern of accessibility variation in cis across the genome recapitulates chromosome topological domains de novo, linking single-cell accessibility variation to three-dimensional genome organization. All together, single-cell analysis of DNA accessibility provides new insight into cellular variation of the “regulome.” Profiles of single cell epigenomes, assayed using scATAC-seq, across 8 cell types and 4 targeted cell manipulations. The complete data set contains a total of 1,632 assayed wells.