Gene expression data of allodiploid embryonic stem cells.
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
SUBMITTER: Xiaolong Cui
PROVIDER: E-GEOD-74533 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:RNA-Seq was used to assess the gene expression profiles of 4 allodiploid embryonic stem cell lines and 4 control embryonic stem cell lines. Moreover, single-cell RNA-Seq was used to quantify the transcriptomes of 87 allodiploid single cells.
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Project description:We sequenced the mRNAs of embryonic stem cells (ESCs) cultured in different conditions. The two lines M (male) and F (female) used in this study were derived from E4 blastocysts of the same cross between a C57BL/6J (Mus musculus domesticus) and CAST/EiJ (Mus castaneus) male. mESCs were cultured in 2i and LIF as the ground state condition or in serum and LIF as the conventional condition. Epistem cell lines were also generated from the two lines by culturing them with Activin A and FGF2. In order to study more advanced development, we differentiated the two mESC lines through embryonic body formation to postmitotic motor neurons using retinoic acid and the smoothened agonist SAG. This differentiation process also results in the derivation of several types of interneurons. We picked single cells from all different conditions and generated sequencing libraries using the Smart-seq2 and Tn5 protocol. For simplicity, we designate the different condition as ES2i, ES, Epi and Neuron from hereon. We also obtained preimplantation inner cell mass and epiblast cells from E3.5 ICM (inner cell mass) and E4.5 blastocysts of the crossbred mice (male CAST/EiJ Ã female C57BL/6J) as well as postimplantation epiblast cells from E5.5 embryos of C57BL/6J mice Examination of gene expression profile in individual male and female embryonic stem cell lines along developmental progression
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Project description:Current gene-expression databases for the haematopoietic system provide information on gene expression profiles present in bulk populations. Although informative, these studies lack the resolution that can be gained at a single-cell level. In particular, population-average data assumes homogeneity within the population and may as such obscure the ability to detect the heterogeneity of decision-making processes in individual cells. Here we report 1656 single cell transcriptomes analysed by single-cell RNA sequencing. Cells were FACS sorted on broad gates encompassing haematopoietic stem and progenitor populations (HSPCs), with index sorting data collected to permit retrospective identification of populations by surface marker expression. Our dataset thus represents the gene expression landscape of HSPCs at single-cell resolution, capturing the heterogeneity in and between cell populations. Pseudotime analysis visualized haematopoietic stem (HSC) to progenitor transitions, identified HSC as well as lineage-specific transcriptional programs, and also highlighted putative lineage branching points. To provide access to the wider scientific community, a user-friendly website was developed with intuitive search and display functionality. Single cell RNA sequencing of haematopoeitic stem and progenitor cells
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Project description:We used microfluidic single cell RNA-seq on 198 individual mouse lung epithelial cells at 4 different stages throughout development to measure the transcriptional states which define the developmental and cellular hierarchy of the distal mouse lung epithelium. We classified 80 cells comprising the distal lung epithelium at E18.5 into distinct populations using an unbiased genome-wide approach that did not require a priori knowledge of the underlying cell types or prior purification of cell types. This M-bM-^@M-^\reverse tissue engineeringM-bM-^@M-^] approach confirmed the basic outlines of the conventional model of cell type diversity in the distal lung and led to the discovery of a large number of novel transcriptional regulators and cell type markers that discriminate between the different populations. Moreover, we reconstructed the steps during maturation of bipotential progenitors into both alveolar lineages based on the presence of undifferentiated, differentiated as well as differentiation intermediate cells at the single time point E18.5. Finally, we followed Sftpc-positive cells throughout their lifecycle (E14.5, E16.5, E18.5, adult) and identified 7 gene sets that differentiate between the multipotential, bipotential, mature, as well as intermediate states of the AT2 lineage. 198 single-cell transcriptomes from mouse lung epithelium were analyzed in total, two 200-cell bulk control samples as well as one no-cell control; All single cell and control samples contain 92 external RNA spike-ins; For time point E18.5, three individual experiments were performed using 3 different pregnant mice (3 biological replicates): Replicate 1 (pooled sibling lungs) yielded 20 single cell transcriptomes, replicate 2 (one single embryonic lung) yielded 34 single cell transcriptomes and replicate 3 (pooled siibling lungs) yielded 26 single cell transcriptomes; In addition, a 200-cell bulk control sample was prepared for E18.5 replicate 1 and E18.5 replicate 3 experiments; A no-cell control sample was generated for the E18.5 replicate 1 experiment; For time point E14.5, one experiment (one pregnant mouse, pooled sibling lungs) was performed yielding 45 single cell transcriptomes; For time point E16.5, one experiment (one pregnant mouse, pooled sibling lungs) was performed yielding 27 single cell transcriptomes; For the adult time point, one 107 day old mouse was used and transcriptomes of 46 single cells were obtained; All single cell samples were processed on the microfluidic platform, 200-cell-bulk and no-cell control samples were processed in microliter volumes in PCR tubes.
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Project description:Single-cell whole-transcriptome analysis is a powerful tool for quantifying gene expression heterogeneity in populations of cells. Many techniques have, thus, been recently developed to perform transcriptome sequencing (RNA-Seq) on individual cells. To probe subtle biological variation between samples with limiting amounts of RNA, more precise and sensitive methods are still required. We adapted a previously developed strategy for single-cell RNA-Seq that has shown promise for superior sensitivity and implemented the chemistry in a microfluidic platform for single-cell whole transcriptome analysis. In this approach, single cells are captured and lysed in a microfluidic device, where mRNAs with poly(A) tails are reverse-transcribed into cDNA. Double-stranded cDNA is then collected and sequenced using a next-generation sequencing platform. We prepared 94 libraries consisting of single mouse embryonic cells and technical replicates of extracted RNA and thoroughly characterized the performance of this technology. Microfluidic implementation increased mRNA detection sensitivity as well as improved measurement precision compared with tube-based protocols. With 0.2M reads per cell, we were able to reconstruct a majority of the bulk transcriptome with 10 single cells. We also quantified variation between and within different types of mouse embryonic cells and found that enhanced measurement precision, detection sensitivity, and experimental throughput aided the distinction between biological variability and technical noise. With this work, we validated the advantages of an early approach to single-cell RNA-Seq and showed that the benefits of combining microfluidic technology with high-throughput sequencing will be valuable for large-scale efforts in single-cell transcriptome analysis. We investigated gene expression in mouse embryonic cells using microfluidic-facilitated RNA-Seq to analyze 56 single mouse ES cell (mESC) transcriptomes and 6 single mouse embryonic fibroblast (MEF) transcriptomes. To quantitatively evaluate the sensitivity and precision of our technique, we also sequenced 23 libraries from extracted mESC RNA, representing three sets of technical replicates with varying starting amounts of material.
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Project description:<p>Human embryonic stem cells (hESCs) are a powerful tool for the study of human development and can form the basis of cellular disease models or therapies. However, the genetic make-up and stability of hESCs has not been systematically studied at a genome-wide level with single nucleotide resolution. We therefore sequenced the whole exomes of widely available hESCs. The data generated has provided new insights about the nature of acquired variation and the genomic integrity of the cell lines. We anticipate that this online resource will enable investigators to access raw sequencing data in order to interrogate cell lines for different disease and trait-associated genetic variants.</p>
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