Physically interacting cell sequencing of mouse embryonic endoderm at E7.5, E8.5, and E9.5
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA756041 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15530265.fastq.gz | Fastqsanger.gz | |||
| SRR15530266.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 2 of 2 |
Similar Datasets
Project description:Cells continuously communicate with the neighboring cells during development. Direct interaction of different cell types can induce molecular signals dictating lineage specification and cell fate decisions. The current single-cell RNAseq (scRNAseq) technology cannot study cell contact dependent (or niche specific) gene expression due to the loss of spatial information. To overcome this issue and determine cell contact specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PICseq) and assessed alongside our single cell transcriptomes (scRNAseq) derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of PICseq data identifies an interesting suite of gene expression signatures depending on neighboring cell types. For instance, neural progenitor (NP) cells expressed Nkx2-1 when interacting with definitive endoderm (DE) and DE cells expressed Gsc when interacting with NP. Based on the identified cell contact specific genes, we devised a means to predict the neighboring cell types from individual cell transcriptome. We further developed spatial-tSNE to show the pseudo-spatial distribution of cells in a 2-dimensional space. In sum, we suggest an approach to study niche specific gene regulation during embryogenesis.
2022-08-03 | GSE182393 | GEO
Project description:Primordial germ cell mRNA profiles from cells microdissected from e6.5, e7.5 and e8.5 embryos, e7.5 somatic neighbours and Blimp1-KO mice were generated by single cell library construction and sequencing in duplicate using Applied Biosystems SOLiD sequencer. Single cell library construction is described in: Tang f. et. al, Nature Protocols (2010), Vol. 5, p.516.
2013-07-01 | E-MTAB-1178 | biostudies-arrayexpress
Project description:Understanding processes how the early stage kidney precursor gives rise to metanephric mesenchyme, which is a committed progenitor cells of adult kidney is important for the regeneration of kidney in vitro. The combination of fluorescent activated cell sorting (FACS) plus microarray analysis offers a powerful, efficient and effective method for the creation of global gene expression profiles of the developing kidney precursors. Those gene expression data provides insights into not only the stage specific marker genes but also the signals working in each population, which should be informative for the directed differentiation of pluripotent stem cells in vitro. Osr1-GFP knock-in mice were used to isolate kidney precursor cells from embryos at E8.5, E9.5 and E11.5. At E9.5 and E11.5 embryos, to identify the differences between nephron progenitors and surrounding mesenchyme, nephron progenitor populations were further enriched by gating Osr1-GFP positive Integrin alpha8 positive Pdgfr alpha negative population and compared with Osr1-GFP positive cells other than that gate. RNA was isolated from cells and the gene expression profiles were determined by microarrays.
2015-01-13 | E-GEOD-51054 | biostudies-arrayexpress
Project description:We analyzed wildtype and miR-302 knockout embryos at E7.5 and sorted neural crest using Wnt1-Cre at E8.5 and Sox9 at E9.5 to capture miRNA differences during neural crest development
2020-03-09 | GSE137240 | GEO
Project description:We analyzed wildtype and miR-302 knockout embryos at E7.5 and sorted neural crest using Wnt1-Cre at E8.5 and Sox9 at E9.5 to capture transcriptomic differences during neural crest development
2020-03-09 | GSE137227 | GEO
Project description:Transcription profiling by high throughput sequencing of primordial germ cells from e6.5, e7.5, e8.5 embryos and e7.5 somatic neighbours and Blimp1-KO mice
| PRJEB3110 | ENA
Project description:The placenta is a transient organ that is repsonsible for multiple processes during pregnancy. Here we aim to study the development of the placenta using mouse models at embryonic day (e) e7.5, e8.5 and e9.5 by integrating RNA-seq data at the three timepoints to identify important genes, inferring their interaction networks, and predict novel regulators of placental development.
2022-11-21 | GSE202243 | GEO
Project description:Dissecting cellular crosstalk by sequencing physically interacting cells
| PRJNA558696 | ENA
Project description:Mammalian embryogenesis is characterized by rapid cellular proliferation and diversification. Within a few weeks, a single cell zygote gives rise to millions of cells expressing a panoply of molecular programs, including much of the diversity that will subsequently be present in adult tissues. Although intensively studied, a comprehensive delineation of the major cellular trajectories that comprise mammalian development in vivo remains elusive. For mouse embryogenesis in particular, we and others have performed single cell or single nucleus RNA-seq data (scRNA-seq) during implantation, gastrulation and organogenesis. Here we set out to integrate several single cell RNA-seq datasets (scRNA-seq) that collectively span mouse gastrulation and organogenesis. However, a technical challenge that we faced is that the datasets that we sought to integrate were generated by different groups at different times using different scRNA-seq technologies. In particular, probably because there was no overlapping timepoint, the integration of scRNA-seq data generated at E8.5 (cells, 10X Genomics) and E9.5 (nuclei, sci-RNA-seq3) was challenging (Cao et al. 2019; Pijuan-Sala et al. 2019). To address this, we set out to generate new data at E8.5 that might serve to “bridge” these two datasets. Because of how quickly changes are occurring during this window of development, we focused on individual, somite-resolved E8.5 embryos using a simplified, optimized version of sci-RNA-seq3. We selected 12 embryos from 2 separate litters harvested at E8.5, including a single primitive streak stage embryo (prior to somitogenesis) and 11 embryos staged in 1-somite increments from 2 to 12 somites. The optimized sci-RNA-seq3 method markedly improved data quality, with 9-fold higher UMIs and 6-fold higher gene detection per nucleus, relative to (Cao et al. 2019). Overall, we collected published data (Cheng et al. 2019; Mohammed et al. 2017; Pijuan-Sala et al. 2019), the new E8.5 data, and published data from one study spanning E9.5 to E13.5 but with deeper sequencing of those libraries (Cao et al. 2019). Altogether, we define cell states at each of 19 successive stages spanning E3.5 to E13.5, heuristically connect them to their pseudo-ancestors and pseudo-descendants. Despite being constructed through automated procedures, the resulting trajectories of mammalian embryogenesis (TOME) are largely consistent with our contemporary understanding of mammalian development. In addition, the new E8.5 data itself comprises a foundational resource for mammalian developmental biology (especially for the early somitogenesis), and are made available in a way that will facilitate their ongoing annotation by the research community.
2022-03-11 | GSE186069 | GEO
Project description:Crosstalk between neighboring cells underlines many biological processes, such as cell activation, differentiation and signaling. Current single-cell genomic technologies lack information of cell-cell interactions, as each cell is profiled separately following tissue dissociation. Here we present Physically Interacting Cells sequencing (PIC-seq), which combines fluorescently activated cell sorting of physically interacting cells along with massively parallel single-cell RNA-sequencing and computational modeling to systematically map in situ cellular interactions and characterize their molecular crosstalk. Focusing on interactions between T cells and dendritic cells (DC) in vitro and in vivo, we map T-DC interaction preferences, and discover regulatory T cells as the major T cell subtype interacting with DC. Analysis of T-DC pairs characterized interaction-specific transcription, including upregulation of a costimulatory program in rare interactions between pathogen-presenting DC and T cells. In summary, PIC-seq provides a new technology to profile cell-cell interactions and characterize interaction-specific pathways and target genes at high resolution.
2020-03-12 | GSE135382 | GEO