Preparing for the first breath at single cell level [single cell Fluidigm C1]
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ABSTRACT:
PROVIDER: PRJNA504644 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Preparing for the first breath at single cell level
| PRJNA504641 | ENA
Project description:The respiratory system undergoes remarkable structural, biochemical, and functional changes necessary for adaptation to air breathing at birth. To identify dynamic changes in gene expression in the diverse pulmonary cells at birth, we performed Drop-seq based massive parallel single-cell RNA sequencing. An iterative cell type identification strategy was used to unbiasedly identify the heterogeneity of murine pulmonary cell types on postnatal day 1. Distinct populations of epithelial, endothelial, mesenchymal, and immune cells were identified, each containing distinct subpopulations. Cell type predictions and signature genes identified using Drop-seq were cross-validated using an independent single cell isolation platform. Temporal changes in RNA expression patterns were compared before and after birth to identify signaling pathways selectively activated in specific pulmonary cell types, demonstrating activation of UPR signaling during perinatal adaptation of the lung. Present data provide the first single cell view of the adaptation to air breathing after birth. All data from the present study are freely accessed at https://research.cchmc.org/pbge/lunggens/SCLAB.html.
2019-01-06 | GSE122330 | GEO
Project description:Preparing for the first breath at single cell level [single cell Drop-seq]
| PRJNA504643 | ENA
Project description:Preparing for the first breath at single cell level [whole lung time course]
| PRJNA504645 | ENA
Project description:This study provides an assessment of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. The system combines microfluidic technology and nanoliter-scale reactions. We sequenced 622 cells allowing identification of 341 islet cells with high-quality gene expression profiles. The cells clustered into populations of alpha-cells (5%), beta-cells (92%), delta-cells (1%) and PP-cells (2%). We identified cell-type specific transcription factors and pathways primarily involved in nutrient sensing and oxidation and cell signaling. Unexpectedly, 281 cells had to be removed from the analysis due to low viability (23%), low sequencing quality (13%) or contamination resulting in the detection of more than one islet hormone (64%). Collectively, we provide a resource for identification of high-quality gene expression datasets to help expand insights into genes and pathways characterizing islet cell types. We reveal limitations in the C1 Fluidigm cell capture process resulting in contaminated cells with altered gene expression patterns. This calls for caution when interpreting single-cell transcriptomics data using the C1 Fluidigm system. Single-cell RNA sequencing of mouse C57BL/6 pancreatic islet cells
2016-02-19 | E-GEOD-77980 | biostudies-arrayexpress
Project description:This study provides an assessment of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. The system combines microfluidic technology and nanoliter-scale reactions. We sequenced 622 cells allowing identification of 341 islet cells with high-quality gene expression profiles. The cells clustered into populations of alpha-cells (5%), beta-cells (92%), delta-cells (1%) and PP-cells (2%). We identified cell-type specific transcription factors and pathways primarily involved in nutrient sensing and oxidation and cell signaling. Unexpectedly, 281 cells had to be removed from the analysis due to low viability (23%), low sequencing quality (13%) or contamination resulting in the detection of more than one islet hormone (64%). Collectively, we provide a resource for identification of high-quality gene expression datasets to help expand insights into genes and pathways characterizing islet cell types. We reveal limitations in the C1 Fluidigm cell capture process resulting in contaminated cells with altered gene expression patterns. This calls for caution when interpreting single-cell transcriptomics data using the C1 Fluidigm system.
2016-02-19 | GSE77980 | GEO
Project description:A cost effective single cell RNA sequencing approach for the Fluidigm C1
| PRJNA315001 | ENA
Project description:The respiratory system undergoes remarkable structural, biochemical, and functional changes necessary for adaptation to air breathing at birth. To identify dynamic changes in gene expression in the diverse pulmonary cells at birth, we performed Drop-seq based massive parallel single-cell RNA sequencing. An iterative cell type identification strategy was used to unbiasedly identify the heterogeneity of murine pulmonary cell types on postnatal day 1. Distinct populations of epithelial, endothelial, mesenchymal, and immune cells were identified, each containing distinct subpopulations. Cell type predictions and signature genes identified using Drop-seq were cross-validated using an independent single cell isolation platform. Temporal changes in RNA expression patterns were compared before and after birth to identify signaling pathways selectively activated in specific pulmonary cell types, demonstrating activation of UPR signaling during perinatal adaptation of the lung. Present data provide the first single cell view of the adaptation to air breathing after birth. All data from the present study are freely accessed at https://research.cchmc.org/pbge/lunggens/SCLAB.html.
2019-01-06 | GSE122329 | GEO
Project description:Single cell RNA seq of adult mouse mammary gland using Fluidigm C1 platform.
| PRJNA650512 | ENA
Project description:We used microfluidic single cell RNA-seq on mixed e16.5 mouse lung cells in order to determine the potential cell types present based on differential transcriptional profiles of the entire population using minimal cell selection bias. whole lung mouse e16.5 cells were pooled and loaded onto the Fluidigm C1 device. microwells that contained intact single cells were recorded, the labchip was processed for the generation of cDNA from each cell, and cDNA generated from each accepted well was used to generate amplified and barcoded DNA library that was loaded into an Iluumina HiSeq machine for HTS analysis.
2015-06-11 | E-GEOD-69761 | biostudies-arrayexpress