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.
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
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.
Project description:Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing based methods for cell lineage analysis depend on low resolution bulk analysis or rely on extensive single cell sequencing which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way towards large-scale human cell lineage discovery.