Project description:Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.
Project description:Single cell RNA-seq of the human alveolar rhabdomyosarcoma cell line Rh41. We also inlcude a bulk RNA-seq study of unsorted and sorted cells using CD44 as a marker Overall design: Single cell RNA-seq of Rh41 using the 10x Genomics. For bulk RNA-seq, there are three batches, each corresponds to three samples: sorted CD44 high, sorted CD44 low and unsorted. C2, C6, C7: CD44high subspopulation (Batch 1/2/3); C3, C4, C5: CD44low subpopulation (Batch 1/2/3); C1, C8, C9: CD44 unsorted (Batch 1/2/3)
Project description:High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in several of the reaction steps of Quartz-Seq2 allow us to effectively convert initial reads to UMI counts (at a rate of 30%–50%). To demonstrate the power of Quartz-Seq2, we analyzed transcriptomes from a cell population of in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of sequence reads. Preprint: http://www.biorxiv.org/content/early/2017/07/21/159384 Overall design: Technical validation of high-throughput single-cell RNA-seq methods using 10 pg total RNA. Demonstration of Quartz-Seq2 using 4,484 single-cells of embryonic stem cell and 1,052 single-cells of stromal vascular fraction (SVF).
Project description:SMART-seq2 was performed on single cells isolated from visually staged zebrafish embryos. Overall design: Samples were all sequenced in one batch. Some were generated with a 5' UMI-tagged method, and others are full-length SMART-seq2.
Project description:Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity amongst single cells. Here we identify two major sources of technical variability, sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this and after validation by single-molecule FISH experiments, we apply these models to demonstrate that growing mES cells in 2i instead of serum/LIF globally reduces gene expression variability. J1 mouse embryonic stem cells (mESCs) were cutured in 2i or in serum medium. Cells were dissociated into a single cell suspension and picked under a stereomicroscope using a 30μm glass capillary and mouth pipette. Picked cells were deposited in the lid of an 0.5ml LoBind eppendorf tube and snap frozen in liquid nitrogen. For the pool and split controls, approximately 1 million cells were lysed, the amount of RNA was quantified on a bioanalyzer (Agilent) using the Eukaryote Total RNA pico kit. 20pg aliquots of total RNA were used for each pool and split control. Single cells and controls were processed using the previously described CEL-seq technique, with a few alterations. A 4bp random barcode as unique molecular identifier (UMI) was added to the primer in between the cell specific barcode and the poly T stretch. Libraries were sequenced on an Illumina HighSeq 2500 using 50bp paired end sequencing. For cells and controls, two libraries were sequenced on two lanes in total for each condition.
Project description:To improve our understanding of the effect of differential methylation on gene expression between normal and tumor breast cells, we employed high-throughput sequencing technology to determine and compare CpG methylation of normal breast and four breast tumor genomes at single-base resolution. By comparing the methylation profiles between normal and tumor, we identified large hypomethylated zones in associated with large tissue-specific genes and gene deserts. We identified small hypomethylated regions in the methylomes and termed these sites as unmethylated islands (UMI). The UMI are highly correlated with positive regulatory chromatin marks and exhibit differential methylation mainly at the island shores. Our analysis showed there is a complex relationship between genome-wide promoter differential methylation and gene expression. Four other data sets from this study were also deposited at ArrayExpress under accession numbers E-MTAB-1935, E-MTAB-1952, E-MTAB-1958 and E-MTAB-1961.
Project description:This SuperSeries is composed of the following subset Series: GSE32967: Modeling lethal prostate cancer variant with small cell carcinoma features [expression profile] GSE33053: Modeling lethal prostate cancer variant with small cell carcinoma features [genomic profile] Refer to individual Series
Project description:Single cell transcriptomics has emerged as a powerful approach to dissecting phenotypic heterogeneity in complex, unsynchronized cellular populations. However, many important biological questions demand quantitative analysis of large numbers of individual cells. Hence, new tools are urgently needed for efficient, inexpensive, and parallel manipulation of RNA from individual cells. We report a simple microfluidic platform for trapping single cell lysates in sealed, picoliter microwells capable of “printing” RNA on glass or capturing RNA on polymer beads. To demonstrate the utility of our system for single cell transcriptomics, we developed a highly scalable technology for genome-wide, single cell RNA-Seq. The current implementation of our device is pipette-operated, profiles hundreds of individual cells in parallel with library preparation costs of ~$0.10-$0.20/cell, and includes five lanes for simultaneous experiments. We anticipate that this system will ultimately serve as a general platform for large-scale single cell transcriptomics, compatible with both imaging and sequencing readouts.!Series_type = Expression profiling by high throughput sequencing A microfluidic device that pairs sequence-barcoded mRNA capture beads with individual cells was used to barcode cDNA from individual cells which was then pre-amplified by in vitro transcription in a pool and converted into an Illumina RNA-Seq library. Libraries were generated from ~600 individual cells in parallel and extensive analysis was done on 396 cells from the U87 and MCF10a cell lines and from ~500 individual cells with extensive analysis on 247 cells from the U87 and WI-38 cell lines. Sequencing was done on the 3'-end of the transcript molecules. The first read contains cell-identifying barcodes that were present on the capture bead and the second read contains a unique molecular identifier (UMI) barcode, a lane-identifying barcode, and then the sequence of the transcript.
Project description:This set features unfiltered, aligned, UMI-based single cell RNA sequencing count data for 5290 Blood, intraepithelial ileum (IEL) and lamina propria ileum (LPL) T cells from Crohn's disease patients as published in Uniken Venema et al, Gastroenterology 2019