Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Ramsköld D
PROVIDER: S-ECPF-GEOD-38495 | biostudies-other | 2012 Aug
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Genome-wide transcriptome analyses are routinely used to monitor tissue-, disease- and cell type–specific gene expression, but it has been technically challenging to generate expression profiles from single cells. Here we describe a robust mRNA-Seq protocol (Smart-Seq) that is applicable down to single cell levels. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which enhances detailed analyses of alternative transcript isoforms and identification of single-nucleotide polymorphisms. We determined the sensitivity and quantitative accuracy of Smart-Seq for single-cell transcriptomics by evaluating it on total RNA dilution series. We found that although gene expression estimates from single cells have increased noise, hundreds of differentially expressed genes could be identified using few cells per cell type. Applying Smart-Seq to circulating tumor cells from melanomas, we identified distinct gene expression patterns, including candidate biomarkers for melanoma circulating tumor cells. Our protocol will be useful for addressing fundamental biological problems requiring genome-wide transcriptome profiling in rare cells.
| S-EPMC3467340 | biostudies-literature
Project description:We assessed Smart-Seq, a new single-cell RNA-Seq library preparation method, on a variety of mouse and human RNA samples or cells. We generated RNA-Seq libraries for dilution series of MAQC reference RNA and mouse brain RNA to assess technical reproducibility, and for a variety of individual cells including putative circulating tumour cells.
2012-07-22 | E-GEOD-38495 | biostudies-arrayexpress
Project description:We assessed Smart-Seq, a new single-cell RNA-Seq library preparation method, on a variety of mouse and human RNA samples or cells.
2012-07-22 | GSE38495 | GEO
Project description:Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
| PRJNA170814 | ENA
Project description:Single-cell characterization techniques, such as mRNA-seq, have been applied to a diverse range of applications in cancer biology, yielding great insight into mechanisms leading to therapy resistance and tumor clonality. While single-cell techniques can yield a wealth of information, a common bottleneck is the lack of throughput, with many current processing methods being limited to the analysis of small volumes of single cell suspensions with cell densities on the order of 107 per mL. In this work, we present a high-throughput full-length mRNA-seq protocol incorporating a magnetic sifter and magnetic nanoparticle-antibody conjugates for rare cell enrichment, and Smart-seq2 chemistry for sequencing. We evaluate the efficiency and quality of this protocol with a simulated circulating tumor cell system, whereby non-small-cell lung cancer cell lines (NCI-H1650 and NCI-H1975) are spiked into whole blood, before being enriched for single-cell mRNA-seq by EpCAM-functionalized magnetic nanoparticles and the magnetic sifter. We obtain high efficiency (> 90%) capture and release of these simulated rare cells via the magnetic sifter, with reproducible transcriptome data. In addition, while mRNA-seq data is typically only used for gene expression analysis of transcriptomic data, we demonstrate the use of full-length mRNA-seq chemistries like Smart-seq2 to facilitate variant analysis of expressed genes. This enables the use of mRNA-seq data for differentiating cells in a heterogeneous population by both their phenotypic and variant profile. In a simulated heterogeneous mixture of circulating tumor cells in whole blood, we utilize this high-throughput protocol to differentiate these heterogeneous cells by both their phenotype (lung cancer versus white blood cells), and mutational profile (H1650 versus H1975 cells), in a single sequencing run. This high-throughput method can help facilitate single-cell analysis of rare cell populations, such as circulating tumor or endothelial cells, with demonstrably high-quality transcriptomic data.
| S-EPMC5706670 | biostudies-literature
Project description:The broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.
| S-EPMC7893963 | biostudies-literature
Project description:We present FLASH-seq (FS), a full-length single-cell RNA sequencing (scRNA-seq) method with increased sensitivity and reduced hands-on time compared to Smart-seq3. The entire FS protocol can be performed in ~4.5 hours, is simple to automate and can be easily miniaturized to decrease resource consumption. The FS protocol can also use unique molecular identifiers (UMIs) for molecule counting while displaying reduced strand-invasion artifacts. FS will be especially useful for characterizing gene expression at high resolution across multiple samples.
| S-EPMC9546769 | biostudies-literature
Project description:The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.
| S-EPMC9977151 | biostudies-literature
Project description:BACKGROUND:Read alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly. RESULTS:The Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5-16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7-16.5x compared to performing transcript assembly on a highly optimised computer. CONCLUSION:Falco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at https://github.com/VCCRI/Falco.
| S-EPMC6936136 | biostudies-literature
Project description:Because of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy.We conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development.Simulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2.Supplementary data are available at Bioinformatics online.
| S-EPMC4673975 | biostudies-literature