Genome Biol. Accounting for technical noise in single-cell RNA-seq experiments. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq.
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.
Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation.
Cell Stem Cell. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. T cell fate and clonality inference from single-cell transcriptomes. Defining the three cell lineages of the human blastocyst by single-cell RNA-seq.
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Sci Immunol. Deterministic and stochastic allele specific gene expression in single mouse blastomeres.
PLoS One. Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq. Nat Genet. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression.
Nat Commun. Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression.
Liu S, Trapnell C. Single-cell transcriptome sequencing: recent advances and remaining challenges. Revealing the vectors of cellular identity with single-cell genomics. Comparative analysis of single-cell RNA sequencing methods. Mol Cell. Power analysis of single-cell RNA-sequencing experiments. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity.
Nucleic Acids Res. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. In BioRxiv. Scaling single cell transcriptomics through split pool barcoding.
Effective detection of variation in single-cell transcriptomes using MATQ-seq. Counting absolute numbers of molecules using unique molecular identifiers. Donati G. The niche in single-cell technologies. Immunol Cell Biol. Ten years of next-generation sequencing technology.
Trends Genet. Quantitative assessment of single-cell RNA-sequencing methods. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Massively parallel digital transcriptional profiling of single cells. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing.
Nat Neurosci. Single-cell genomics unveils critical regulators of Th17 cell pathogenicity. The Human Cell Atlas. Multiplexing droplet-based single cell RNA-sequencing using natural genetic barcodes. Single-cell transcriptome conservation in cryopreserved cells and tissues. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biol. Fixed single-cell transcriptomic characterization of human radial glial diversity. The technology and biology of single-cell RNA sequencing.
Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Consortium ERC. BMC Genomics. Article Google Scholar. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Immunol. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.
Genome Res. Theory Biosci. Mammalian genes are transcribed with widely different bursting kinetics. Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise. Mol Biol Cell. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.
Barron M, Li J. Identifying and removing the cell-cycle effect from single-cell RNA-sequencing data. Sci Rep. Janes KA. Single-cell states versus single-cell atlases - two classes of heterogeneity that differ in meaning and method.
Curr Opin Biotechnol. Validation of noise models for single-cell transcriptomics. Bacher R, Kendziorski C. Design and computational analysis of single-cell RNA-sequencing experiments. Granatum: a graphical single-cell RNA-seq analysis pipeline for genomics scientists. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. Computational and analytical challenges in single-cell transcriptomics.
Nat Rev Genet. Classification of low quality cells from single-cell RNA-seq data. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. SCell: integrated analysis of single-cell RNA-seq data.
Single-cell transcriptomics bioinformatics and computational challenges. Front Genet. Computational approaches for interpreting scRNA-seq data. FEBS Lett. Avoiding common pitfalls when clustering biological data. Sci Signal. SC3: consensus clustering of single-cell RNA-seq data. BMC Bioinf.
Xu C, Su Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. PLoS Comput Biol. Comparison of methods to detect differentially expressed genes between single-cell populations. Brief Bioinform. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape.
Wishbone identifies bifurcating developmental trajectories from single-cell data. Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development.
Diffusion maps for high-dimensional single-cell analysis of differentiation data. Diffusion pseudotime robustly reconstructs lineage branching. ArXiv preprint arXiv JingleBells: a repository of immune-related single-cell RNA-sequencing datasets. J Immunol. Dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens.
Single-cell multiomics: multiple measurements from single cells. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Download references. We are grateful to Valentine Svensson for useful discussions during the preparation of this manuscript. This method is advantageous for biologists studying processes such as differentiation, proliferation, and tumorigenesis. Achieve cost-effective RNA exome analysis using sequence-specific capture of the coding regions of the transcriptome.
Ideal for low-quality samples or limited starting material. Accurately measure gene and transcript abundance and detect both known and novel features in coding and multiple forms of noncoding RNA. Isolate and sequence small RNA species, such as microRNA, to understand the role of noncoding RNA in gene silencing and posttranscriptional regulation of gene expression.
Deeply sequence ribosome-protected mRNA fragments to gain a complete view of the ribosomes active in a cell at a specific time point, and predict protein abundance. Transcriptomics and whole-genome shotgun sequencing provide researchers and pharmaceutical companies with data to refine drug discovery and development.
Learn about read length and depth requirements for RNA-Seq and find resources to help with experimental design.
Advances in RNA-Seq library prep are revolutionizing the study of the transcriptome. Our enhanced RNA-Seq library prep portfolio spans multiple types of sequencing studies.
These solutions offer rapid turnaround time, broad study flexibility, and sequencing scalability. A fast, flexible, and mobile-friendly tool, our Custom Protocol Selector helps you generate RNA sequencing protocols tailored to your experiment. A simple, scalable, cost-effective, rapid single-day solution for analyzing the coding transcriptome leveraging as little as 25 ng input of standard non-degraded RNA.
These cost-efficient, user-friendly, mid-throughput benchtop sequencers offer extreme flexibility to support new and emerging applications. The new enhancements deliver solutions for studying RNA that provide rapid turnaround time, broad study flexibility and sequencing scalability, while delivering exceptional data quality for infectious disease, oncology and genetic disease research. Learn about Illumina solutions for next-generation RNA sequencing applications.
RNA sequencing provides deeper insights for complex research. See how RNA-Seq is helping this lab move beyond gene expression. Demystifying data analysis with answers to some of the most common frequently asked questions. Host genetic differences and individual responses to the SARS-CoV-2 virus play a part in disease susceptibility and severity.
The Visium Spatial Gene Expression enables you to visualize tissue morphology overlaid with gene activity, revealing the spatial relationships between cells and how they contribute to tissue development, function, and disease state.
RNA Sequencing. Early RNA-seq techniques used Sanger sequencing technology, a technique that although innovative at the time was also low-throughput and costly. An RNA-seq workflow has several steps, which can be broadly summarized as:. The cDNA is then fragmented, and adapters are added to each end of the fragments. These adapters contain functional elements which permit sequencing, for example, the amplification element which facilitates clonal amplification of the fragments and the primary sequencing priming site.
Following processes of amplification, size selection, clean-up and quality checking, the cDNA library is then analyzed by NGS, producing short sequences that correspond to all or part of the fragment from which it was derived. The depth to which the library is sequenced varies depending on the purpose for which the output data will be used for. Sequencing may follow either single-end or paired-end sequencing methods. Paired-end methods sequence from both ends and are therefore more expensive 6 , 7 but offer advantages in post-sequencing data reconstruction.
A further choice must be made between strand-specific and non-strand-specific protocols. The former method means the information about which DNA strand was transcribed is retained. The value of extra information obtained from strand-specific protocols make them the favorable option. These reads, of which there will be many millions by the end of the workflow, can then be aligned to a reference genome if available or assembled de novo to produce an RNA sequence map that spans the transcriptome.
RNA-seq is widely regarded as superior to other technologies, such as microarray hybridization. Not limited to genomic sequences — unlike hybridization-based approaches, which may require species-specific probes, RNA-seq can detect transcripts from organisms with previously undetermined genomic sequences. This makes it fundamentally superior for the detection of novel transcripts, SNPs or other alterations. Low background signal — the cDNA sequences used in RNA-seq can be mapped to targeted regions on the genome, which makes it easy to remove experimental noise.
Furthermore, issues with cross-hybridization or sub-standard hybridization, which can plague microarray experiments, are not an issue in RNA-seq experiments.
More quantifiable - Microarray data is only ever displayed as values relative to other signals detected on the array, whilst RNA-seq data is quantifiable. RNA-seq also avoids the issues microarrays have in detecting very high or very low transcription levels. Figure 2: A workflow for RNA-seq.
To sum up, modern-day RNA-seq is well established as the superior option to microarrays and will likely remain the preferred option for the time being. Significant progress has been made in the field of RNA-seq over the last decade or so.
The associated costs have reduced significantly while throughput has increased, sequence fidelity is far superior to earlier iterations of the NGS technologies and the availability of data analysis tools and pipelines has improved tremendously. However, there remain a number of challenges for scientists to bear in mind when considering RNA-seq experiments.
These include:. Isolating sufficient, high-quality RNA — while the sample quantity requirements for RNA-seq analysis have reduced drastically, it is still important to ensure you are able to obtain sufficient RNA to fulfill all your analysis requirements, including repeats if necessary.
It is also important to bear in mind that, while you may isolate total RNA, depending upon your experimental question, you are likely only to be sequencing a fraction of this typically messenger RNA mRNA , further reducing your sample quantity.
0コメント