CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification
TL;DR: It is shown that CEL-Seq gives more reproducible, linear, and sensitive results than a PCR-based amplification method, and will be useful for transcriptomic analyses of complex tissues containing populations of diverse cell types.
About: This article is published in Cell Reports.The article was published on 2012-09-27 and is currently open access. It has received 1166 citations till now. The article focuses on the topics: RNA-Seq & Gene expression.
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TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
5,506 citations
Cites methods from "CEL-Seq: Single-Cell RNA-Seq by Mul..."
...However, methods of preparing cells for profiling have been applicable in practice to just hundreds (Hashimshony et al., 2012; Picelli et al., 2013) or (with automation) a few thousand cells (Jaitin et al., 2014), typically after first separating the cells by flow sorting (Shalek et al., 2013) or…...
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TL;DR: Monocle is described, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points that revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation.
Abstract: Defining the transcriptional dynamics of a temporal process such as cell differentiation is challenging owing to the high variability in gene expression between individual cells. Time-series gene expression analyses of bulk cells have difficulty distinguishing early and late phases of a transcriptional cascade or identifying rare subpopulations of cells, and single-cell proteomic methods rely on a priori knowledge of key distinguishing markers. Here we describe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. We validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.
4,119 citations
TL;DR: Seurat is a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns, and correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.
Abstract: Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
3,465 citations
01 May 2015
TL;DR: Drop-seq as discussed by the authors analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin, and identifies 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes.
Abstract: Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.
3,365 citations
TL;DR: This work has developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing, which shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays.
2,894 citations
Cites methods from "CEL-Seq: Single-Cell RNA-Seq by Mul..."
...The method showed an excellent linear readout of the ERCC spike-in input concentration (Figure 4D) down to concentrations of 0.5 molecules/droplet on average; below that limit, we tended to over-count transcripts, a bias seen previously (Grün et al., 2014; Hashimshony et al., 2012)....
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...(Grün et al., 2014; Hashimshony et al., 2012)....
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...After barcoding, material from all cells is combinedbybreaking thedroplets, and thecDNA library is sequenced using established methods (CEL-seq) (Hashimshony et al., 2012; Jaitin et al., 2014)....
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References
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TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Abstract: Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Availability: http://samtools.sourceforge.net
Contact: [email protected]
45,957 citations
TL;DR: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
Abstract: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems
23,312 citations
TL;DR: A new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format, which allows the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks.
Abstract: Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing webbased methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools
18,858 citations
TL;DR: Human blastocyst-derived, pluripotent cell lines are described that have normal karyotypes, express high levels of telomerase activity, and express cell surface markers that characterize primate embryonic stem cells but do not characterize other early lineages.
Abstract: Human blastocyst-derived, pluripotent cell lines are described that have normal karyotypes, express high levels of telomerase activity, and express cell surface markers that characterize primate embryonic stem cells but do not characterize other early lineages. After undifferentiated proliferation in vitro for 4 to 5 months, these cells still maintained the developmental potential to form trophoblast and derivatives of all three embryonic germ layers, including gut epithelium (endoderm); cartilage, bone, smooth muscle, and striated muscle (mesoderm); and neural epithelium, embryonic ganglia, and stratified squamous epithelium (ectoderm). These cell lines should be useful in human developmental biology, drug discovery, and transplantation medicine.
15,555 citations
TL;DR: The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
Abstract: RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
11,528 citations
"CEL-Seq: Single-Cell RNA-Seq by Mul..." refers methods in this paper
...666 Cell Reports 2, 666–673, September 27, 2012 ª2012 The Author With the dramatically decreasing costs of sequencing, RNASeq (Wang et al., 2009) has emerged as the preferred method for transcriptomic analyses, overtaking microarrays, providing an imperative for any transcriptomic method to be adapted for RNA-Seq....
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...666 Cell Reports 2, 666–673, September 27, 2012 ª2012 The Author With the dramatically decreasing costs of sequencing, RNASeq (Wang et al., 2009) has emerged as the preferred method for transcriptomic analyses, overtaking microarrays, providing an imperative for any transcriptomic method to be…...
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...666 Cell Reports 2, 666–673, September 27, 2012 a2012 The Author With the dramatically decreasing costs of sequencing, RNASeq (Wang et al., 2009) has emerged as the preferred method for transcriptomic analyses, overtaking microarrays, providing an imperative for any transcriptomic method to be adapted for RNA-Seq....
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