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Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

ABSTRACT: Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. Using this technique, we analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility and low noise of this high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE65525 | GEO | 2015/05/20

SECONDARY ACCESSION(S): PRJNA274274

REPOSITORIES: GEO

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Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

Project description:Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. Using this technique, we analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after LIF withdrawal. The reproducibility and low noise of this high-throughput single cell data allowed us to deconstruct cell populations and infer gene expression relationships. A total of 8 single cell data sets are submitted: 3 for mouse embryonic stem (ES) cells (1 biological replicate, 2 technical replicates); 3 samples following LIF withdrawal (days 2,4, 7); one pure RNA data set (from human lymphoblast K562 cells); and one sample of single K562 cells.

2015-05-20 | E-GEOD-65525 | biostudies-arrayexpress

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Project description:It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.

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Quantitative single-cell RNA-seq

Project description:Purpose: We applied cDNA molecule counting using unique molecular identifiers combined with high-throughput sequencing to study the transcriptome of individual mouse embryonic stem cells, with spike-in controls to monitor technical performance. We further examined transcriptional noise in the embryonic stem cells.

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Exploring the effect of different surgical methods on the prognosis of patients with colorectal cancer based on multiple omics

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Project description:Droplet microfluidic methods have massively increased the throughput of single-cell RNA sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples as well as lower overall RNA capture efficiency. These drawbacks stem from the lack of strategies to enrich for high-quality material at the moment of cell encapsulation and the lack of implementable multi-step enzymatic processes that increase RNA capture. Here we alleviate both bottlenecks by deploying fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei or fixed cells and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half, depending on sample quality. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development using highly damaged input material. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

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Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

Project description:Droplet barcoding for single cell transcriptomics applied to embryonic stem cells

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Ultra-parallel ChIP-seq by barcoding of intact nuclei

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