Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Quantitative assessment of single-cell RNA sequencing methods

ABSTRACT: We generated single-cell transcriptomes from a large number of single cells using several commercially available platforms, in both microliter and nanoliter volumes, and compared performance between them. We benchmarked each method to conventional RNA-seq of the same sample using bulk total RNA, as well as to multiplexed qPCR, which is the current gold standard for quantitative single-cell gene expression analysis. In doing so, we were able to systematically evaluate the sensitivity, precision, and accuracy of various approaches to single-cell RNA-seq. Our results show that it is possible to use single-cell RNA-seq to perform quantitative transcriptome measurements of individual cells, that it is possible to obtain quantitative and accurate gene expression measurements with a relatively small number of sequencing reads, and that when such measurements are performed on large numbers of cells, one can recapitulate the bulk transcriptome complexity, and the distributions of gene expression levels found by single-cell qPCR. 109 single-cell human transcriptomes were analyzed in total; 96 using nanoliter volume sample processing on a microfluidic platform, Nextera library prep (biological replicates); 3 using the SMARTer cDNA synthesis kit, Nextera library prep (biological replicates); 3 using the Transplex cDNA synthesis kit, Nextera library prep (biological replicates); 7 using the Ovation Nugen cDNA synthesis kit (biological replicates) where 3 used Nextera library prep and 4 used NEBNext library prep. In addition, 4 bulk RNA samples were sequenced: bulk RNA generated using ~1 million pooled cells was used to make bulk libraries, 2 of which were made using SMARTer cDNA synthesis kit (technical replicates) and 2 made using Superscript RT kit with no amplification (technical replicates). All 4 bulk samples were made into libraries using Nextera.

ORGANISM(S): Homo sapiens

SUBMITTER: Angela Wu

PROVIDER: E-GEOD-51254 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Quantitative assessment of single-cell RNA-sequencing methods.

Wu Angela R AR Neff Norma F NF Kalisky Tomer T Dalerba Piero P Treutlein Barbara B Rothenberg Michael E ME Mburu Francis M FM Mantalas Gary L GL Sim Sopheak S Clarke Michael F MF Quake Stephen R SR

Nature methods 20131020 1

Interest in single-cell whole-transcriptome analysis is growing rapidly, especially for profiling rare or heterogeneous populations of cells. We compared commercially available single-cell RNA amplification methods with both microliter and nanoliter volumes, using sequence from bulk total RNA and multiplexed quantitative PCR as benchmarks to systematically evaluate the sensitivity and accuracy of various single-cell RNA-seq approaches. We show that single-cell RNA-seq can be used to perform accu ...[more]

PMID: 24141493

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Project description:We sought to characterize cellular heterogeneity in the human cerebral cortex at a molecular level during cortical neurogenesis. We captured single cells and generated sequencing libraries using the C1TM Single-Cell Auto Prep System (Fluidigm), the SMARTer Ultra Low RNA Kit (Clontech), and the Nextera XT DNA Sample Preparation Kit (Illumina). We performed unbiased clustering of the single cells and further examined transcriptional variation among cell groups interpreted as radial glia. Within this population, the major sources of variation related to cell cycle progression and the stem cell niche from which radial glia were captured. We found that outer subventricular zone radial glia (oRG cells) preferentially express genes related to extracellular matrix formation, migration, and stemness, including TNC, PTPRZ1, FAM107A, HOPX, and LIFR and related this transcriptional state to the position, morphology, and cell behaviors previously used to classify the cell type. Our results suggest that oRG cells maintain the subventricular niche through local production of growth factors, potentiation of growth factor signals by extracellular matrix proteins, and activation of self-renewal pathways, thereby contributing to the developmental and evolutionary expansion of the human neocortex. For study version 2, we have updated this data set to include additional primary cells that we infer to represent microglia, endothelial cells, and immature astrocytes, as well as additional cells from the developing neural retina, and from iPS-cell derived cerebral organoids. The genes distinguishing these cell populations may reveal biological processes supporting the diverse functions of these cell types as well as vulnerabilities of specific cell types in human genetic diseases and in viral infections. For study version 3, we have updated the data set to include additional primary cells, including those published in Nowakowski, et al., Science 2017: "Spatiotemporal Gene Expression Trajectories Reveal Developmental Hierarchies of the Human Cortex" (in press)

Dataset Information

Quantitative assessment of single-cell RNA sequencing methods

Publications

Quantitative assessment of single-cell RNA-sequencing methods.

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