Quantitative single-cell RNA-seq with unique molecular identifiers

Islam, Saiful; Zeisel, Amit; Joost, Simon; Manno, Gioele La; Zając, Paweł; Kasper, Maria; Lönnerberg, Peter; Linnarsson, Sten

doi:10.1038/nmeth.2772

Cited by 1,055 publications

(1,013 citation statements)

References 24 publications

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“…Quality control for exclusion of debris or doublets was performed after each capture experiment. Following lysis, cDNA synthesis, amplification, and tagmentation, high‐throughput RNA sequencing was performed on an Illumina HiSeq 2000 sequencer (Islam et al , 2014). Next, the dataset was processed with the BackSpinV2 algorithm (Romanov et al , 2017a,b) and first grouped for main cell lineages.…”

Section: Methodsmentioning

confidence: 99%

Hypothalamic CNTF volume transmission shapes cortical noradrenergic excitability upon acute stress

et al. 2018

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Stress‐induced cortical alertness is maintained by a heightened excitability of noradrenergic neurons innervating, notably, the prefrontal cortex. However, neither the signaling axis linking hypothalamic activation to delayed and lasting noradrenergic excitability nor the molecular cascade gating noradrenaline synthesis is defined. Here, we show that hypothalamic corticotropin‐releasing hormone‐releasing neurons innervate ependymal cells of the 3rd ventricle to induce ciliary neurotrophic factor (CNTF) release for transport through the brain's aqueductal system. CNTF binding to its cognate receptors on norepinephrinergic neurons in the locus coeruleus then initiates sequential phosphorylation of extracellular signal‐regulated kinase 1 and tyrosine hydroxylase with the Ca2+‐sensor secretagogin ensuring activity dependence in both rodent and human brains. Both CNTF and secretagogin ablation occlude stress‐induced cortical norepinephrine synthesis, ensuing neuronal excitation and behavioral stereotypes. Cumulatively, we identify a multimodal pathway that is rate‐limited by CNTF volume transmission and poised to directly convert hypothalamic activation into long‐lasting cortical excitability following acute stress.

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Section: Methodsmentioning

confidence: 99%

Hypothalamic CNTF volume transmission shapes cortical noradrenergic excitability upon acute stress

et al. 2018

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“…These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .…”

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confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...

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“…When the apparent heterogeneity in gene expression is simply due to technical issues, the data can lead to erroneous conclusions of biological heterogeneity. Other methods include the use of unique molecular identifiers in the primers for counting individual transcripts 37. In cases in which replicate measurements can be performed, such as has been shown with single cell proteomics,38 duplicate proteomic measurements from individual cells can be performed.…”

Section: Unique Challenges and Opportunities Posed By Single‐cell Anamentioning

confidence: 99%

“…Multiplex protein expression (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37) …”

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confidence: 99%

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation

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Taylor

Plant

et al. 2018

Clinical Translational Sci

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The high‐content interrogation of single cells with platforms optimized for the multiparameter characterization of cells in liquid and solid biopsy samples can enable characterization of heterogeneous populations of cells ex vivo. Doing so will advance the diagnosis, prognosis, and treatment of cancer and other diseases. However, it is important to understand the unique issues in resolving heterogeneity and variability at the single cell level before navigating the validation and regulatory requirements in order for these technologies to impact patient care. Since 2013, leading experts representing industry, academia, and government have been brought together as part of the Foundation for the National Institutes of Health (FNIH) Biomarkers Consortium to foster the potential of high‐content data integration for clinical translation.

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Quantitative single-cell RNA-seq with unique molecular identifiers

Cited by 1,055 publications

References 24 publications

Hypothalamic CNTF volume transmission shapes cortical noradrenergic excitability upon acute stress

Hypothalamic CNTF volume transmission shapes cortical noradrenergic excitability upon acute stress

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Opportunities and Challenges in Implementation of Multiparameter Single Cell Analysis Platforms for Clinical Translation

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