Revealing the vectors of cellular identity with single-cell genomics

Wagner, Allon; Regev, Aviv; Yosef, Nir

doi:10.1038/nbt.3711

Cited by 577 publications

(514 citation statements)

References 222 publications

(403 reference statements)

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“…Specifically, we fit a zero-inflated negative binomial model using the zeroinfl function 48 from the pscl R package 49 , version 1.4.9. The zero-inflated negative binomial model combines a count component and a point mass at zero, which is relevant for scRNA-seq data where zero values are inflated owing to the technology not capturing expressed genes, particularly those with low expression 11 . The model requires a substantial amount of data to fit, making it well suited to data generated by massively parallel methods.…”

Section: Methodsmentioning

confidence: 99%

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The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

Wallrapp

Riesenfeld

Burkett

et al. 2017

Nature

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Type 2 innate lymphoid cells (ILC2s) both contribute to mucosal homeostasis and initiate pathologic inflammation in allergic asthma. However, the signals that direct ILC2s to promote homeostasis versus inflammation are unclear. To identify such molecular cues, we profiled mouse lung-resident ILCs using single-cell RNA sequencing at steady state and after in vivo stimulation with the alarmin cytokines IL-25 and IL-33. ILC2s were transcriptionally heterogeneous after activation, with subpopulations distinguished by expression of proliferative, homeostatic and effector genes. The neuropeptide receptor Nmur1 was preferentially expressed by ILC2s at steady state and after IL-25 stimulation. Neuromedin U (NMU), the ligand of NMUR1, activated ILC2s in vitro, and in vivo co-administration of NMU with IL-25 strongly amplified allergic inflammation. Loss of NMU–NMUR1 signalling reduced ILC2 frequency and effector function, and altered transcriptional programs following allergen challenge in vivo. Thus, NMUR1 signalling promotes inflammatory ILC2 responses, highlighting the importance of neuro-immune crosstalk in allergic inflammation at mucosal surfaces.

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Section: Methodsmentioning

confidence: 99%

“…Single-cell genomics, especially single-cell RNA-sequencing (scRNA-seq) 10,11 , can identify such diversity, both when changes in cell states are continuous across a population 12 and when there are discrete subpopulations of varying sizes, including in intestinal ILCs 13–15 .…”

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confidence: 99%

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

Wallrapp

Riesenfeld

Burkett

et al. 2017

Nature

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496

480

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“…However, these are rather arbitrary distinctions limited by current knowledge and available resources. Advances in single cell 'omics' provide a more complex picture of identity, often revealing continuums of multiple facets of cellular plasticity [47]. Therefore, the distinction between DNA methylation changes at single loci within a canonical cell type (intrinsic in Horvath's definition [19]) and changed cell fate characterized by DNA methylation changes at multiple loci (extrinsic by Horvath's definition) is just one of scale.…”

Section: What Is a Cell Type Anyway?mentioning

confidence: 99%

Is Cellular Heterogeneity Merely a Confounder to be Removed from Epigenome-Wide Association Studies?

et al. 2017

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Excitement about DNA methylation biomarkers has been tempered by a growing appreciation of the complex causal relations with cell fate. Intersample differences in DNA methylation can be partitioned into those that are independent of cellular heterogeneity and those that are caused by differential mixtures of cell types. Generally, the field has assumed that the former are more likely to be causative of disease. The latter has been considered a likely consequence of disease and a confounder to be removed. We argue that the conceptual separation of these signals is artificial and not necessarily informative about causation. DNA methylation is a very sensitive measure of cell fate mix and therefore reveals much about underlying disease etiology including aspects of causation. DNA methylation marks integrate genetic & environmental influences & hence have potential as prognostic & stratification biomarkers & also as read-outs of intervention efficacyLoci-specific DNA 5-methylcytosine levels at CpG sites are easily measured at scale in biological samples. They vary across individuals and this variation has been robustly associated with a range of disease phenotypes and environmental exposures [1][2][3][4][5][6]. As interindividual DNA methylation is specified by the interaction of both genotypic and environmental influences [7,8], it may be a more powerful prognostic biomarker than either genotypic or lifestyle factors alone [9]. DNA methylation is dynamic throughout the lifecourse and is potentially modifiable by therapeutic interventions. This raises the possibility of sensitive real-time biomarkers of disease status to track intervention efficacy [10].Locus-specific observations were first to demonstrate the role of early life environmental influence on interindividual variation in methylation. For instance, postnatal maternal care in rats or childhood trauma in humans, affects DNA methylation levels at the NR3C1 gene in blood and the hippocampus; methylation levels at this locus then predict adult psychopathology [11][12][13]. Observations such as this have inspired many epigenome wide association studies (EWAS) to determine the epigenetic consequences of early life influences. Although epigenetic programming was originally envisioned to pertain to very early life factors (i.e., fetal programming, see for example [14]), it could be generalized to account for environmental influences throughout the lifecourse. Some of the best evidence for the later life effect of the environment on DNA methylation is from Fasanelli and colleagues, who showed (by EWAS) that smoking reversibly caused hypomethylation of the AHRR and FR2L3 genes in adults and that

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“…It has enabled previously impractical, studies of cell type heterogeneity, differentiation, and developmental trajectories 1 . However, the adaptation of RNA sequencing techniques from bulk samples to single cells did not progress without challenges.…”

Section: Introductionmentioning

confidence: 99%

netSmooth: Network-smoothing based imputation for single cell RNA-seq

Ronen

Akalin

2018

F1000Res

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Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth. This article is included in the gateway. RPackage Altuna Akalin ( )

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Revealing the vectors of cellular identity with single-cell genomics

Cited by 577 publications

References 222 publications

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

The neuropeptide NMU amplifies ILC2-driven allergic lung inflammation

Is Cellular Heterogeneity Merely a Confounder to be Removed from Epigenome-Wide Association Studies?

netSmooth: Network-smoothing based imputation for single cell RNA-seq

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