Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

1] Department of Chemistry and Chemical Biology and Department of Physics, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA [2].
Nature (Impact Factor: 41.46). 05/2013; 498(7453). DOI: 10.1038/nature12172
Source: PubMed


Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

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Available from: Alex K Shalek, May 19, 2014
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    • "Some recent work has investigated cell–cell variability in RNA splicing. While a study using single-molecule FISH on two genes found that variability in splice isoform ratio exceeds the variability expected from random partitioning to a relatively modest level, quantitatively similar to the level we observed for polyadenylation isoforms (Waks et al, 2011), a single-cell transcriptomic study reported much more widespread bimodality in splice isoform usage (Shalek et al, 2013). BATBayes could help to reconcile these findings by accounting for both technical noise of single-cell transcriptomics and the probabilistic distribution of RNA molecules to isoforms. "
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    ABSTRACT: Cell-to-cell variability in gene expression is important for many processes in biology, including embryonic development and stem cell homeostasis. While heterogeneity of gene expression levels has been extensively studied, less attention has been paid to mRNA polyadenylation isoform choice. 3' untranslated regions regulate mRNA fate, and their choice is tightly controlled during development, but how 3' isoform usage varies within genetically and developmentally homogeneous cell populations has not been explored. Here, we perform genome-wide quantification of polyadenylation site usage in single mouse embryonic and neural stem cells using a novel single-cell transcriptomic method, BATSeq. By applying BATBayes, a statistical framework for analyzing single-cell isoform data, we find that while the developmental state of the cell globally determines isoform usage, single cells from the same state differ in the choice of isoforms. Notably this variation exceeds random selection with equal preference in all cells, a finding that was confirmed by RNA FISH data. Variability in 3' isoform choice has potential implications on functional cell-to-cell heterogeneity as well as utility in resolving cell populations. © 2015 The Authors. Published under the terms of the CC BY 4.0 license.
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    • "Third, after mapping reads to a known reference genome, several metrics can help diagnose possible problems with a sequencing library, and facilitate identification of cells that contain degraded RNA. These metrics include examining the expression levels of housekeeping genes (Treutlein et al., 2014), the number of expressed genes (Islam et al., 2014), the number of mapped reads (Shalek et al., 2014), the fraction of reads mapped to the endogenous genes (Shalek et al., 2013, 2014), the ratio of the number of reads mapped to the endogenous genes to the external RNA spike-ins (Brennecke et al., 2013), and the fraction of reads mapped to mitochondrial genes (Islam et al., 2014) (Figure 2A). Finally, evaluating the correlation between each gene's expression level in bulk-level control data, if it exists, and the gene's average expression across single cells can provide insights into the overall quality of the filtered single-cell data. "
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    ABSTRACT: The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications. Copyright © 2015 Elsevier Inc. All rights reserved.
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    • "Differential response between subjects could be explored in more depth with the goal of correlating and attributing genetic or environment factors (for example allergies that immuno activate cells and immuno inhibitory anti-histamines) to mechanistic changes in cell specific signaling pathways. Additionally, improvements are being made in single cell mRNA detection technologies and these are an attractive alternative to the CyTof technology due to enhanced flexibility of nucleic acid detection technologies (Ståhlberg and Bengtsson, 2010; Shalek et al., 2013). From these types of studies, predictive and stratifying biomarkers could be built and deployed to build clinical trials with better signal to noise ratios. "
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