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Beyond comparisons of means: understanding changes in gene expression at the single-cell level.

ABSTRACT: Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method's performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS.

SUBMITTER: Vallejos CA

PROVIDER: S-EPMC4832562 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Beyond comparisons of means: understanding changes in gene expression at the single-cell level.

Vallejos Catalina A CA Richardson Sylvia S Marioni John C JC

Genome biology 20160415

Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to- ...[more]

PMID: 27083558

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Project description:Key microbial processes in many bacterial species are heterogeneously expressed in single cells of bacterial populations. However, the paucity of adequate molecular tools for live, real-time monitoring of multiple-gene expression at the single-cell level has limited the understanding of phenotypic heterogeneity. To investigate phenotypic heterogeneity in the ubiquitous opportunistic pathogen Pseudomonas aeruginosa, a genetic tool that allows gauging multiple-gene expression at the single-cell level has been generated. This tool, named pRGC, consists of a promoter-probe vector for transcriptional fusions that carries three reporter genes coding for the fluorescent proteins mCherry, green fluorescent protein (GFP), and cyan fluorescent protein (CFP). The pRGC vector has been characterized and validated via single-cell gene expression analysis of both constitutive and iron-regulated promoters, showing clear discrimination of the three fluorescence signals in single cells of a P. aeruginosa population without the need for image processing for spectral cross talk correction. In addition, two pRGC variants have been generated for either (i) integration of the reporter gene cassette into a single neutral site of P. aeruginosa chromosome that is suitable for long-term experiments in the absence of antibiotic selection or (ii) replication in bacterial genera other than Pseudomonas The easy-to-use genetic tools generated in this study will allow rapid and cost-effective investigation of multiple-gene expression in populations of environmental and pathogenic bacteria, hopefully advancing the understanding of microbial phenotypic heterogeneity.IMPORTANCE Within a bacterial population, single cells can differently express some genes, even though they are genetically identical and experience the same chemical and physical stimuli. This phenomenon, known as phenotypic heterogeneity, is mainly driven by gene expression noise and results in the emergence of bacterial subpopulations with distinct phenotypes. The analysis of gene expression at the single-cell level has shown that phenotypic heterogeneity is associated with key bacterial processes, including competence, sporulation, and persistence. In this study, new genetic tools have been generated that allow easy cloning of up to three promoters upstream of distinct fluorescent genes, making it possible to gauge multiple-gene expression at the single-cell level by fluorescence microscopy without the need for advanced image-processing procedures. A proof of concept has been provided by investigating iron uptake and iron storage gene expression in response to iron availability in P. aeruginosa.

Dataset Information

Beyond comparisons of means: understanding changes in gene expression at the single-cell level.

Publications

Beyond comparisons of means: understanding changes in gene expression at the single-cell level.

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