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Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry.

ABSTRACT: Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneous relative growth and protein synthesis rates of multiplexed microbial populations across many conditions. These measurements generate quantitative profiles of dynamically evolving protein synthesis and degradation rates. We demonstrate this setup in the context of gene regulation of the unfolded protein response (UPR) of Saccharomyces cerevisiae and uncover a dynamic and complex landscape of gene expression, growth dynamics and proteolysis following perturbations.

SUBMITTER: Zuleta IA

PROVIDER: S-EPMC4016179 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry.

Zuleta Ignacio A IA Aranda-Díaz Andrés A Li Hao H El-Samad Hana H

Nature methods 20140309 4

Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneo ...[more]

PMID: 24608180

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Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Dataset Information

Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry.

Publications

Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry.

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