Evaluating gene expression dynamics using pairwise RNA fish data

Matthieu Wyart, David Botstein, Ned S. Wingreen

    Research output: Contribution to journalArticle

    Abstract

    Recently, a novel approach has been developed to study gene expression in single cells with high time resolution using RNA Fluorescent In Situ Hybridization (FISH). The technique allows individual mRNAs to be counted with high accuracy in wildtype cells, but requires cells to be fixed; thus, each cell provides only a ''snapshot'' of gene expression. Here we show how and when RNA FISH data on pairs of genes can be used to reconstruct real-time dynamics from a collection of such snapshots. Using maximum-likelihood parameter estimation on synthetically generated, noisy FISH data, we show that dynamical programs of gene expression, such as cycles (e.g., the cell cycle) or switches between discrete states, can be accurately reconstructed. In the limit that mRNAs are produced in short-lived bursts, binary thresholding of the FISH data provides a robust way of reconstructing dynamics. In this regime, prior knowledge of the type of dynamics - cycle versus switch - is generally required and additional constraints, e.g., from triplet FISH measurements, may also be needed to fully constrain all parameters. As a demonstration, we apply the thresholding method to RNA FISH data obtained from single, unsynchronized cells of Saccharomyces cerevisiae. Our results support the existence of metabolic cycles and provide an estimate of global gene-expression noise. The approach to FISH data presented here can be applied in general to reconstruct dynamics from snapshots of pairs of correlated quantities including, for example, protein concentrations obtained from immunofluorescence assays.

    Original languageEnglish (US)
    Article numbere1000979
    JournalPLoS computational biology
    Volume6
    Issue number11
    DOIs
    StatePublished - Nov 1 2010

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    ASJC Scopus subject areas

    • Ecology, Evolution, Behavior and Systematics
    • Modeling and Simulation
    • Ecology
    • Molecular Biology
    • Genetics
    • Cellular and Molecular Neuroscience
    • Computational Theory and Mathematics

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