Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking

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    16 Citations (Scopus)

    Abstract

    Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of >70% and a false-positive detection rate of

    Original languageEnglish
    Pages (from-to)4057-4062
    Number of pages6
    JournalMolecular Biology of the Cell (Print)
    Volume26
    Issue number22
    DOIs
    Publication statusPublished - 2015

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