ArticleFrontiers in bioengineering and biotechnology2023
PLATERO: A calibration protocol for plate reader green fluorescence measurements.
Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Host-aware Identification of Intrinsic Gene Expression Biopart Parameters using Combinatorial Libraries.Nature communications · 2026Article
- Room-temperature mL-to-μL quantitative liquid concentration device for cyclone flow.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2024Article
- Parsley: a web app for parsing data from plate readers.Bioinformatics (Oxford, England) · 2023Article
- PLATERO: A calibration protocol for plate reader green fluorescence measurements.Frontiers in bioengineering and biotechnology · 2023Article
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6 authors.
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Abstract
One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by a certain fluorescent molecule, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we describe PLATERO, a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression that returns normalized, gain-independent, and comparable data, even if the acquisition was done at different device gain levels. Most important, the PLATERO embeds a Linearity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluates the sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, and perform the uncertainty and variability assessment are available in an open access repository.
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Registered trials
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