PLATERO: A calibration protocol for plate reader green fluorescence measurements

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.

Parsley: a web app for parsing data from plate readers

“…A growing number of analytical packages have been developed in recent years to automate the analysis of plate reader data. A list of currently available packages is shown in Supplementary Table S1 ( Fernandez-Ricaud et al 2016 , Sprouffske and Wagner 2016 , Coutin 2019a , Fernandez 2019 , Martin et al 2019 , Coutin et al 2020 , Fedorec et al 2020 , Kamrad et al 2020 , Small et al 2020 , Sprouffske 2020 , Cheng and Thrash 2021 , 2023 , Csibra 2021 , Kamrad 2021 , Midani et al 2021 , Shterev et al 2021 , Warchal 2021 , Wirth 2021 , Yáñez Feliú et al 2021 , Csibra and Stan 2022 , Montaño-Gutierrez et al 2022 , Osthege 2022 , Osthege et al 2022 , Small and McCormick 2023 , Swain 2023 , Blazanin 2023a , b , Fedorec 2023 , Giordano 2023 , González-Cebrián et al 2023 , González-Cebrián and Vignoni 2023 , Midani 2023 ). These tools fall broadly into three categories: those that tackle the analysis of microbial growth curves, those that quantify fluorescent protein expression, and those that evaluate phenotype (or drug) screening assays.…”

“…Data parsing functions can be found in a variety of packages [ Supplementary Table S2 ( Fernandez-Ricaud et al 2016 , Hughes 2016 , 2022 , Coutin 2019a , b , c , d , e , Fernandez 2019 , Martin et al 2019 , Coutin et al 2020 , Fedorec et al 2020 , Palakkan 2020 , Palakkan and Davies 2020 , Vegh 2020 , Csibra 2021 , Mante 2021 , Shterev et al 2021 , Wirth 2021 , Csibra and Stan 2022 , Montaño-Gutierrez et al 2022 , Osthege 2022 , Osthege et al 2022 , Blazanin 2023a , b , Fedorec 2023 , Giordano 2023 , González-Cebrián and Vignoni 2023 , González-Cebrián et al 2023 , Midani 2023 , Samineni et al 2023 , Swain 2023 )]. Of the 22 tools identified, parsers in 15 of these function in data extraction, 16 in data tidying and 11 in metadata joining, though capabilities in each area vary.…”

Statistical Machine Learning in Biomedical Engineering

“…Finally, Section 10.4.4 gives the results obtained for a second plate reader, showing how in this case, PLATERO warned of problems related to the consistency of measurements caused by the device. The datasets we used in all cases and the Matlab scripts running PLATERO on these datasets can be obtained from the Zenodo repository [215].…”

“…Thus, each observation combined a concentration level, a well, the gain used for its acquisition, and the number of replicas. The resulting data and test software are publicly available as a Zenodo repository [215].…”

“…We used the dataset obtained from the first plate reader [215]. This dataset was divided into two subsets: one for the Model Building process and another for the Model Validation.…”