Comparative analysis of three studies measuring fluorescence from engineered bacterial genetic constructs

Reproducibility is a key challenge of synthetic biology, but the foundation of reproducibility is only as solid as the reference materials it is built upon. Here we focus on the reproducibility of fluorescence measurements from bacteria transformed with engineered genetic constructs. This comparative analysis comprises three large interlaboratory studies using flow cytometry and plate readers, identical genetic constructs, and compatible unit calibration protocols. Across all three studies, we find similarly high precision in the calibrants used for plate readers. We also find that fluorescence measurements agree closely across the flow cytometry results and two years of plate reader results, with an average standard deviation of 1.52-fold, while the third year of plate reader results are consistently shifted by more than an order of magnitude, with an average shift of 28.9-fold. Analyzing possible sources of error indicates this shift is due to incorrect preparation of the fluorescein calibrant. These findings suggest that measuring fluorescence from engineered constructs is highly reproducible, but also that there is a critical need for access to quality controlled fluorescent calibrants for plate readers.

How to certify reference materials: by voting or by exact weights

The problems of using reference materials (RM) of oil and petroleum products humidity is discussed for different programs of certification: according to accuracy of the preparation procedure (RM-PP), according to the results of the interlaboratory studies (RM-INTERLAB) and using reference methods (RM-RM). Similar NIST samples (SRM 2271 and 272) were used for comparison. The problem of using CO-INTERLAB for control the accuracy of standard methods is the absence of the true value, this task is not even raised. "Accuracy control" has a different emphasis in this situation: RM-INTERLAB allow to select, "voting by majority" among the general population of the laboratory and cut the others. Therefore, their main application is qualification tasks. This approach is basically incorrect for analyzers. In the case of RM-PP and RM-RM, the main problem is the difference in the composition of real samples and ideal matrices of RM’s. Their main application is the control the accuracy of the analyzers in the absence of interfering influences. The "cheap" RM’s production technologies do not allow the omprehensive control of the real oil samples. The complication of oil technologies and the use of heavy oils in refining could provide the progress in RM’s.

Bivariate confidence probability plots as a method to test the accuracy and variability of microbiological measures

IntroductionIn an interlaboratory calibration analysis to validate a methodology that will be proposed as a European standard for domestic laundry disinfection, tests were carried out to detect if there are different behaviors in the measurements regarding accuracies and variabilities. Interlaboratory tests using different doses of disinfectant and microorganisms were carried out. ISO 5725-2 and ISO 13528 form the basis of validations of quantitative methods, providing validation specifications for interlaboratory studies. However, a need for a simple graphical method to detect interlaboratory differences in accuracy and variability was observed.ObjectivesThe general goal of this work is to present a new exploratory methodology, graphical and easy to interpret, that can determine the accuracy and variability (precision) of a variable, and compare it to the methodology applied in ISO 5725-2 and ISO 13528.MethodsWe used confidence probability plots of the multivariate Student’s t-distribution to observe the accuracy and variability of microbiological measures carried out by different laboratories during a ring trial exercise. A function in R was built for this purpose: Miriam.analysis.ellipse(Y, factor_a, eel.plot = “ t-Student”). The different observations of accuracy and variability are represented in the ellipses. If any of the points are outside the ellipse with 95% confidence, we can assume a deviation in accuracy and / or variability.ResultsTwo examples are provided with real microbiological data (logarithmic unit reductions (LR) for Pseudomonas aeruginosa, Escherichia coli, Staphilococcus aureus, Enterococcus hirae, Candida albicans and microbial counts in water (WW)). The proposed new method allowed us to detect possible deviations in the WWMEA variable and we believe it has future application for the rapid control of microbiological measures.