Enzyme activity deviates due to spatial and temporal temperature profiles in commercial microtiter plate readers

The Edge Effect in High-Throughput Proteomics: A Cautionary Tale

In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift. These batch effects can confound the detection of true signal differences, resulting in incorrect conclusions being drawn about significant biological effects or lack thereof. Here, we present an intraplate batch effect termed the edge effect arising from temperature gradients in multiwell plates, commonly reported in preclinical cell culture studies but not yet reported in a clinical proteomics setting. We present methods herein to ameliorate the phenomenon including proper assessment of heating techniques for multiwell plates and incorporation of surrogate standards, which can normalize for intraplate variation.

On the reproducibility of enzyme reactions and kinetic modelling

Enzyme reactions are highly dependent on reaction conditions. To ensure reproducibility of enzyme reaction parameters, experiments need to be carefully designed and kinetic modeling meticulously executed. Furthermore, to enable quality control of enzyme reaction parameters, the experimental conditions, the modeling process as well as the raw data need to be reported comprehensively. By taking these steps, enzyme reaction parameters can be open and FAIR (findable, accessible, interoperable, re-usable) as well as repeatable, replicable and reproducible. This review discusses these requirements and provides a practical guide to designing initial rate experiments for the determination of enzyme reaction parameters and gives an open, FAIR and re-editable example of the kinetic modeling of an enzyme reaction. Both the guide and example are scripted with Python in Jupyter Notebooks and are publicly available (https://fairdomhub.org/investigations/483/snapshots/1). Finally, the prerequisites of automated data analysis and machine learning algorithms are briefly discussed to provide further motivation for the comprehensive, open and FAIR reporting of enzyme reaction parameters.

The edge effect: A global problem. The trouble with culturing cells in 96-well plates

BACKGROUND

The use of 96-well plates is ubiquitous in preclinical studies. Corner and edge wells have been observed to be more prone to evaporation compared to interior wells.

METHODS

Mammalian cells were cultured in 96-well plates over a period of 72 h. VWR and Greiner plates were tested. MTS reagent was added, and metabolic activity was determined after 2 h.

RESULTS

When using VWR plates, cells showed a highly heterogeneous pattern of cell growth. The outer wells showed 35% lower metabolic activity than the central wells. Cells grown in rows two and three also grew sub-optimally (25% and 10% reduction compared to central wells). Greiner plates showed better homogeneity. Cells grown in the outer wells showed 16% lower metabolic activity while cells in rows two and three showed reductions of 7 and 1%, respectively. This edge effect was partially mitigated by storing the plates in loosely sealed wrapping during incubation. Placing a buffer between the wells of the plate further improved homogeneity for the Greiner plates.

CONCLUSION

Different brands of 96-well plates show different levels of the edge effect. Some clearly are inappropriate for such studies.

GENERAL SIGNIFICANCE

Each laboratory needs to determine their own optimum conditions for culturing cells empirically before continuing to use multiwell plates. Otherwise, large artifacts may arise, affecting the quality of data, with the potential of introducing type I or type II errors.

Biofilms vs. cities and humans vs. aliens - a tale of reproducibility in biofilms

Biofilms are complex and dynamic structures that include many more components than just viable cells. Therefore, the apparently simple goal of growing reproducible biofilms is often elusive. One of the challenges in defining reproducibility for biofilm research is that different research fields use a spectrum of parameters to define reproducibility for their particular application. For instance, is the researcher interested in achieving a similar population density, height of biofilm structures, or function of the biofilm in a certain ecosystem/industrial context? Within this article we categorize reproducibility into four different levels: level 1, no reproducibility; level 2, standard reproducibility; level 3, potential standard reproducibility; and level 4, total reproducibility. To better understand the need for these different levels of reproducibility, we expand on the 'cities of microbes' analogy for biofilms by imagining that a new civilization has reached the Earth's outskirts and starts studying the Earth's cities. This will provide a better sense of scale and illustrate how small details can impact profoundly on the growth and behavior of a biofilm and our understanding of reproducibility.

Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion

Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.

Customized microscale approach for optimizing two-phase bio-oxidations of alkanes with high reproducibility

Background

Numerous challenges remain to achieve industrially competitive space–time yields for bio-oxidations. The ability to rapidly screen bioconversion reactions for characterization and optimization is of major importance in bioprocess development and biocatalyst selection; studies at conventional lab scale are time consuming and labor intensive with low experimental throughput. The direct ω-oxyfunctionalization of aliphatic alkanes in a regio- and chemoselective manner is efficiently catalyzed by monooxygenases such as the AlkBGT enzyme complex from Pseudomonas putida under mild conditions. However, the adoption of microscale tools for these highly volatile substrates has been hindered by excessive evaporation and material incompatibility.

Results

This study developed and validated a robust high-throughput microwell platform for whole-cell two-liquid phase bio-oxidations of highly volatile n-alkanes. Using microwell plates machined from polytetrafluoroethylene and a sealing clamp, highly reproducible results were achieved with no significant variability such as edge effects determined. A design of experiment approach using a response surface methodology was adopted to systematically characterize the system and identify non-limiting conditions for a whole cell bioconversion of dodecane. Using resting E. coli cells to control cell concentration and reducing the fill volume it is possible to operate in non-limiting conditions with respect to oxygen and glucose whilst achieving relevant total product yields (combining 1-dodecanol, dodecanal and dodecanoic acid) of up to 1.5 mmol g_DCW⁻¹.

Conclusions

Overall, the developed microwell plate greatly improves experimental throughput, accelerating the screening procedures specifically for biocatalytic processes in non-conventional media. Its simplicity, robustness and standardization ensure high reliability of results.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0788-4) contains supplementary material, which is available to authorized users.