A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation.

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies

H2-Tankstellen-Konfigurator

Anhand einer Fallstudie wird die Arbeit mit dem von der Freiberg Institut für Energie und Klimaökonomie GmbH erschaffenen H2-Tankstellen-Konfigurators dargestellt. Durch die Variation der Wasserstoffversorgung einer fiktiv geplanten H2-Tankstelle wird verdeutlicht, wie die Optimierungssoftware Edgar die Dimensionierung der Anlagentechnik für eine optimale Konfiguration und minimale Kosten verändert.A case study is used to illustrate the work with the H2 filling station configurator created by the Freiberg Institute for Energy and Climate Economics GmbH. By varying the hydrogen supply of a fictitiously planned H2 filling station, it is illustrated how the optimization software Edgar changes the dimensioning of the plant technology for an optimal configuration and minimal costs

H2-Tankstellen-Konfigurator

Anhand einer Fallstudie wird die Arbeit mit dem von der Freiberg Institut für Energie und Klimaökonomie GmbH erschaffenen H2-Tankstellen-Konfigurators dargestellt. Durch die Variation der Wasserstoffversorgung einer fiktiv geplanten H2-Tankstelle wird verdeutlicht, wie die Optimierungssoftware Edgar die Dimensionierung der Anlagentechnik für eine optimale Konfiguration und minimale Kosten verändert.A case study is used to illustrate the work with the H2 filling station configurator created by the Freiberg Institute for Energy and Climate Economics GmbH. By varying the hydrogen supply of a fictitiously planned H2 filling station, it is illustrated how the optimization software Edgar changes the dimensioning of the plant technology for an optimal configuration and minimal costs

H2-Tankstellen-Konfigurator

Anhand einer Fallstudie wird die Arbeit mit dem von der Freiberg Institut für Energie und Klimaökonomie GmbH erschaffenen H2-Tankstellen-Konfigurators dargestellt. Durch die Variation der Wasserstoffversorgung einer fiktiv geplanten H2-Tankstelle wird verdeutlicht, wie die Optimierungssoftware Edgar die Dimensionierung der Anlagentechnik für eine optimale Konfiguration und minimale Kosten verändert.A case study is used to illustrate the work with the H2 filling station configurator created by the Freiberg Institute for Energy and Climate Economics GmbH. By varying the hydrogen supply of a fictitiously planned H2 filling station, it is illustrated how the optimization software Edgar changes the dimensioning of the plant technology for an optimal configuration and minimal costs

Reproducibility and accuracy of microscale thermophoresis in the NanoTemper Monolith: a multi laboratory benchmark study (European Biophysics Journal, (2021), 50, 3-4, (411-427), 10.1007/s00249-021-01532-6)

Publisher Copyright: © The Author(s) 2021.The article “Reproducibility and accuracy of microscale thermophoresis in the NanoTemper Monolith: a multi laboratory benchmark study” written by López-Méndez, B., Baron, B., Brautigam, C. A., Jowitt, T. A., Knauer, S. H., Uebel, S., Williams, M. A., Sedivy, A., Abian, O., Abreu, C., Adamczyk, M., Bal, W., Berger, S., Buell, A. K., Carolis, C., Daviter, T., Fish, A., Garcia-Alai, M., Guenther, C., Hamacek, J., Holková, J., Houser, J., Johnson, C., Kelly, S., Leech, A., Mas, C., Matulis, D., McLaughlin, S. H., Montserret, R., Nasreddine, R., Nehmé, R., Nguyen, Q., Ortega-Alarcón, D., Perez, K., Pirc, K., Piszczek, G., Podobnik, M., Rodrigo, N., Rokov-Plavec, J., Schaefer, S., Sharpe, T., Southall, J., Staunton, D., Tavares, P., Vanek, O., Weyand, M., Wu, D. was originally published Online First without Open Access. After publication in volume 50, issue 3–4, pages 411–427 the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed topublishersversionpublishe

Reproducibility and accuracy of microscale thermophoresis in the NanoTemper Monolith: a multi laboratory benchmark study

Microscale thermophoresis (MST), and the closely related Temperature Related Intensity Change (TRIC), are synonyms for a recently developed measurement technique in the field of biophysics to quantify biomolecular interactions, using the (capillary-based) NanoTemper Monolith and (multiwell plate-based) Dianthus instruments. Although this technique has been extensively used within the scientific community due to its low sample consumption, ease of use, and ubiquitous applicability, MST/TRIC has not enjoyed the unambiguous acceptance from biophysicists afforded to other biophysical techniques like isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR). This might be attributed to several facts, e.g., that various (not fully understood) effects are contributing to the signal, that the technique is licensed to only a single instrument developer, NanoTemper Technology, and that its reliability and reproducibility have never been tested independently and systematically. Thus, a working group of ARBRE-MOBIEU has set up a benchmark study on MST/TRIC to assess this technique as a method to characterize biomolecular interactions. Here we present the results of this study involving 32 scientific groups within Europe and two groups from the US, carrying out experiments on 40 Monolith instruments, employing a standard operation procedure and centrally prepared samples. A protein–small molecule interaction, a newly developed protein–protein interaction system and a pure dye were used as test systems. We characterized the instrument properties and evaluated instrument performance, reproducibility, the effect of different analysis tools, the influence of the experimenter during data analysis, and thus the overall reliability of this method

Examples for the determination of radial magnification errors.

<p>(A) Radial intensity profile measured in scans of the precision mask. Blue lines are experimental scans, and shaded areas indicate the regions expected to be illuminated on the basis of the known mask geometry. In this example, the increasing difference between the edges corresponds to a calculated radial magnification error of -3.1%. (B—D) Examples for differences between the experimentally measured positions of the light/dark transitions (blue circles, arbitrarily aligned for absolute mask position) and the known edge distances of the mask. The solid lines indicate the linear or polynomial fit. (B) Approximately linear magnification error with a slope corresponding to an error of -0.04%. Also indicated as thin lines are the confidence intervals of the linear regression. (C) A bimodal shift pattern of left and right edges, likely resulting from out-of-focus location of the mask, with radial magnification error of -1.7%. (D) A non-linear distortion leading to a radial magnification error of -0.53% in the <i>s</i>-values from the analysis of back-transformed data. The thin grey lines in C and D indicate the best linear fit through all data points.</p

Distributions of calculated BSA monomer signals for the different kits and the different optical systems.

<p>The box-and-whisker plots indicate the central 50% of the data as solid line and draw the smaller and larger 25% percentiles as individual circles. The median for each group is displayed as vertical line.</p