# Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determination

Klijn, Marieke E.; Hubbuch, Jürgen

##### Abstract:
The protein cloud-point temperature (T$_{Cloud}$) is a known representative of protein–protein interaction strength and provides valuable information during the development and characterization of protein-based products, such as biopharmaceutics. A high-throughput low volume T$_{Cloud}$ detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent T$_{Cloud}$ (T$_{Cloud,app}$). As an understanding of the apparent nature is imperative to facilitate inter-study data comparability, the current work was performed to systematically evaluate the influence of 3 image analysis strategies and 2 experimental parameters (sample volume and cooling rate) on T$_{Cloud,app}$ detection of lysozyme. Different image analysis strategies showed that T$_{Cloud,app}$ is detectable by means of total pixel intensity difference and the total number of white pixels, but the latter is also able to extract the ice nucleation temperature. Experimental parameter variation showed a T$_{Cloud,app}$ depression for increasing cooling rates (0.1–0.5 °C/min), and larger sample volumes (5–24 μL). Exploratory thermographic data indicated this resulted from a temperature discrepancy between the measured temperature by the cryogenic device and the actual sample temperature. ... mehr

 Zugehörige Institution(en) am KIT Institut für Bio- und Lebensmitteltechnik (BLT) Publikationstyp Zeitschriftenaufsatz Publikationsjahr 2020 Sprache Englisch Identifikator ISSN: 0178-515X, 1432-0797, 1615-7591, 1615-7605 KITopen-ID: 1000127682 Erschienen in Bioprocess and biosystems engineering Verlag Springer Schlagwörter Freezing · Protein stability · Colloidal stability · Nucleation temperature · High-throughput screening · Liquid– liquid phase separation Nachgewiesen in ScopusWeb of Science
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