Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud-point temperature determ
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RESEARCH PAPER
Influence of image analysis strategy, cooling rate, and sample volume on apparent protein cloud‑point temperature determination Marieke E. Klijn1 · Jürgen Hubbuch1 Received: 24 June 2020 / Accepted: 16 October 2020 © The Author(s) 2020
Abstract The protein cloud-point temperature (TCloud) 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 TCloud detection method was introduced in preceding work, where it was concluded that the extracted value is an apparent TCloud (TCloud,app). As an understanding of the apparent nature is imperative to facilitate interstudy 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 TCloud,app detection of lysozyme. Different image analysis strategies showed that TCloud,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 TCloud,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. Literature validation confirmed that the discrepancy does not affect the relative inter-study comparability of the samples, regardless of the image analysis strategy or experimental parameters. Additionally, high measurement precision was demonstrated, as TCloud,app changes were detectable down to a sample volume of only 5 μL and for 0.1 °C/min cooling rate increments. This work explains the apparent nature of the TCloud detection method, showcases its detection precision, and broadens the applicability of the experimental setup. Keywords Freezing · Protein stability · Colloidal stability · Nucleation temperature · High-throughput screening · Liquid– liquid phase separation
Introduction Quantification of protein–protein interactions can be used to assess the colloidal stability of biopharmaceutical formulations [1] or to identify environmental conditions which induce desired phase transitions for separation techniques [2] and 3-D structure determination of proteins [3]. One of the empirical parameters to quantify protein–protein Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00449-020-02465-8) contains supplementary material, which is available to authorized users. * Jürgen Hubbuch [email protected] 1
Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Fritz‑Haber‑Weg 2, 76131 Karlsruhe, Germany
interactions is the protein cloud-point temperature (TCloud) [4]. This t
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