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

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. 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 T$_{Cloud,app}$ 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 T$_{Cloud}$ detection method, showcases its detection precision, and broadens the applicability of the experimental setup