Apparent protein cloud point temperature determination using a low volume high-throughput cryogenic device in combination with automated imaging

Klijn, Marieke E.; Wöll, Anna Katharina; Hubbuch, Jürgen

doi:10.1007/s00449-019-02239-x

Cited by 3 publications

(11 citation statements)

References 61 publications

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“…The protein cloud-point temperature was determined using the setup as described by Klijn et al [18]. This setup employs a cryogenic device (EF600M 105, Grant Instruments, Cambridgeshire, UK) and a GoPro Hero4 camera (GoPro Inc., San Mateo, CA, USA) for image acquisition.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…The temperature at which LLPS is detected is mainly determined by protein-protein interaction strength, where higher T Cloud values are found for strong attractive forces and low T Cloud values for weaker attractive forces [5]. The accompanying increase in solution turbidity when LLPS occurs is a characteristic that is often exploited for T Cloud measurements by means of spectroscopic methods [6,7], light scattering detection [5,[8][9][10][11][12][13], or imaging and microscopy [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Preceding work presented a novel experimental setup to detect T Cloud in a high-throughput manner (60 samples per experiment) using a low sample volume (24 μL) [18]. To cover a wide range of protein-protein interaction strengths, the experimental setup included a cryogenic device.…”

Section: Introductionmentioning

confidence: 99%

“…Work reported by other research groups that employ visible light-based methodologies to determine T Cloud extract the mean gray level (MGL) [17], light intensity [14], or manually determine clouding by means of microscopy [15,16]. The methodology developed in our lab employs the total intensity difference (TID) of images, where each image is compared to the starting image to detect change over time [18]. The current work investigates the effects of different visible light image analysis strategies and determines whether differences in absolute T Cloud,app values occur as a result of the analysis strategies.…”

Section: Introductionmentioning

confidence: 99%

“…In the scope of this research, three image analysis strategies were used to investigate the effects on T Cloud,app determination and to explore a broader application of the developed experimental setup. This included two previously reported image analysis strategies for T Cloud detection, namely MGL [17] and TID [18]. As the camera used in this work captures color images, the mean red, blue, and green color pixel values were explored in addition to the gray color level.…”

Section: Introductionmentioning

confidence: 99%

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

Klijn

Hubbuch

2020

Bioprocess Biosyst Eng

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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 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 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.

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Section: Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Klijn

Hubbuch

2020

Bioprocess Biosyst Eng

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Investigation of the reversibility of freeze/thaw stress-induced protein instability using heat cycling as a function of different cryoprotectants

Wöll

Hubbuch

2020

Bioprocess Biosyst Eng

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Formulation conditions have a significant influence on the degree of freeze/thaw (FT) stress-induced protein instabilities. Adding cryoprotectants might stabilize the induced FT stress instabilities. However, a simple preservation of protein stability might be insufficient and further methods are necessary. This study aims to evaluate the addition of a heat cycle following FT application as a function of different cryoprotectants with lysozyme as exemplary protein. Sucrose and glycerol were shown to be the most effective cryoprotectants when compared to PEG200 and Tween20. In terms of heat-induced reversibility of aggregates, glycerol showed the best performance followed by sucrose, NaCl and Tween20 systems. The analysis was performed using a novel approach to visualize complex interplays by a clustering and data reduction scheme. In addition, solubility and structural integrity were measured and confirmed the obtained results. Graphic abstract

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Application of ultraviolet, visible, and infrared light imaging in protein-based biopharmaceutical formulation characterization and development studies

Klijn

Hubbuch

2021

European Journal of Pharmaceutics and Biopharmaceutics

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Apparent protein cloud point temperature determination using a low volume high-throughput cryogenic device in combination with automated imaging

Cited by 3 publications

References 61 publications

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

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

Investigation of the reversibility of freeze/thaw stress-induced protein instability using heat cycling as a function of different cryoprotectants

Application of ultraviolet, visible, and infrared light imaging in protein-based biopharmaceutical formulation characterization and development studies

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