Time-Dependent Multi-Light-Source Image Classification Combined With Automated Multidimensional Protein Phase Diagram Construction for Protein Phase Behavior Analysis

Abstract

Image-based protein phase diagram analysis is key for understanding and exploiting protein phase behavior in the biopharmaceutical field. However, required data analysis has become a notorious timeconsuming task since high-throughput screening approaches were implemented. A variety of computational tools have been developed to support analysis, but these tools primarily use end point visible light images. This study investigates the combined effect of end point and time-dependent image features obtained from cross-polarized and ultraviolet light features, supplementary to visible light, on protein phase diagram image classification. In addition, external validation was performed to evaluate the classification algorithm's applicability to support protein phase diagram scoring. The predicted protein phase behavior classes were subsequently used to automatically construct multidimensional protein phase diagrams to prevent image information loss without complicating the used image classification algorithm. Combining end point and time-dependent features from 3 light sources resulted in a balanced accuracy of 86.4 ± 4.3%, which is comparable to or better than more complex classifiers reported in literature. External validation resulted in a correct formulation classification rate of 91.7%. Subsequent automated construction of the multidimensional protein phase diagrams, using predicted classes, allowed visualization of details such as crystallization rate and protein phase behavior type coexistence.

Spectroscopic insights into multi-phase protein crystallization in complex lysate using Raman spectroscopy and a particle-free bypass

“…Compared to crystals of a chemical substance, the larger size of a biological molecule increases the complexity of the protein crystal. Therefore, extensive empirical screenings ( Haas and Drenth, 1999 ; Asherie, 2004 ), precise, automated high-throughput (HT) techniques ( Baumgartner et al, 2015 ; Huettmann et al, 2015 ), and HT analytics ( Klijn and Hubbuch, 2020 ; Klijn and Hubbuch, 2021 ; Wegner et al, 2022 ; Wegner and Hubbuch, 2022 ) are essential to find optimal process conditions.…”

Rapid Analysis for Multicomponent High-Throughput Crystallization Screening: Combination of UV–Vis Spectroscopy and Chemometrics

“…Occurrences during protein crystallization are commonly investigated in empirical phase diagrams in high-throughput (HT) screenings. Applied analytics are image-based analytical tools, , single-crystal X-ray diffraction, ,, or dynamic light scattering (DLS) measurements. − These analytics are optimized for pure solutions or crystals, do not allow fast differentiation between different species, and can only distinguish between salt and protein crystals to some extent. When protein crystals are grown in protein mixtures or complex harvest broth, a fast, accurate, and reliable analytical method is essential for screening purposes.…”