Holographic Imaging of Insect Cell Cultures: Online Non-Invasive Monitoring of Adeno-Associated Virus Production and Cell Concentration

Abstract

The insect cell-baculovirus vector system has become one of the favorite platforms for the expression of viral vectors for vaccination and gene therapy purposes. As it is a lytic system, it is essential to balance maximum recombinant product expression with harvest time, minimizing product exposure to detrimental proteases. With this purpose, new bioprocess monitoring solutions are needed to accurately estimate culture progression. Herein, we used online digital holographic microscopy (DHM) to monitor bioreactor cultures of Sf9 insect cells. Batches of baculovirus-infected Sf9 cells producing recombinant adeno-associated virus (AAV) and non-infected cells were used to evaluate DHM prediction capabilities for viable cell concentration, culture viability and AAV titer. Over 30 cell-related optical attributes were quantified using DHM, followed by a forward stepwise regression to select the most significant (p < 0.05) parameters for each variable. We then applied multiple linear regression to obtain models which were able to predict culture variables with root mean squared errors (RMSE) of 7 × 105 cells/mL, 3% for cell viability and 2 × 103 AAV/cell for 3-fold cross-validation. Overall, this work shows that DHM can be implemented for online monitoring of Sf9 concentration and viability, also permitting to monitor product titer, namely AAV, or culture progression in lytic systems, making it a valuable tool to support the time of harvest decision and for the establishment of controlled feeding strategies.

Analytical methods for process and product characterization of recombinant adeno-associated virus-based gene therapies

“…Recently, the monitoring of rAAV production culture and prediction of the intra- and extracellular rAAV titer, among other process-related quality attributes, with in situ FS spectroscopy 110 and online digital holographic microscopy, 111 were reported. These methods employ soft sensors—estimators in the form of regression models or neural networks trained on existing process data—to process sensor data and predict process variables in real time.…”

Advancements in molecular design and bioprocessing of recombinant adeno‐associated virus gene delivery vectors using the insect‐cell baculovirus expression platform

“…[43,68] Infrared sensors for measuring the carbon dioxide evolution rate of cell culture [68] and recently reported on-line digital holographic microscopy for AAV production yield and cell concentration measurement have also been documented. [85] These tools provide a practical approach for process optimization and process control, including making the decision on culture harvest time.…”

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

“…Soft sensors have become an important tool within the QbD/PAT framework, as reviewed by Mandenius and Gustavsson (2015) , Randek and Mandenius (2018) , and Rathore et al (2021) . One reason is that they are often the only means of determining critical process parameters (CPP) or critical quality attributes (CQA) online at all ( Capito et al, 2015 ; Melcher et al, 2015 ; Sauer et al, 2019 ; Spann et al, 2019 ; Walch et al, 2019 ; Pais et al, 2020 ; Wasalathanthri et al, 2020a ). Making these quantities measurable by means of soft sensors, in turn, allows CPPs or CQAs to be closed-loop controlled ( Birle et al, 2015 ; Matthews et al, 2016 ; Voss et al, 2017 ; Brunner et al, 2020 ; Gomis-Fons et al, 2020 ).…”