Real-time online monitoring of insect cell proliferation and baculovirus infection using digital differential holographic microscopy and machine learning

Jort J. Altenburg*, Maarten Klaverdijk, Damien Cabosart, Laurent Desmecht, Sonja S. Brunekreeft-Terlouw, Joshua Both, Vivian I.P. Tegelbeckers, Marieke L.P.M. Willekens, Linda van Oosten, Tessy A.H. Hick, Tom M.H. van der Aalst, Gorben P. Pijlman, Monique M. van Oers, René H. Wijffels, Dirk E. Martens

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

Real-time, detailed online information on cell cultures is essential for understanding modern biopharmaceutical production processes. The determination of key parameters, such as cell density and viability, is usually based on the offline sampling of bioreactors. Gathering offline samples is invasive, has a low time resolution, and risks altering or contaminating the production process. In contrast, measuring process parameters online provides more safety for the process, has a high time resolution, and thus can aid in timely process control actions. We used online double differential digital holographic microscopy (D3HM) and machine learning to perform non-invasive online cell concentration and viability monitoring of insect cell cultures in bioreactors. The performance of D3HM and the machine learning model was tested for a selected variety of baculovirus constructs, products, and multiplicities of infection (MOI). The results show that with online holographic microscopy insect cell proliferation and baculovirus infection can be monitored effectively in real time with high resolution for a broad range of process parameters and baculovirus constructs. The high-resolution data generated by D3HM showed the exact moment of peak cell densities and temporary events caused by feeding. Furthermore, D3HM allowed us to obtain information on the state of the cell culture at the individual cell level. Combining this detailed, real-time information about cell cultures with methodical machine learning models can increase process understanding, aid in decision-making, and allow for timely process control actions during bioreactor production of recombinant proteins.

Original languageEnglish
JournalBiotechnology Progress
Volume39
Issue number2
Early online date13 Dec 2022
DOIs
Publication statusPublished - Mar 2023

Keywords

  • bioengineering
  • biotechnology
  • cell culture
  • process sensing and control

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