Bioprocess and Biosystems Engineering

, Volume 42, Issue 5, pp 665–675 | Cite as

Automated image processing as an analytical tool in cell cryopreservation for bioprocess development

  • Sarah Gretzinger
  • Stefanie Limbrunner
  • Jürgen HubbuchEmail author
Research Paper


The continuous availability of cells with defined cell characteristics represents a crucial issue in the biopharmaceutical and cell therapy industry. Here, development of cell banks with a long-term stability is essential and ensured by a cryopreservation strategy. The strategy needs to be optimized for each cell application individually and usually comprises controlled freezing, storage at ultra-low temperature, and fast thawing of cells. This approach is implemented by the development of master and working cell banks. Currently, empirical cryopreservation strategy development is standard, but a knowledge-based approach would be highly advantageous. In this article, we report the development of a video-based tool for the characterisation of freezing and thawing behaviour in cryopreservation process to enable a more knowledge-based cryopreservation process development. A successful tool validation was performed with a model cryopreservation process for the β-cell line INS-1E. Performance was evaluated for two working volumes (1.0 mL and 2.0 mL), based on freezing-thawing rates (20 °C to − 80 °C) and cell recovery and increase of biomass, to determine tool flexibility and practicality. Evaluation confirmed flexibility by correctly identifying a delay in freezing and thawing for the larger working volume. Further more, a decrease in cell recovery from 0.94 (± 0.14) % using 1.0 mL working volume to 0.61 (± 0.05) % using a 2.0 mL working volume displays tool practicality. The video-based tool proposed in this study presents a powerful tool for cell-specific optimisation of cryopreservation protocols. This can facilitate faster and more knowledge-based cryopreservation process development

Graphical abstract

In this study, a video-based analytical tool was developed for the characterisation of freezing and thawing behaviour in cryopreservation process development. Evaluation of the practicality and flexibility of the developed tool was done based on a scale-up case study with the cell line INS-1E. Here, the influence of sample working volume on process performance was investigated. Increasing the volume from 1to 2 mL led to a delay in freezing and thawing behaviour which caused cell recovery loss. We believe that the developed tool will facilitate more directed and systematic cryopreservation process development.


Automated image processing Controlled freezing Cryopreservation Process development tool Thawing 



Dimethyl sulfoxide


Phosphate-buffered saline


Room temperature


Working volume


Standard deviation






Height to diameter ratio


Good manufacturing practice



We thank Prof. Hartwig and co-workers from the Institute of Food Chemistry at the Karlsruhe Institute of Technology (KIT) for sharing their labs and equipment and, additionally, the staff of the workshops form the Institute of Functional Interface and Institute for Inorganic Chemistry at the KIT for their assistance. We would also like to thank Prof. Maechler, from the Department of Cell Physiology and Metabolism at the University of Geneva Medical Centre, Switzerland for providing the rat beta-cell line INS-1E. This work was supported by the Helmholtz Program “BioInterfaces in Technology and Medicine (BIFTM).

Compliance with ethical standards

Conflict of interest

The authors have no conflicts to declare.


  1. 1.
    Grout B, Morris J, McLellan M (1990) Cryopreservation and the maintenance of cell lines. Trends Biotechnol 8:293–297CrossRefGoogle Scholar
  2. 2.
    Heidemann R, Lünse S, Tran D, Zhang C (2010) Characterization of cell-banking parameters for the cryopreservation of mammalian cell lines in 100-ML cryobags. Biotechnol Prog 26:1154–1163Google Scholar
  3. 3.
    Kleman MI, Oellers K, Lullau E (2008) Optimal conditions for freezing CHO-S and HEK293-EBNA cell lines: Influence of ME2SO, freeze density, and PEI-mediated transfection on devitalization and growth of cells, and expression of recombinant protein. Biotechnol Bioeng 100:911–922CrossRefGoogle Scholar
  4. 4.
    Higgins AZ, Cullen DK, LaPlaca MC, Karlsson JOM (2011) Effects of freezing profile parameters on the survival of cryopreserved rat embryonic neural cells. J Neurosci Methods 201:9–16CrossRefGoogle Scholar
  5. 5.
    Wong CC, Loewke KE, Bossert NL, Behr B et al (2010) Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat Biotechnol 28:1115–1121CrossRefGoogle Scholar
  6. 6.
    Zhou Y, Fowler Z, Cheng A, Sever R (2012) Improve process uniformity and cell viability in cryopreservation. Bioprocess Int 10:70–76Google Scholar
  7. 7.
    Massie I, Selden C, Hodgson H, Fuller B et al (2014) GMP cryopreservation of large volumes of cells for regenerative medicine: active control of the freezing process. Tissue Eng C Methods 20:693–702CrossRefGoogle Scholar
  8. 8.
    Vertes AA, Qureshi N, Caplan AI, Babiss LE (eds) (2015) Stem cells in regenerative medicine: science, regulation, and business strategies. Wiley, OxfordGoogle Scholar
  9. 9.
    Li Y, Ma T (2012) Bioprocessing of cryopreservation for large-scale banking of human pluripotent stem cells. Biores Open Access 1:205–214CrossRefGoogle Scholar
  10. 10.
    Hunt CJ (2007) The banking and cryopreservation of human embryonic stem cells. Transfus Med Hemother 34:293–304CrossRefGoogle Scholar
  11. 11.
    Freimark D, Sehl C, Weber C, Hudel K et al (2011) Systematic parameter optimization of a Me2SO- and serum-free cryopreservation protocol for human mesenchymal stem cells. Cryobiology 63:67–75CrossRefGoogle Scholar
  12. 12.
    De Loecker W, Koptelov VA, Grischenko VI, De Loecker P (1998) Effects of cell concentration on viability and metabolic activity during cryopreservation. Cryobiology 37:103–109CrossRefGoogle Scholar
  13. 13.
    Ware CB, Nelson AM, Blau CA (2005) Controlled-rate freezing of human ES cells. Biotechniques 38:879–883CrossRefGoogle Scholar
  14. 14.
    Routledge C, Armitage WJ (2003) Cryopreservation of cornea: a low cooling rate improves functional survival of endothelium after freezing and thawing. Cryobiology 46:277–283CrossRefGoogle Scholar
  15. 15.
    Woods EJ, Perry BC, Hockema JJ, Larson L et al (2009) Optimized cryopreservation method for human dental pulp-derived stem cells and their tissues of origin for banking and clinical use. Cryobiology 59:150–157CrossRefGoogle Scholar
  16. 16.
    Kilbride P, Lamb S, Milne S, Gibbons S et al (2016) Spatial considerations during cryopreservation of a large volume sample. Cryobiology 73:47–54CrossRefGoogle Scholar
  17. 17.
    Kilbride P, Morris GJ, Milne S, Fuller B et al (2014) A scale down process for the development of large volume cryopreservation. Cryobiology 69:367–375CrossRefGoogle Scholar
  18. 18.
    Karlsson JO, Eroglu A, Toth TL, Cravalho EG et al (1996) Fertilization and development of mouse oocytes cryopreserved using a theoretically optimized protocol. Hum Reprod 11:1296–1305CrossRefGoogle Scholar
  19. 19.
    Dumont F, Marechal P-A, Gervais P (2004) Cell size and water permeability as determining factors for cell viability after freezing at different cooling rates. Appl Environ Microbiol 70:268–272CrossRefGoogle Scholar
  20. 20.
    Buhl T, Legler TJ, Rosenberger A, Schardt A et al (2012) Controlled-rate freezer cryopreservation of highly concentrated peripheral blood mononuclear cells results in higher cell yields and superior autologous T-cell stimulation for dendritic cell-based immunotherapy. Cancer Immunol Immunother 61:2021–2031CrossRefGoogle Scholar
  21. 21.
    Hunt CJ (2011) Cryopreservation of human stem cells for clinical application: a review. Transfus Med Hemother 38:107–123CrossRefGoogle Scholar
  22. 22.
    Borsos Á, Szilágyi B, Agachi P, Nagy ZK (2017) Real-time image processing based online feedback control system for cooling batch crystallization. Org Process Res Dev 21:511–519CrossRefGoogle Scholar
  23. 23.
    Moeglein WA, Griswold R, Mehdi BL, Browning ND et al (2017) Applying shot boundary detection for automated crystal growth analysis during in situ transmission electron microscope experiments. Adv Struct Chem Imaging 3:2CrossRefGoogle Scholar
  24. 24.
    Kumar K, Kumar V, Lal J, Kaur H et al (2017) A simple 2D composite image analysis technique for the crystal growth study of L -ascorbic acid. Microsc Res Tech 80:615–626CrossRefGoogle Scholar
  25. 25.
    Smith MAL, Spomer LA (1987) Direct quantification of in vitro cell growth through image analysis. Vitr Cell Dev Biol 23:67–70CrossRefGoogle Scholar
  26. 26.
    Thomas CR, Paul GC (1996) Applications of image analysis in cell technology. Curr Opin Biotechnol 7:35–45CrossRefGoogle Scholar
  27. 27.
    Selinummi J, Seppälä J, Yli-Harja O, Puhakka J (2005) Software for quantification of labeled bacteria from digital microscope images by automated image analysis. Biotechniques 39:859–863CrossRefGoogle Scholar
  28. 28.
    Levin-Schwarz Y, Sparta DR, Cheer JF, Adali T(2017) Parameter-free automated extraction of neuronal signals from calcium imaging data. In: IEEE Int. Conf. Acoust. Speech Signal Process, pp 1033–1037Google Scholar
  29. 29.
    Liang C-C, Park AY, Guan J-L (2007) In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro. Nat Protoc 2:329–333CrossRefGoogle Scholar
  30. 30.
    Spindler R, Rosenhahn B, Hofmann N, Glasmacher B (2012) Video analysis of osmotic cell response during cryopreservation. Cryobiology 64:250–260CrossRefGoogle Scholar
  31. 31.
    Merglen A, Theander S, Rubi B, Chaffard G et al (2004) Glucose sensitivity and metabolism-secretion coupling studied during two-year continuous culture in INS-1E insulinoma cells. Endocrinology 145:667–678CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Sarah Gretzinger
    • 1
    • 2
  • Stefanie Limbrunner
    • 2
  • Jürgen Hubbuch
    • 1
    • 2
    Email author
  1. 1.Institute of Functional Interfaces (IFG)Karlsruhe Institute of Technology (KIT)Eggenstein-LeopoldshafenGermany
  2. 2.Institute of Engineering in Life Sciences, Section IV: Biomolecular Separation EngineeringKarlsruhe Institute of Technology (KIT)KarlsruheGermany

Personalised recommendations