ABSTRACT
Animal cell culture is widely used in biotechnology for the production of many biological products. In situ microscopes acquire images directly from cell suspensions and analyze the images in matters of cell concentration, cell size distribution and cell morphology. Their applicability was already proven for yeast and suspended mammalian cell cultivations. In this work the in situ microscope was utilized to measure the level of colonization of fibroblasts on microcarrier surfaces during cultivation. For this study the murine cell line NIH-3T3 was used in combination with Cytodex 1 microcarriers. Cultivations were carried out in a 5 L stirred tank bioreactor equipped with the in situ microscope. Images were obtained sequentially with the in situ microscope over the whole cultivation time (900 images per sequence, 7.5 h per sequence on average). For the microcarrier analysis an image analysis algorithm based on a neural network was developed and implemented in the microscope analysis software.
Subject(s)
Bioreactors , Cell Culture Techniques/instrumentation , Fibroblasts/cytology , Image Interpretation, Computer-Assisted/instrumentation , Image Interpretation, Computer-Assisted/methods , Microscopy/instrumentation , Animals , Cell Culture Techniques/methods , Computer Systems , Equipment Design , Equipment Failure Analysis , Mice , Microscopy/methods , NIH 3T3 Cells , Online SystemsABSTRACT
State variables throughout non-induced and induced cultivations of Pichia pastoris for the heterologous Rhizopus oryzae lipase (ROL) production were monitored with a multi-wavelength on-line fluorescence sensor. Based on this work, the use of in situ multi-wavelength fluorometry combined with chemometrics models (PLS-1 models) provided a quantitative prediction of biomass and substrates (glycerol and methanol) during non-induced and induced ROL production. The mean prediction errors for both variables were about 7% and 10%, respectively. ROL is also quite satisfactory estimated in the exponential growth phase with prediction errors similar to biomass and substrate variables. However, in the stationary phase, where proteolytic degradation of ROL is observed, the prediction error could get a value about 20%. This fact is due to the lower reproducibility of protein production from batch to batch.