On-line bioprocess monitoring with a multi-wavelength fluorescence sensor using multivariate calibration

Advancing 2D fluorescence online monitoring in microtiter plates by separating scattered light and fluorescence measurement, using a tunable emission monochromator

Online fluorescence monitoring has become a key technology in modern bioprocess development, as it provides in-depth process knowledge at comparably low costs. In particular, the technology is widely established for high-throughput microbioreactor cultivation systems, due to its noninvasive character. For microtiter plates, previously also multi-wavelength 2D fluorescence monitoring was developed. To overcome an observed limitation of fluorescence sensitivity, this study presents a modified spectroscopic setup, including a tunable emission monochromator. The new optical component enables the separation of the scattered and fluorescent light measurements, which allows for the adjustment of integration times of the charge-coupled device detector. The resulting increased fluorescence sensitivity positively affected the performance of principal component analysis for spectral data of Escherichia coli batch cultivation experiments with varying sorbitol concentration supplementation. In direct comparison with spectral data recorded at short integration times, more biologically consistent signal dynamics were calculated. Furthermore, during partial least square regression for E. coli cultivation experiments with varying glucose concentrations, improved modeling performance was observed. Especially, for the growth-uncoupled acetate concentration, a considerable improvement of the root-mean-square error from 0.25 to 0.17 g/L was achieved. In conclusion, the modified setup represents another important step in advancing 2D fluorescence monitoring in microtiter plates.

Online 2D Fluorescence Monitoring in Microtiter Plates Allows Prediction of Cultivation Parameters and Considerable Reduction in Sampling Efforts for Parallel Cultivations of Hansenula polymorpha

Multi-wavelength (2D) fluorescence spectroscopy represents an important step towards exploiting the monitoring potential of microtiter plates (MTPs) during early-stage bioprocess development. In combination with multivariate data analysis (MVDA), important process information can be obtained, while repetitive, cost-intensive sample analytics can be reduced. This study provides a comprehensive experimental dataset of online and offline measurements for batch cultures of Hansenula polymorpha. In the first step, principal component analysis (PCA) was used to assess spectral data quality. Secondly, partial least-squares (PLS) regression models were generated, based on spectral data of two cultivation conditions and offline samples for glycerol, cell dry weight, and pH value. Thereby, the time-wise resolution increased 12-fold compared to the offline sampling interval of 6 h. The PLS models were validated using offline samples of a shorter sampling interval. Very good model transferability was shown during the PLS model application to the spectral data of cultures with six varying initial cultivation conditions. For all the predicted variables, a relative root-mean-square error (RMSE) below 6% was obtained. Based on the findings, the initial experimental strategy was re-evaluated and a more practical approach with minimised sampling effort and elevated experimental throughput was proposed. In conclusion, the study underlines the high potential of multi-wavelength (2D) fluorescence spectroscopy and provides an evaluation workflow for PLS modelling in microtiter plates.

Excitation–emission matrix fluorescence spectroscopy for cell viability testing in UV-treated cell culture

Monitoring of cells viability is essential in a number of biomedical applications, including cell-based sensors, cell-based microsystems, and cell-based assays. The use of spectroscopic techniques for such purposes is especially advantageous since they are non-invasive, label-free, and non-destructive. However, such an approach must include chemometric analysis of the data to assess the information on cells viability. In the presented article we demonstrate, that excitation–emission matrix (EEM) fluorescence spectroscopy can be applied for reliable determination of cells viability due to the high correlation of EEM fluorescence data with the MTT test data. A375 cells (malignant melanoma) were exposed to UV radiation as a physical stress factor, resulting in a decrease of viability up to ca. 20%, confirmed by the standard MTT test. They were also characterized by means of EEM fluorescence spectroscopy coupled with unfolded partial least squares (UPLS) regression. Statistical evaluation revealed high accordance of the two methods of viability testing in terms of accuracy, precision, and correlation. The presented results are very promising for the development of spectroscopic soft sensors that can be applied for drug screening, biocompatibility testing, tissue engineering, and pharmacodynamic studies.

Excitation-emission matrix fluorescence spectroscopy can be applied for label-free and non-destructive determination of cells viability, which is promising methodology for drug screening, biocompatibility testing, or pharmacodynamic studies.

Soft sensor based on 2D-fluorescence and process data enabling real-time estimation of biomass in Escherichia coli cultivations

In bioprocesses, specific process responses such as the biomass cannot typically be measured directly on‐line, since analytical sampling is associated with unavoidable time delays. Accessing those responses in real‐time is essential for Quality by Design and process analytical technology concepts. Soft sensors overcome these limitations by indirectly measuring the variables of interest using a previously derived model and actual process data in real time. In this study, a biomass soft sensor based on 2D‐fluorescence data and process data, was developed for a comprehensive study with a 20‐L experimental design, for Escherichia coli fed‐batch cultivations. A multivariate adaptive regression splines algorithm was applied to 2D‐fluorescence spectra and process data, to estimate the biomass concentration at any time during the process. Prediction errors of 4.9% (0.99 g/L) for validation and 3.8% (0.69 g/L) for new data (external validation), were obtained. Using principal component and parallel factor analyses on the 2D‐fluorescence data, two potential chemical compounds were identified and directly linked to cell metabolism. The same wavelength pairs were also important predictors for the regression‐model performance. Overall, the proposed soft sensor is a valuable tool for monitoring the process performance on‐line, enabling Quality by Design.

Fluorescence-based real time monitoring and diagnostics of recombinant Pichia pastoris cultivations in a bioreactor

This study describes the application of the multivariate curve resolution (MCR) analysis technique for real-time analysis of culture fluorescence during recombinant Pichia pastoris cultivation in a bioreactor. Fluorescence spectra were acquired with an on-line dual excitation wavelength fluorometer and then used to develop a real time MCR-based bioprocess monitoring and diagnostics tool. Initial bioreactor experiments using two similar recombinant antibody secreting P. pastoris cell lines showed significant differences in protein production. To distinguish between the contributions of operating conditions and the specific cell line's genetic composition to the observed differences in protein production, the bioreactor experiments were repeated and accompanied by real time MCR analysis. The tests demonstrated high sensitivity of MCR-derived "pure concentration" profiles to growth as well as to initial conditions, thus enabling real-time cultivation process trend diagnostics and fault detection. © 2018 Her Majesty the Queen in Right of Canada © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2761, 2019.

Accuracy Improvement of In-line Near-Infrared Spectroscopic Moisture Monitoring in a Fluidized Bed Drying Process

An exploratory analysis of a large representative dataset obtained in a fluidized bed drying process of a pharmaceutical powder has revealed a significant correlation of spectral intensity with granulate humidity in the whole studied range of 1091.8–2106.5 nm. This effect was explained by the dependence of powder refractive properties, and hence light penetration depth, on the water content. The phenomenon exhibited a close spectral similarity to the well-known stochastic variation of spectral intensities caused by the process turbulence (the so-called “scatter effect”). Therefore, any traditional scatter-corrective preprocessing incidentally eliminates moisture-correlated variance from the data. To preserve this additional information for a more precise moisture calibration, a time-domain averaging of spectral variables has been suggested. Its application resulted in a distinct improvement of prediction accuracy, as compared to the scatter-corrected data. Further improvement of the model performance was achieved by the application of a dynamic focusing strategy when adjusting the model to a drying process stage. Probe fouling was shown to have a minor effect on prediction accuracy. The study resulted in a considerable reduction of the root-mean-square error of in-line moisture monitoring to 0.1%, which is close to the reference method's reproducibility and significantly better than previously reported results.

In-situ electrochemical analysis of microbial activity

Microbes have a wide range of metabolic capabilities available that makes them industrially useful organisms. Monitoring these metabolic processes is a crucial component in efficient industrial application. Unfortunately, monitoring these metabolic processes can often be invasive and time consuming and expensive, especially within an anaerobic environment. Electrochemical techniques, such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) offer a non-invasive approach to monitor microbial activity and growth. EIS and CV were used to monitor Clostridium phytofermentans, an anaerobic and endospore-forming bacterium. C. phytofermentans ferments a wide range of sugars into hydrogen, acetate, and ethanol as fermentation by-products. For this study, both traditional microbiological and electrochemical techniques were used to monitor the growth of C. phytofermentans and the formation of fermentation products. An irreversible reduction peak was observed using CV beginning at mid-logarithmic phase of growth. This peak was associated with C. phytofermentans and not the spent medium and was indicative of a decrease in carbon and energy sources to the cells. Additionally, EIS analysis during growth provided information related to increased charge transfer resistance of the culture also as a function of carbon and energy source depletion. Results demonstrate that CV and EIS are useful tools in the monitoring the physiological status of bioprocesses.

Comparison of spectroscopy technologies for improved monitoring of cell culture processes in miniature bioreactors

Cell culture process development requires the screening of large numbers of cell lines and process conditions. The development of miniature bioreactor systems has increased the throughput of such studies; however, there are limitations with their use. One important constraint is the limited number of offline samples that can be taken compared to those taken for monitoring cultures in large‐scale bioreactors. The small volume of miniature bioreactor cultures (15 mL) is incompatible with the large sample volume (600 µL) required for bioanalysers routinely used. Spectroscopy technologies may be used to resolve this limitation. The purpose of this study was to compare the use of NIR, Raman, and 2D‐fluorescence to measure multiple analytes simultaneously in volumes suitable for daily monitoring of a miniature bioreactor system. A novel design‐of‐experiment approach is described that utilizes previously analyzed cell culture supernatant to assess metabolite concentrations under various conditions while providing optimal coverage of the desired design space. Multivariate data analysis techniques were used to develop predictive models. Model performance was compared to determine which technology is more suitable for this application. 2D‐fluorescence could more accurately measure ammonium concentration (RMSE_CV 0.031 g L⁻¹) than Raman and NIR. Raman spectroscopy, however, was more robust at measuring lactate and glucose concentrations (RMSE_CV 1.11 and 0.92 g L⁻¹, respectively) than the other two techniques. The findings suggest that Raman spectroscopy is more suited for this application than NIR and 2D‐fluorescence. The implementation of Raman spectroscopy increases at‐line measuring capabilities, enabling daily monitoring of key cell culture components within miniature bioreactor cultures. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:337–346, 2017

Automated measurement and monitoring of bioprocesses: key elements of the M(3)C strategy

The state-of-routine monitoring items established in the bioprocess industry as well as some important state-of-the-art methods are briefly described and the potential pitfalls discussed. Among those are physical and chemical variables such as temperature, pressure, weight, volume, mass and volumetric flow rates, pH, redox potential, gas partial pressures in the liquid and molar fractions in the gas phase, infrared spectral analysis of the liquid phase, and calorimetry over an entire reactor. Classical as well as new optical versions are addressed. Biomass and bio-activity monitoring (as opposed to "measurement") via turbidity, permittivity, in situ microscopy, and fluorescence are critically analyzed. Some new(er) instrumental analytical tools, interfaced to bioprocesses, are explained. Among those are chromatographic methods, mass spectrometry, flow and sequential injection analyses, field flow fractionation, capillary electrophoresis, and flow cytometry. This chapter surveys the principles of monitoring rather than compiling instruments.

Advancing biopharmaceutical process development by system-level data analysis and integration of omics data

Development of efficient bioprocesses is essential for cost-effective manufacturing of recombinant therapeutic proteins. To achieve further process improvement and process rationalization comprehensive data analysis of both process data and phenotypic cell-level data is essential. Here, we present a framework for advanced bioprocess data analysis consisting of multivariate data analysis (MVDA), metabolic flux analysis (MFA), and pathway analysis for mapping of large-scale gene expression data sets. This data analysis platform was applied in a process development project with an IgG-producing Chinese hamster ovary (CHO) cell line in which the maximal product titer could be increased from about 5 to 8 g/L.Principal component analysis (PCA), k-means clustering, and partial least-squares (PLS) models were applied to analyze the macroscopic bioprocess data. MFA and gene expression analysis revealed intracellular information on the characteristics of high-performance cell cultivations. By MVDA, for example, correlations between several essential amino acids and the product concentration were observed. Also, a grouping into rather cell specific productivity-driven and process control-driven processes could be unraveled. By MFA, phenotypic characteristics in glycolysis, glutaminolysis, pentose phosphate pathway, citrate cycle, coupling of amino acid metabolism to citrate cycle, and in the energy yield could be identified. By gene expression analysis 247 deregulated metabolic genes were identified which are involved, inter alia, in amino acid metabolism, transport, and protein synthesis.

Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations

Fed-batch cultivations of Streptomyces coelicolor, producing the antibiotic actinorhodin, were monitored online by multiwavelength fluorescence spectroscopy and off-gas analysis. Partial least squares (PLS), locally weighted regression, and multilinear PLS (N-PLS) models were built for prediction of biomass and substrate (casamino acids) concentrations, respectively. The effect of combination of fluorescence and gas analyzer data as well as of different variable selection methods was investigated. Improved prediction models were obtained by combination of data from the two sensors and by variable selection using a genetic algorithm, interval PLS, and the principal variables method, respectively. A stepwise variable elimination method was applied to the three-way fluorescence data, resulting in simpler and more accurate N-PLS models. The prediction models were validated using leave-one-batch-out cross-validation, and the best models had root mean square error of cross-validation values of 1.02 g l(-1) biomass and 0.8 g l(-1) total amino acids, respectively. The fluorescence data were also explored by parallel factor analysis. The analysis revealed four spectral profiles present in the fluorescence data, three of which were identified as pyridoxine, NAD(P)H, and flavin nucleotides, respectively.

Evaluation of Multiwavelength Culture Fluorescence for Monitoring the Aroma Compound 4-Hydroxy-2(or 5)-ethyl-5(or 2)-methyl-3(2H)-furanone (HEMF) Production

Fluorescence spectra of a 4-hydroxy-2(or 5)-ethyl-5(or 2)-methyl-3(2H)-furanone (HEMF) fermentation culture broth were combined with measurable process variables for off-line and on-line process monitoring. Culture broth fluorescence in UV and visible ranges was acquired by a fiber optic LCD array spectrometer. Process dynamics was followed on-line using a fiber optic probe attached to an external recirculation loop of the bioreactor. Partial least squares and stepwise regression methods were used to correlate measurable process parameters with the components of the fluorescence spectra. Both methods provided adequate approximation of yeast density, HEMF, glucose, and ethanol concentrations from fluorescence spectra. HEMF production was observed during the oxido-reductive growth phase when there was a lack of measurable oxygen in the culture broth and an excess of glucose. The addition of glucose resulted in the rapid production of HEMF and other metabolite intermediates such as ethanol, acetate, and glycerol.

Plant Cell Culture Monitoring Using an in Situ Multiwavelength Fluorescence Probe

A multiwavelength fluorescence probe is proposed for in situ monitoring of Eschscholtzia californica and Catharanthus roseus plant cell cultures. The potential of the probe as a tool for real-time estimation of biomass and production in secondary metabolites has been studied. The probe excitation range is 270-550 nm and the emission range is 310-590 nm, with a step of 20 nm for both excitation and emission filters. Many endogenous fluorophores such as NAD(P)H, riboflavins (riboflavin and derivatives such as FMN, FAD), tryptamine and tryptophan, and fluorescent secondary metabolites were analyzed simultaneously. NAD(P)H fluorescence signal (350/450 nm) showed to be an adequate signal for estimating cells activity. Riboflavins fluorescence signal (450/530 nm) followed C. roseus cell concentration both for the growth phase and after elicitation with jasmonic acid. Fluorescence from the alkaloids interfered with NAD(P)H signal during the production phase. For C. roseus, tryptophan, tryptamine, ajmalicine and serpentine were monitored by the probe. For E. californica, fluorescence from alkaloids overlapped with riboflavins preventing from using the probe to follow cell growth but global alkaloids production could be followed using the probe.

Rivoflavin may interfere with on-line monitoring of secreted green fluorescence protein fusion proteins in Pichia pastoris

BACKGROUND

Together with the development of optical sensors, fluorometry is becoming an increasingly attractive tool for the monitoring of cultivation processes. In this context, the green fluorescence protein (GFP) has been proposed as a molecular reporter when fused to target proteins to study their subcellular localization or secretion behaviour. The present work evaluates the use of the GFP fusion partner for monitoring extracellular production of a Rhizopus oryzae lipase (ROL) in Pichia pastoris by means of 2D-fluorimetric techniques

RESULTS

In this study, the GFP-ROL fusion protein was successfully produced as a secreted fusion form in P. pastoris batch cultivations. Furthermore, both the fusion enzyme and the fluorescent protein (GFP S65T mutant) retained their biological activity. However, when multiwavelength spectrofluorometry was used for extracellular fusion protein monitoring, riboflavin appeared as a major interfering component with GFP signal. Only when riboflavin was removed by ultrafiltration from cultivation supernatants, GFP fluorescence signal linearly correlated to lipase activity

CONCLUSION

P. pastoris appears to secrete/excrete significant amounts of riboflavin to the culture medium. When attempting to monitor extracellular protein production in P. pastoris using GFP fusions combined with multiwavelength spectrofluorimetric techniques, riboflavin may interfere with GFP fluorescence signal, thus limiting the application of some GFP variants for on-line extracellular recombinant protein quantification and monitoring purposes.

An improved method for two-dimensional fluorescence monitoring of complex bioreactors

An improved method for deconvoluting complex spectral maps from bidimensional fluorescence monitoring is presented, relying on a combination of principal component analysis (PCA) and feedforward artificial neural networks (ANN). With the aim of reducing ANN complexity, spectral maps are first subjected to PCA, and the scores of the retained principal components are subsequently used as ANN input vector. The method is presented using the case study of an extractive membrane biofilm reactor, where fluorescence maps of a membrane-attached biofilm were analysed, which were collected under different reactor operating conditions. During ANN training, the spectral information is associated with process performance indicators. Originally, 231 excitation/emission pairs per fluorescence map were used as ANN input vector. Using PCA, each fluorescence map could be represented by a maximum of six principal components, thereby catching 99.5% of its variance. As a result, the dimension of the ANN input vector and hence the complexity of the artificial neural network was significantly reduced, and ANN training speed was increased. Correlations between principal components and ANN predicted process performance parameters were good with correlation coefficients in the order of 0.7 or higher.

Chemometric analysis of in-line multi-wavelength fluorescence measurements obtained during cultivations with a lipase producingAspergillus oryzae strain

The filamentous fungus, Aspergillus oryzae, was cultivated in batch and fed-batch cultivations in order to investigate the use of multi-wavelength fluorescence for monitoring course of events during filamentous fungi cultivations. The A. oryzae strain applied expressed a fungal lipase from Thermomyces lanuginosus. Spectra of multi-wavelength fluorescence were collected every 5 min with the BioView system (DELTA, Denmark) and both explorative and predictive models, correlating the fluorescence data with cell mass and lipase activity, were built. During the cultivations, A. oryzae displayed dispersed hyphal growth and under these conditions no fouling of the multi-wavelength fluorescence sensor was observed. The scores of a parallel factor analysis (PARAFAC) model, based on the fluorescence spectra, gave clear evidence of, for example, the on-set of the feeding phase. The predictive models, estimating the cell mass, showed correlations between 0.73 and 0.97 with root mean square error of cross validation (RMSECV) values between 1.48 and 0.77 g . kg(-1). A model estimating the lipase activity was also constructed for the fed-batch cultivations with a correlation of 0.93. The results presented here clearly show that multi-wavelength fluorescence is a useful tool for monitoring fed-batch cultivations of filamentous fungi.

State variables monitoring by in situ multi-wavelength fluorescence spectroscopy in heterologous protein production by Pichia pastoris

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.

Improvement of bioprocess monitoring: development of novel concepts

The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.

Determination of cell mass and polymyxin using multi-wavelength fluorescence

Multi-wavelength fluorescence was applied for on-line monitoring of cell mass and the antibiotic polymyxin B in Bacillus polymyxa cultivations. By varying the phosphate and nitrogen content of the medium different polymyxin-cell mass ratios could be obtained. Using this strategy, it was possible to investigate if multi-wavelength fluorescence is able to give independent prediction of the two parameters. Partial least square (PLS) regression was applied to establish mathematical relationships between off-line determined cell mass and polymyxin concentrations and on-line collected fluorescence data. For polymyxin one universal PLS model, with a correlation of 0.95 and a root mean square error of cross validation (RMSECV) of 35 mgl(-1), could be constructed. However, correlation between fluorescence and cell mass dry weight could not be established including data from all three types of cultivations. For data from each type of cultivation, separate models with high correlation and low RMSECV values were built. A large variation in cellular composition as a result of the different levels of nitrogen and phosphorus in the cultivations was the probable reason to the necessity of building three models. The results of the present investigation indicate that production of polymyxin is concomitantly regulated by phosphate and nitrogen as the highest polymyxin yield on cell mass, 0.17+/-0.01 gg(-1), was reached in the cultivations where both nitrogen and phosphate concentrations were kept low.

Manufacturing of recombinant therapeutic proteins in microbial systems

Recombinant therapeutic proteins have gained enormous importance for clinical applications. The first recombinant products have been produced in E. coli more than 20 years ago. Although with the advent of antibody-based therapeutics mammalian expression systems have experienced a major boost, microbial expression systems continue to be widely used in industry. Their intrinsic advantages, such as rapid growth, high yields and ease of manipulation, make them the premier choice for expression of non-glycosylated peptides and proteins. Innovative product classes such as antibody fragments or alternative binding molecules will further expand the use of microbial systems. Even more, novel, engineered production hosts and integrated technology platforms hold enormous potential for future applications. This review summarizes current applications and trends for development, production and analytical characterization of recombinant therapeutic proteins in microbial systems.

Sensor combination and chemometric modelling for improved process monitoring in recombinant E. coli fed-batch cultivations

The key objective for the optimisation of recombinant protein production in bacteria is to optimize the exploitation of the host cell's synthesis potential. Recent studies show that the novel concept of transcription rate control allows the tuning of recombinant gene expression in relation to the metabolic capacity of the host cell. To adjust the inducer-biomass ratio to a tolerable level, real-time knowledge about key process variables is paramount. Since there are no reliable online-sensors for key variables such as biomass or recombinant product, it is necessary to relate available online signals to process variables by mathematical models. To improve chemometric modelling of process variables, dielectric spectroscopy and a multi-wavelength online fluorescence sensor for two-dimensional fluorescence spectroscopy were applied in a series of recombinant Escherichia coli fed-batch cultivations applying two different process operation states. Dielectric spectroscopy signals were closely correlated to biomass, while two-dimensional fluorescence spectroscopy allowed the monitoring of fluorescent biogenic components. Chemometric modelling of key process variables with two different modelling techniques showed that this sensor combination greatly improved the estimation (i.e. reduce error magnitude) of process variables in recombinant E. coli cultivations, thereby enhancing process monitoring capabilities.

On-line cell mass monitoring of Saccharomyces cerevisiae cultivations by multi-wavelength fluorescence

The catalyst in bioprocesses, i.e. the cell mass, is one of the most challenging and important variables to monitor in bioprocesses. In the present study, cell mass in cultivations with Saccharomyces cerevisiae was monitored on-line with a non-invasive in situ placed sensor measuring multi-wavelength culture fluorescence. The excitation wavelength ranged from 270 to 550 nm with 20 nm steps and the emission wavelength range was from 310 to 590 nm also with 20 nm steps. The obtained spectra were analysed chemometrically with the multi-way technique, parallel factor analysis (PARAFAC), resulting in a decomposition of the multivariate fluorescent landscape, whereby underlying spectra of the individual intrinsic fluorophors present in the cell mass were estimated. Furthermore, gravimetrically determined cell mass concentration was used together with the fluorescence spectra for calibration and validation of multivariate partial least squares (PLS) regression models. Both two- and three-way models were calculated, the models behaved similarly giving root mean square error of prediction (RMSEPs) of 0.20 and 0.19 g l(-1), respectively, when test set validation was used. Based on this work, it is evident that on-line monitoring of culture fluorescence can be used for estimation of the cell mass concentration during cultivations.

Spectral analysis and fingerprinting for biomedia characterisation

Classical culture media, as well as domestic and/or industrial wastewater treated by biological processes, have a complex composition. The on-line and/or in situ determination of some substances is possible, but expensive, as sample collection and pre-treatment are often necessary with strict rules of sterility. More global methods can be used to detect rapidly "accidents" such as the appearance of an undesirable by-product in a fermentation broth or of a toxic substance in wastewater. These methods combine a "hard" part, for sensing, and a "soft" part, for data treatment. Among potential "hard" candidates, spectroscopy can be the basis for non-invasive and non-destructive measuring systems. Some of them have been already tested in situ: ultra-violet-visible, infra-red (mid or near), fluorescence (mono-dimensional, two-dimensional or synchronous), dielectric, while others, more sophisticated, such as mass spectrometry, coupled or not to pyrolysis, nuclear magnetic resonance and Raman spectroscopy, have been proposed. All these methods provide spectra, i.e. large sets of data, from which meaningful information should be rapidly extracted, either for analysis or fingerprinting. The recourse to data-mining techniques (the "soft" part) such as principal components analysis, projection on latent structures or artificial neural networks, is a necessary step for that task. A review of techniques, mostly based on spectroscopy, with examples taken in the bioengineering field in general is proposed.

Application of multi-wavelength fluorometry for on-line monitoring of an anaerobic digestion process

This work examined the use of multi-wavelength fluorometry for on-line monitoring of an anaerobic digestion process. Experiments were carried out in a laboratory-scale anaerobic digestor fed with either synthetic or agricultural (cheese factory) wastewater. An in-line fiber optic probe installed in the external recirculation loop of the reactor was used to acquire fluorescence spectra with an interval of 5-10 min. The spectra were compared with analytical measurements taken at the same time to develop regression models, which were then used to predict concentrations of chemical oxygen demand, volatile fatty acids, and other key process parameters. A comparison of partial least squares (PLS), nonlinear principal components regression, and step-wise regression models on an independent set of data showed that the PLS model gave the best prediction accuracy.

Single-cell variability in growing Saccharomyces cerevisiae cell populations measured with automated flow cytometry

Cell cultures normally are heterogeneous due to factors such as the cell cycle, inhomogeneous cell microenvironments, and genetic differences. However, distributions of cell properties usually are not taken into account in the characterization of a culture when only population averaged values are measured. In this study, the cell size, green fluorescence protein (Gfp) content, and viability after automated staining with propidium iodide (PI) are monitored at the single-cell level in Saccharomyces cerevisiae cultures growing in a batch bioreactor using an automated flow injection flow cytometer system. To demonstrate the wealth of information that can be obtained with this system, three cultures containing three different plasmids are compared. The first plasmid is a centromeric plasmid expressing under the control of a TEF2 promoter the S65T mutant form of Gfp. The other two plasmids are 2 microm plasmids and express the FM2 mutant of Gfp under the control of either the TEF1 or the TEF2 promoter. The automated sampling, cell preparation, and analysis permitted frequent quantification of the culture characteristics. The time course of the data representing not only population average values but also their variability, provides a detailed and reproducible "fingerprint" of the culture dynamics. The data demonstrate that small changes in the genetic make up of the recombinant system can result in large changes in the culture Gfp production and viability. Thus, the developed instrumentation is valuable for rapidly testing promoter strength, plasmid stability, cell viability, and culture variability.

Evaluation of the GFP signal and its aptitude for novel on-line monitoring strategies of recombinant fermentation processes

A high number of economically important recombinant proteins are produced in Escherichia coli based host/vector systems. The major obstacle for improving current processes is a lack of appropriate on-line in situ methods for the monitoring of metabolic burden and critical state variables. Here, a pre-evaluation of the reporter green fluorescent protein (GFP) was undertaken to assess its use as a reporter of stress associated promoter regulation. The investigation of GFP and its blue fluorescent variant BFP was done in model fermentations using E. coli HMS 174(DE3)/pET11 aGFPmut3.1 and E. coli HMS174(DE3)/pET1aBFP host/vector systems cultured in fed-batch and chemostat regime. Our results prove the suitability of the fluorescent reporter proteins for the design of new strategies of on-line bioprocess monitoring. GFPmut3.1 variant can be detected after a short lag-phase of only 10 min, it shows a high fluorescence yield in relation to the amount of reporter protein, a good signal to noise ratio and a low detection limit. The fluorescence-signal and the amount of fluorescent protein, determined by ELISA, showed a close correlation in all fermentations performed. A combination of reporter technology with state of the art sensors helps to develop new strategies for efficient on-line monitoring needed for industrial process optimisation. The development of efficient monitoring will contribute to advanced control of recombinant protein production and accelerate the development of optimised production processes.

Monitoring the growth and stress responses of yeast cells by two-dimensional fluorescence spectroscopy: first results

S. cerevisiae growth and responses to different treatments were monitored by two-dimensional fluorescence spectroscopy, which simultaneously detects the fluorescence of a number of cells' own fluorophores. Growth curves of cultures of free cells were measured by means of tryptophan fluorescence in nonfluorescent culture medium and a flow-through system at a suitable excitation/emission beam geometry. Fast responses of the cells to anaerobic-aerobic transition or addition of glucose, methanol or cyanide, which could not be measured in this system because of the time delay inherent in transporting the cells from the culture flask to the cuvette, were monitored with cells immobilized in alginate. The major fluorescence changes caused by these treatments belonged to NAD(P)H which is a good indicator of the redox state of the cells.

Evaluation of spectrofluorometry as a tool for estimation in fed-batch fermentations

Native culture fluorescence was investigated as an additional source of information for predicting biomass and glucose concentrations in a fed-batch fermentation of Alcaligenes eutrophus. Partial least squares (PLS) regression and a feed forward neural network (FFNN) coupled with principle component analysis (PCA) were each used to model the kinetics of the fermentation. Data from three fermentations was combined to form a training set for model calibration and data from a fourth fermentation was used as the testing set. The fluorescent soft-sensors were compared with a previously developed feed forward neural network soft-sensor model which used oxygen uptake rate (OUR), carbon dioxide evolution rate (CER), aeration rate, feed rate, and fermentor volume to estimate biomass and glucose concentrations. The best model performance for predicting both biomass and glucose concentrations was achieved using the native fluorescence-based models. Real data predictions of the biomass concentration in the testing set were obtained using both the PLS and FFNN PCA modeling utilizing fluorescence measurements plus the rate of change of the fluorescence measurements. Accurate predictions of the glucose concentration in the testing set were obtained using the FFNN PCA modeling technique utilizing the rate of change of the fluorescence measurements. Substrate exhaustion was indicated qualitatively by a first-order PLS model utilizing the rate of change of fluorescence measurements. These results indicate that native culture fluorescence shows promise for providing additional valuable information to enhance predictive modeling which cannot be extracted from other easily acquired measurements.

Optical sensor systems for bioprocess monitoring

Bioreactors are closed systems in which microorganisms can be cultivated under defined, controllable conditions that can be optimized with regard to viability, reproducibility, and product-oriented productivity. To drive the biochemical reaction network of the biological system through the desired reaction optimally, the complex interactions of the overall system must be understood and controlled. Optical sensors which encompass all analytical methods based on interactions of light with matter are efficient tools to obtain this information. Optical sensors generally offer the advantages of noninvasive, nondestructive, continuous, and simultaneous multianalyte monitoring. However, at this time, no general optical detection system has been developed. Since modern bioprocesses are extremely complex and differ from process to process (e.g., fungal antibiotic production versus mammalian cell cultivation), appropriate analytical systems must be set up from different basic modules, designed to meet the special demands of each particular process. In this minireview, some new applications in bioprocess monitoring of the following optical sensing principles will be discussed: UV spectroscopy, IR spectroscopy, Raman spectroscopy, fluorescence spectroscopy, pulsed terahertz spectroscopy (PTS), optical biosensors, in situ microscope, surface plasmon resonance (SPR), and reflectometric interference spectroscopy (RIF).