Analytical performance of thrombospondin-1 and cathepsin D immunoassays part of a novel CE-IVD marked test as an aid in the diagnosis of prostate cancer.

The Prostate Specific Antigen (PSA) test suffers from low specificity for the diagnosis of Prostate Cancer (PCa). We originally discovered two cancer-related proteins thrombospondin-1 (THBS1) and cathepsin D (CTSD) using a mass-spectrometry-based proteomics approach. The two serum proteins were shown to improve the diagnosis of high-grade PCa. Thus, we developed quantitative ELISAs for the determination of their concentration in human serum. Here we report their analytical performance in terms of limit of detection, specificity, precision, linearity and interferences, which were determined based on CLSI guidelines. Further, we investigated the influence of pre-analytical factors on concentration measurements. For this, blood from 4-6 donors was collected in different tubes and stored at room temperature for different times prior to centrifugation at different centrifugal forces and temperatures. Stability of THBS1 and CTSD under different storage temperatures was also evaluated. Our results show that the assays are specific, linear and sensitive enough to allow measurement of clinical samples. Precision in terms of repeatability and total within-laboratory coefficient of variation (CV) are 5.5% and 8.1% for THBS1 and 4.3% and 7.2% for CTSD, respectively. Relative laboratory-to-laboratory differences were -6.3% for THBS1 and -3% for CTSD. Both THBS1 and CTSD were stable in serum samples, with 80-120% recoveries of concentrations across donors, sample preparation and storage. In conclusion, the ELISAs as part of the novel commercial in vitro diagnostic test Proclarix are suitable for the use in clinical practice. THBS1 and CTSD can be accurately measured for their intended use independent of the lot and laboratory when conditions consistent with routine practice for PSA sampling and storage are used.

Development of a Scale-Down Model of hydrodynamic stress to study the performance of an industrial CHO cell line under simulated production scale bioreactor conditions.

The objective of this study was to develop a Scale-Down Model of a hydrodynamic stress present in large scale production bioreactors to investigate the performance of CHO cells under simulated production bioreactor conditions. Various levels of hydrodynamic stress were generated in 2L bioreactors mimicking those present in different locations of a large scale stirred tank bioreactor. In general, it was observed that tested cells are highly robust against the effect of hydrodynamic stress. However, at elevated hydrodynamic stress equivalent to an average energy dissipation rate, ε, equal to 0.4W/kg, the specific monoclonal antibody productivity, qmAb, decreased by 25% compared to the cultivation conditions corresponding to ε equal to 0.01W/kg. Even stronger decrease of qmAb, in the order of 30%, was observed when ε was periodically oscillating between 0.01 and 0.4W/kg to simulate the repeated passage of cells through the highly turbulent impeller discharge zone of a production scale bioreactor. Despite this effect, no changes in metabolite consumption or byproduct formation were observed. Furthermore, considering the experimental error product quality was independent of the applied ε. To achieve a molecular insight into the observed drop of cellular productivity, a transcriptome analysis using mRNA microarrays was performed. It was found that transcripts related to DNA damage and repair mechanisms were upregulated when high ε was applied for cultivation.

On-line infrared spectroscopy for bioprocess monitoring.

One of the major aims of bioprocess engineering is the real-time monitoring of important process variables. This is the basis of precise process control and is essential for high productivity as well as the exact documentation of the overall production process. Infrared spectroscopy is a powerful analytical technique to analyze a wide variety of organic compounds. Thus, infrared sensors are ideal instruments for bioprocess monitoring. The sensors are non-invasive, have no time delay due to sensor response times, and have no influence on the bioprocess itself. No sampling is necessary, and several components can be analyzed simultaneously. In general, the direct monitoring of substrates, products, metabolites, as well as the biomass itself is possible. In this review article, insights are provided into the different applications of infrared spectroscopy for bioprocess monitoring and the complex data interpretation. Different analytical techniques are presented as well as example applications in different areas.

On-line monitoring of human prostate cancer cells in a perfusion rotating wall vessel by near-infrared spectroscopy.

PC-3 human prostate cancer cells have been cultivated in a rotating wall vessel in which glucose, lactate, and glutamine profiles were monitored noninvasively and in real time by near-infrared (NIR) spectroscopy. The calibration models were based on off-line spectra from tissue culture experiments described previously (Rhiel et al., Biotechnol Bioeng 77:73-82). Monitoring performance was improved by Fourier filtering of the spectra and initial off-set adjustment. The resulting standard errors of predictions were 0.95, 0.74, and 0.39 mM for glucose, lactate, and glutamine, respectively. The concentration of ammonia could not be accurately measured from the same spectra. In addition, metabolite uptake and production rates were determined for PC-3 prostate cancer cells during exponential growth in batch-mode cultivation. Cells grew with a doubling time of 21 h and consumed glucose and glutamine at rates of 6.8 and 1.8 x 10(-17) mol/cell.s, respectively. This resulted in lactate and ammonia production rates of 11.9 and 1.3 x 10(-17) mol/cell.s, respectively. Compared with other monitoring technologies, this technology has many advantages for spaceflights and stand-alone units; for instance, calibration can be performed at one time and then applied in a reagentless, low-maintenance way at a later time. The resulting concentration information can be incorporated into closed-loop control schemes, thereby leading to better in vitro models of in vivo behavior.

On-line monitoring of Phaffia rhodozyma fed-batch process with in situ dispersive Raman spectroscopy.

Since the yeast Phaffia rhodozyma was first described some 35 years ago, there has been significant interest in the development of commercial processes to exploit its ability to produce carotenoids (approximately 80% astaxanthin). However, the optimal conditions for carotenoid production are not well understood. A key limitation has been the lack of an appropriate sensor for on-line carotenoid quantification. In this study, an in situ Raman spectroscopy probe was used to monitor intracellular carotenoid production for three consecutive P. rhodozyma fed-batch experiments. Raman spectroscopy is particularly well suited to the study of carotenoids due to a resonance effect, which greatly enhances the intensity of the three fundamental carotenoid bands, nu(1) (1513 cm(-1), C(-) (-)C stretch), nu(2) (1154 cm(-1), C-C stretch), and nu(3) (1003 cm(-1), CH(3) rock). For all three cultures, the peak height of these bands was linearly correlated with intracellular carotenoid content (1 to 45 mg/L) to a precision of better than 5%, and the correlation from one experiment was directly applicable to others.

Methodology for real-time, multianalyte monitoring of fermentations using an in-situ mid-infrared sensor.

An in-situ, mid-infrared sensor was used to monitor the major analyte concentrations involved in the cultivation of Gluconacetobacter xylinus and the production of gluconacetan, a food-grade exopolysaccharide. To predict the analyte concentrations, three different sets of standard spectra were used to develop calibration models, applying partial least-squares regression. It was possible to build a valid calibration model to predict the 700 spectra collected during the complete time course of the cultivation, using only 12 spectra collected every 10 h as standards. This model was used to reprocess the concentration profiles from 0 to 15 g/L of nine different analytes with a mean standard error of validation of 0.23 g/L. However, this calibration model was not suitable for real-time monitoring as it was probably based on non-specific spectral features, which were correlated only with the measured analyte concentrations. Valid calibration models capable of real-time monitoring could be established by supplementing the set of 12 fermentation spectra with 42 standards of measured analytes. A pulse of 5 g/L ethanol showed the robustness of the model to sudden disturbances. The prediction of the models drifted, however, toward the end of the fermentation. The most robust calibration model was finally obtained by the addition of 34 standard spectra of non-measured analytes. Although the spectra did not contain analyte-specific information, it was believed that this addition would increase the variability space of the calibration model. Therefore, an expanded calibration model containing 88 spectra was used to monitor, in real time, the concentration profiles of fructose, acetic acid, ethanol and gluconacetan and allowed standard errors of prediction of 1.11, 0.37, 0.22, and 0.79 g/L, respectively.

The influence of correlated calibration samples on the prediction performance of multivariate models based on mid-infrared spectra of animal cell cultures.

The effect of the presence of metabolism-induced concentration correlations in the calibration samples on the prediction performance of partial least-squares regression (PLSR) models and mid-infrared spectra from Chinese hamster ovary cell cultures was investigated. Samples collected from batch cultures contained highly correlated metabolite concentrations as a result of metabolic relations. Calibrations based on such samples could only be used to predict concentrations in new samples if a similar correlation structure was present and failed when the new samples were randomly spiked with the analytes. On the other hand, such models were able to predict glucose correctly even if they were based on a spectral range in which glucose does not absorb, provided that the correlations in the calibration and in the new samples were similar. If however, samples from a calibration culture were randomly spiked with the main analytes, much more robust PLSR models resulted. It was possible to predict analyte concentrations in new samples irrespective of whether the correlation structure was maintained or not. Validity of all established models for any given use could be predicted a priori by computing the space inclusion and observer conditions. Predictions from these computations agreed in all cases with the experimental test of model validity.

Nondestructive near-infrared spectroscopic measurement of multiple analytes in undiluted samples of serum-based cell culture media.

An adaptive calibration procedure is used to build selective multivariate calibration models for the measurement of glucose, lactate, glutamine, and ammonia in undiluted serum-based cell culture media. This adaptive procedure removes metabolism-induced covariance between these analytes in a series of calibration samples collected during the cultivation of PC-3 human prostate cancer cells. Partial least-squares calibration models are generated from single-beam near-infrared (NIR) spectra collected over the 4800- to 4200-cm(-1) combination spectral range. Calibration models were generated with both the full spectral range and optimized spectral ranges. In both cases, the number of model factors was optimized and model validity was determined by comparing analyte concentrations predicted from a series of independent and unaltered samples that were obtained during a subsequent cultivation of the PC-3 cells. Similar analytical performance was achieved with fewer model factors when the optimized spectral range was used. The lowest standard errors of prediction were 0.82, 0.94, 0.55, and 0.76 mM for glucose, lactate, glutamine, and ammonia, respectively. Different spectral ranges were optimal for each analyte and the optimized spectral range coincided with the distinguishing spectral features of the analyte. The results of this study demonstrate that NIR spectroscopy can be used effectively in the off-line measurement of important nutrients (glucose and glutamine) and byproducts (lactate and ammonia) in a serum-based animal cell culture medium.

Real-time in situ monitoring of freely suspended and immobilized cell cultures based on mid-infrared spectroscopic measurements.

Glucose and lactate profiles in Chinese hamster ovary cell cultures were accurately monitored in real time and in situ during three bioreactor batch cultures lasting 11,15, and 15 days performed within a 60-day period. Monitoring was accomplished using in situ-collected mid-infrared spectra analyzed with a priori one-time established partial least-squares regression models. The robustness of the technique was demonstrated by application of these models without modification after 2.3 years. Neither recalibration nor instrument maintenance was required during the 2.3-year period, except for the daily filling of liquid nitrogen for detector cooling during operation. The lactate calibration model yielded accurate absolute concentration estimations during each of the batch cultures with standard errors of estimate from 1 to 3 mM. The a priori-established glucose calibration model yielded concentration estimations with an off-set, which was constant throughout a culture. Adjustment of the off-set before inoculation resulted in accurate concentration estimations with Standard errors of estimate of approximately 1 mM for each of the bioreactor cultures. Sensitivity in detecting differences of 0.5 mM and selectivity against variation of one metabolite while the other was kept constant was demonstrated during standard additions of either glucose or lactate. The sensor system proved to be reliable, simple, accurate, sterile, and capable of long-term automatic operation and is considered to be mature enough to be routinely applied for in situ (on-line) cell culture monitoring.