High-throughput analysis of animal cell cultures using two-dimensional fluorometry

We report a new method which combines fluorescence spectroscopy at microtiter plate scale with multivariate statistical analysis for rapid and high-throughput analysis of secreted recombinant protein and viable cell growth in animal cell cultures. The potential of the method is demonstrated by application to cultures of three Chinese Hamster Ovary (CHO) cell clones with distinct IgG₄ antibody yields. Supernatant samples collected throughout culture time were analysed by two-dimensional fluorometry; significant changes were observed in the regions of tryptophan, metabolic cofactors and vitamins. Partial least squares regression was then used to correlate the entire fluorescence map with measured concentrations of antibody and viable cells. For both target variables, a model was calibrated with representative data from the two less productive clones and validated with data from the best producer clone; this allowed viable cell density to be predicted for the validation clone with an average error of 10%; even better, the secreted antibody could be predicted with an average error of 7%, proving the predictive capacity of the model beyond the calibration region. All the main spectral regions were required to establish the best correlations for both targeted variables. In conclusion, this method effectively analyzes cellular productivity in 96-well plate format, shortening the time spent in early phases of bioprocess development.     

Direct determination of alginate content in brown algae by near infra-red (NIR) spectroscopy

The methods used to quantify total alginate in brown algal tissue are time-consuming and may also be misleading, so faster and simpler methods for measuring alginate content would be beneficial in a variety of applications. This study reports on the use of near infra-red (NIR) analysis to monitor the alginate content of Laminaria hyperborea stipe during biodegradation. NIR reflectance spectra were recorded for 78 different freeze-dried samples of its stipe. The samples were collected during several biological degradation experiments and the total alginate content varied from 2.2 to 40.8% Na-alginate (w/w), determined by established methods based on ion exchange. Data analysis was performed using multivariate calibration methods in order to relate the spectral data to the alginate content. PLS2 analysis revealed some dependence on material type, probably reflecting differences in polyphenol content. In the end, a PLS1 model with 9 components was selected. The calculated model was validated both with internal data and with an external test set. Internal full cross validation explained 96.6% of the variance in alginate content. The external validation showed that the PLS1 model was able to predict the alginate concentration with a root mean square prediction accuracy of 2.1%. This revised version was published online in June 2006 with corrections to the Cover Date.     

Synchronous fluorescence spectroscopy as a novel tool to enable PAT applications in bioprocesses

In this work, synchronous fluorescence spectroscopy (SFS) is evaluated as a new tool for real-time bioprocess monitoring of animal cell cultures. This technique presents several advantages over the traditional two-dimensional (2D) fluorometry since it provides data on various fluorescent compounds in a single spectrum, showing improved peak resolution and recording speed. Bioreactor cultures of three monoclonal antibody-producing CHO cell lines were followed in situ by both 2D and synchronous fluorometry techniques. The time profiles of the main spectral features in each data type present some differences, but principal component analysis indicated both as containing enough information to distinguish the cultures. Partial least squares regression models were then independently developed for viable cell density and antibody levels on the basis of the different fluorescence signals recorded, hiding half of the dataset for subsequent validation purposes. Regardless of the signal used, model predictions fit very well the off-line measurements; still, the synchronous spectra collected at a wavelength difference of 20 nm allowed comparable and superior performances for cell density and antibody titer, respectively, with validation accuracies higher than 91%. Therefore, SFS compares favorably with the traditional 2D approach, becoming an improved, faster option for real-time monitoring of cells and product titer over culture time. The readiness in data acquisition facilitates the design of process control strategies meeting the requirements of a PAT application.     

Validation of chemometric models for the determination of deoxynivalenol on maize by mid-infrared spectroscopy

Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800–1800 cm⁻¹) of 21 maize samples that were contaminated with 300 – 2600 μg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported. Presented at the 25^th Mykotoxin Workshop in Giessen, Germany, May 19–21, 2003     

Multiblock PLS analysis of an industrial pharmaceutical process

The performance of an industrial pharmaceutical process (production of an active pharmaceutical ingredient by fermentation, API) was modeled by multiblock partial least squares (MBPLS). The most important process stages are inoculum production and API production fermentation. Thirty batches (runs) were produced according to an experimental planning. Rather than merging all these data into a single block of independent variables (as in ordinary PLS), four data blocks were used separately (manipulated and quality variables for each process stage). With the multiblock approach it was possible to calculate weights and scores for each independent block. It was found that the inoculum quality variables were highly correlated with API production for nominal fermentations. For the nonnominal fermentations, the manipulations of the fermentation stage explained the amount of API obtained (especially the pH and biomass concentration). Based on the above process analysis it was possible to select a smaller set of variables with which a new model was built. The amount of variance predicted of the final API concentration (cross-validation) for this model was 82.4%. The advantage of the multiblock model over the standard PLS model is that the contributions of the two main process stages to the API volumetric productivity were determined.     

A multivariate calibration procedure for UV/VIS spectrometric monitoring of BHK‐21 cell metabolism and growth

Monitoring mammalian cell culture with UV–vis spectroscopy has not been widely explored. The aim of this work was to calibrate Partial Least Squares (PLS) models from off‐line UV–vis spectral data in order to predict some nutrients and metabolites, as well as viable cell concentrations for mammalian cell bioprocess using phenol red in culture medium. The BHK‐21 cell line was used as a mammalian cell model. Spectra of samples taken from batches performed at different dissolved oxygen concentrations (10, 30, 50, and 70% air saturation), in two bioreactor configurations and with two strategies to control pH were used to calibrate and validate PLS models. Glutamine, glutamate, glucose, and lactate concentrations were suitably predicted by means of this strategy. Especially for glutamine and glucose concentrations, the prediction error averages were lower than 0.50 ± 0.10 mM and 2.21 ± 0.16 mM, respectively. These values are comparable with those previously reported using near infrared and Raman spectroscopy in conjunction with PLS. However, viable cell concentration models need to be improved. The present work allows for UV–vis at‐line sensor development, decrease cost related to nutrients and metabolite quantifications and establishment of fed‐batch feeding schemes. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 30:241–248, 2014     

近红外光谱分析法测定东北黑土有机碳和全氮含量

以我国东北黑土为研究对象,分析了2004-2005年采集的136个土壤样品在3699～12000 cm^-1范围的近红外光谱,利用偏最小二乘法建立了原始光谱吸光度与土壤有机碳、全氮和碳氮比之间的定量分析模型.结果表明：土壤有机碳和全氮的模型拟合效果良好,决定系数R²分别为0.92和0.91（P<0.001）,相对分析误差RPD分别为3.45和3.36,利用该模型对验证样本土壤有机碳和全氮的预测值与实测值之间的相关系数分别为0.94和0.93（P<0.001）,表明可以用近红外光谱分析法对黑土有机碳和全氮含量进行测定.但是利用近红外光谱分析法对土壤碳氮比的预测并不理想,虽然验证样本集黑土碳氮比模型预测值与实测值呈显著相关（r＝0.74,P<0.001）,但是校正模型的R²为0.61,RPD仅为1.61,建立的模型不能对黑土碳氮比做出合理的估测.     

Heart design: free ADP scales with absolute mitochondrial and myofibrillar volumes from mouse to human

Our aim was to estimate a number of bioenergetic parameters in the beating mouse, rat and guinea pig heart in situ and compare the values to those in hearts of mammals over a 2000-fold range in body mass. For the mouse, rat and guinea pig heart, we report a phosphorylation ratio of 1005+/-50 (n=16), 460+/-32 (n=10) and 330+/-22 (n=5) mM(-1) and a free cytosolic [ADP] concentration of 13, 18 and 22 microM, respectively. When each parameter was plotted against body mass, they scaled closely to the quarter power (-0.28, r=0.99 and -0.23, r=0.97). A similar regression slope was found when the inverse of free [ADP] was plotted against absolute mitochondrial (slope=-0.26, r=0.99) and myofibrillar volumes (slope=-0.24, r=0.99). The similar slopes indicate that the ratio of absolute mitochondria and myofibrillar volumes in the healthy mammalian heart is a constant, and independent of body size. In conclusion, our study supports the hypothesis that the mammalian heart has a number of highly conserved thermodynamic and kinetic parameters that obey quarter-power laws linking the phosphorylation ratio, ATP turnover rates, free [ADP] and absolute mitochondrial volumes to body size. The results are discussed in terms of possible mechanisms and potential deviations from these laws in some disease states.     

提高重组原动蛋白2表达水平的优化策略

原动蛋白2(PK2)是近年发现的一个具有多种生物学功能的蛋白质因子。采用Design-Expert 7.0.0软件对重组PK2表达的最优诱导条件进行响应面分析。结果表明,诱导起始时机与诱导物浓度是影响重组PK2表达水平的显著性因子。同时,诱导时间-诱导时机、诱导时机-诱导物浓度之间的相互作用对PK2的表达水平具有显著性影响。试验数据拟合与极值分析表明最优诱导条件为:工程菌OD600为0.60时,采用终浓度为0.62 mmol/L的IPTG在37℃诱导培养2.82 h,此时重组PK2的表达水平为170.73 mg/L。优化后的诱导条件导致重组PK2的表达水平提高了51.1%,而所需IPTG减少38.0%,诱导时间缩短53.0%,有利于大量制备重组PK2进行后续功能研究及应用研究。     

Investigation of parameters affecting a fixed bed bioreactor process for recombinant cell lines

A BHK 21 cell line expressing a recombinant antibody was grown in a fixed bed reactor (FBR) system using a porous support made of Siran glass beads. The contribution of five process variables (bead and inoculum sizes; circulation and dilution rates; glutamine concentration of the feed) to the productivity of the process (defined as production rate, effluent product concentration or yield of product on medium supplied) was investigated using a partial factorial experimental design. Individually, none of the variables tested had a significant affect upon productivity. The combination of smaller bead and inoculum sizes, higher circulation and dilution rates, plus higher feed glutamine concentration gave a markedly higher productivity than any other combination of variable levels tested. This combination of variable levels suggested that better results shold be obtained using a fluidised bed reactor system. However, comparison of the productivities of the two systems showed that the FBR gave the better results. This result can be explained in terms of the relationship of Qs_rAb to .Abbreviations C concentration - D dilution rate - FBR fixed bed bioreactor - FIBR fluidised bed bioreactor - Gln glutamine - Qs cell specific rate - Qv volumetric rate - rAb recombinant antibody - X_v viable cell density - specific growth rate     

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−¹³C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.We acknowledge the support of NIH Grant DK58533 and the DuPont-MIT Alliance.     

Relation of bone mineral density and content to mineral content and density of the fat-free mass.

Differences in the mineral fraction of the fat-free mass (M(FFM)) and in the density of the FFM (D(FFM)) are often inferred from measures of bone mineral content (BMC) or bone mineral density (BMD). We studied the relation of BMC and BMD to the M(FFM) and D(FFM) in a heterogeneous sample of 216 young men (n = 115) and women (n = 101), which included whites (n = 155) and blacks (n = 61) and collegiate athletes ( n = 132) and nonathletes (n = 84). Whole body BMC and BMD were determined by dual-energy X-ray absorptiometry (DXA; Hologic QDR-1000W, enhanced whole body analysis software, version 5.71). FFM was estimated using a four-component model from measures of body density by hydrostatic weighing, body water by deuterium dilution, and bone mineral by DXA. There was no significant relation of BMD to M(FFM) (r = 0.01) or D(FFM) (r = -0.06) or of BMC to M(FFM) (r = -0.11) and a significant, weak negative relation of BMC to D(FFM) (r = -0.14, P = 0.04) in all subjects. Significant low to moderate relationships of BMD or BMC to M(FFM) or D(FFM) were found within some gender-race-athletic status subgroups or when the effects of gender, race, and athletic status were held constant using multiple regression, but BMD and BMC explained only 10-17% of the variance in M(FFM) and 0-2% of the variance in D(FFM) in addition to that explained by the demographic variables. We conclude that there is not a significant positive relation of BMD and BMC to M(FFM) or D(FFM) in young adults and that BMC and BMD should not be used to infer differences in M(FFM) or D(FFM).     

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.