A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line bioprocess monitoring of Chinese hamster ovary cell cultivations—Part II

This study was performed in order to evaluate a new LED‐based 2D‐fluorescence spectrometer for in‐line bioprocess monitoring of Chinese hamster ovary (CHO) cell culture processes. The new spectrometer used selected excitation wavelengths of 280, 365, and 455 nm to collect spectral data from six 10‐L fed‐batch processes. The technique provides data on various fluorescent compounds from the cultivation medium as well as from cell metabolism. In addition, scattered light offers information about the cultivation status. Multivariate data analysis tools were applied to analyze the large data sets of the collected fluorescence spectra. First, principal component analysis was used to accomplish an overview of all spectral data from all six CHO cultivations. Partial least square regression models were developed to correlate 2D‐fluorescence spectral data with selected critical process variables as offline reference values. A separate independent fed‐batch process was used for model validation and prediction. An almost continuous in‐line bioprocess monitoring was realized because 2D‐fluorescence spectra were collected every 10 min during the whole cultivation. The new 2D‐fluorescence device demonstrates the significant potential for accurate prediction of the total cell count, viable cell count, and the cell viability. The results strongly indicated that the technique is particularly capable to distinguish between different cell statuses inside the bioreactor. In addition, spectral data provided information about the lactate metabolism shift and cellular respiration during the cultivation process. Overall, the 2D‐fluorescence device is a highly sensitive tool for process analytical technology applications in mammalian cell cultures.     

In‐situ monitoring of Saccharomyces cerevisiae ITV01 bioethanol process using near‐infrared spectroscopy NIRS and chemometrics

The application feasibility of in‐situ or in‐line monitoring of S. cerevisiae ITV01 alcoholic fermentation process, employing Near‐Infrared Spectroscopy (NIRS) and Chemometrics, was investigated. During the process in a bioreactor, in the complex analytical matrix, biomass, glucose, ethanol and glycerol determinations were performed by a transflection fiber optic probe immersed in the culture broth and connected to a Near‐Infrared (NIR) process analyzer. The NIR spectra recorded between 800 and 2,200 nm were pretreated using Savitzky‐Golay smoothing and second derivative in order to perform a partial least squares regression (PLSR) and generate the calibration models. These calibration models were tested by external validation and then used to predict concentrations in batch alcoholic fermentations. The standard errors of calibration (SEC) for biomass, ethanol, glucose and glycerol were 0.212, 0.287, 0.532, and 0.296 g/L and standard errors of prediction (SEP) were 0.323, 0.369, 0.794, and 0.507 g/L, respectively. Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:510–517, 2016     

On‐line yeast propagation process monitoring and control using an intelligent automatic control system

The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real‐time. This work presents a system for on‐line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on‐line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on‐line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on‐line biomass estimation. The root mean squared error of prediction is 4 × 10⁶ cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy‐based expert system allows to provide the desired yeast cell concentration of 100–120 × 10⁶ cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.     

Ratiometric fluorescent detection of Cu2+ based on dual‐emission ZIF‐8@rhodamine‐B nanocomposites

Zeolitic imidazolate framework‐8 (ZIF‐8) loading rhodamine‐B (ZIF‐8@rhodamine‐B) nanocomposites was proposed and used as ratiometric fluorescent sensor to detect copper(II) ion (Cu²⁺). Scanning electron microscopy, Fourier transform infrared spectroscopy, X‐ray powder diffraction, nitrogen adsorption/desorption isotherms and fluorescence emission spectroscopy were employed to characterize the ZIF‐8@rhodamine‐B nanocomposites. The results showed the rhodamine‐B was successfully assembled on ZIF‐8 based on the π‐π interaction and the hydrogen bond between the nitrogen atom of ZIF‐8 and –COOH of rhodamine‐B. The as‐obtained ZIF‐8@rhodamine‐B nanocomposites were octahedron with size about 150–200 nm, had good water dispersion, and exhibited the characteristic fluorescence emission of ZIF‐8 at 335 nm and rhodamine‐B at 575 nm. The Cu²⁺ could quench fluorescence of ZIF‐8 rather than rhodamine‐B. The ZIF‐8 not only acted as the template to assemble rhodamine‐B, but also was employed as the signal fluorescence together with the fluorescence of rhodamine‐B as the reference to construct a novel ratiometric fluorescent sensor to detect Cu²⁺. The resulted ZIF‐8@rhodamine‐B nanocomposite fluorescence probe showed good linear range (68.4 nM to 125 μM) with a low detection limit (22.8 nM) for Cu²⁺ combined with good sensitivity and selectivity. The work also provides a better way to design ratiometric fluorescent sensors from ZIF‐8 and other fluorescent molecules.     

Real‐time monitoring of ethanol production during Pichia stipitis NRRL Y‐7124 alcoholic fermentation using transflection near infrared spectroscopy

The application of in situ near‐infrared spectroscopy monitoring of xylose metabolizing yeast such as Pichia stipitis for ethanol production with semisynthetic media, applying chemometrics, was investigated. During the process in a bioreactor, biomass, glucose, xylose, ethanol, acetic acid, and glycerol determinations were performed by a transflection probe immersed in the culture broth and connected to a near‐infrared process analyzer. Wavelength windows in near‐infrared spectra recorded between 800 and 2200 nm were pretreated using Savitzky–Golay smoothing, second derivative and multiplicative scattering correction in order to perform a partial least squares regression and generate the calibration models. These calibration models were tested by external validation (78 samples). Calibration and validation criteria were defined and evaluated in order to generate robust and reliable models for an alcoholic fermentation process matrix. Moreover, regressions coefficients (β) and variable influence in the projection plots were used to assess the results. A novelty is the use of β versus VIP dispersion plots to determine which vectors have more influence on the response in order to improve process comprehension and operability. Validated models were used in a real‐time monitoring during P. stipitis NRRL Y7124 semisynthetic media fermentations.     

Two‐dimensional fluorescence as soft sensor in the monitoring of biotransformation performed by yeast

Soft sensors are powerful tools for bioprocess monitoring due to their ability to perform online, noninvasive measurement, and possibility of detection of multiple components in cultivation media, which in turn can provide tools for the quantification of more than one metabolite/substrate/product in real time. In this work, soft sensor based on excitation‐emission fluorescence is for the first time applied for the monitoring of biotransformation production of 2‐phenylethanol (2‐PE) by yeast strains. Main process parameters—such as optical density, glucose, and 2‐PE concentrations—were determined with high accuracy and precision by fluorescence fingerprinting coupled with partial least squares regression. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:299–307, 2017     

A novel LED‐based 2D‐fluorescence spectroscopy system for in‐line monitoring of Chinese hamster ovary cell cultivations – Part I

A new two‐dimensional fluorescence sensor system was developed for in‐line monitoring of mammalian cell cultures. Fluorescence spectroscopy allows for the detection and quantification of naturally occurring intra‐ and extracellular fluorophores in the cell broth. The fluorescence signals correlate to the cells’ current redox state and other relevant process parameters. Cell culture pretests with twelve different excitation wavelengths showed that only three wavelengths account for a vast majority of spectral variation. Accordingly, the newly developed device utilizes three high‐power LEDs as excitation sources in combination with a back‐thinned CCD‐spectrometer for fluorescence detection. This setup was first tested in a lab design of experiments study with process relevant fluorophores proving its suitability for cell culture monitoring with LOD in the μg/L range. The sensor was then integrated into a CHO‐K1 cell culture process. The acquired fluorescence spectra of several batches were evaluated using multivariate methods. The resulting batch evolution models were challenged in deviating and “golden batch” validation runs. These first tests showed that the new sensor can trace the cells’ metabolic state in a fast and reliable manner. Cellular distress is quickly detected as a deviation from the “golden batch”.     

A New Evaluation Method for 2‐D Fluorescence Spectra Based on Theoretical Modeling

This article presents a new evaluation procedure of 2‐D fluorescence spectra obtained during a yeast cultivation without performing a calibration measurement. The 2‐D fluorescence spectra are used to predict the process variables biomass, glucose and ethanol. The new calibration procedure uses a theoretical model of these process variables, i.e., differential equations, to replace any calibration measurement. The theoretical model parameters are identified simultaneously during the calculation of the chemometric models. The root mean square error of prediction of the chemometric models with respect to off‐line measurements are 1.5 g/L, 0.40 g/L and 0.56 g/L for glucose, biomass and ethanol, respectively.     

Non‐destructive allometric estimates of above‐ground and below‐ground biomass of high‐mountain vegetation in the Andes

Aim

Studies that monitor high‐mountain vegetation, such as paramo grasslands in the Andes, lack non‐destructive biomass estimation methods. We aimed to develop and apply allometric models for above‐ground, below‐ground and total biomass of paramo plants.

Location

The paramo of southern Colombia between 1°09′N and 077°50′W, at 3,400 and 3,700 m a.s.l.

Methods

We established 61 1‐m² plots at random locations, excluding disturbed, inaccessible and peat bog areas. We measured heights and basal diameters of all vascular plants in these plots and classified them into seven growth forms. Near each plot, we sampled the biomass from plants of abundant genera, after having measured their height and basal diameter. Hence, we measured the biomass of 476 plants (allometric set). For each growth form we applied power‐law functions to develop allometric models of biomass against basal diameter, height, height x basal diameter and height × basal area. The best models were selected using AIC_c weights. Using the observed and predicted plant biomass of the allometric set we calculated absolute percentage errors using cross‐validation. The biomass of a plot was estimated by summing the predicted biomass of all plants in a plot. Confidence limits around these sums were calculated by bootstrapping.

Results

For groups of <20 plants the biomass predictions yielded large (>15%) errors. Applying groups that resembled the 1‐m² plots in density and composition, the errors for above‐ground and total biomass estimates were <15%. Across all plots, we obtained an above‐ground, below‐ground and total plot biomass of 329 ± 190, 743 ± 486 and 1011 ± 627 g/m² (mean ± SD), respectively. These values were within the range of biomass estimates obtained destructively in the tropical Andes.

Conclusions

In new applications, if target vegetation samples are similar regarding growth forms and genera to our allometric set, their biomass might be predicted applying our equations, provided they contain at least 50–100 plants. In other situations, we would recommend gathering additional biomass measurements from local plants to evaluate new regression equations.     

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

Upstream bioprocess characterization and optimization are time and resource‐intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time‐resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed‐batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data‐driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time‐resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black‐box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time‐resolved trajectories. This showcases the high potential of hybrid models for soft‐sensing and model predictive control.     

Advanced near‐infrared monitor for stable real‐time measurement and control of Pichia pastoris bioprocesses

Near‐infrared spectroscopy is considered to be one of the most promising spectroscopic techniques for upstream bioprocess monitoring and control. Traditionally the nature of near‐infrared spectroscopy has demanded multivariate calibration models to relate spectral variance to analyte concentrations. The resulting analytical measurements have proven unreliable for the measurement of metabolic substrates for bioprocess batches performed outside the calibration process. This paper presents results of an innovative near‐infrared spectroscopic monitor designed to follow the concentrations of glycerol and methanol, as well as biomass, in real time and continuously during the production of a monoclonal antibody by a Pichia pastoris high cell density process. A solid state instrumental design overcomes the ruggedness limitations of conventional interferometer‐based spectrometers. Accurate monitoring of glycerol, methanol, and biomass is demonstrated over 274 days postcalibration. In addition, the first example of feedback control to maintain constant methanol concentrations, as low as 1 g/L, is presented. Postcalibration measurements over a 9‐month period illustrate a level of reliability and robustness that promises its adoption for online bioprocess monitoring throughout product development, from early laboratory research and development to pilot and manufacturing scale operation. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:749–759, 2014     

Novel fluorescent sensor using molecularly imprinted silica microsphere‐coated CdSe@CdS quantum dots and its application in the detection of 2,4,6‐trichlorophenol from environmental water samples

In this study, a high fluorescence sensitivity and selectivity, molecularly imprinted nanofluorescent polymer sensor (MIP@SiO₂@QDs) was prepared using a reverse microemulsion method. 2,4,6‐Trichlorophenol (2,4,6‐TCP) was detected using fluorescence quenching. Tetraethyl orthosilicate (TEOS), quantum dots (QDs) and 3‐aminopropyltriethoxysilane (APTS) were used as cross‐linker, signal sources and functional monomer respectively. The sensor (MIP@SiO₂@QDs) and the non‐imprinted polymer sensor (NIP@SiO₂@QDs) were characterized using infra‐red (IR) analysis, X‐ray diffraction (XRD), transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The selectivity of MIP@SiO₂@QDs was examined by comparing 2,4,6‐TCP with other similar functional substances including 2,4‐dichlorophenol (2,4‐DCP), 2,6‐dichlorophenol (2,6‐DCP) and 4‐chlorophenol (4‐CP). Results showed that MIP@SiO₂@QDs had better selectivity for 2,4,6‐TCP than the other compounds. Fluorescence quenching efficiency displayed a good linear response at the 2,4,6‐TCP concentration range 5–1000 μmol/L. The limit of detection (LOD) was 0.9 μmol/L (3σ, n = 9). This method was equally applicable for testing actual samples with a recovery rate of 98.0–105.8%. The sensor had advantages of simple pretreatment, good sensitivity and selectivity, and wide linear range and could be applied for the rapid detection of 2,4,6‐TCP in actual samples.     

Glucose and Nitrogen Amendments Can Mitigate Wastewater‐Borne Bacteria Competition Effect Against Algal Growth in Wastewater‐Based Systems

The reuse of wastewater is important for reducing costs involved with algal lipid production. However, nutrient limitations, wastewater‐borne microbes, and mixotrophic growth can significantly affect biomass yields and lipid/biomass ratios. This research compared the growth performances of both Chlorella vulgaris and Pseudokirchneriella subcapitata on domestic wastewater effluent. The experiments were conducted in the presence and absence of wastewater‐borne bacteria, while additionally assessing the impact of distinct nitrate and glucose supplementations. When compared to the sterilized controls, the presence of wastewater‐borne bacteria in the effluent reduced C. vulgaris and P. subcapitata total biomass production by 37% and 46%, respectively. In the corresponding treatments supplemented with glucose and nitrate, total biomass production increased by 12% and 61%, respectively. The highest biomass production of 1.11 and 0.72 g · L^?1 was, however, observed in the sterilized treatments with both glucose and nitrate supplementations for C. vulgaris and P. subcapitata, respectively. Lipid to biomass ratios were, on average, threefold higher when only nitrate was introduced in the sterilized treatments for both species (0.4 and 0.5, respectively). Therefore, the combination of nitrate and glucose supplementation is shown to be an important strategy for enhancing algal lipid and biomass production when those algae are grown in the presence of wastewater‐borne bacteria. On the other hand, in the absence of wastewater‐borne bacteria, only nitrate supplementation can significantly improve lipid/biomass ratios.     

An experimental test of the hypothesis of non‐homeostatic consumer stoichiometry in a plant litter–microbe system

Stoichiometric homeostasis of heterotrophs is a common, but not always well‐examined premise in ecological stoichiometry. We experimentally evaluated the relationship between substrate (plant litter) and consumer (microorganisms) stoichiometry for a tropical terrestrial decomposer system. Variation in microbial C : P and N : P ratios tracked that of the soluble litter fraction, but not that of bulk leaf litter material. Microbial N and P were not isometrically related, suggesting higher rates of P than N sequestration in microbial biomass. Shifts in microbial stoichiometry were related to changes in microbial community structure. Our results indicate that P in dissolved form is a major driver of terrestrial microbial stoichiometry, similar to aquatic environments. The demonstrated relative plasticity in microbial C : P and N : P and the critical role of P have important implications for theoretical modelling and contribute to a process‐based understanding of stoichiometric relationships and the flow of elements across trophic levels in decomposer systems.     

Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta‐training, meta‐testing and validation sets. The meta‐training set was used to build risk prediction model, and the other four data sets were employed for validation. By log‐rank test and univariate COX regression analysis as well as LASSO‐COX, AML patients were divided into high‐risk and low‐risk groups based on AML risk score (AMLRS) which was constituted by 10 survival‐related genes. In meta‐training, meta‐testing and validation sets, the patient in the low‐risk group all had a significantly longer OS (overall survival) than those in the high‐risk group (P < .001), and the area under ROC curve (AUC) by time‐dependent ROC was 0.5854‐0.7905 for 1 year, 0.6652‐0.8066 for 3 years and 0.6622‐0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1‐year, 3‐year and 5‐year OS. Finally, we created a web‐based prognostic model to predict the prognosis of AML patients ( https://tcgi.shinyapps.io/amlrs_nomogram/ ).     

Synthesis of hydrophobic nanoparticles for real‐time lysozyme detection using surface plasmon resonance sensor

Diagnostic biomarkers such as proteins and enzymes are generally hard to detect because of the low abundance in biological fluids. To solve this problem, the advantages of surface plasmon resonance (SPR) and nanomaterial technologies have been combined. The SPR sensors are easy to prepare, no requirement of labelling and can be detected in real time. In addition, they have high specificity and sensitivity with low cost. The nanomaterials have also crucial functions such as efficiency improvement, selectivity, and sensitivity of the detection systems. In this report, an SPR‐based sensor is developed to detect lysozyme with hydrophobic poly (N‐methacryloyl‐(L)‐phenylalanine) (PMAPA) nanoparticles. The SPR sensor was first characterized by attenuated total reflection‐Fourier transform infrared, atomic force microscope, and water contact angle measurements and performed with aqueous lysozyme solutions. Various concentrations of lysozyme solution were used to calculate kinetic and affinity coefficients. The equilibrium and adsorption isotherm models of interactions between lysozyme solutions and SPR sensor were determined and the maximum reflection, association, and dissociation constants were calculated by Langmuir model as 4.87, 0.019 nM⁻¹, and 54 nM, respectively. The selectivity studies of SPR sensor were investigated with competitive agents, hemoglobin, and myoglobin. Also, the SPR sensor was used four times in adsorption/desorption/recovery cycles and results showed that, the combination of optical SPR sensor with hydrophobic ionizable PMAPA nanoparticles in one mode enabled the detection of lysozyme molecule with high accuracy, good sensivity, real‐time, label‐free, and a low‐detection limit of 0.66 nM from lysozyme solutions. Lysozyme detection in a real sample was performed by using chicken egg white to evaluate interfering molecules present in the medium.     

Efficient racemization of N‐phenylacetyl‐D‐glufosinate for L‐glufosinate production

Most amino acids contain chiral centres and exist as both D‐enantiomer and L‐enantiomer. The optically pure enantiomer is often more valuable than the racemate. Enzymatic resolution provides an effective strategy to obtain optically pure amino acids but often results in large amounts of unwanted isomer. In this study, optically pure L‐glufosinate (L‐PPT) was obtained by coupling amidase‐mediated hydrolysis of N‐phenylacetyl‐D,L‐glufosinate with racemization of N‐phenylacetyl‐D‐glufosinate (NPDG), which exclusively exhibits effective herbicidal properties compared with its D‐enantiomer. To improve the yield of L‐PPT, the racemization reaction conditions were optimized, and through single‐factor experiments, the optimal reaction temperature, reaction time, and mole ratio of phenylacetic acid to NPDG were determined to be 150°C, 30 minutes, and 1.5, respectively. The response surface methodology was applied to further optimize the racemization conditions, and the final yield of L‐PPT reached 96.13% with optimum reaction temperature of 154°C, reaction time of 23 minutes, and phenylacetic acid/NPDG mole ratio of 1.7, respectively. Moreover, adding a small amount of acetic anhydride further raised the yield of L‐PPT to 97.02%.     

Two‐channel near‐infrared fluorescence Ag+ ion sensing of a new star‐shaped dendrimer

A new near‐infrared fluorescence sensor PDI‐PD for Ag⁺ ions was successfully prepared and its structure characterized by ¹H nuclear magnetic resonance (NMR), ¹³C NMR and high‐resolution mass spectrometry; matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (HRMS MALDI‐TOF). The probe exhibited rapid, sensitive, and selective two‐channel fluorescence responses towards Ag⁺ ions and protons. The probe has a marked high binding affinity and high sensitivity for Ag⁺, with a detection limit of 1.4 × 10^?6 M. An approximately five‐fold enhanced core emission at 784 nm was attributed to fluorescence resonance energy transfer (FRET). The enhanced core emission of the probe with Ag⁺ ions based on photo‐induced electron transfer and FRET is discussed. In addition, the probe presented a visible colour change. All experimental results demonstrated that PDI‐PD is an efficient tool for the selective, sensitive and rapid detection of Ag⁺ ions and protons using two‐channel fluorescence responses.