Analysis of chemometric models applied to Raman spectroscopy for monitoring key metabolites of cell culture

The Food and Drug Administration (FDA) initiative of Process Analytical Technology (PAT) encourages the monitoring of biopharmaceutical manufacturing processes by innovative solutions. Raman spectroscopy and the chemometric modeling tool partial least squares (PLS) have been applied to this aim for monitoring cell culture process variables. This study compares the chemometric modeling methods of Support Vector Machine radial (SVMr), Random Forests (RF), and Cubist to the commonly used linear PLS model for predicting cell culture components—glucose, lactate, and ammonia. This research is performed to assess whether the use of PLS as standard practice is justified for chemometric modeling of Raman spectroscopy and cell culture data. Model development data from five small-scale bioreactors (2 × 1 L and 3 × 5 L) using two Chinese hamster ovary (CHO) cell lines were used to predict against a manufacturing scale bioreactor (2,000 L). Analysis demonstrated that Cubist predictive models were better for average performance over PLS, SVMr, and RF for glucose, lactate, and ammonia. The root mean square error of prediction (RMSEP) of Cubist modeling was acceptable for the process concentration ranges of glucose (1.437 mM), lactate (2.0 mM), and ammonia (0.819 mM). Interpretation of variable importance (VI) results theorizes the potential advantages of Cubist modeling in avoiding interference of Raman spectral peaks. Predictors/Raman wavenumbers (cm⁻¹) of interest for individual variables are X1139–X1141 for glucose, X846–X849 for lactate, and X2941–X2943 for ammonia. These results demonstrate that other beneficial chemometric models are available for use in monitoring cell culture with Raman spectroscopy.     

Real time monitoring of multiple parameters in mammalian cell culture bioreactors using an in-line Raman spectroscopy probe

The FDA's process analytical technology initiative encourages drug manufacturers to apply innovative ideas to better understand their processes. There are many challenges to applying these techniques to monitor mammalian cell culture bioreactors for biologics manufacturing. These include the ability to monitor multiple components in complex medium formulations non-invasively and in-line. We report results that demonstrate, for the first time, the technical feasibility of the in-line application of Raman spectroscopy for monitoring a mammalian cell culture bioreactor. A Raman probe was used for the simultaneous prediction of culture parameters including glutamine, glutamate, glucose, lactate, ammonium, viable cell density, and total cell density.     

Bioprocess in-line monitoring using Raman spectroscopy and Indirect Hard Modeling (IHM): A simple calibration yields a robust model

To increase the process productivity and product quality of bioprocesses, the in-line monitoring of critical process parameters is highly important. For monitoring substrate, metabolite, and product concentrations, Raman spectroscopy is a commonly used Process Analytical Technology (PAT) tool that can be applied in-situ and non-invasively. However, evaluating bioprocess Raman spectra with a robust state-of-the-art statistical model requires effortful model calibration. In the present study, we in-line monitored a glucose to ethanol fermentation by Saccharomyces cerevisiae (S. cerevisiae) using Raman spectroscopy in combination with the physics-based Indirect Hard Modeling (IHM) and showed successfully that IHM is an alternative to statistical models with significantly lower calibration effort. The IHM prediction model was developed and calibrated with only 16 Raman spectra in total, which did not include any process spectra. Nevertheless, IHM's root mean square errors of prediction (RMSEPs) for glucose (3.68 g/L) and ethanol (1.69 g/L) were comparable to the prediction quality of similar studies that used statistical models calibrated with several calibration batches. Despite our simple calibration, we succeeded in developing a robust model for evaluating bioprocess Raman spectra.     

Raman spectroscopy and order in biological systems

The Raman spectra in the low 5–200 cm⁻¹ frequency region of metabolically activeE. coli cells have been analyzed to determine whether they are indicators of a possible in vivo underlying order by applying standard concepts derived from the Raman spectroscopy of crystalline systems with varying degrees of order. The analysis suggests that in-vivo space-time ordered structures involving amino acids associated with DNA exist since the low frequency lines of metabolically active cells can be assigned to lines seen in the spectra of crystals of given amino acids known to associated with DNA early in the lifetime of a cell.     

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.     

A novel urine analysis technique combining affinity chromatography with Au nanoparticle based surface enhanced Raman spectroscopy for potential applications in non‐invasive cancer screening

Modified nucleoside in urine samples is one of the most common biomarkers for cancer screening. Therefore, we developed a novel detection method for modified nucleoside detection in human urine. In this work, the modified nucleoside from real cancer patient's urine samples was first separated and purified using the affinity chromatography (AC) technology relying on its specific adsorption capacity. Then, surface‐enhanced Raman spectroscopy (SERS) technology with the capability of single molecular detection was used to sensitively characterize the biomolecular features of modified nucleoside. A total of 141 high‐quality SERS spectra of urinary modified nucleoside can be obtained from 50 gastric cancer patients and 43 breast cancer patients, as well as 48 healthy volunteers. Using principal component analysis combined with linear discriminant analysis (PCA‐LDA), the diagnostic sensitivities for identifying gastric cancer vs normal, breast cancer vs normal, gastric cancer vs breast cancer were 84.0%, 76.7% and 82.0%, respectively, and the corresponding diagnostic specificities for each combination were 95.8%, 87.5% and 90.7%, respectively. These results show that this novel method based on urinary modified nucleoside detection combining AC and SERS technologies holds promising potential for developing a specific, non‐invasive and label‐free tool for cancer screening.      

Superpixel Raman spectroscopy for rapid skin cancer margin assessment

Spontaneous Raman micro‐spectroscopy has been demonstrated great potential in delineating tumor margins; however, it is limited by slow acquisition speed. We describe a superpixel acquisition approach that can expedite acquisition between ~×100 and ×10 000, as compared to point‐by‐point scanning by trading off spatial resolution. We present the first demonstration of superpixel acquisition on rapid discrimination of basal cell carcinoma tumor from eight patients undergoing Mohs micrographic surgery. Results have been demonstrated high discriminant power for tumor vs normal skin based on the biochemical differences between nucleus, collagen, keratin and ceramide. We further perform raster‐scanned superpixel Raman imaging on positive and negative margin samples. Our results indicate superpixel acquisition can facilitate the use of Raman microspectroscopy as a rapid and specific tool for tumor margin assessment.     

Oligosaccharide identification and mixture quantification using Raman spectroscopy and chemometric analysis

This work demonstrates the feasibility of using Raman spectroscopy for the analysis of small quantities of chemically similar oligosaccharides and their mixtures. Raman spectra were obtained from 10-microL aliquots of 1 mM solutions of maltotetraose and/or stachyose after deposition onto an electrochemically roughened silver substrate (and the resulting spectral features are attributed to a combination of normal and surface-enhanced Raman scattering). These compounds were selected because they are representative of glycans derived from post-translationally modified proteins which, like these compounds, often consist of isomers of equal mass and similar shape. Replicate spectral measurements were recorded and processed using a partial-least-squares (PLS) classification and quantification algorithms with a leave-one-batch-out (LOBO) training and testing procedure. Spectra derived from solutions of individual sugars were identified with 100% accuracy, and mixtures of the two sugars were quantified with an average error of 2.7% in the relative maltotetraose/stachyose composition for mixtures with a total oligosaccharide concentration of 1 mM.     

Purification of cell culture-derived human influenza A virus by size-exclusion and anion-exchange chromatography

A process comprising of size-exclusion chromatography (SEC) and anion-exchange chromatography (AEC) was investigated for downstream processing of cell culture-derived influenza A virus. Human influenza virus A/PR/8/34 (H1N1) was propagated in serum-free medium using MDCK cells as a host. Concentrates of the virus were prepared from clarified and inactivated cell culture supernatants by cross-flow ultrafiltration as described before. SEC on Sepharose 4 FF resulted in average product yields of 85% based on hemagglutination (HA) activity. Productivity was maximized to 0.15 column volumes (cv) of concentrate per hour yielding a reduction in total protein and host cell DNA (hcDNA) to 35 and 34%, respectively. AEC on Sepharose Q XL was used to separate hcDNA from virus at a salt concentration of 0.65 M sodium chloride. Product yields >80% were achieved for loads >160 kHAU/mL of resin. The reduction in hcDNA was 67-fold. Split peak elution and bimodal particle volume distributions suggested aggregation of virions. Co-elution with hcDNA and constant amounts of hcDNA per dose indiciated association of virions to hcDNA. An overall product yield of 52% was achieved. Total protein was reduced more than 19-fold; hcDNA more than 500-fold by the process. Estimation of the dose volume from HA activity predicted a protein content at the limit for human vaccines. Reduction of hcDNA was found insufficient (about 500 ng per dose) requiring further optimization of AEC or additional purification steps. All operations were selected to be scalable and independent of the virus strain rendering the process suitable for vaccine production.     

A computationally efficient Monte-Carlo model for biomedical Raman spectroscopy

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through “isoweight,” a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 − 2000 cm⁻¹ with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.     

光镊捕获的单个解冻人血红细胞的拉曼光谱研究

红细胞的冰冻与解冻的实施为血液的长期保存提供了有效的方法。在过去的几十年中,红细胞冻存有过数种方法,其中高甘油冻存法比较普遍,因而用此方法保存红细胞是否对红细胞产生了影响及产生了什么影响是值得研究的。作者利用激光光镊拉曼光谱系统,通过拉曼光谱对冻存人血红细胞进行研究;结果显示,红细胞冻存前后的拉曼光谱有一定的变化,解冻提取后也产生了一定的变化,这些变化主要体现在对应拉曼光谱的位置和强度上。研究结果对进一步研究红细胞的冻存具有一定的参考价值。     

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.     

Raman spectroscopy as a method to replace off-line pH during mammalian cell culture processes

Raman spectroscopy is a robust, well-established tool utilized for measuring important cell culture process variables for example, feed, metabolites, and biomass in real-time. This study further expands the functionality of in-line Raman spectroscopy coupled with partial least squares (PLS) regression modelling to develop a pH measurement tool. Cell line specific models were developed to enhance the robustness for processes with different pH setpoints, deadbands, and cellular metabolism. The modelling strategy further improved robustness by reducing the temporal complexity of pH shifts by splitting data sets into two time zones reflective of major changes in pH. In addition, models were developed to assess if lactate and partial pressure of carbon dioxide (pCO₂) could be used in a PLS model for pH. Splitting the data sets into early and late for the process resulted in errors of 0.035 pH and 0.034 pH for the two respective Raman cell lines models which was within acceptance criteria. The lactate and pCO₂ PLS model with values provided by Raman models had a further 0.001 pH error reduction. This study illustrates the potential to eliminate off-line samples to correct for in-line measurements of pH and further illustrates the capabilities of Raman to measure additional process variables.     

The effect of cell fixation on the discrimination of normal and leukemia cells with laser tweezers Raman spectroscopy

Laser tweezers Raman spectroscopy (LTRS) was used to characterize the effect of different chemical fixation procedures on the Raman spectra of normal and leukemia cells. Individual unfixed, paraformaldehyde-fixed, and methanol-fixed normal and transformed lymphocytes from three different cell lines were analyzed with LTRS. When compared to the spectra of unfixed cells, the fixed cell spectra show clear, reproducible changes in the intensity of specific Raman markers commonly assigned to DNA, RNA, protein, and lipid vibrations (e.g. 785, 1230, 1305, 1660 cm(-1)) in mammalian cells, many of which are important markers that have been used to discriminate between normal and cancer lymphocytes. Statistical analyses of the Raman data and classification using principal component analysis and linear discriminant analysis indicate that methanol fixation induces a greater change in the Raman spectra than paraformaldehyde. In addition, we demonstrate that the spectral changes as a result of the fixation process have an adverse effect on the accurate Raman discrimination of the normal and cancer cells. The spectral artifacts created by the use of fixatives indicate that the method of cell preparation is an important parameter to consider when applying Raman spectroscopy to characterize, image, or differentiate between different fixed cell samples to avoid potential misinterpretation of the data.     

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     

Sub millimetre flexible fibre probe for background and fluorescence free Raman spectroscopy

Using the shifted-excitation Raman difference spectroscopy technique and an optical fibre featuring a negative curvature excitation core and a coaxial ring of high numerical aperture collection cores, we have developed a portable, background and fluorescence free, endoscopic Raman probe. The probe consists of a single fibre with a diameter of less than 0.25 mm packaged in a sub-millimetre tubing, making it compatible with standard bronchoscopes. The Raman excitation light in the fibre is guided in air and therefore interacts little with silica, enabling an almost background free transmission of the excitation light. In addition, we used the shifted-excitation Raman difference spectroscopy technique and a tunable 785 nm laser to separate the fluorescence and the Raman spectrum from highly fluorescent samples, demonstrating the suitability of the probe for biomedical applications. Using this probe we also acquired fluorescence free human lung tissue data.     

Non-invasive analysis of cell cycle dynamics in single living cells with Raman micro-spectroscopy

Raman micro-spectroscopy is a laser-based technique which enables rapid and non-invasive biochemical analysis of cells and tissues without the need for labels, markers or stains. Previous characterization of the mammalian cell cycle using Raman micro-spectroscopy involved the analysis of suspensions of viable cells and individual fixed and/or dried cells. Cell suspensions do not provide cell-specific information, and fixing/drying can introduce artefacts which distort Raman spectra, potentially obscuring both qualitative and quantitative analytical results. In this article, we present Raman spectral characterization of biochemical changes related to cell cycle dynamics within single living cells in vitro. Raman spectra of human osteosarcoma cells synchronized in G(0)/G(1), S, and G(2)/M phases of the cell cycle were obtained and multivariate statistics applied to analyze the changes in cell spectra as a function of cell cycle phase. Principal components analysis identified spectral differences between cells in different phases, indicating a decrease in relative cellular lipid contribution to Raman spectral signatures from G(0)/G(1) to G(2)/M, with a concurrent relative increase in signal from nucleic acids and proteins. Supervised linear discriminant analysis of spectra was used to classify cells according to cell cycle phase, and exhibited 97% discrimination between G(0)/G(1)-phase cells and G(2)/M-phase cells. The non-invasive analysis of live cell cycle dynamics with Raman micro-spectroscopy demonstrates the potential of this approach to monitoring biochemical cellular reactions and processes in live cells in the absence of fixatives or labels.     

In‐line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics models

The application of PAT for in‐line monitoring of biopharmaceutical manufacturing operations has a central role in developing more robust and consistent processes. Various spectroscopic techniques have been applied for collecting real‐time data from cell culture processes. Among these, Raman spectroscopy has been shown to have advantages over other spectroscopic techniques, especially in aqueous culture solutions. Measurements of several process parameters such as glucose, lactate, glutamine, glutamate, ammonium, osmolality and VCD using Raman‐based chemometrics models have been reported in literature. The application of Raman spectroscopy, coupled with calibration models for amino acid measurement in cell cultures, has been assessed. The developed models cover four amino acids important for cell growth and production: tyrosine, tryptophan, phenylalanine and methionine. The chemometrics models based on Raman spectroscopy data demonstrate the significant potential for the quantification of tyrosine, tryptophan and phenylalanine. The model for methionine would have to be further refined to improve quantification.     

Raman microprobe spectroscopy: A new tool for biofouling diagnostics

Raman vibrational spectra were obtained from crystalline amino acids and acid molecules adsorbed at the surfaces of silver electrodes in aqueous solutions. Results revealed that aromatic acids such as phenylalanine adsorbed via their aromatic ring while bases such as alanine were coordinated by their amine functional groups. Sulphur containing acids (cysteine and cystine) were found to bond through their sulphur groups. In all cases, adsorption increased towards the point of zero charge of silver, as would be expected for uncharged species. Similar experiments carried out on α‐amylase solutions showed that the enzyme molecule changes its coordination to the silver surface as a function of electrode potential, indicating that C‐S, C‐N, and aromatic ring functional groups are all present on the outer surface of the enzyme structure.