Raman spectroscopy provides valuable process insights for cell-derived and cellular products

Prediction of cardiac differentiation in human induced pluripotent stem cell-derived cardiomyocyte supernatant using surface-enhanced Raman spectroscopy and machine learning

The efficient manufacturing of cardiomyocytes from human-induced pluripotent stem cells (hiPSCs) is essential for advancing regenerative therapies for myocardial injuries. However, ensuring cell quality during production is challenging since traditional methods are invasive, destructive, and time-consuming. In this study, we monitored cardiomyocyte differentiation of WTC11 hiPSCs by analyzing conditioned media collected at various stages using Raman spectroscopy, multivariate analysis, and machine learning. Differentiation efficiency was confirmed via flow cytometry and immunostaining. Raman spectra were processed using standard normal variate and second derivative transformations before performing a principal component analysis (PCA) and machine learning (Random Forest, K-Nearest Neighbors, and Deep Neural Networks [DNN]). Results show that PCA was unable to distinguish cells based on differentiation stages, while machine learning could reliably predict cell differentiation early in the cardiac cell manufacturing process. DNN models achieved accuracies exceeding 82 % in predicting differentiation, highlighting their potential as quality control tools. These findings underscore the potential of Raman spectroscopy coupled with machine learning as a tool for real-time monitoring of cardiomyocyte production.

Raman analysis of lipids in cells: Current applications and future prospects

Lipids play an important role in the regulation of cell life processes. Although there are various lipid detection methods, Raman spectroscopy, a non-invasive technique, provides the detailed chemical composition of lipid profiles without a complex sample preparation procedure and possesses greater potential in basic biology, clinical diagnosis and disease therapy. In this review, we summarized the characteristics and advantages of Raman-based techniques and their primary contribution to illustrating cellular lipid metabolism.

Clinical study of the diagnosis of thyroid tumours using Raman spectroscopy

OBJECTIVE

The feasibility of the RS for the clinical diagnosis of thyroid tumours was explored.

METHODS

The tumour specimens from 30 benign patients and 30 malignant patients were collected. The collected specimens were subjected to RS and histopathological analysis. The Raman peak intensities of all the specimens were calculated, and the data were analysed using discriminant analysis.

RESULTS

(1) The prevalence rate of malignant tumours in females was as high as 76.7%. Central lymph node metastasis of malignant thyroid tumours accounted for 33.3% of cases, and lateral cervical lymph node metastasis accounted for only 6.7%. (2) The spectral intensity of malignant thyroid tumours was significantly greater than benign thyroid tumours at 1309 cm-1, which should be the characteristic peak of thyroid cancer. The accuracy, sensitivity, and specificity of the RS for differentiating benign from malignant thyroid tumours were 95%, 83.3% and 89.2%.

CONCLUSION

RS is feasible for the diagnosis of thyroid tumours. This study provides experimental and clinical support for the wider application of RS in the evaluation of thyroid tissue.

LEVELS OF EVIDENCE

Levels 4.

In-line monitoring of bioreactor by Raman spectroscopy: Direct use of a standard-based model through cell-scattering correction

Raman spectroscopy and machine learning have become popular in in-line monitoring of bioreactors. However, traditional modeling processes typically entail extensive fermentation batches to collect learning datasets, which are significantly time-consuming and laborious. In addition, these models are limited to configurations with the same conditions as the training batches. The present work proposes a reproducible and adaptable modeling approach by combining standard spectra as a training dataset, with a simple means of correcting for cell scattering. Alcoholic fermentation by Saccharomyces cerevisiae is used as a benchmark. Initially, a partial least squares (PLS) regression model was developed based on the spectra of pure solutions of glucose and ethanol. Then, a mathematical expression was defined to estimate yeast concentration, allowing the correction of Raman intensity attenuated by cell scattering. The corrected spectra demonstrate close alignment with reference spectra in both shape and intensity. Validation of the methodology was conducted across numerous batches and one fed-batch bioreactor. As a result, the developed method enables the simultaneous monitoring of glucose, ethanol, and yeast concentrations, effectively addressing the challenge of implementing an independent standards based PLS model to manage the intricate compositional dynamics in bio-processes. The conclusion underscores the effectiveness of the proposed method and offers new prospects in biotechnological industries.

Harnessing Raman spectroscopy for cell therapy bioprocessing

Cell therapy manufacturing requires precise monitoring of critical parameters to ensure product quality, consistency and to facilitate the implementation of cost-effective processes. While conventional analytical methods offer limited real-time insights, integration of process analytical technology tools such as Raman spectroscopy in bioprocessing has the potential to drive efficiency and reliability during the manufacture of cell-based therapies while meeting stringent regulatory requirements. The non-destructive nature of Raman spectroscopy, combined with its ability to be integrated on-line with scalable platforms, allows for continuous data acquisition, enabling real-time correlations between process parameters and critical quality attributes. Herein, we review the role of Raman spectroscopy in cell therapy bioprocessing and discuss how simultaneous measurement of distinct parameters and attributes, such as cell density, viability, metabolites and cell identity biomarkers can streamline on-line monitoring and facilitate adaptive process control. This, in turn, enhances productivity and mitigates process-related risks. We focus on recent advances integrating Raman spectroscopy across various manufacturing stages, from optimizing culture media feeds to monitoring bioprocess dynamics, covering downstream applications such as detection of co-isolated contaminating cells, cryopreservation, and quality control of the drug product. Finally, we discuss the potential of Raman spectroscopy to revolutionize current practices and accelerate the development of advanced therapy medicinal products.

Use of spectroscopic process analytical technology for rapid quality evaluation during preparation of CHO cell culture media

Media preparation parameters contribute significantly to media quality, cell culture performance, productivity, and product quality. Establishing proper media preparation procedures is critical for ensuring a robust CHO cell culture process. Process analytical technology (PAT) enables unique ways to quantify assessments and improve media quality. Here, cell culture media were prepared under a wide range of temperatures (40-80°C) and pH (7.6-10.0). Media quality profiles were compared using three real-time PATs: Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and excitation-emission matrix (EEM) spectroscopy. FTIR and Raman spectroscopies identified shifts in media quality under high preparation temperature (80°C) and at differing preparation pH which negatively impacted monoclonal antibody (mAb) production. In fed-batch processes for production of three different mAbs, viable cell density (VCD) and cell viability were mostly unaffected under all media preparation temperatures, while titer and cell specific productivity of mAb decreased when cultured in basal and feed media prepared at 80°C. High feed preparation pH alone was tolerated but cell growth and productivity profiles deviated from the control condition. Further, charge variants (main, acidic, basic species) and glycosylation (G0F, afucosylation, and high mannose) were examined. Statistically significant differences were observed for one or more of these quality attributes with any shifts in media preparation. In this study, we demonstrated strong associations between media preparation conditions and cell growth, productivity, and product quality. The rapid evaluation of media by PAT implementation enabled more comprehensive understanding of different parameters on media quality and consequential effects on CHO cell culture.

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC-MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells.

Real-time monitoring strategies for optimization of in vitro transcription and quality control of RNA

RNA-based therapeutics and vaccines are opening up new avenues for modern medicine. To produce these useful RNA-based reagents, in vitro transcription (IVT) is an important reaction that primarily determines the yield and quality of the product. Therefore, IVT condition should be well optimized to achieve high yield and purity of transcribed RNAs. To this end, real-time monitoring of RNA production during IVT, which allows for fine tuning of the condition, would be required. Currently, light-up RNA aptamer and fluorescent dye pairs are considered as useful strategies to monitor IVT in real time. Fluorophore-labeled antisense probe-based methods can also be used for real-time IVT monitoring. In addition, a high-performance liquid chromatography (HPLC)-based method that can monitor IVT reagent consumption has been developed as a powerful tool to monitor IVT reaction in near real-time. This mini-review briefly introduces some strategies and examples for real-time IVT monitoring and discusses pros and cons of IVT monitoring methods.