(T1030-02-10) Enhancing Analytical Sensitivity in Upstream Bioreactions Using Time-Gated Raman Spectroscopy

Purpose: The growing complexity of bioreactions in biomanufacturing has increased the demand for Process Analytical Technology (PAT) tools for real-time process monitoring and quality control. Raman spectroscopy has proven effective for tracking key metabolites due to its water interference resistance, bioreactor compatibility, high chemical specificity, rapid-data collection, and biological safety. Consequently, Raman remains a leading PAT tool and is increasingly integrated into full-scale biomanufacturing. Despite its advantages as a PAT tool, Raman is limited by fluorescence interference from bioreaction constituents, which weakens signal-to-noise ratio (SNR) and obscures Raman signals, with a detection limit of 0.5–2.0 mM for glucose and lactate [1], traditional Raman spectroscopy (TRS) struggles to detect key metabolites present at low concentrations, reducing its effectiveness in process monitoring. Time-gated Raman Spectroscopy (TGRS) provides an advanced solution by exploiting the temporal difference between Raman scattering and fluorescence emission. By synchronizing laser excitation with a time-gated detector, TGRS selectively captures Raman signals while minimizing fluorescence interference, improving detection sensitivity. This approach has significant implications for bioprocess monitoring where precise chemical detection in complex environments is required. Technological advancements have made TGRS increasingly feasible for real-time and in-situ applications, enhancing its role in analytical and industrial settings. This study aims to compare TRS and TGRS in shaker-flask bioreactions by evaluating their SNR and LOD. Data were collected from six independent shaker flasks using both Raman instruments. Pure component modeling was employed to determine the NAS for five key cell culture metabolites: glucose, lactate, ammonia, glutamine, and alanine. The NAS values were subsequently utilized to calculate the SNR and LOD for these metabolites in both TRS and TGRS. The findings of this study demonstrate that TGRS effectively reduces fluorescence interference, leading to improved SNR and LOD for the five metabolites in shaker flask bioreactions highlighting it's potential as a viable PAT tool for real-time monitoring of upstream bioprocesses.

Methods: The NISTCHO cell line, a clonal CHO-K1 line producing cNISTmAb (IgG1κ), was cultured in serum-free EX-CELL Advanced Fed Batch Medium with 6mM L-Glutamine. A total of 6 cultures were conducted with two cell density levels (low: 5 × 10⁵ to 5 × 10⁶, high: >5 × 10⁶, figure-1E) to measure five key metabolites: Glucose, Lactate, Ammonia, Glutamine and Alanine. Net Analyte Signal (NAS): Each chemical component has a distinct NAS in a mixture spectrum that remains orthogonal to the signals of other constituents. The NAS determines the detectability of a specific chemical component in relation to the presence of other components across all wavelengths [2]. The pure NAS for a given component K, (NAS_pure,i) with a pure spectrum M_pure can be derived from the pure component spectra matrix (K). NAS_pure,i = (I – K`<sub>i</sub> * (K<sub>i</sub> * K`_i)^-1 * K_i) * M_pure` Net Analyte Signal Noise (NAS<sub>noise</sub>): NAS<sub>noise</sub> represents the portion of the spectrum that is not accounted for by the pure component spectra and quantifies the unexplained spectral contribution, including noise and unmodeled variations. NAS<sub>noise</sub> = (I – K`K) * M`_media). Signal-to-Noise Ratio: Estimating the SNR in spectral samples can be achieved by defining a NAS_noise vector. This method involves computing NAS_noise by orthogonalizing [2, 3] the mixture spectrum against the complete pure component matrix K. SNR = || NAS_mix ||/std(NAS_noise) Limit of Detection: LOD represents the minimum concentration of an analyte that can be reliably detected using a specific analytical method within a given sample matrix. In this formulation, NAS_{noise from media} represents the net analyte signal corresponding to the noise in the medium, while ||SEN|| denotes the norm of the sensitivity vector, reflecting the instrument’s response to variations in analyte concentration [4]. SEN_i = NAS_i/y_i LOD = 3.3 NAS_{noise from media} 1/||SEN||

Results: Figure 1 illustrates the NAS of five key metabolites, along with their respective signature peaks. From the preprocessed figures (C & D), clear signature peaks were identified for all the metabolites: Glucose, Lactate, Ammonia, Glutamine, and Alanine using TGRS and TRS. Additionally, after preprocessing of the data, the TGRS-derived NAS spectra exhibit sharper, more well-defined peaks and less noisy compared to the TRS spectra, suggesting improved spectral resolution and reduced background or fluorescence interference in the TGRS method. Figure 2 illustrates the comparison of SNR and LOD calculation of TGRS and TRS for low-density cell culture data. For all 5 metabolites, TGRS showed higher SNR and lower LOD compared to TRS. Superior SNR and LOD results supported that TGRS enhances signal clarity and reduces background noise, improving metabolite differentiation and quantification over TRS. Similarly, in Figure 3 high-density cell culture showed high SNR and lower LOD for TGRS compared to TRS

Conclusion: Signature peaks of five key metabolites were identified using NAS calculations from both TGRS and TRS, with each showing distinct Raman peaks. TGRS outperformed TRS by effectively removing fluorescence, leading to better SNR and LOD. Integrating these probes into a bioreactor will enable real-time metabolite prediction using chemometric models.

References: 1. Moretto, J., et al., Raman-process Raman spectroscopy for in-line CHO cell culture monitoring. American Pharmaceutical Review, 2011. 14(3): p. 18.
2. Lorber A, Faber K, Kowalski BR. Net analyte signal calculation in multivariate calibration. Analytical Chemistry. 1997 Apr 15;69(8):1620-6.
3. Lorber A. Error propagation and figures of merit for quantification by solving matrix equations. Analytical Chemistry. 1986 May 1;58(6):1167-72.
4. Li M, Ebel B, Chauchard F, Guédon E, Marc A. Parallel comparison of in situ Raman and NIR spectroscopies to simultaneously measure multiple variables toward real-time monitoring of CHO cell bioreactor cultures. Biochemical Engineering Journal. 2018 Sep 15;137:205-13.

Acknowledgements: This work was performed in collaboration with Pfizer, Inc and was performed under a Project Award Agreement from the National Institute for Innovation in Manufacturing Biopharmaceuticals (NIIMBL) and financial assistance award 70NANB17H002 from the U.S. Department of Commerce, National Institute of Standards and Technology.


Figure 1: These plots represent the pure component spectra and Net analyte signal (NAS) of five key metabolites (A) is the non-processed pure component spectra of metabolites from TGRS, (B) is the non-processed pure component spectra of metabolites from TRS. (C) is the preprocessed pure component NAS with signature peaks of metabolites from TGRS, (D) is the preprocessed pure component NAS with signature peaks of metabolites from TRS and (E) Six cell culture samples with their density and cell viability values.


Figure 2: These plots represent the SNR & LOD of low cell density (LCD) culture calculated from TGRS and TRS. (A) illustrate the comparison of SNR calculation of five key metabolites from TGRS and TRS. (B) illustrate the comparison of LOD calculation of five key metabolites from TGRS and TRS


Figure 3: These plots represent the SNR & LOD of high cell density (HCD) culture calculated from TGRS and TRS. (A) illustrate the comparison of SNR calculation of five key metabolites from TGRS and TRS. (B) illustrate the comparison of LOD calculation of five key metabolites from TGRS and TRS