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The accurate and sensitive analysis of sub-proteomic samples, such as host cell proteins (HCPs) in recombinant products and stem cells in medical devices, is crucial for ensuring product safety and efficacy in the biopharmaceutical industry. However, current analytical techniques, such as conventional analytical-flow LC-MS/MS, face limitations in sensitivity due to the low concentrations of target proteins and the complexity of the sample matrix. In this study, a highly sensitive and repeatable micro-flow LC-MS/MS strategy was developed by replacing analytical-flow tubing with micro-flow tubing on an existing analytical-flow LC-MS system for sub-proteomic sample analysis. Method optimization and evaluation were first conducted with monoclonal antibody (mAb) digestion, focusing on enhancing sensitivity and repeatability. Over 8 days, relative standard deviations (RSDs) for retention time and mass area were less than 5 % and 10 %, respectively. Sensitivity improved by 2.91-4.14 times compared to the analytical-flow LC-MS/MS method. After confirming the reliability of the method, the micro-flow LC-MS/MS method was compared to the nano-flow LC-MS/MS method and the analytical-flow LC-MS/MS method in sub-proteomic sample analysis. For HCPs, the micro-flow LC-MS/MS method demonstrated superior qualitative and much better reproducibility than the nano-flow LC-MS/MS method, with more than 98 % of proteins showing intensity RSD values below 20 %. In the analysis of mesenchymal stem cells (MSCs), the micro-flow method demonstrated good reproducibility and better sensitivity than the analytical-flow method. Taking the analysis of the 20 th generation of MSC products as an example, the sample analyzed by micro-flow LC-MS/MS resulted in the identification of 68 % and 8.5 % more peptides and proteins, respectively. Moreover, micro-flow maintained stable system pressure while analyzing umbilical cord stem cells, where nano-flow methods often encounter blockages. This micro-flow LC-MS/MS method is notable for its sensitivity, reproducibility, and straightforward operation, making it highly adaptable for diverse sub-proteomic analyses in biopharmaceutical laboratories., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Raman chemical mapping is an inherently slow analysis tool. Accurate and robust multivariate analysis algorithms, which require least amount of time and effort in method development are desirable. Calibration-free regression and resolution approaches such as classical least squares (CLS) and multivariate curve resolution using alternating least squares (MCR-ALS), respectively, help in reducing the resources required for method development. However, conventional CLS does not consider appropriate constraints, which may result in negative and/or greater than 100 % Raman concentration scores, while MCR-ALS may not always be as accurate as regression-based algorithms. Linear iterative optimization technology (IOT) is another calibration-free algorithm, which with appropriate constraints has previously shown promise in online and offline pharmaceutical mixture composition determination. This paper aims to evaluate the performance of the linear IOT algorithm for Raman chemical mapping of the active pharmaceutical ingredient (API), diluent, and lubricant in pharmaceutical tablets. Two pre-processing strategies were applied to the raw Raman mapping spectra. The results were compared with CLS (current reference method) and MCR-ALS. Special emphasis was given to mapping at low Raman exposure times to enable feasible total acquisition times (< 5 h). The quality of IOT/CLS/MCR-ALS estimated Raman concentration predictions were assessed by calculating a correlation factor between the spectrum corresponding to the maximum predicted concentration (or resolved spectra) of a component for IOT/CLS (or MCR-ALS) and the pure powder component spectrum. The Raman chemical maps were visualized, and the average Raman concentrations scores were compared. The results demonstrated the utility of IOT in Raman chemical mapping of pharmaceutical tablets. The diluent (lactose) and API (semi-fine APAP) used in this study were reliably estimated by IOT at relatively short Raman exposure times. On the other hand, as expected, the lubricant (magnesium stearate) could not be detected in any of the cases investigated here, irrespective of the algorithm used. Overall, for the API and diluent used in this formulation as well as the chemical mapping conditions, linear IOT seemed to better estimate the pure spectrum intensities and the average Raman scores (closer to CLS) in comparison to MCR-ALS. Moreover, application of appropriate constraints in linear IOT avoided the presence of negative and/or greater than 100 % Raman concentration scores, as observed in CLS-based Raman chemical maps., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Depression is currently the main disease which endangers human health and emotion. However, the existing antidepressants have the disadvantages of slowly taking effect and high recurrence rate. Numerous studies have shown that depression causes disorders in energy metabolism, but the specific metabolic pathways remain unclear. The stable isotope-resolved metabolomics (SIRM) can clarify the specific metabolic pathways of energy metabolism disorders in depression and provide a strong basis for the pathogenesis of depression and new targets for the development of new antidepressants. We applied this method to the chronic unpredictable mild stress (CUMS) model, and the metabolites related to energy metabolism were comprehensively analyzed on HILIC and T3 columns through LC-MS. Conventional untargeted metabolomics found 50 differential metabolites, and most of them were focused on the energy metabolism pathway. SIRM exhibited that 78 metabolites related to energy metabolism were labeled, and 28 of them were observed significantly changed in the model group compared with control. Our results revealed depression inhibited TCA cycle and activated gluconeogenesis pathway, and abnormally increased pyruvic acid may participate in pyrimidine biosynthesis, phospholipid synthesis, and amino acid metabolism pathways. Pyruvate carboxylase (PC), pyruvate dehydrogenase (PDH), aspartate aminotransferase (AST) and phosphoenolpyruvate carboxykinase (PEPC) may be the new targets for depression. We established a SIRM method at animal level. To the best of our knowledge, it is the first time to apply the method for the study of depression. The method is of great significance and value for further explaining the pathogenesis of depression and the development of antidepressants., Competing Interests: Declaration of Competing Interest The authors report no declarations of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

A method based on liquid chromatography/high resolution tandem mass spectrometry coupled with electrophoretic separation, for determination and relative quantification of the protein composition of exhaled breath condensate (EBC), was developed. Application of the procedure to a sample of EBC, pooled from nine healthy subjects, resulted in the identification of 167 unique gene products, 113 of which not previously reported in EBC samples. The abundance of the protein identified was estimated by means of the exponentially modified protein abundance index protocol (emPAI). Cytokeratins were by far the most abundant proteins in EBC samples. Many of the identified proteins were associated with multiple cellular location with cytoplasm constituting the largest group. Cytosol, nucleus, membrane, cytoskeleton and extracellular were other abundantly represented locations. No amylase was detected, suggesting the absence of saliva protein contamination. The profile obtained represents the most comprehensive protein characterization of EBC so far reported and demonstrates that this approach provides a powerful tool for investigating the protein profile of EBC samples. Compared with analogous investigations, this study also shows that the protein profile of EBC is strongly affected by the sampling method adopted., (Copyright © 2014 Elsevier B.V. All rights reserved.)