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Itkonen J, Ghemtio L, Pellegrino D, Jokela (née Heinonen) PJ, Xhaard H, Casteleijn MG. Analysis of Biologics Molecular Descriptors towards Predictive Modelling for Protein Drug Development Using Time-Gated Raman Spectroscopy. Pharmaceutics 2022; 14:1639. [PMID: 36015265 PMCID: PMC9413954 DOI: 10.3390/pharmaceutics14081639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/29/2022] [Accepted: 08/03/2022] [Indexed: 11/16/2022] Open
Abstract
Pharmaceutical proteins, compared to small molecular weight drugs, are relatively fragile molecules, thus necessitating monitoring protein unfolding and aggregation during production and post-marketing. Currently, many analytical techniques take offline measurements, which cannot directly assess protein folding during production and unfolding during processing and storage. In addition, several orthogonal techniques are needed during production and market surveillance. In this study, we introduce the use of time-gated Raman spectroscopy to identify molecular descriptors of protein unfolding. Raman spectroscopy can measure the unfolding of proteins in-line and in real-time without labels. Using K-means clustering and PCA analysis, we could correlate local unfolding events with traditional analytical methods. This is the first step toward predictive modeling of unfolding events of proteins during production and storage.
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Affiliation(s)
- Jaakko Itkonen
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00100 Helsinki, Finland
| | - Leo Ghemtio
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00100 Helsinki, Finland
| | - Daniela Pellegrino
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00100 Helsinki, Finland
| | - Pia J. Jokela (née Heinonen)
- Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, 00100 Helsinki, Finland
- Orion Pharma, 02101 Espoo, Finland
| | - Henri Xhaard
- Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, 00100 Helsinki, Finland
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Wang H, Li J, Qin J, Li J, Chen Y, Song D, Zeng H, Wang S. Investigating the cellular responses of osteosarcoma to cisplatin by confocal Raman microspectroscopy. J Photochem Photobiol B 2022; 226:112366. [PMID: 34826719 DOI: 10.1016/j.jphotobiol.2021.112366] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/03/2021] [Accepted: 11/16/2021] [Indexed: 06/13/2023]
Abstract
Confocal Raman Microspectroscopy (CRM) was employed to clarify the cellular response of cisplatin in osteosarcoma (OS) cells with different dosages and incubation times. The K7M2 mouse osteosarcoma cells were treated by cisplatin in 0 μM (UT group), 20 μM (20 T group), and 40 μM (40 T group) doses for 24-h (24H group) and 48-h (48H group), respectively. Raman spectroscopy was utilized to analyze the drug induced variations of intracellular biochemical components in osteosarcoma cells. The spectral results shows that the main changes in its biochemical composition come from nucleic acids. By adopting three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)), principal component analysis combined with support vector machine models (PCA-SVM) was built to address the spectral variations among all investigated groups. Meanwhile, multivariate curve resolution alternating least squares (MCR-ALS) was further utilized to discuss on the chemical interpretation on the acquired spectral results. Moreover, Raman spectral images, which is reconstructed by K-means cluster analysis (KCA) with point-scanned hyperspectral dataset, was applied to illustrate the drug induced compositional and morphological variations in each subcellular region. The achieved results not only prove the application potential of Raman based analytical technique in non-labeled intracellular studies, but also illustrate the detailed compositional and structural information of cisplatin induced OS cell responses from the perspective of multivariate analysis and imaging of Raman spectroscopy.
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Affiliation(s)
- Haifeng Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Jing Li
- Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China
| | - Jie Qin
- Department of Orthopedics, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710004, China.
| | - Jie Li
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Yishen Chen
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Dongliang Song
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China
| | - Haishan Zeng
- Imaging Unit - Integrative Oncology Department, BC Cancer Research Center, Vancouver, BC, V5Z1L3, Canada
| | - Shuang Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China.
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Song D, Chen Y, Li J, Wang H, Ning T, Wang S. A graphical user interface (NWUSA) for Raman spectral processing, analysis and feature recognition. J Biophotonics 2021; 14:e202000456. [PMID: 33547854 DOI: 10.1002/jbio.202000456] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 01/20/2021] [Accepted: 02/04/2021] [Indexed: 05/08/2023]
Abstract
It is a practical necessity for non-professional users to interpret biologically derived Raman spectral information for obtaining accurate and reliable analytical results. An integrated Raman spectral analysis software (NWUSA) was developed for spectral processing, analysis, and feature recognition. It provides a user-friendly graphical interface to perform the following preprocessing tasks: spectral range selection, cosmic ray removal, polynomial fitting based background subtraction, Savitzky-Golay smoothing, area-under-curve normalization, mean-centered procedure, as well as multivariate analysis algorithms including principal component analysis (PCA), linear discriminant analysis, partial least squares-discriminant analysis, support vector machine (SVM), and PCA-SVM. A spectral dataset obtained from two different samples was utilized to evaluate the performance of the developed software, which demonstrated that the analysis software can quickly and accurately achieve functional requirements in spectral data processing and feature recognition. Besides, the open-source software can not only be customized with more novel functional modules to suit the specific needs, but also benefit many Raman based investigations, especially for clinical usages.
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Affiliation(s)
- Dongliang Song
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Yishen Chen
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Jie Li
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Haifeng Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Tian Ning
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
| | - Shuang Wang
- State Key Laboratory of Photon-Technology in Western China Energy, Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi, China
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Li H, Ning T, Yu F, Chen Y, Zhang B, Wang S. Raman Microspectroscopic Investigation and Classification of Breast Cancer Pathological Characteristics. Molecules 2021; 26:molecules26040921. [PMID: 33572420 PMCID: PMC7916258 DOI: 10.3390/molecules26040921] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 02/01/2021] [Accepted: 02/05/2021] [Indexed: 02/07/2023] Open
Abstract
Breast cancer is one of the major cancers of women in the world. Despite significant progress in its treatment, an early diagnosis can effectively reduce its incidence rate and mortality. To improve the reliability of Raman-based tumor detection and analysis methods, we conducted an ex vivo study to unveil the compositional features of healthy control (HC), solid papillary carcinoma (SPC), mucinous carcinoma (MC), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissue samples. Following the identification of biological variations occurring as a result of cancer invasion, principal component analysis followed by linear discriminate analysis (PCA-LDA) algorithm were adopted to distinguish spectral variations among different breast tissue groups. The achieved results confirmed that after training, the constructed classification model combined with the leave-one-out cross-validation (LOOCV) method was able to distinguish the different breast tissue types with 100% overall accuracy. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.
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MESH Headings
- Adenocarcinoma, Mucinous/pathology
- Adenocarcinoma, Mucinous/surgery
- Algorithms
- Breast Neoplasms/classification
- Breast Neoplasms/pathology
- Breast Neoplasms/surgery
- Carcinoma, Ductal, Breast/pathology
- Carcinoma, Ductal, Breast/surgery
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Carcinoma, Intraductal, Noninfiltrating/surgery
- Carcinoma, Papillary/pathology
- Carcinoma, Papillary/surgery
- Case-Control Studies
- Discriminant Analysis
- Female
- Follow-Up Studies
- Humans
- Middle Aged
- Principal Component Analysis
- Spectrum Analysis, Raman/methods
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