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Pečinka L, Moráň L, Kovačovicová P, Meloni F, Havel J, Pivetta T, Vaňhara P. Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing. Heliyon 2024; 10:e29936. [PMID: 38707401 PMCID: PMC11066331 DOI: 10.1016/j.heliyon.2024.e29936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression of TUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations of TUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures.
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Affiliation(s)
- Lukáš Pečinka
- Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
| | - Lukáš Moráň
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Research Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Petra Kovačovicová
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Francesca Meloni
- Chemical and Geological Sciences Department, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Josef Havel
- Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
| | - Tiziana Pivetta
- Chemical and Geological Sciences Department, University of Cagliari, Cittadella Universitaria, Monserrato, Italy
| | - Petr Vaňhara
- International Clinical Research Center, St. Anne's University Hospital Brno, Czech Republic
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
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Luu GT, Ge C, Tang Y, Li K, Cologna SM, Godwin AK, Burdette JE, Su J, Sanchez LM. An Integrated Approach to Protein Discovery and Detection From Complex Biofluids. Mol Cell Proteomics 2023; 22:100590. [PMID: 37301378 PMCID: PMC10388710 DOI: 10.1016/j.mcpro.2023.100590] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 05/22/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023] Open
Abstract
Ovarian cancer, a leading cause of cancer-related deaths among women, has been notoriously difficult to screen for and diagnose early, as early detection significantly improves survival. Researchers and clinicians seek routinely usable and noninvasive screening methods; however, available methods (i.e., biomarker screening) lack desirable sensitivity/specificity. The most fatal form, high-grade serous ovarian cancer, often originate in the fallopian tube; therefore, sampling from the vaginal environment provides more proximal sources for tumor detection. To address these shortcomings and leverage proximal sampling, we developed an untargeted mass spectrometry microprotein profiling method and identified cystatin A, which was validated in an animal model. To overcome the limits of detection inherent to mass spectrometry, we demonstrated that cystatin A is present at 100 pM concentrations using a label-free microtoroid resonator and translated our workflow to patient-derived clinical samples, highlighting the potential utility of early stage detection where biomarker levels would be low.
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Affiliation(s)
- Gordon T Luu
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California, USA
| | - Chang Ge
- Wyant College of Optical Sciences, University of Arizona, Tucson, Arizona, USA
| | - Yisha Tang
- Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
| | - Kailiang Li
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Andrew K Godwin
- Department of Pathology and Laboratory Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA; Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA; The University of Kansas Cancer Center, Kansas City, Kansas, USA
| | - Joanna E Burdette
- Department of Pharmaceutical Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Judith Su
- Wyant College of Optical Sciences, University of Arizona, Tucson, Arizona, USA; Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA.
| | - Laura M Sanchez
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California, USA.
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Dueñas ME, Peltier‐Heap RE, Leveridge M, Annan RS, Büttner FH, Trost M. Advances in high-throughput mass spectrometry in drug discovery. EMBO Mol Med 2022; 15:e14850. [PMID: 36515561 PMCID: PMC9832828 DOI: 10.15252/emmm.202114850] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/03/2022] [Accepted: 10/07/2022] [Indexed: 12/15/2022] Open
Abstract
High-throughput (HT) screening drug discovery, during which thousands or millions of compounds are screened, remains the key methodology for identifying active chemical matter in early drug discovery pipelines. Recent technological developments in mass spectrometry (MS) and automation have revolutionized the application of MS for use in HT screens. These methods allow the targeting of unlabelled biomolecules in HT assays, thereby expanding the breadth of targets for which HT assays can be developed compared to traditional approaches. Moreover, these label-free MS assays are often cheaper, faster, and more physiologically relevant than competing assay technologies. In this review, we will describe current MS techniques used in drug discovery and explain their advantages and disadvantages. We will highlight the power of mass spectrometry in label-free in vitro assays, and its application for setting up multiplexed cellular phenotypic assays, providing an exciting new tool for screening compounds in cell lines, and even primary cells. Finally, we will give an outlook on how technological advances will increase the future use and the capabilities of mass spectrometry in drug discovery.
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Affiliation(s)
- Maria Emilia Dueñas
- Laboratory for Biomedical Mass Spectrometry, Biosciences InstituteNewcastle UniversityNewcastle‐upon‐TyneUK
| | - Rachel E Peltier‐Heap
- Discovery Analytical, Screening Profiling and Mechanistic Biology, GSK R&DStevenageUK
| | - Melanie Leveridge
- Discovery Analytical, Screening Profiling and Mechanistic Biology, GSK R&DStevenageUK
| | - Roland S Annan
- Discovery Analytical, Screening Profiling and Mechanistic Biology, GSK R&DStevenageUK
| | - Frank H Büttner
- Drug Discovery Sciences, High Throughput BiologyBoehringer Ingelheim Pharma GmbH&CoKGBiberachGermany
| | - Matthias Trost
- Laboratory for Biomedical Mass Spectrometry, Biosciences InstituteNewcastle UniversityNewcastle‐upon‐TyneUK
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Impact of Skin Tissue Collection Method on Downstream MALDI-Imaging. Metabolites 2022; 12:metabo12060497. [PMID: 35736430 PMCID: PMC9227925 DOI: 10.3390/metabo12060497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/06/2022] [Accepted: 05/26/2022] [Indexed: 02/04/2023] Open
Abstract
MALDI imaging is a novel technique with which to study the pathophysiologies of diseases. Advancements in the field of metabolomics and lipidomics have been instrumental in mapping the signaling pathways involved in various diseases, such as cancer and neurodegenerative diseases (Parkinson’s). MALDI imaging is flexible and can handle many sample types. Researchers primarily use either formalin-fixed paraffin-embedded (FFPE) or fresh frozen tissue samples to answer their scientific questions. FFPE samples allow for easy long-term storage, but the requirement for extensive sample processing may limit the ability to provide a clear picture of metabolite distribution in biological tissue. Frozen samples require less handling, but present logistical challenges for collection and storage. A few studies, mostly focused on cancer cell lines, have directly compared the results of MALDI imaging using these two tissue fixation approaches. Herein, we directly compared FFPE and fresh frozen sample preparation for murine skin samples, and performed detailed pathway analysis to understand how differences in processing impact MALDI results from otherwise identical tissues. Our results indicate that FFPE and fresh frozen methods differ significantly in the putative identified metabolite content and distribution. The fixation methods shared only 2037 metabolites in positive mode and only 4079 metabolites in negative ion mode. However, both fixation approaches allowed for downstream fluorescent staining, which may save time and resources for samples that are clinically precious. This work represents a direct comparison of the impacts of the two main tissue processing methods on subsequent MALDI-MSI. While our results are similar to previous work in cancer tissue, they provide novel insights for those using MALDI-MSI in skin.
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Luu GT, Sanchez LM. Toward improvement of screening through mass spectrometry-based proteomics: ovarian cancer as a case study. INTERNATIONAL JOURNAL OF MASS SPECTROMETRY 2021; 469:116679. [PMID: 34744497 PMCID: PMC8570641 DOI: 10.1016/j.ijms.2021.116679] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.
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Affiliation(s)
- Gordon T Luu
- Department of Chemistry and Biochemistry, University of California Santa Cruz, 1156 High St. Santa Cruz, CA, 95064
| | - Laura M Sanchez
- Department of Chemistry and Biochemistry, University of California Santa Cruz, 1156 High St. Santa Cruz, CA, 95064
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Heap RE, Segarra-Fas A, Blain AP, Findlay GM, Trost M. Profiling embryonic stem cell differentiation by MALDI TOF mass spectrometry: development of a reproducible and robust sample preparation workflow. Analyst 2020; 144:6371-6381. [PMID: 31566633 DOI: 10.1039/c9an00771g] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
MALDI TOF mass spectrometry (MS) is widely used to characterise and biotype bacterial samples, but a complementary method for profiling of mammalian cells is still underdeveloped. Current approaches vary dramatically in their sample preparation methods and are not suitable for high-throughput studies. In this work, we present a universal workflow for mammalian cell MALDI TOF MS analysis and apply it to distinguish ground-state naïve and differentiating mouse embryonic stem cells (mESCs), which can be used as a model for drug discovery. We employed a systematic approach testing many parameters to evaluate how efficiently and reproducibly each method extracted unique mass features from four different human cell lines. These data enabled us to develop a unique mammalian cell MALDI TOF workflow involving a freeze-thaw cycle, methanol fixing and a CHCA matrix to generate spectra that robustly phenotype different cell lines and are highly reproducible in peak identification across replicate spectra. We applied our optimised workflow to distinguish naïve and differentiating populations using multivariate analysis and reproducibly identify unique features. We were also able to demonstrate the compatibility of our optimised method for current automated liquid handling technologies. Consequently, our MALDI TOF MS profiling method enables identification of unique features and robust phenotyping of mESC differentiation in under 1 hour from culture to analysis, which is significantly faster and cheaper when compared with conventional methods such as qPCR. This method has the potential to be automated and can in the future be applied to profile other cell types and expanded towards cellular MALDI TOF MS screening assays.
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Affiliation(s)
- Rachel E Heap
- Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle-upon-Tyne, UK.
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Galey MM, Young AN, Petukhova VZ, Wang M, Wang J, Salvi A, Russo A, Burdette JE, Sanchez LM. Detection of Ovarian Cancer Using Samples Sourced from the Vaginal Microenvironment. J Proteome Res 2020; 19:503-510. [PMID: 31738564 DOI: 10.1021/acs.jproteome.9b00694] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Mass spectrometry (MS) offers high levels of specificity and sensitivity in clinical applications, and we have previously been able to demonstrate that matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS is capable of distinguishing two-component cell mixtures at low limits of detection. Ovarian cancer is notoriously difficult to detect due to the lack of diagnostic techniques available to the medical community. By sampling a local microenvironment, such as the vaginal canal and cervix, a MS based method is presented for monitoring disease progression from proximal samples to the diseased tissue. A murine xenograft model of high grade serous ovarian carcinoma (HGSOC) was used for this study, and vaginal lavages were obtained from mice on a weekly basis throughout disease progression and subjected to our MALDI-TOF MS workflow followed by statistical analyses. Proteins in the 4-20 kDa region of the mass spectrum yielded a fingerprint that we could consistently measure over time that correlated with disease progression. These fingerprints were found to be largely stable across all mice, with the protein fingerprint converging toward the end point of the study. MALDI-TOF MS serves as a unique analytical technique for measuring a sampled vaginal microenvironment in a specific and sensitive manner for the detection of HGSOC in a murine model.
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Affiliation(s)
- Melissa M Galey
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
| | - Alexandria N Young
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
| | - Valentina Z Petukhova
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
| | - Mingxun Wang
- Ometa Laboratories , 3210 Merryfield Row , San Diego , California 92121 , United States
| | - Jian Wang
- Ometa Laboratories , 3210 Merryfield Row , San Diego , California 92121 , United States
| | - Amrita Salvi
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
| | - Angela Russo
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
| | - Joanna E Burdette
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
| | - Laura M Sanchez
- Department of Pharmaceutical Sciences , University of Illinois at Chicago , 833 S Wood Street , Chicago , Illinois 60612 , United States
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