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Applied Sciences
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New Mass Spectrometry Approaches for Clinical Diagnostics
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New Mass Spectrometry Approaches for Clinical Diagnostics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 11883

Special Issue Editors

Dr. Enrico Dávoli

E-Mail Website
Guest Editor
Mass Spectrometry Research Center for Health and Environment, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, 20156 Milano, Italy
Interests: new analytical approaches based on mass spectrometry; Mass Spectrometry Imaging (MSI); 2D and 3D MSI quantitative analysis
Prof. Sen Takeda

E-Mail Website
Guest Editor
Department of Anatomy and Cell Biology, Graduate School of Medicine, University of Yamanashi, 1110 Shimo-Kateau, Chuo, Yamanashi 409-3898, Japan
Interests: application of ambient mass spectrometry to clinical diagnosis; cancer diagnosis; machine learning, cell biology

Special Issue Information

Dear Colleagues,

Few analytical instruments have a broader range of applications than mass spectrometry, being applied in virtually every scientific discipline. Laboratories use it for studies in chemistry, biology, pharmacology, physics, environmental sciences, agriculture, and geology. Moreover, many other applications now use mass spectrometers directly in the field, out of the laboratory, to monitor processes involving chemical or biological reactions. Its sensitivity and specificity provide unsurpassed quality in analytical data. While analytical chemistry remains one of the principal applications of MS, there remains a particular analytical environment where its potential is little used: the world of clinical diagnostics.

Rules are defined throughout the world for what applies to any medical device, or any instrument, intended to be used for human beings’ diagnostics. These rules set essential requirements for in vitro diagnostic medical devices and their accessories to regulate any instrument placed on the market, in the interest of uniform standards.

This Special Issue will consist of a comprehensive review and original research articles featuring important and recent developments in the instrumentation available for clinical diagnostics and potential solutions available from the scientific world, for mass spectrometry-based diagnosis.

We are pleased to invite you to contribute to this Special Issue comprehensive review and original research articles up to the end of this year, December 2021.

Dr. Enrico Dávoli
Prof. Sen Takeda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mass spectrometry (MS)
  • clinical analysis
  • in vitro diagnostics (IVD)
  • newborn screening
  • tumor diagnosis
  • surgery
  • medical devices
  • therapeutic drug monitoring (TDM)

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Published Papers (5 papers)

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Research

12 pages, 959 KiB  
Article
A Validated HPLC-MS/MS Method for Quantification of Fingolimod and Fingolimod-Phosphate in Human Plasma: Application to Patients with Relapsing–Remitting Multiple Sclerosis
by Claudia Fracasso, Alice Passoni, Laura Brambilla, Renato Mantegazza, Silvia Rossi, Marco Gobbi and Jacopo Lucchetti
Appl. Sci. 2022, 12(12), 6102; https://doi.org/10.3390/app12126102 - 15 Jun 2022
Cited by 1 | Viewed by 2241
Abstract
Fingolimod is a sphingosine 1-phosphate-receptor modulator approved for the oral treatment of relapsing–remitting multiple sclerosis (RRMS), a form of MS characterized by a pattern of exacerbation of neurological symptoms followed by recovery. Here, we validated a simple and rapid liquid chromatography–tandem mass spectrometry [...] Read more.
Fingolimod is a sphingosine 1-phosphate-receptor modulator approved for the oral treatment of relapsing–remitting multiple sclerosis (RRMS), a form of MS characterized by a pattern of exacerbation of neurological symptoms followed by recovery. Here, we validated a simple and rapid liquid chromatography–tandem mass spectrometry method for the measurement of the concentrations of Fingolimod and its active metabolite Fingolimod-Phosphate (Fingolimod-P) in human plasma. The lower limits of quantification were set at 0.3 and 1.5 ng/mL for Fingolimod and Fingolimod-P, respectively, and the linearity was in the range 0.3–150 ng Fingolimod/mL and 1.5–150 ng Fingolimod-P/mL. After protein precipitation, the extraction recoveries of both analytes were always above 60% with minimal matrix effect. The method was accurate and precise, satisfying the criteria set in the European Medicine Agency guidelines for bioanalytical method validation. The method was then applied to measure Fingolimod and Fingolimod-P concentrations in the plasma of 15 RRMS patients under chronic treatment with Fingolimod, administered daily at the dose of 0.5 mg for up to 24 months. No significant differences were observed between samples collected at 6, 12 and 24 months for both analytes, indicating that the drug’s bioavailability was unaffected by multiple daily doses up to 24 months. The levels of Fingolimod-P were about two-fold higher than the levels of the parent compound. The availability of this analytical method can allow the monitoring of the impact of plasma levels of the drug and its metabolite on inter-individual variability in clinical responses. Full article
(This article belongs to the Special Issue New Mass Spectrometry Approaches for Clinical Diagnostics)
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12 pages, 2248 KiB  
Article
Fast Classification of Thyroid Nodules with Ultrasound Guided-Fine Needle Biopsy Samples and Machine Learning
by Ye Wang, Zhenhe Chen, Lin Zhang, Dingrong Zhong, Jinxi Di, Xiaodong Li, Yajuan Lei, Jie Li, Yao Liu, Ruiying Jiang and Lei Cao
Appl. Sci. 2022, 12(11), 5364; https://doi.org/10.3390/app12115364 - 25 May 2022
Cited by 1 | Viewed by 1822
Abstract
A rapid classification method was developed for the malignant and benign thyroid nodules with ultrasound guided-fine needle aspiration biopsy (FNAB) samples. With probe electrospray ionization mass spectrometry, the mass-scan data of FNAB samples were used as datasets for machine learning. The patients were [...] Read more.
A rapid classification method was developed for the malignant and benign thyroid nodules with ultrasound guided-fine needle aspiration biopsy (FNAB) samples. With probe electrospray ionization mass spectrometry, the mass-scan data of FNAB samples were used as datasets for machine learning. The patients were marked as malignant (98 patients), benign (110 patients) or undetermined (42 patients) by experienced doctors in terms of ultrasound, the B-Raf (BRAF) gene, and cytopathology inspections. Pairwise coupling was performed on 163 ions to generate 3630 ion ratios as new features for classifier training. With the new features, the performance of the multilayer perception (MLP) classifier is much better than that with the 163 ions as features directly. After training, the accuracy of the MLP classifier is as high as 92.0%. The accuracy of the single-blind test is 82.4%, which proved the good generalization ability of the MLP classifier. The overall concordance is 73.0% between prediction and six-month follow-up for patients in the undetermined group. Especially, the classifier showed high accuracy for the undetermined patients with suspicious for papillary carcinoma diagnosis (90.9%). In summary, the machine learning method based on FNAB samples has potential for real clinical applications. Full article
(This article belongs to the Special Issue New Mass Spectrometry Approaches for Clinical Diagnostics)
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10 pages, 1317 KiB  
Article
Versatile Mass Spectrometry-Based Intraoperative Diagnosis of Liver Tumor in a Multiethnic Cohort
by Silvia Giordano, Angela Marika Siciliano, Matteo Donadon, Cristiana Soldani, Barbara Franceschini, Ana Lleo, Luca Di Tommaso, Matteo Cimino, Guido Torzilli, Hidekazu Saiki, Hiroki Nakajima, Sen Takeda and Enrico Davoli
Appl. Sci. 2022, 12(9), 4244; https://doi.org/10.3390/app12094244 - 22 Apr 2022
Cited by 4 | Viewed by 1620
Abstract
Currently used techniques for intraoperative assessment of tumor resection margins are time-consuming and laborious and, more importantly, lack specificity. Moreover, pathological diagnosis during surgery does not often give a clear outcome. Recent advances in mass spectrometry (MS) and instrumentation have made it possible [...] Read more.
Currently used techniques for intraoperative assessment of tumor resection margins are time-consuming and laborious and, more importantly, lack specificity. Moreover, pathological diagnosis during surgery does not often give a clear outcome. Recent advances in mass spectrometry (MS) and instrumentation have made it possible to obtain detailed molecular information from tissue specimens in real-time, with minimal sample pre-treatment. Probe Electro Spray Ionization MS (PESI-MS), combined with artificial intelligence (AI), has demonstrated its effectiveness in distinguishing liver cancer tissues from healthy tissues in a large Italian population group. As the MS profile can reflect the patient’s ethnicity, dietary habits, or particular operating room procedures, the AI algorithm must be well trained to distinguish different groups. We used a large dataset composed of liver tumor and healthy specimens, from the Italian and Japanese populations, to develop a versatile algorithm free from ethnic bias. The system can classify tissues with discrepancies <5% from the pathologist’s diagnosis. These results demonstrate the potential of the PESI-MS system to distinguish tumor from surrounding non-tumor tissues in patients, with minimal bias from race/ethnicity or etiological characteristics or operating room procedures. Full article
(This article belongs to the Special Issue New Mass Spectrometry Approaches for Clinical Diagnostics)
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10 pages, 1661 KiB  
Article
Clinical Mass Spectrometry in Immunosuppressant Analysis: Toward a Full Automation?
by Chiara Fania, Marco Bagnati, Marina Albertario, Carlotta Ferraris, Marta Lamonaca and Umberto Dianzani
Appl. Sci. 2022, 12(7), 3695; https://doi.org/10.3390/app12073695 - 6 Apr 2022
Cited by 3 | Viewed by 2709
Abstract
The analysis of immunosuppressive drugs allows the physician to monitor, and eventually correct, immunosuppressive therapy. The panel of molecules under evaluation includes cyclosporine A (CsA), tacrolimus, sirolimus, and everolimus. Initially, assays were performed by immunometric methods, but in the past few years this [...] Read more.
The analysis of immunosuppressive drugs allows the physician to monitor, and eventually correct, immunosuppressive therapy. The panel of molecules under evaluation includes cyclosporine A (CsA), tacrolimus, sirolimus, and everolimus. Initially, assays were performed by immunometric methods, but in the past few years this methodology has been largely superseded by a more accurate and specific technique, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), which is now considered the “gold standard” for immunosuppressant analysis. Both LC-MS/MS and often also immunoassays require a preanalytical manual sample preparation, which involves time-consuming sequential operations whose traceability is often hampered and adds up to the probability of gross errors. The aim of this work was to compare an “open” LC-MS/MS with a fully automated system, consisting of LC instrumentation combined with a triple quadrupole MS, named Thermo ScientificTM CascadionTM SM Clinical Analyzer (Cascadion). Such automated systems suit the requirements of the reference method and are designed to completely eliminate all of the manual procedures. More than 2000 immunosuppressant samples were analyzed both with the open LC-MS/MS and with Cascadion. Statistics allowed the evaluation of linearity, intra- and inter-assay CV%, bias %, limit of detection and of quantitation, and Passing–Bablok and Bland–Altman plots. Results indicated a good correlation between the two methods. In both cases, methods confirmed their suitability for diagnostic settings. Cascadion could provide support when the presence of specialized personnel is lacking, and/or when great productivity and continuous workflow are required. Full article
(This article belongs to the Special Issue New Mass Spectrometry Approaches for Clinical Diagnostics)
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16 pages, 2662 KiB  
Article
A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum
by Ricardo J. Pais, Raminta Zmuidinaite, Jonathan C. Lacey, Christian S. Jardine and Ray K. Iles
Appl. Sci. 2022, 12(6), 3030; https://doi.org/10.3390/app12063030 - 16 Mar 2022
Cited by 5 | Viewed by 2531
Abstract
Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in [...] Read more.
Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening. Full article
(This article belongs to the Special Issue New Mass Spectrometry Approaches for Clinical Diagnostics)
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