Course: Clinical mass spectrometry

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Course title Clinical mass spectrometry
Course code ACH/KHS
Organizational form of instruction Lecture + Exercise
Level of course Master
Year of study 2
Semester Winter
Number of ECTS credits 2
Language of instruction Czech
Status of course Compulsory-optional, Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Havlíček Vladimír, prof. Ing. Dr.
  • Kuzma Marek, Ing. Ph.D.
Course content
1. Fundamentals and instrumentation. Ion formation (EI, CI, ESI/APCI, MALDI), ambient ionization (DESI, REIMS); quadrupole, TOF, ion traps, Orbitrap and FT-ICR; resolving power, mass accuracy, dynamic range and ion mobility. 2. Tandem MS and spectral interpretation. CID/HCD, ETD/ECD; isotope envelopes, accurate mass, molecular formula assignment, fragmentation rules, MSn, spectral libraries and database searching. 3. Quantitative clinical MS. LC-MS/MS and GC-MS; internal standards, calibration, matrix effects, carry-over, accuracy and precision, LOD/LOQ, reference materials, method validation, QC/EQA and harmonization. Examples include therapeutic drug monitoring, steroid analysis, toxicology and newborn screening. 4. Peptides, proteins and structural MS. Deconvolution, bottom-up and top-down analysis, targeted proteomics, post-translational modifications, native MS, HDX-MS and chemical cross-linking. 5. Metabolomics and lipidomics. Targeted and untargeted workflows, biofluids, metabolite annotation and dereplication, normalization and multivariate analysis; requirements for translating a biomarker into a clinical test. 6. Clinical microbiology and infection. MALDI-TOF microbial identification, direct analysis of clinical samples and positive blood cultures, opportunities and limitations for resistance detection and strain typing; microbial metabolites and metallophores. 7. Spatial and intraoperative MS. MALDI-MSI, DESI and SIMS principles; spatial metabolomics/proteomics, multimodal imaging, REIMS/iKnife and SpiderMass; trade-offs among spatial resolution, sensitivity and molecular-identification confidence. 8. Data, software and AI. CycloBranch, mMass and public databases; data-quality control, reproducibility, machine-learning classification and responsible reporting of results. Practical training and learning outcomes ? Data acquisition on MALDI/ESI instruments using model or student-provided samples; charge-state and molecular-mass calculations; MS/MS interpretation and software/database work. ? After completing the course, students will be able to select an appropriate MS strategy for a clinical question, justify analyte identification, outline a basic validation/QC plan, and distinguish biomarker discovery from a clinically validated diagnostic test. ? The colloquium assesses theory, calculations, spectrum interpretation and critical appraisal of a selected clinical application.

Learning activities and teaching methods
Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming)
Learning outcomes
The course integrates the physicochemical foundations of mass spectrometry with methods used in clinical laboratories and emerging translational applications. Emphasis is placed on selecting appropriate ionization and mass-analysis strategies, interpreting MS and MS/MS data, quantitative analysis and validation, critical appraisal of biomarkers, and hands-on work with real spectra. The course is designed for students of chemistry, biochemistry, molecular biology and biomedical disciplines; prior specialization in mass spectrometry is not required.

Prerequisites
unspecified

Assessment methods and criteria
Oral exam

The knowledge of basics of organic, analytical chemistry and biochemistry is recommended.
Recommended literature
  • Barber A.R.J. et al. Rapid evaporative ionization mass spectrometry in surgery: a systematic review. Br. J. Surg. 112(11), znaf228 (2025). doi:10.1093/bjs/znaf228..
  • Guo T., Steen J.A., Mann M. Mass-spectrometry-based proteomics: from single cells to clinical applications. Nature 638, 901?911 (2025). doi:10.1038/s41586-025-08584-0..
  • Havlíček V., Lemr K., Tureček F. (eds.). Hmotnostní spektrometrie. Chemické listy 114(2), 85?160 (2020) a 114(3), 161?248 (2020). Dvě tematická čísla: fyzikálně-chemické základy, instrumentace, tandemová MS, interpretace, kvantifikace a klinické/aplikační kapitoly..
  • Lee W. et al. Clinical metabolomics: analytical workflows, data interpretation, and translational considerations. J. Anal. Sci. Technol. 17, 46 (2026). doi:10.1186/s40543-026-00567-8..
  • Lira K.E., May J.C., McLean J.A. Ion mobility spectrometry and ion mobility-mass spectrometry in clinical chemistry. Adv. Clin. Chem. 124, 123?160 (2025). doi:10.1016/bs.acc.2024.10.003..
  • Spraggins J.M. et al. Matrix-assisted laser desorption/ionization imaging mass spectrometry. Nat. Rev. Methods Primers 6, 44 (2026). doi:10.1038/s43586-026-00492-5..
  • Vogeser M., Habler K. Applications of mass spectrometry in the routine diagnostic medical laboratory ? a status report 2025. J. Chromatogr. B 1273, 124958 (2026). doi:10.1016/j.jchromb.2026.124958.
  • Weiss Z.F., Basu S.S. The Mass Spectrometry Revolution in Clinical Microbiology Part 2: Emerging Applications. Clin. Lab. Med. 45, 15?26 (2025). doi:10.1016/j.cll.2024.10.012..


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Physical Chemistry (2021) Category: Chemistry courses 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Biotechnology and Genetic Engineering (2019) Category: Chemistry courses - Recommended year of study:-, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Bioinformatics (2021) Category: Informatics courses - Recommended year of study:-, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Analytical Chemistry (2021) Category: Chemistry courses 2 Recommended year of study:2, Recommended semester: Winter