Detailed Information on Publication Record
2023
Biostatistic and machine learning in MALDI mass spectrometry research
PEČINKA, Lukáš, Lukáš MORÁŇ, Monika VLACHOVÁ, Adam BŘEZINA, Petra KOVAČOVICOVÁ et. al.Basic information
Original name
Biostatistic and machine learning in MALDI mass spectrometry research
Authors
PEČINKA, Lukáš (203 Czech Republic, belonging to the institution), Lukáš MORÁŇ (203 Czech Republic, belonging to the institution), Monika VLACHOVÁ (203 Czech Republic, belonging to the institution), Adam BŘEZINA (203 Czech Republic, belonging to the institution), Petra KOVAČOVICOVÁ (703 Slovakia, belonging to the institution), Martina MARCHETTI-DESCHMANN (40 Austria), Sabina ŠEVČÍKOVÁ (203 Czech Republic, belonging to the institution), Josef HAVEL (203 Czech Republic, belonging to the institution), Aleš HAMPL (203 Czech Republic, belonging to the institution) and Petr VAŇHARA (203 Czech Republic, guarantor, belonging to the institution)
Edition
XXII. Interdisciplinary meeting of young life scientists, 2023
Other information
Language
English
Type of outcome
Konferenční abstrakt
Field of Study
30400 3.4 Medical biotechnology
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
RIV identification code
RIV/00216224:14110/23:00132354
Organization unit
Faculty of Medicine
Keywords in English
mass spectrometry; biotyping; machine learning; tissue engineering
Změněno: 14/12/2023 13:12, doc. RNDr. Petr Vaňhara, Ph.D.
Abstract
V originále
With increasing demands on precise analyses of biological samples in complex biological matrices, there is also a need to develop and optimize mass spectrometric (MS) methods. MS analysis of whole cells, plasma samples, and other biological materials is of great importance for monitoring and elucidating biological processes in the organism and provides important information regarding organism pheno/genotype. In two topics presented herein, different techniques for whole cell samples and peripheral blood plasma will be presented. The whole cell MALDI TOF MS is already used in clinical microbiology and diagnostics. In recent years it has been introduced also to cell biology, immunology, and cancer biology. The first project focuses on classifying ovarian cancer cells with different percentages of cell populations with a knockout of a single gene (TUSC3). Different cell types (4 in total) from different organisms (human and mouse) were introduced to MS analysis. MS method was combined with multivariate statistical and machine learning algorithms (PLS-DA, ANN, and RF for example) using an R programming language. Data obtained from MS were analysed via an in-house developed R-script. In total 5 optimized classifiers based on different algorithms were established and compared for 175 mass spectra divided into 5 groups. PLS-DA was determined as a model with the best performance with 100% accuracy (95% confidence interval, Cl = 94.7-100%) for the test data. The method described above was further used for other studies; to follow the differentiation process of hESCs to ELEPs for example. We visualized the full differentiation trajectory based on spectral data only and revealed also some phenotypic abnormalities linked to passage number, and by proxy aneuploidy status of hESCs. The second project is dealing with the development method for the analysis of human plasma samples using MALDI TOF MS. This project aims to discriminate multiple myeloma (MM) patients and patients with similar diseases like plasma cell leukemia (PCL) and extramedullary multiple myeloma (EMD). The two steps protein extraction protocol was developed for the classification of MM, PCL, and EMD patients. Intensity across the whole m/z range increased approx. 50 times when extraction protocol was used (compare to dilute direct plasma samples). The accuracy of classification models using ML algorithms (RF, PLS-DA, and ANN) was 80-90% for the training dataset and 80-85% for the test dataset. These findings may help accelerate the integration of MALDI MS into a clinical application as the diagnosis of MM, PCL, and EMD is rather inaccurate nowadays.
Links
| LX22NPO5102, research and development project |
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| MUNI/A/1298/2022, interní kód MU |
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| MUNI/A/1370/2022, interní kód MU |
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| MUNI/11/ACC/3/2022, interní kód MU |
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| NU21-03-00076, research and development project |
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