2023
Biostatistic and machine learning in MALDI mass spectrometry research
PEČINKA, Lukáš, Lukáš MORÁŇ, Monika VLACHOVÁ, Adam BŘEZINA, Petra KOVAČOVICOVÁ et. al.Základní údaje
Originální název
Biostatistic and machine learning in MALDI mass spectrometry research
Autoři
PEČINKA, Lukáš (203 Česká republika, domácí), Lukáš MORÁŇ (203 Česká republika, domácí), Monika VLACHOVÁ (203 Česká republika, domácí), Adam BŘEZINA (203 Česká republika, domácí), Petra KOVAČOVICOVÁ (703 Slovensko, domácí), Martina MARCHETTI-DESCHMANN (40 Rakousko), Sabina ŠEVČÍKOVÁ (203 Česká republika, domácí), Josef HAVEL (203 Česká republika, domácí), Aleš HAMPL (203 Česká republika, domácí) a Petr VAŇHARA (203 Česká republika, garant, domácí)
Vydání
XXII. Interdisciplinary meeting of young life scientists, 2023
Další údaje
Jazyk
angličtina
Typ výsledku
Konferenční abstrakt
Obor
30400 3.4 Medical biotechnology
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Kód RIV
RIV/00216224:14110/23:00132354
Organizační jednotka
Lékařská fakulta
Klíčová slova anglicky
mass spectrometry; biotyping; machine learning; tissue engineering
Změněno: 14. 12. 2023 13:12, doc. RNDr. Petr Vaňhara, Ph.D.
Anotace
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.
Návaznosti
| LX22NPO5102, projekt VaV |
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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, projekt VaV |
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