Biostatistics and data evaluation of intact cell MS application in stem cells and progenitors biotyping
| Autoři | |
|---|---|
| Rok publikování | 2021 |
| Druh | Další prezentace na konferencích |
| Citace | |
| Popis | Intact Cell Matrix Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (IC MALDI TOF MS) emerged as a promising analytical approach for discrimination and classification of cell types or their (patho) physiological states, based on their spectral profiles. The intact MALDI TOF MS is already used in clinical microbiology and diagnostics. In recent years it has been introduced to cell biology, immunology, or cancer biology. In our previous work, we used the intact cell MALDI TOF MS as a quality control tool for revealing phenotypic and metabolic changes occurring in cultured embryonic stem cells. [1]. The human embryonic stem cells (hESC) are a promising cell source for regenerative medicine or tissue engineering. The hESC proliferate indefinitely but retain a full differentiation capacity. However, in prolonged in vitro cultures the cell acquires chromosomal aberrations, gain unwanted phenotypic traits, and lose their potential for clinical application [2]. In this work, we applied the IC MALDI TOF MS coupled with cluster analysis and unsupervised methods based on machine learning on hESCs differentiated to expandable lung-like epithelial (ELEP) cells to monitor their differentiation trajectory and reveal phenotypic abnormalities linked to passage number of hESCs (low v.s. high). We used the mass range of 2-20 kDa (peptide fingerprint) for monitoring of such alterations of hESCs. Raw mass spectra were exported and processed by various biostatistical methods in R programming environment. Mass spectra were pre-processed using MALDIquant package which provide complete analysis pipeline for MALDI TOF MS data. Data obtained from mass spectra were visualised by several methods including principal component analysis (PCA), heatmap, and boxplots. Data were also analysed by unsupervised methods (cluster analysis) and supervised methods (decision tree, random forest, and artificial neural networks). Our methodological pipeline using simple intact cell MALDI TOF MS protocol coupled with semiautomated analysis by multivariate statistical methods and machine learning is especially suitable for routine monitoring of hESCs cultures and identification of divergencies from optimal conditions. The work was supported by Masaryk University (project no. MUNI/A/1390/2020, ROZV/28/LF/2020) and Ministry of Health of the Czech Republic, (grant no. NV18-08-00299. All rights reserved). |
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