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@article{2400717,
author = {Pečinka, Lukáš and Moráň, Lukáš and Kovačovicová, Petra and Meloni, Francesca and Havel, Josef and Pivetta, Tiziana and Vaňhara, Petr},
article_location = {CAMBRIDGE},
article_number = {9},
doi = {http://dx.doi.org/10.1016/j.heliyon.2024.e29936},
keywords = {Bioinformatics; Biotyping; Cell culture; Intact cell MALDI TOF MS; Machine learning; Quality control; R programming language; TUSC3},
language = {eng},
issn = {2405-8440},
journal = {Heliyon},
title = {Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing.},
url = {https://www.cell.com/heliyon/fulltext/S2405-8440(24)05967-X},
volume = {10},
year = {2024}
}
TY - JOUR
ID - 2400717
AU - Pečinka, Lukáš - Moráň, Lukáš - Kovačovicová, Petra - Meloni, Francesca - Havel, Josef - Pivetta, Tiziana - Vaňhara, Petr
PY - 2024
TI - Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing.
JF - Heliyon
VL - 10
IS - 9
SP - 1-10
EP - 1-10
PB - CELL PRESS
SN - 24058440
KW - Bioinformatics
KW - Biotyping
KW - Cell culture
KW - Intact cell MALDI TOF MS
KW - Machine learning
KW - Quality control
KW - R programming language
KW - TUSC3
UR - https://www.cell.com/heliyon/fulltext/S2405-8440(24)05967-X
N2 - Intact (whole) cell MALDI TOF mass spectrometry is a commonly used tool in clinical microbiology for several decades. Recently it was introduced to analysis of eukaryotic cells, including cancer and stem cells. Besides targeted metabolomic and proteomic applications, the intact cell MALDI TOF mass spectrometry provides a sufficient sensitivity and specificity to discriminate cell types, isogenous cell lines or even the metabolic states. This makes the intact cell MALDI TOF mass spectrometry a promising tool for quality control in advanced cell cultures with a potential to reveal batch-to-batch variation, aberrant clones, or unwanted shifts in cell phenotype. However, cellular alterations induced by change in expression of a single gene has not been addressed by intact cell mass spectrometry yet. In this work we used a well-characterized human ovarian cancer cell line SKOV3 with silenced expression of a tumor suppressor candidate 3 gene (TUSC3). TUSC3 is involved in co-translational N-glycosylation of proteins with well-known global impact on cell phenotype. Altogether, this experimental design represents a highly suitable model for optimization of intact cell mass spectrometry and analysis of spectral data. Here we investigated five machine learning algorithms (k-nearest neighbors, decision tree, random forest, partial least squares discrimination, and artificial neural network) and optimized their performance either in pure populations or in two-component mixtures composed of cells with normal or silenced expression of TUSC3. All five algorithms reached accuracy over 90 % and were able to reveal even subtle changes in mass spectra corresponding to alterations of TUSC3 expression. In summary, we demonstrate that spectral fingerprints generated by intact cell MALDI-TOF mass spectrometry coupled to a machine learning classifier can reveal minute changes induced by alteration of a single gene, and therefore contribute to the portfolio of quality control applications in routine cell and tissue cultures
ER -
PEČINKA, Lukáš, Lukáš MORÁŇ, Petra KOVAČOVICOVÁ, Francesca MELONI, Josef HAVEL, Tiziana PIVETTA and Petr VAŇHARA. Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing. \textit{Heliyon}. CAMBRIDGE: CELL PRESS, 2024, vol.~10, No~9, p.~1-10. ISSN~2405-8440. Available from: https://dx.doi.org/10.1016/j.heliyon.2024.e29936.
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