Intact cell mass spectrometry coupled with machine learning
reveals minute changes induced by single gene silencing.

J 2024

Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing.

PEČINKA, Lukáš, Lukáš MORÁŇ, Petra KOVAČOVICOVÁ, Francesca MELONI, Josef HAVEL et. al.

Basic information

Original name

Intact cell mass spectrometry coupled with machine learning reveals minute changes induced by single gene silencing.

Authors

PEČINKA, Lukáš (203 Czech Republic, belonging to the institution), Lukáš MORÁŇ (203 Czech Republic, belonging to the institution), Petra KOVAČOVICOVÁ (703 Slovakia, belonging to the institution), Francesca MELONI (380 Italy), Josef HAVEL (203 Czech Republic, belonging to the institution), Tiziana PIVETTA (380 Italy) and Petr VAŇHARA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Heliyon, CAMBRIDGE, CELL PRESS, 2024, 2405-8440

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30400 3.4 Medical biotechnology

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 4.000 in 2022

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1016/j.heliyon.2024.e29936

UT WoS

001243807900001

Keywords in English

Bioinformatics; Biotyping; Cell culture; Intact cell MALDI TOF MS; Machine learning; Quality control; R programming language; TUSC3

Abstract

V originále

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

Links

MUNI/A/1298/2022, interní kód MU

Name: Základní a aplikovaný výzkum a vývoj metod chemické a fyzikálně chemické analýzy pro studium přírody a pokročilé technologie

Investor: Masaryk University, Basic and applied research and development of chemical and physicochemical analytical methods for the study of nature and advanced technology

MUNI/A/1301/2022, interní kód MU

Name: Zdroje pro tkáňové inženýrství 13

Investor: Masaryk University

MUNI/11/ACC/3/2022, interní kód MU

Name: Bioanalytical quality control of cGMP/ATMP-grade stem cells and progenitors

Investor: Masaryk University, Accelerate

NU23-08-00241, research and development project

Name: Vývoj ex-vivo buněčných modelů pro adenokarcinom pankreatu: markery a cíle pro precizní medicínu

Investor: Ministry of Health of the CR, Development of ex vivo cellular models for pancreatic adenocarcinoma: markers and targets for precision medicine, Subprogram 1 - standard

Citovat

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. 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.

@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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