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:

Impact factor

Impact factor: 4.000 in 2022

Organization unit

Faculty of Medicine

UT WoS

001243807900001

Keywords in English

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

Tags

Tags

International impact, Reviewed
Změněno: 2/7/2024 10:08, Mgr. Tereza Miškechová

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