Multivariate Calibration Approach for Quantitative
Determination of Cell-Line Cross Contamination by Intact Cell
Mass Spectrometry and Artificial Neural Networks

J 2016

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks

VALLETTA, Elisa; Lukáš KUČERA; Lubomír PROKEŠ; Filippo AMATO; Tiziana PIVETTA et. al.

Basic information

Original name

Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks

Authors

VALLETTA, Elisa (380 Italy, belonging to the institution); Lukáš KUČERA (203 Czech Republic, belonging to the institution); Lubomír PROKEŠ (203 Czech Republic, belonging to the institution); Filippo AMATO (380 Italy, belonging to the institution); Tiziana PIVETTA (380 Italy); Aleš HAMPL (203 Czech Republic, belonging to the institution); Josef HAVEL (203 Czech Republic, belonging to the institution) and Petr VAŇHARA (203 Czech Republic, guarantor, belonging to the institution)

Edition

Plos One, San Francisco, Public Library of Science, 2016, 1932-6203

Other information

Language

English

Type of outcome

Article in a journal

Field of Study

Genetics and molecular biology

Country of publisher

United States of America

Confidentiality degree

is not subject to a state or trade secret

Impact factor

Impact factor: 2.806

RIV identification code

RIV/00216224:14110/16:00089350

Organization unit

Faculty of Medicine

DOI

https://doi.org/10.1371/journal.pone.0147414

UT WoS

000369528400026

EID Scopus

2-s2.0-84958825099

Keywords in English

EMBRYONIC STEM-CELLS; LASER-DESORPTION/IONIZATION-TIME; LEAST-SQUARES REGRESSION; MALDI-TOF; EXPERIMENTAL-DESIGN; CANCER-DIAGNOSIS; MAMMALIAN-CELLS; CLASSIFICATION; SPECTRA; CULTURE

Abstract

In the original language

Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.

Links

EE2.3.20.0185, research and development project

Name: Centrum analýz a modelování tkání a orgánů

MUNI/A/1558/2014, interní kód MU

Name: Zdroje pro tkáňové inženýrství 5 (Acronym: TissueEng5)

Investor: Masaryk University, Category A

MUNI/M/0041/2013, interní kód MU

Name: Bioanalytical Cell and Tissue Authentication using Physical Chemistry Methods and Artificial Intelligence

Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects

ROZV/20/LF/2015, interní kód MU

Name: LF - Příspěvek IP 2015

Investor: Ministry of Education, Youth and Sports of the CR

Cite

VALLETTA, Elisa; Lukáš KUČERA; Lubomír PROKEŠ; Filippo AMATO; Tiziana PIVETTA; Aleš HAMPL; Josef HAVEL and Petr VAŇHARA. Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks. Plos One. San Francisco: Public Library of Science, 2016, vol. 11, No 1, p. 1-14. ISSN 1932-6203. Available from: https://doi.org/10.1371/journal.pone.0147414.

@article{1334235,
   author = {Valletta, Elisa and Kučera, Lukáš and Prokeš, Lubomír and Amato, Filippo and Pivetta, Tiziana and Hampl, Aleš and Havel, Josef and Vaňhara, Petr},
   article_location = {San Francisco},
   article_number = {1},
   doi = {https://doi.org/10.1371/journal.pone.0147414},
   keywords = {EMBRYONIC STEM-CELLS; LASER-DESORPTION/IONIZATION-TIME; LEAST-SQUARES REGRESSION; MALDI-TOF; EXPERIMENTAL-DESIGN; CANCER-DIAGNOSIS; MAMMALIAN-CELLS; CLASSIFICATION; SPECTRA; CULTURE},
   language = {eng},
   issn = {1932-6203},
   journal = {Plos One},
   title = {Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks},
   volume = {11},
   year = {2016}
}

TY  - JOUR
ID  - 1334235
AU  - Valletta, Elisa - Kučera, Lukáš - Prokeš, Lubomír - Amato, Filippo - Pivetta, Tiziana - Hampl, Aleš - Havel, Josef - Vaňhara, Petr
PY  - 2016
TI  - Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks
JF  - Plos One
VL  - 11
IS  - 1
SP  - 1-14
EP  - 1-14
PB  - Public Library of Science
SN  - 19326203
KW  - EMBRYONIC STEM-CELLS
KW  - LASER-DESORPTION/IONIZATION-TIME
KW  - LEAST-SQUARES REGRESSION
KW  - MALDI-TOF
KW  - EXPERIMENTAL-DESIGN
KW  - CANCER-DIAGNOSIS
KW  - MAMMALIAN-CELLS
KW  - CLASSIFICATION
KW  - SPECTRA
KW  - CULTURE
N2  - Cross-contamination of eukaryotic cell lines used in biomedical research represents a highly relevant problem. Analysis of repetitive DNA sequences, such as Short Tandem Repeats (STR), or Simple Sequence Repeats (SSR), is a widely accepted, simple, and commercially available technique to authenticate cell lines. However, it provides only qualitative information that depends on the extent of reference databases for interpretation. In this work, we developed and validated a rapid and routinely applicable method for evaluation of cell culture cross-contamination levels based on mass spectrometric fingerprints of intact mammalian cells coupled with artificial neural networks (ANNs). We used human embryonic stem cells (hESCs) contaminated by either mouse embryonic stem cells (mESCs) or mouse embryonic fibroblasts (MEFs) as a model. We determined the contamination level using a mass spectra database of known calibration mixtures that served as training input for an ANN. The ANN was then capable of correct quantification of the level of contamination of hESCs by mESCs or MEFs. We demonstrate that MS analysis, when linked to proper mathematical instruments, is a tangible tool for unraveling and quantifying heterogeneity in cell cultures. The analysis is applicable in routine scenarios for cell authentication and/or cell phenotyping in general.
ER  -

VALLETTA, Elisa; Lukáš KUČERA; Lubomír PROKEŠ; Filippo AMATO; Tiziana PIVETTA; Aleš HAMPL; Josef HAVEL and Petr VAŇHARA. Multivariate Calibration Approach for Quantitative Determination of Cell-Line Cross Contamination by Intact Cell Mass Spectrometry and Artificial Neural Networks. \textit{Plos One}. San Francisco: Public Library of Science, 2016, vol.~11, No~1, p.~1-14. ISSN~1932-6203. Available from: https://doi.org/10.1371/journal.pone.0147414.

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