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.

Základní údaje

Originální název

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

Autoři

VALLETTA, Elisa; Lukáš KUČERA; Lubomír PROKEŠ; Filippo AMATO; Tiziana PIVETTA; Aleš HAMPL; Josef HAVEL a Petr VAŇHARA

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

Genetika a molekulární biologie

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.806

Kód RIV

RIV/00216224:14110/16:00089350

Organizační jednotka

Lékařská fakulta

DOI

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

UT WoS

000369528400026

EID Scopus

2-s2.0-84958825099

Klíčová slova anglicky

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

Štítky

EL OK, podil

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 5. 12. 2016 16:59, Ing. Mgr. Věra Pospíšilíková

Anotace

V originále

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.

Návaznosti

EE2.3.20.0185, projekt VaV

Název: Centrum analýz a modelování tkání a orgánů

MUNI/A/1558/2014, interní kód MU

Název: Zdroje pro tkáňové inženýrství 5 (Akronym: TissueEng5)

Investor: Masarykova univerzita, Zdroje pro tkáňové inženýrství 5, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

MUNI/M/0041/2013, interní kód MU

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

Investor: Masarykova univerzita, Bioanalytical Cell and Tissue Authentication using Physical Chemistry Methods and Artificial Intelligence, INTERDISCIPLINARY - Mezioborové výzkumné projekty

ROZV/20/LF/2015, interní kód MU

Název: LF - Příspěvek IP 2015

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, LF - Příspěvek IP 2015

Citovat

VALLETTA, Elisa; Lukáš KUČERA; Lubomír PROKEŠ; Filippo AMATO; Tiziana PIVETTA; Aleš HAMPL; Josef HAVEL a 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, roč. 11, č. 1, s. 1-14. ISSN 1932-6203. Dostupné z: 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 a 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, roč.~11, č.~1, s.~1-14. ISSN~1932-6203. Dostupné z: https://doi.org/10.1371/journal.pone.0147414.

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