SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985823%3A_____%2F23%3A00567074" target="_blank" >RIV/67985823:_____/23:00567074 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22310/23:43925032 RIV/44555601:13440/23:43897699

Výsledek na webu

<a href="https://doi.org/10.1016/j.snb.2022.132812" target="_blank" >https://doi.org/10.1016/j.snb.2022.132812</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.snb.2022.132812" target="_blank" >10.1016/j.snb.2022.132812</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium

Popis výsledku v původním jazyce

The development of advanced methods of SERS-CNN data analysis seems to provide a perfect analytical system that is capable of solving the sophisticated task of determining the species and the behavior of microorganisms. Unlike the widely-used analytical approach, machine learning allows precise analysis even of very complex spectra of biological samples, and can provide precise decisions for a specific biochemical or microbiological task. In this article, we show for the first time the utilization of the SERS-CNN approach for remote observation of mesenchymal stem cell behavior. Our approach is based on SERS measurements of the biochemical changes taking place in the surrounding culture media due to stem cell proliferation and their biochemical activity. The cells were cultivated on various substrates supporting random or oriented cell growth, and also on „surface-toxic“ substrates. SERS-CNN analysis reveals the ability to perform „remote“ non-invasive estimation (i.e. using the surrounding medium analysis) of the degree of cell survival and the proliferation rate, using Raman measurements and advanced spectra data processing. It should be noted that the proposed approach makes it possible to analyze cell behavior without disrupting cell growth, and it can also be performed by untrained staff with the use of widely-available equipment.

Název v anglickém jazyce

SERS-CNN approach for non-invasive and non-destructive monitoring of stem cell growth on a universal substrate through an analysis of the cultivation medium

Popis výsledku anglicky

The development of advanced methods of SERS-CNN data analysis seems to provide a perfect analytical system that is capable of solving the sophisticated task of determining the species and the behavior of microorganisms. Unlike the widely-used analytical approach, machine learning allows precise analysis even of very complex spectra of biological samples, and can provide precise decisions for a specific biochemical or microbiological task. In this article, we show for the first time the utilization of the SERS-CNN approach for remote observation of mesenchymal stem cell behavior. Our approach is based on SERS measurements of the biochemical changes taking place in the surrounding culture media due to stem cell proliferation and their biochemical activity. The cells were cultivated on various substrates supporting random or oriented cell growth, and also on „surface-toxic“ substrates. SERS-CNN analysis reveals the ability to perform „remote“ non-invasive estimation (i.e. using the surrounding medium analysis) of the degree of cell survival and the proliferation rate, using Raman measurements and advanced spectra data processing. It should be noted that the proposed approach makes it possible to analyze cell behavior without disrupting cell growth, and it can also be performed by untrained staff with the use of widely-available equipment.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30404 - Biomaterials (as related to medical implants, devices, sensors)

Projekt

<a href="/cs/project/GA21-06065S" target="_blank" >GA21-06065S: Nové funkcionalizované senzory založené na plazmonech jako nástroje pro monitorování buněk a pro pokročilé tkáňové inženýrství</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors and Actuators B - Chemical

ISSN

0925-4005

e-ISSN

0925-4005

Svazek periodika

375

Číslo periodika v rámci svazku

15 January

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

9

Strana od-do

132812

Kód UT WoS článku

000904973600005

EID výsledku v databázi Scopus

2-s2.0-85140356459