Bioinspired Superhydrophobic SERS Substrates for Machine Learning Assisted miRNA Detection in Complex Biomatrix Below Femtomolar Limit

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F23%3A43927109" target="_blank" >RIV/60461373:22310/23:43927109 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.aca.2023.341708" target="_blank" >https://doi.org/10.1016/j.aca.2023.341708</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aca.2023.341708" target="_blank" >10.1016/j.aca.2023.341708</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bioinspired Superhydrophobic SERS Substrates for Machine Learning Assisted miRNA Detection in Complex Biomatrix Below Femtomolar Limit

Popis výsledku v původním jazyce

Surface-enhanced Raman spectroscopy (SERS) is an analytical method with high potential in the field of medicine. The design of SERS substrates, based on specific morphology and/or chemical modification, allow the recognition of the presence of specific analytes with precision close to a single-molecule detection limit. However, the SERS analysis of real samples is significantly complicated by the presence of a large number of “minor” molecules that can shield the signal from the target analyte and make it impossible to determine it in practice. In this work, an advanced SERS approach was used for the detection of cancer-related miRNA-21 in blood plasma, used as a molecular model background. The approach was based on the combination of the biomimetic plasmon-active SERS substrate, its tuned surface chemistry and advanced SERS data analysis, making use of artificial machine learning. In the first step, biomimetic SERS substrates were created using a butterfly wing as a starting template. The substrates were covered by thin Au layer and covalently grafted with hydrophobic chemical moieties to introduce superhydrophobic and water-adhesive properties. The self-concentration of the analyte on the substrates was achieved by minimizing the contact area between the analyte drop and the substrate, which is facilitated by surface superhydrophobicity and additionally enhanced by drop evaporation on the flipped over substrate. Due to the presence of cancer miRNA and blood plasma background, the measured SERS spectra represent a complex of interfering peaks. Thus, their interpretation was carried out using a specially trained machine learning model. As a result, reliable and repeatable quantitative detection of miRNAs below the femtomolar level (up to 10−16 M) on the background of human blood plasma becomes possible.

Název v anglickém jazyce

Bioinspired Superhydrophobic SERS Substrates for Machine Learning Assisted miRNA Detection in Complex Biomatrix Below Femtomolar Limit

Popis výsledku anglicky

Surface-enhanced Raman spectroscopy (SERS) is an analytical method with high potential in the field of medicine. The design of SERS substrates, based on specific morphology and/or chemical modification, allow the recognition of the presence of specific analytes with precision close to a single-molecule detection limit. However, the SERS analysis of real samples is significantly complicated by the presence of a large number of “minor” molecules that can shield the signal from the target analyte and make it impossible to determine it in practice. In this work, an advanced SERS approach was used for the detection of cancer-related miRNA-21 in blood plasma, used as a molecular model background. The approach was based on the combination of the biomimetic plasmon-active SERS substrate, its tuned surface chemistry and advanced SERS data analysis, making use of artificial machine learning. In the first step, biomimetic SERS substrates were created using a butterfly wing as a starting template. The substrates were covered by thin Au layer and covalently grafted with hydrophobic chemical moieties to introduce superhydrophobic and water-adhesive properties. The self-concentration of the analyte on the substrates was achieved by minimizing the contact area between the analyte drop and the substrate, which is facilitated by surface superhydrophobicity and additionally enhanced by drop evaporation on the flipped over substrate. Due to the presence of cancer miRNA and blood plasma background, the measured SERS spectra represent a complex of interfering peaks. Thus, their interpretation was carried out using a specially trained machine learning model. As a result, reliable and repeatable quantitative detection of miRNAs below the femtomolar level (up to 10−16 M) on the background of human blood plasma becomes possible.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Analytica Chimica Acta

ISSN

0003-2670

e-ISSN

—

Svazek periodika

1278

Číslo periodika v rámci svazku

OCT 16 2023

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

24

Strana od-do

"341708/1"-24

Kód UT WoS článku

001076457700001

EID výsledku v databázi Scopus

2-s2.0-85169050550