Zia ul Quasim Syed, Sathya Samaraweera, Zhuo Wang and Sadagopan Krishnan
{"title":"Colorimetric nano-biosensor for low-resource settings: insulin as a model biomarker","authors":"Zia ul Quasim Syed, Sathya Samaraweera, Zhuo Wang and Sadagopan Krishnan","doi":"10.1039/D4SD00197D","DOIUrl":null,"url":null,"abstract":"<p >Biomarkers provide critical molecular insights into diseases and abnormal conditions. However, detecting them at ultra-low concentrations is a challenge, particularly in areas with limited resources and access to sophisticated instruments. Our research is primarily focused on mitigating this challenge. In this report, we introduce a colorimetric immunosensor for detecting insulin, an essential hormone biomarker that regulates glucose metabolism, at picomolar concentrations using citrate-functionalized magnetic particles. This immunosensor utilizes a two-antibody sandwich immunoassay: one antibody is covalently conjugated to the nanoparticles to capture and isolate the target marker, while the other is labeled with horseradish peroxidase for colorimetric detection of insulin. We conducted comparative analyses of insulin detection in buffer, saliva, and serum samples, offering valuable analytical insights. Our findings indicate a detection limit of 10 pM, with dynamic ranges of 10 pM to 1 nM, 10 pM to 10 nM, and 50 pM to 1 nM for insulin detection in buffer solution, 2-fold diluted serum, and 20-fold diluted artificial saliva, respectively. We demonstrate the application of the color immunosensor to type 1 diabetes and healthy human serum samples. For human saliva analysis, the detection limit needs to be improved in our future studies. Overall, our study enhances biomarker analysis in biofluids through an equipment-free colorimetric method, which is particularly relevant for point-of-need applications.</p>","PeriodicalId":74786,"journal":{"name":"Sensors & diagnostics","volume":" 10","pages":" 1659-1671"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/sd/d4sd00197d?page=search","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sensors & diagnostics","FirstCategoryId":"1085","ListUrlMain":"https://pubs.rsc.org/en/content/articlelanding/2024/sd/d4sd00197d","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Biomarkers provide critical molecular insights into diseases and abnormal conditions. However, detecting them at ultra-low concentrations is a challenge, particularly in areas with limited resources and access to sophisticated instruments. Our research is primarily focused on mitigating this challenge. In this report, we introduce a colorimetric immunosensor for detecting insulin, an essential hormone biomarker that regulates glucose metabolism, at picomolar concentrations using citrate-functionalized magnetic particles. This immunosensor utilizes a two-antibody sandwich immunoassay: one antibody is covalently conjugated to the nanoparticles to capture and isolate the target marker, while the other is labeled with horseradish peroxidase for colorimetric detection of insulin. We conducted comparative analyses of insulin detection in buffer, saliva, and serum samples, offering valuable analytical insights. Our findings indicate a detection limit of 10 pM, with dynamic ranges of 10 pM to 1 nM, 10 pM to 10 nM, and 50 pM to 1 nM for insulin detection in buffer solution, 2-fold diluted serum, and 20-fold diluted artificial saliva, respectively. We demonstrate the application of the color immunosensor to type 1 diabetes and healthy human serum samples. For human saliva analysis, the detection limit needs to be improved in our future studies. Overall, our study enhances biomarker analysis in biofluids through an equipment-free colorimetric method, which is particularly relevant for point-of-need applications.