Yang, Jaeyoung7; Palla, Mirko7; Bosco, Filippo1; Rindzevicius, Tomas6; Alstrøm, Tommy Sonne3; Schmidt, Michael Stenbæk6; Boisen, Anja6; Ju, Jingyue7; Lin, Qiao7
1 Department of Micro- and Nanotechnology, Technical University of Denmark2 Nanoprobes, Department of Micro- and Nanotechnology, Technical University of Denmark3 Department of Applied Mathematics and Computer Science, Technical University of Denmark4 Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark5 Columbia University6 Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Center, Technical University of Denmark7 Columbia University
Surface-enhanced Raman spectroscopy (SERS) has been used in a variety of biological applications due to its high sensitivity and specificity. Here, we report a SERS-based biosensing approach for quantitative detection of biomolecules. A SERS substrate bearing gold-decorated silicon nanopillars is functionalized with aptamers for sensitive and specific detection of target molecules. In this study, TAMRA-labeled vasopressin molecules in the picomolar regime (1 pM to 1 nM) are specifically captured by aptamers on the nanostructured SERS substrate and monitored by using an automated SERS signal mapping technique. From the experimental results, we show concentration-dependent SERS responses in the picomolar range by integrating SERS signal intensities over a scanning area. It is also noted that our signal mapping approach significantly improves statistical reproducibility and accounts for spot-to-spot variation in conventional SERS quantification. Furthermore, we have developed an analytical model capable of predicting experimental intensity distributions on the substrates for reliable quantification of biomolecules. Lastly, we have calculated the minimum needed area of Raman mapping for efficient and reliable analysis of each measurement. Combining our SERS mapping analysis with an aptamer-functionalized nanopillar substrate is found to be extremely efficient for detection of low-abundance biomolecules.