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Nano Lett., Jun 13 2024, | https://doi.org/10.1021/acs.nanolett.4c01654 Simultaneous Protein Colorful Imaging via Raman Signal Classification

Dongkwon Seo^1,2, Hayeon Sun^2,3, Yeonho Choi ^1,2,3,4

¹Department of Bio-convergence Engineering, Korea University, Seoul 02841, Republic of Korea.

²Interdisciplinary Program in Precision Public Health, Korea University, Seoul 02841, Republic of Korea.

³Department of Biomedical Engineering, Korea University, Seoul 02841, Republic of Korea.

⁴School of Biomedical Engineering, Korea University, Seoul 02841, Republic of Korea.

Corresponding Author: Yeonho Choi

Abstract

Protein imaging aids diagnosis and drug development by revealing protein-drug interactions or protein levels. However, the challenges of imaging multiple proteins, reduced sensitivity, and high reliance on specific protein properties such as Raman peaks or refractive index hinder the understanding. Here, we introduce multiprotein colorful imaging through Raman signal classification. Our method utilized machine learning-assisted classification of Raman signals, which are the distinctive features of label-free proteins. As a result, three types of proteins could be imaged simultaneously. In addition, we could quantify individual proteins from a mixture of multiple proteins over a wide detection range (10 fg/mL-1 μg/mL). These results showed a 1000-fold improvement in sensitivity and a 30-fold increase in the upper limit of detection compared to existing methods. These advances will enhance our understanding of biology and facilitate the development of disease diagnoses and treatments.

논문정보

형식Research article
게재일2024년 06월 (BRIC 등록일 2024-06-17)
연구진국내 연구진
분야 바이오·의료융합 > 바이오센싱 및 나노바이오물질

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