機械学習でナノ粒子の「色」を見分け、タンパク質を高精度に検出－色の異なる2種類の金属ナノ粒子と暗視野顕微鏡を組み合わせ、従来法では難しかった抗体タンパク質などの大きな分子の検出を実現－

2026-08-04

2026-08-03 愛媛大学

愛媛大学と理化学研究所の共同研究グループは、色の異なる2種類の金属ナノ粒子と暗視野顕微鏡、機械学習を組み合わせることで、タンパク質などの大きな分子を高精度に検出する新しいバイオセンシング技術を開発した。従来の金属ナノ粒子を用いた検出法では、タンパク質のような大きな分子ではナノ粒子間距離が広がるため、凝集状態の違いを十分に識別できないという課題があった。研究では、散乱光の色が異なる2種類の金属ナノ粒子を利用し、標的分子によって形成されるヘテロ二量体を暗視野顕微鏡で観察し、その色の違いを機械学習で分類する手法を構築した。その結果、標的タンパク質を高感度かつ高精度に検出できるだけでなく、非特異的凝集や異物などのノイズも識別可能となった。本技術は、疾患マーカーや抗体検査などの高精度なバイオセンシングへの応用が期待される。研究成果は学術誌『RSC Advances』に掲載された。

＜関連情報＞

暗視野観察を用いた2種類のナノ粒子の機械学習に基づく分子検出

Machine learning-based molecular detection using dark-field observation of two different nanoparticles

Yuki Yano;Gen Hirao;Ryosuke Izumi;Yu Muto;Misato Yasuoka;Tsuyoshi Asahi;Takuo Tanaka;Mizuo Maeda;Tamotsu Zako

RSC Advances Published:31 July 2026

DOI:https://doi.org/10.1039/d6ra03733j

Metal nanoparticles, such as gold nanoparticles (AuNPs), are widely used as biosensing materials. In previous studies, dark-field microscopy (DFM) has been utilised to examine target-induced AuNP aggregation for molecular sensing. The intensity of scattering light of each spot observed by DFM was analysed at the single-cluster level for sensitive molecular detection. However, changes in the intensity and colour of AuNP aggregates were not significant when the inter-particle distance was large because of the insufficient effect of the surface plasmon resonance, suggesting difficulty in the sensitive detection of large molecules such as proteins. In this study, we developed a machine learning-based method to distinguish target-induced dimers from monomers by DFM, given large inter-particle distance, using two types of nanoparticles with different spot colours. When the two types of nanoparticles form a dimer (heterodimer), observation of a new spot colour derived from the heterodimer could be expected. As a proof-of-concept study, Protein A-modified silver nanoparticles and BSA-modified gold nanourchins were used to detect anti-BSA antibody; in the presence of the target, heterodimer was formed. The colours of individual spots observed by DFM at the single-cluster level were utilised for machine learning-based classification, and spots derived from heterodimers were identified for molecular detection. Our results demonstrate that the heterodimer formation increased in a target concentration-dependent manner. Furthermore, scattered lights from non-specific aggregates and impurities such as dust can be discriminated by this method. This assay is expected to be applicable to the detection of large molecules, such as proteins.