Lee Hankeun, Li Siyan, Liu Leyang, Wang Weijing, Ayupova Takhmina, Tibbs Joseph, Kim Chansong, Fang Ying, Do Minh N, Cunningham Brian T
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA; Nick Holonyak Jr. Micro and Nanotechnology Lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Department of Pathobiology, College of Veterinary Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Biosens Bioelectron. 2025 Aug 1;281:117455. doi: 10.1016/j.bios.2025.117455. Epub 2025 Apr 9.
Accurate molecular biomarker detection with digital-resolution sensitivity is essential for applications such as disease diagnostics, therapeutic studies, and biomedical research. Here, we present LOCA-PRAM (LOcalization with Context Awareness), a deep learning-based method integrated with a Photonic Resonator Absorption Microscope (PRAM) to achieve digital-resolution detection of biomolecules using gold nanoparticles (AuNPs) as molecular tags. LOCA-PRAM leverages photonic crystal (PC)-AuNP resonant-coupling to enhance signal contrast, facilitating precise quantification of target molecules without partitioning the sample into droplets or enzymatic amplification. Through registration of PRAM images with Scanning Electron Microscopy (SEM) images, we empirically obtain the point spread function (PSF) of AuNP tags, enabling realistic training data generation for the deep learning framework. LOCA-PRAM surpasses conventional image processing method in accuracy and sensitivity, achieving reliable AuNP detection and localization even in high-density conditions, minimizing false-positive and false-negative quantifications and expending the dynamic range of assay. Benchmarking with SEM-derived ground truth confirms LOCA-PRAM's sub-pixel resolution and ability to accurately quantify AuNPs with overlapping PSF. Overall, the PRAM combined with LOCA-based AuNP digital counting enables real-time, high-precision detection of molecular biomarkers, advancing digital-resolution biosensing for biomedical research and diagnostics.
以数字分辨率灵敏度进行精确的分子生物标志物检测对于疾病诊断、治疗研究和生物医学研究等应用至关重要。在此，我们展示了LOCA-PRAM（上下文感知定位），这是一种基于深度学习的方法，与光子谐振器吸收显微镜（PRAM）集成，以使用金纳米颗粒（AuNP）作为分子标签实现生物分子的数字分辨率检测。LOCA-PRAM利用光子晶体（PC）-AuNP共振耦合来增强信号对比度，无需将样品分成液滴或进行酶扩增就能促进目标分子的精确定量。通过将PRAM图像与扫描电子显微镜（SEM）图像配准，我们凭经验获得了AuNP标签的点扩散函数（PSF），从而为深度学习框架生成逼真的训练数据。LOCA-PRAM在准确性和灵敏度方面超越了传统图像处理方法，即使在高密度条件下也能实现可靠的AuNP检测和定位，最大限度地减少假阳性和假阴性定量，并扩展了检测的动态范围。与SEM得出的地面真值进行基准测试证实了LOCA-PRAM的亚像素分辨率以及准确量化具有重叠PSF的AuNP的能力。总体而言，PRAM与基于LOCA的AuNP数字计数相结合，能够实时、高精度地检测分子生物标志物，推动了用于生物医学研究和诊断的数字分辨率生物传感技术的发展。
