Electrical Engineering Department, University of California, Los Angeles, CA 90095, USA.
Sci Rep. 2013;3:1699. doi: 10.1038/srep01699.
Computational microscopy tools, in particular lensfree on-chip imaging, provide a large field-of-view along with a long depth-of-field, which makes it feasible to rapidly analyze large volumes of specimen using a compact and light-weight on-chip imaging architecture. To bring molecular specificity to this high-throughput platform, here we demonstrate the use of plasmon-resonant metallic nanoparticles to automatically recognize different cell types based on their plasmon-enhanced lensfree holograms, detected and reconstructed over a large field-of-view of e.g., ~24 mm².
计算显微镜工具,特别是无透镜片上成像,提供了大的视场和长的景深,这使得使用紧凑和轻量级的片上成像架构快速分析大体积的样本成为可能。为了将分子特异性引入这个高通量平台,我们在这里展示了使用等离子体共振金属纳米粒子根据它们的等离子体增强无透镜全息图自动识别不同细胞类型的方法,该全息图可以在例如~24mm²的大视场中进行检测和重建。