Wu Jiamin, Xiong Bo, Lin Xing, He Jijun, Suo Jinli, Dai Qionghai
Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, 100084, China.
Sci Rep. 2016 Apr 22;6:24624. doi: 10.1038/srep24624.
The comprehensive analysis of biological specimens brings about the demand for capturing the spatial, temporal and spectral dimensions of visual information together. However, such high-dimensional video acquisition faces major challenges in developing large data throughput and effective multiplexing techniques. Here, we report the snapshot hyperspectral volumetric microscopy that computationally reconstructs hyperspectral profiles for high-resolution volumes of ~1000 μm × 1000 μm × 500 μm at video rate by a novel four-dimensional (4D) deconvolution algorithm. We validated the proposed approach with both numerical simulations for quantitative evaluation and various real experimental results on the prototype system. Different applications such as biological component analysis in bright field and spectral unmixing of multiple fluorescence are demonstrated. The experiments on moving fluorescent beads and GFP labelled drosophila larvae indicate the great potential of our method for observing multiple fluorescent markers in dynamic specimens.
对生物样本的综合分析引发了同时捕捉视觉信息的空间、时间和光谱维度的需求。然而，这种高维视频采集在开发大数据吞吐量和有效的多路复用技术方面面临重大挑战。在此，我们报告了快照高光谱体积显微镜，它通过一种新颖的四维（4D）去卷积算法，以视频速率为约1000μm×1000μm×500μm的高分辨率体积计算重建高光谱轮廓。我们通过用于定量评估的数值模拟和原型系统上的各种实际实验结果验证了所提出的方法。展示了不同的应用，如明场中的生物成分分析和多种荧光的光谱解混。对移动荧光珠和GFP标记的果蝇幼虫的实验表明了我们的方法在观察动态样本中的多个荧光标记方面的巨大潜力。
