Department of Electrical Engineering, University of California, Los Angeles, CA 90095, USA.
Proc Natl Acad Sci U S A. 2012 Jul 17;109(29):11630-5. doi: 10.1073/pnas.1204718109. Epub 2012 Jul 2.
Optical microscopy is one of the most widely used diagnostic methods in scientific, industrial, and biomedical applications. However, while useful for detailed examination of a small number (< 10,000) of microscopic entities, conventional optical microscopy is incapable of statistically relevant screening of large populations (> 100,000,000) with high precision due to its low throughput and limited digital memory size. We present an automated flow-through single-particle optical microscope that overcomes this limitation by performing sensitive blur-free image acquisition and nonstop real-time image-recording and classification of microparticles during high-speed flow. This is made possible by integrating ultrafast optical imaging technology, self-focusing microfluidic technology, optoelectronic communication technology, and information technology. To show the system's utility, we demonstrate high-throughput image-based screening of budding yeast and rare breast cancer cells in blood with an unprecedented throughput of 100,000 particles/s and a record false positive rate of one in a million.
光学显微镜是科学、工业和生物医学应用中最广泛使用的诊断方法之一。然而，尽管在对少量（<10000 个）微观实体进行详细检查时非常有用，但由于其低通量和有限的数字内存大小，传统的光学显微镜无法以高精度对大量（>100000000 个）进行具有统计学意义的筛选。我们提出了一种自动化的流动式单颗粒光学显微镜，通过在高速流动时进行敏感的无模糊图像采集和不间断的实时图像记录和分类微粒子，克服了这一限制。这是通过集成超快光学成像技术、自聚焦微流控技术、光电通信技术和信息技术实现的。为了展示该系统的实用性，我们演示了以 100000 个/秒的空前通量和百万分之一的记录假阳性率对血液中的出芽酵母和罕见乳腺癌细胞进行基于图像的高通量筛选。
