High-throughput single-microparticle imaging flow analyzer

Keisuke Goda(California NanoSystems Institute), Ali Ayazi(University of California, Los Angeles), Daniel R. Gossett(California NanoSystems Institute), Jagannath Sadasivam(University of California, Los Angeles), Cejo Konuparamban Lonappan(University of California, Los Angeles), Elodie Sollier(California NanoSystems Institute), Ali Fard(California NanoSystems Institute), Soojung Hur(California NanoSystems Institute), Jost Adam(University of California, Los Angeles), Coleman Murray(University of California, Los Angeles), Chao Wang(University of California, Los Angeles), Nora Brackbill(University of California, Los Angeles), Dino Di Carlo(California NanoSystems Institute), Bahram Jalali(California NanoSystems Institute)
Proceedings of the National Academy of Sciences
July 2, 2012
Cited by 389Open Access
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Abstract

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.


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