19 March 2014 Apparatus and fast method for cancer cell classification based on high harmonic coherent diffraction imaging in reflection geometry
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Abstract
In cancer treatment it is highly desirable to identify and /or classify individual cancer cells in real time. Nowadays, the standard method is PCR which is costly and time-consuming. Here we present a different approach to rapidly classify cell types: we measure the pattern of coherently diffracted extreme ultraviolet radiation (XUV radiation at 38nm wavelength), allowing to distinguish different single breast cancer cell types. The output of our laser driven XUV light source is focused onto a single unstained and unlabeled cancer cell, and the resulting diffraction pattern is measured in reflection geometry. As we will further show, the outer shape of the object can be retrieved from the diffraction pattern with sub-micron resolution. For classification it is often not necessary to retrieve the image, it is only necessary to compare the diffraction patterns which can be regarded as a spatial fingerprint of the specimen. For a proof-of-principle experiment MCF7 and SKBR3 breast cancer cells were pipetted on gold-coated silica slides. From illuminating each single cell and measuring a diffraction pattern we could distinguish between them. Owing to the short bursts of coherent soft x-ray light, one could also image temporal changes of the specimen, i.e. studying changes upon drug application once the desired specimen is found by the classification method. Using a more powerful laser, even classifying circulating tumor cells (CTC) at a high throughput seems possible. This lab-sized equipment will allow fast classification of any kind of cells, bacteria or even viruses in the near future.
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Michael Zürch, Stefan Foertsch, Mark Matzas, Katharina Pachmann, Rainer Kuth, Christian Spielmann, "Apparatus and fast method for cancer cell classification based on high harmonic coherent diffraction imaging in reflection geometry", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90331O (19 March 2014); doi: 10.1117/12.2043285; https://doi.org/10.1117/12.2043285
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