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2023-08-25T05:49:03Z

Created page with "== Citation == Last, Mart G. F. / Voortman, Lenard M. / Sharp, Thomas H. Measuring cryo-TEM sample thickness using reflected light microscopy and machine learning. 2023. J. S..."

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== Citation ==

Last, Mart G. F. / Voortman, Lenard M. / Sharp, Thomas H. Measuring cryo-TEM sample thickness using reflected light microscopy and machine learning. 2023. J. Structural Biology, Vol. 215, No. 2, p. 107965

== Abstract ==

In cryo-transmission electron microscopy (cryo-TEM), sample thickness is one of the most important parameters
that governs image quality. When combining cryo-TEM with other imaging methods, such as light microscopy,
measuring and controlling the sample thickness to ensure suitability of samples becomes even more critical due
to the low throughput of such correlated imaging experiments. Here, we present a method to assess the sample
thickness using reflected light microscopy and machine learning that can be used prior to TEM imaging of a
sample. The method makes use of the thin-film interference effect that is observed when imaging narrow-band
LED light sources reflected by thin samples. By training a neural network to translate such reflection images into
maps of the underlying sample thickness, we are able to accurately predict the thickness of cryo-TEM samples
using a light microscope. We exemplify our approach using mammalian cells grown on TEM grids, and
demonstrate that the thickness predictions are highly similar to the measured sample thickness. The open-source
software described herein, including the neural network and algorithms to generate training datasets, is freely
available at github.com/bionanopatterning/thicknessprediction. With the recent development of in situ
cellular structural biology using cryo-TEM, there is a need for fast and accurate assessment of sample thickness
prior to high-resolution imaging. We anticipate that our method will improve the throughput of this assessment
by providing an alternative method to screening using cryo-TEM. Furthermore, we demonstrate that our method
can be incorporated into correlative imaging workflows to locate intracellular proteins at sites ideal for highresolution
cryo-TEM imaging.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S104784772300028X

== Related software ==

== Related methods ==

== Comments ==

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WikiSysop: Created page with "== Citation == Last, Mart G. F. / Voortman, Lenard M. / Sharp, Thomas H. Measuring cryo-TEM sample thickness using reflected light microscopy and machine learning. 2023. J. S..."