What makes the CQ1 the primary choice for 3D imaging and analysis?
Even though it is all just “looking at cells” (as per our theme), there are some fundamental differences when you move from standard (or 2D) high content imaging to 3D on spheroids and organoids. Three crucial points are worth considering here, concerning image quality, imaging speed, and data analysis. And they were when the CQ1 was first designed.
While the traditional HC-work deals with a quite transparent cellular monolayer with a maximum thickness of a few dozen micrometers, in 3D the objects of observation can be quite chunky and have diameters of up to 1 mm. It does not take much imagination that imaging these can be challenging from the illumination and transparency side.
Furthermore, as the imaging of thick objects is done by serial images of a large number of z-layers, it is obvious that image acquisition speed can become an issue. Specifically, when time-lapse experiments on these objects are done, any given time point for the whole object should not stretch out for too long in order to avoid blurring of the composed overall image.
Finally, these multi-z-stacked campaigns generate a lot of images which need to be sensibly processed to give the desired information on the location of individual signals in the cell conglomerate. Ideally these analyses are automated and do not require too much manual work, let alone scripting protocols.
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The CQ1 was designed with all these requirements in mind.
The illumination section is equipped with solid state lasers of adequate power. Just simply because if there´s no excitation light reaching to an inner cell in such a clump, obviously there will be little fluorescence and hence no proper image. The microlense enhanced spinning disc with its extra light focusing capability adds further to achieving the necessary image quality.
The big surface chip in the CQ1 camera and Yokogawa´s general engineering of the instrument makes it the fastest one-camera system in the market and hence the most economic option when it comes to decent 3D imaging.
And finally, the data analysis package CellPathFinder has been designed to process large amounts of image data with ease and speed and turn these raw data into properly visualised results.
It may seem trivial, but last and certainly not least: as adequate as they may be for 3D work, none of these features are wasted on good old traditional high content work on 2D cell layers, of course.
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A short selection of literature references:
Klemm, F., Möckl, A., Salamero-Boix, A. et al. Compensatory CSF2-driven macrophage activation promotes adaptive resistance to CSF1R inhibition in breast-to-brain metastasis. Nat Cancer 2, 1086–1101 (2021). https://doi.org/10.1038/s43018-021-00254-0
Title AC, Karsai M, Mir-Coll J, Grining ÖY, Rufer C, Sonntag S, Forschler F, Jawurek S, Klein T, Yesildag B. Evaluation of the Effects of Harmine on β-cell Function and Proliferation in Standardized Human Islets Using 3D High-Content Confocal Imaging and Automated Analysis. Front Endocrinol (Lausanne). 2022 Jul 4;13:854094. doi: 10.3389/fendo.2022.854094. PMID: 35860702; PMCID: PMC9289187.
Vulin, M., Jehanno, C., Sethi, A. et al. A high-throughput drug screen reveals means to differentiate triple-negative breast cancer. Oncogene 41, 4459–4473 (2022). https://doi.org/10.1038/s41388-022-02429-0
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