Not every image you capture on your microscope is suited for quantification, no matter how nice they may look. Even though you might not notice any problems by eye, the tips outlined here for acquiring and storing images can improve the quality of data derived from digital image analysis. These tips are a bit CellProfiler-centric but generally applicable to any quantification you might do.
Biologists are coming up with more and more complex physiologically-relevant assay systems and scaling them up for screens. From co-cultured cells to C. elegans to 3D organoids and tumor spheroids, these assay systems can be challenging, expensive, lower-throughput, and/or rely on materials such as human primary cells that are in short supply.
Might there be a shortcut allowing you to screen a huge chemical library without the expense? Continue reading
So you already know how to put together an image analysis pipeline to measure particular phenotypes of interest? Great!
Have you ever considered looking for the unexpected? Say you are comparing two treatment conditions, such as a negative control vs. a hormone treatment. You may have in mind phenotypes to measure, so you use CellProfiler to accurately quantify them. But did you realize you could also measure everything you can from the images and let the data tell you what distinguishes your two conditions? Continue reading