Help! Why do my output images seem all black?

Double clicking on the output images produced by CellProfiler sometimes opens up a screen in your operating system’s default image viewer that looks all black. This can make it seem like your pipeline didn’t work or didn’t produce the right output. However, this can happen for a couple of reasons:

(a) If you’re exporting objects and have only a few objects in your image
(b) If you’re exporting 16-bit images

Continue reading

Be a histology hero with CellProfiler

Thanks to the rapid advancement in image processing, we now have so many techniques to characterize cellular and subcellular objects (hooray CellProfiler!) Measuring cultured cells in monolayers is (usually) easy…but what about examining how cells interact with each other and their surroundings? Such experiments are often conducted using highly confluent cell cultures, tissue sections, or densely cell-packed organoids. At this level, clusters of cells gather, tightly bind and overlap to form cell niches, and often in a single area multiple clusters of various differentiated cell types can be found with different morphologies and functions. Recognizing and profiling individual cells can be very challenging under these circumstances. Continue reading

Help! Why does CellProfiler say it can’t find any valid image sets?

Defining the input to CellProfiler can be the hardest part of getting your pipeline set up and your analysis underway.  Incoming images are configured in the first 4 modules of CellProfiler – Images, Metadata, NamesAndTypes, and Groups – which offer lots of flexibility. But it’s sometimes confusing what each one does, and it’s not always obvious which ones you need for your experiment. Continue reading

Looking for the Unexpected: Unbiased Image Analysis

The Cell Painting assay (six stains that label eight cellular components, imaged in five channels)

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