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

CellProfiler & Ilastik: Superpowered Segmentation

Joining forces

CellProfiler is capable of accurate and reliable segmentation of cells by utilizing a broad collection of classical image processing methods. Peruse the documentation on the IdentifyPrimaryObjects module, for example, to get a sense of these, e.g., thresholding, declumping, and watershed. However, despite the many problems CellProfiler can readily solve, certain types of images are particularly challenging. For instance, when the biologically relevant objects are defined more by texture and context than raw intensity many classical image processing techiques can be foiled; DIC images of cells are a common biological example.

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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

Making it easier to run image analysis in the cloud: announcing Distributed-CellProfiler

There’s nothing more exciting than getting back a big batch of data from your automated microscope – finally, you have the results of your screen, your timelapse, or whatever you’ve spent the last weeks or months preparing.  That excitement can turn to sadness quickly though when you realize that neither your laptop nor the old general-use computer in the lab are up to analyzing thousands (or tens of thousands, or hundreds of thousands!) of images.  But, congratulations! You’ve reached an elite level of CellProfiler users when you outgrow processing on a single local computer. 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