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 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.
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