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 →
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
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 →
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 →
It can be confusing when you’re trying to set up your first pipeline to figure out which modules to use to generate your objects! A helpful way to understand the difference between Identifying Primary, Secondary, and Tertiary objects: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 →