For those of you who’ve been with us for a long time though, the obvious next question after how to use the new test mode is will my old CellProfiler pipelines work in the new version? We feel the same way – the pipelines you’ve accumulated over the years are precious resources! The good and bad news is that the answer is Yes, mostly. In order to facilitate the speedup and continue the process of streamlining the code, a few things had to go; we also removed some things we felt were causing “option fatigue” for the sake of user friendliness going forward.
As described in our lab’s recent review article, there are so many great reasons to use microscopy images to create signatures of perturbations: identifying phenotypes associated with disease, identifying chemical mechanisms of action, and discovering gene functions, among others. Have you wanted to give this approach a try, but been overwhelmed by the computational options available? Continue reading
Sharing Image Data
At the Imaging Platform we frequently need to send and receive images between collaborators or forum users. Working with images can become challenging simply due to their file size. Memory and disk limitations are often irritants when analyzing images, but in some cases, such as multi-dimensional images and whole-slide scans, images’ file sizes can be large enough that even accessing images becomes a major roadblock, especially when the size starts to rival the size of desktop or laptop hard drives. Continue reading
This post was written by a guest author, Kun-Hsing Yu, who can be reached at Kun-Hsing_Yu@hms.harvard.edu.
Lung cancer causes more than 1.4 million deaths per year. To diagnose lung cancer, pathologists prepare microscopic slides from surgical or biopsy samples, stain them with appropriate chemicals, and observe the visual patterns of cell morphology under the microscope. This manual (and often laborious) approach is the gold standard for lung cancer diagnosis and distinguished lung cancer subtypes. Continue reading
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.
Now that CellProfiler can handle 3D images, how do you view the results? When processing 2D images, the IdentifyPrimaryObjects module will produce a figure that displays the original image next to the segmented image to make validation fast and convenient, a side-by-side comparison. However, the extra dimension in 3D complicates this approach, because the 3D image must be transformed in some manner in order to appear on a flat surface (e.g. the monitor or phone you’re using to read this blog post!). This blog post contains advice on how to look at 3D images and the 3D segmentation image produced by CellProfiler and introduces orthoviews in FIJI. If you would like to explore the example used in this post please check out the demo posted on the CellProfiler forum. Continue reading
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