Examples

Basic Pipelines

• Human cells: Human HT29 cells are fairly smooth and elliptical. This pipeline demonstrates how to accurately identify these cells and how to measurements cellular parameters such as morphology, count, intensity and texture.
  [Download] (0.3 MB)

• Fruit fly cells: In comtrast to the HT29 cells, Drosophila Kc167 cells are a highly textured and clumpy cell type. This pipeline demonstrates how to identify these clumpy cells and obtain morphological, intensity and texture measurements.
  [Download] (4 MB)

• Tumors: A simple pipeline that identifies and counts tumors in a mouse lung, and then measures their size.
  [Download] (0.9 MB)

• Comet assay This is a simple example of a DNA damage assay using single cell gel electrophoresis. Here, the measurement of interest is the length and intensity of the comet tail. Also, illumination correction is used to reduce background flourescence prior to measurement.
  [Download] (0.4 MB)

Specialized pipelines

• Cell/particle counting, and scoring the percentage of stained objects: CellProfiler is commonly used to count cells or other objects as well as percent-positives, by measuring the per-cell staining intensity. This pipeline shows how to do both of these tasks, and demonstrates how various modules may be used to accomplish the same result.
  [Download] (0.2 MB)
  

• Yeast colony classification: This pipeline demonstrates how to classify and count objects on the basis of their measured features. The example identifies uniformly round objects, in this case, yeast colonies growing on a dish. The pipeline also shows how to load a template and align it to a cropped image, as well as how to use illumination correction to subtract for background illumination.
  [Download] (0.2 MB)
  [Tutorial]

• Yeast patch identification: This pipeline identifies patches of yeast growing in a 96 well plate, serving as an introduction to the grid defintion and identification modules.
  [Download] (0.4 MB)

• Tissue Neighbors: Tissue samples often have irregularly shaped cells with adjacent edges. This pipeline shows how to input a color tissue image, split it into its component channels, and then identify individual cells from a particular stain and record the number of neighbors that each cell has.
  [Download] (0.1 MB)

• Wound Healing: In this example, cells are grown as a tissue monolayer. Rather than identifying individual cells, this pipeline quantifies the area occupied by the tissue sample.
  [Download] (1.1 MB)

• Illumination Correction: Illumination correction is often important for both accurate segmentation and for intensity measurements. This example shows how the CorrectIlluminationCalculate and CorrectIlluminationApply modules are used to compensate for the non-uniformities in illumination often present in microscopy images.
  [Download] (14.9 MB)
[Tutorial]

Pipelines using the WormToolbox

• Untangle worms: In this pipeline we identify individual worms and extract shape and intensity measurements. Each step of the pipeline is annotated to describe function and settings. Worm untangling requires a worm model, and such a model is provided together with the pipeline. If adjusting the pipeline to fit your own data, worm detection will likely improve by creating a new worm model based on your own image data and following the steps of the sample pipeline below. More sample images and information on the image data can be found on the BBBC.
  [Download] (1.3 MB)

• Straighten worms and extract intensity measurements using a low-resolution atlas: Once worms are untangled, they can be straightened and aligned with a low-resolution worm atlas to extract localized intensity measurements and compare patterns of reporter signals. This pipeline describes the different steps and provides a sample image. It also includes steps for identifying secondary objects (fluorescent marker signals) and relating these objects to individual worms, enabling count of signals on a per worm basis. More data can be found on the BBBC.
  [Download] (968 KB)

• Create your own worm model: The UntangleWorms module has an "Untangle" mode and a "Train" mode. This pipeline describes how the "Train" mode is used to create worm model. Training consists of providing a large number of images of worms that are representative of the worm variation within the population, and that do not touch or overlap. The selection of worms can either be semi-automatic (visually verifying each worm added to the training set) or fully automated (selecting single worms based on size). This example pipeline includes only two images and will not result in a good model, as it will not be representative of all possible variations of the worm shapes. We recommend using at least 60 worms to create a model. More data, for creating a better model, can be found on the BBBC.
  [Download] (1.6 MB)

• Untangle worms and make measurements bright-field staining pattern phenotype: This pipeline detects individual worms by worm untangling and finds sub-objects (fatty regions stained with oil red O) within the worms. Using bright-field data only, it detects fatty regions by intensity thresholding in a single image channel and relates the fatty regions to individual worms. This enables detection of rare phenotypes in heterogeneous populations, phenotypes that would be missed if population averages were observed. More data can be found on the BBBC.
  [Download] (852 KB)

More Advanced Pipelines

• Human cytoplasm-nucleus translocation assay (SBS Vitra): In this human cytoplasm-nucleus translocation assay, learn how to load a previously calculated illumination correction function for two separate channels, measure protein content in the nucleus and cytoplasm, and calculate the ratio as a measure of translocation. This is a clumpy cell type, so studying the settings in primary object identification may be helpful for users interested in the more advanced options that module offers. More about these images can be found at the BBBC.
  [Download] (13 MB)

• Human cytoplasm-nucleus translocation assay (SBS Bioimage): This example includes an advanced example of illumination correction - creating an illumination correction function from all images in a 96-well plate. This pipeline also demonstrates how to load dosage information via the LoadData module, how to use advanced methods for primary and seecondary object identifcation, and how to calculate the Z' factor, a measure of assay quality. More about these images can be found at the BBBC.
  [Download] (40 MB)
  [Tutorial]

• Speckle Counting: This pipeline shows how to identify smaller objects (foci) within larger objects (nuclei) and how to use the Relate module to establish a relationship between the two as well as perform per-object aggregate measurements (such as number of foci per nucleus).
  [Download] (3 MB)

• Object Tracking and Metadata Management: This example shows an example of object tracking. This pipeline analyzes a time-lapse experiment to identify the cells and track them from frame to frame, which is challenging since the cells are also moving. In addition, this pipeline also extracts metadata from the filename and uses groups the images by metadata in order to independently process several sequences of images and output the measurements of each.
  [Download] (10 MB)

• Sequencing RNA molecules in situ combining CellProfiler with ImageJ plugins. Some image analysis algorithms, such as more advanced image alignment, are not available in CellProfiler. However, functions available as ImageJ plugins can be called from CellProfiler. This pipeline shows how images from subsequent base-calling cycles can be aligned using ImageJ plugins from for RNA sequencing in situ.
  Sequencing substrates are generated using gap-fill padlock probes and rolling circle amplification, followed by the sequencing-by-ligation chemistry (manuscript submitted). Note that additional ImageJ plugins are required and can be downloaded as described in the included README file. MATLAB scripts for sequence visualization are also included in the ZIP file.
  [Download] (45 MB)

File Utilities

• Invert For Printing: Inverts RGB fluorescence microscopy images from a black background to a white background.
  [Download] (0.2 MB)

• Color To Gray Demonstrates how to separate a color image image into its component channels, and how to combine grayscale channel images into an RGB color image.
  [Download] (0.8 MB)

• Convert Image File Format Loads image and saves them in a different file format.
  [Download] (0.9 MB)