Vala Sciences

Count Muscle Fibers Positive for a Particular Myosin Isoform

2009-11-30T19:13:17Z

The Image: Example Monkey Muscle Section

This example using an image from monkey muscle images (courtesy Tatiana Kostrominova). This is an RGB images in which the laminin is visualized in the green channel, and the myosin isoform is visualized in the red channel. There is no information in the blue channel, but that’s ok. This figure is a screenshot of the image loaded into CyteSeer.

Setting Up the Automated Skeletal Muscle Image Segmentation Algorithm

All of the images from the monkey data set in the same directory, so there are multiple lines in the “Wells to run Algorithm On” field (A01, A02, etc.). These files are named using the Vala “Generic naming” convention (Monkey_A01f01d1.tif). Though this can be useful for organization, files don't have to be named like this. CyteSeer recognizes most common formats and names tif, jpg, gif etc. via the “Free Form RGB Image” choice from the drop down choices under “Naming Conventions” in the main menu.

CyteSeer’s main menu. See that I’ve chosen the “Skeletal Muscle Algorithm” in the “Algorithm to run” field. The "Results Folder " will be set automatically but this can be set to any folder. This example uses “single” images from a sample, as opposed to the “tiled” images we get from well plates, when run on an automated system. Make sure that the “Images Within Well” “Images Across” = 1, and “Images Down” =1, and the “Computational Cluster Size” is also set to “Images Across” =1, and “Images down” =1.

Importantly, when you chose the Skeletal Muscle algorithm, CyteSeer immediately assumes that you may wish to analyze both fiber size, utilizing the fiber outlines, and the expression of a protein of interest. There are two fields to correctly assign in the “Channel Folder” column (one for “Skeletal Muscle” – the top choice), and one for the channel which contains the information relevant to the protein of interest (Protein 1). Since your images use the green channel for the fiber outline, I chose the green channel from the “pull down” menu within the Channel Folder field for the “Skeletal Muscle” line. Since the myosin is visualized on the red channel, I chose the “red channel” option for protein 1. For this example, I selected just chose one of the images to analyze (the top image, labeled A01). I pressed the “run” button, and CyteSeer analyzed the image in less than 10 seconds.

How did CyteSeer Perform?

Analysis of the fibers by CyteSeer for the first monkey image. Notice on the “zoomed” portion of the figure, on the right panel, that fiber #130 is red and fiber #128 is not.

View Data --> Display Table

OK… so we have the masks… but how to get to the data? I chose the “View Data’ from the main menu. Next, I chose the Well Data Tab to display the large Data Table.

View

Next, I chose just a few of the dozens of data parameters from the menu.

You can do this manually in two steps:
1) Click "Uncheck All" followed by "Update"
2) Search for the measurements you'd like to use via the "Show" checkboxes and click "Update". The measurements displayed are updated in real-time as you type in your search term.

All data tables displayed on this screen are exportable to comma separated values (csv) files with the "Export" button.

Also note the "Cell ID Count:" is 303/303 [100%]. This means CyteSeer has found 303 fibers in this image.

I find creating a custom measurement set via the "All Measurements" a better option for any analysis performed more than once.

Set up a Custom Measurement Set

Custom measurement sets are useful so that you don't have to find your measurements over an over again. Once we select "Ok" on this screen, we'll have a new measurement set available on the "All Measurements" drop-down menu named "Muscle Set 1".

Select "Muscle Set 1" and click "Update".

Analysis

For this example, I've selected:
Area of Fiber Mask (Area Fm): It is the best estimate of the area (in pixels) for the skeletal muscle fiber
Average Pixel Intensity for Protein-1 of the Fiber Mask (API Pi-1 Fm): The myosin is visualized in the red channel

Note that the API for fiber 130 is 212, and fiber 130 was bright red, whereas API for fiber 128 is just 56, which is low. In fact, the value of 56 is about at background – though API is as low as 45 or so for certain fibers. Thus, Fiber #130 would be scored as positive for the myosin isoform, whereas Fiber #128 is negative.

Count the Myosin Positive Fibers

The upper left panel of this data tables window allows you to find just the myosin positive fibers.
To do this:
1) Click the "New" button
2) Name the gate something appropriate. I've chosen "Myosin Positive" here.
3) Select the measurement of interest. In this example, we'll use the same Average Pixel Intensity of Protein-1 Image on Fiber Mask (API Pi-1 Fm)
4) Select greater than ">" 100 as your "Actual Value".
5) Click "Ok" to save the gate

Count the Myosin Positive Fibers

Select this newly created gate and click the "Gated" button. Two thing happen here.
1) The "Cell ID Count:" now reads 83/303 [27.4%]. This is the myosin positive fiber count!
2) The data table displays data from myosin positive fibers only. Note that Fiber #130 is still shown, as expected.

View an Image Gallery of Only Myosin Positive Fibers

The myosin positive fiber count may be the final numeric result but the gates are available in CyteSeer galleries as well.
1) From the main window, goto View --> Data --> Well Data --> Display Cell Gallery
2) Select the "Myosin Positive" gate and click "Gated" to visualize a gallery on myosin positive fibers. The lower left image is fiber #130.

Vala Sciences Contact Information

Vala Sciences is a cell biology company offering software, kits, services and custom development for analyzing a wide variety of cell types and conditions from adipocytes & stem cells to primary & well established cell lines.
Vala Sciences Inc. | Sales and technical information:
858.461.6861 | support@valasciences.com | http://valasciences.com
Links: | CyteSeer free download and trial | Kit ordering Information | Forum | Tutorials | Application Notes
Services: HCS Screening Services, IC 100 Warranty Service and Hardware Upgrades

Many thanks to Tatiana Kostrominova at Indiana University for her invaluable work on this project.

Custom Algorithm Pipelines in CyteSeer

2009-11-06T01:47:18Z

CyteSeer has a very powerful custom algorithm pipeline development capability. It is a strongly typed environment that doesn't require the user to have sophisticated programming skills. It's also built to take maximum advantage of CyteSeer infrastructure and analysis tools. The main goal is to enable our users to focus on a given segmentation problem. Once a pipeline is built, it is immediately available for use with the image viewer, data viewer, gating tool, cell gallery tool etc. The algorithm pipelines are easily shared via a small XML file, run on arbitrarily large sets of images and take advantage of CyteSeer's optimized plugin and multithreaded subsystems.

This example is available via the downloads page at valasciences.com.

Create a New Algorithm From CyteSeer's Main Window

Input Tab

For the Input tab
- Name your pipeline
- Decide how many channels you'd like to analyze
- It is generally a good idea to name the "Size Parameter" and give it a default size in pixels

These are the inputs into your algorithm and will match what is seen on the Main Window.

Sampling of Available Image Processing Plugins

Pipeline Tab

This is where the work gets done. It will take a minute to go through this layout but is the most important page to understand for custom development.

1) The center Pipeline Plugins panel holds the actual steps of the pipeline
2) The left Available Plugins panel hold the list of plugins available for use in a pipeline. Double click add a plugin to your pipeline.
3) The upper left drop down menus help sort the Available Plugins list. e.g. I only want to see plugins that take a gray image as input and labeled mask as output.
4) The upper center Pipeline Inputs panel shows what is available as input to pipeline. If we had chosen 4 channels as input, we would see this list grow accordingly.
5) The right panel is context sensitive to the selected plugin within your pipeline. The plugin will be red until all the connections to the Pipeline Inputs are made. Use the drop lists to match up which input should go where. In this case, InputImage(GrayImage) is matched with "Channel 0 Image".
6) The lower left Pipeline Outputs panel connects the pipeline to the rest of the program. If these aren't connected, the pipeline will run but none of the results will be saved or used anywhere. In this example, we renamed the specific plugin output with the Details button.

Details Button

The Details button is context sensitive for the highlighted plugin. The Long Description and Output Types are used most often. It's often useful to rename the Output Type. Note that the output type will correlate with the Connect Outputs panel of the Pipeline Tab. In this example, SegmentPrimaryWatershed has an output type of LabeledMask that I've renamed Nuclear Mask. Verify connections whenever names are changed.

This connection writes the masks to disk such that CyteSeer can display the masks in the Image Viewer.

Run It Anywhere

That is all we need. Save it and it will be available as an algorithm on the main CyteSeer window.

Pipeline Output Labeled Masks in the Image Viewer

The LabeledMask output type renamed Nuclear Mask works! You can see the masks and cell ID's in the Image Viewer just like any of the other algorithms in CyteSeer.

The good news is that this is a working pipeline. We need to go through a few more steps to add measurements for every cell.

Measure the Cell Masks

The most common workflow when making pipelines is to string together a series of operations that ultimately generates a labeled mask first. The next step is to make measurements on those labeled masks and use Connect Outputs to get the data in the rest of CyteSeer.

The current set of measurements is fairly generalizable for tasks. The current list includes:
Area, Radius, Perimeter, Roundness, Average Pixel Intensity, Median Pixel Intensity, Total Pixel Intensity, Standard Deviation of Pixel Intensity, Skewness of Pixel Intensity, Bounding Box, Centroids, Manders and Pearsons Colocalizations, Droplets, Elongation, Ellipse Axes, Fluorescence Localized in Nucleus (FLIN), Kurtosis of Pixel Intensity, Wiggle (perimeter/area), Normalized Wiggle (perimeter/square root of area) and Order Statistics.

We add new measurements routinely, so please do contact us with requests. It's also possible to extend the architecture with your own jar file.

Connect the Measurements

Here, we've added Measure Area on a LabeledMask to the Pipeline and connected the InputLabeledMask to the Nuclear Mask we created earlier. We also connected the imageMeasurementList to the default "Measurements" input and to an Output Type of ImageDoubleMeasurement.

Measurements Available Everywhere

Save and rerun the analysis. We have numeric data wherever we would expect to see it with any other algorithm.

Add a Second Color

Pipelines can address an arbitrary number of colors in an image. This addresses anything from single marker biology to hyper-spectral image analysis.

In this example we've increased the Number of Channels to 2 and added a Size Parameter, Size, default of 25.

Segment a Generic Cell

We can build on the nuclear segmentation we've created the the SegmentSecondaryGenericCell plugin. Note that I've connected it – via the primary mask – to the output of SegmentPrimaryWatershed. All of the other input connections are to channel 1 now. I renamed the Output Type, added another LabeledMask to the Connect Outputs list and connected it to the Cell Mask. You can now save and rerun this on two color data!

What's Next

You can build much more complex algorithm pipelines in CyteSeer. Our basic segmentation plugins are a powerful place to start but many sophisticated users may want to build their own algorithms from the ground up. There are more example pipelines on the download page of the web. It's worth taking a look.

Vala Sciences Contact Information

Quantify Co-labeling of Two Markers in Primary Astrocyte Cultures

2009-11-05T21:45:35Z

This example uses CyteSeer to quantify co-labeling of two markers in primary astrocyte cultures.

Each cell in the image is automatically found and measured by CyteSeer. These aren't hand traced cell edges. Each cell has a unique identifying number and size, shape, intensity, location and colocalization properties. This is used for routine total cell counts as well counting cells within a given population. The cell-by-cell cytometry architecture of CyteSeer is designed to interrogate real world biological images automatically.

The power of this approach becomes clear as you start to consider the questions that can be answered:
Count red cells. Count green cells. How many cells are transfected and express a specific protein? Of the cells that are green and red, to what extent is the expression colocalized? How do these results change with respect to the cell cycle?
CyteSeer is built from the ground up for just sort of progressively more sophisticated analysis.

Original Astrocyte Image

Easy Set-Up

Simply point CyteSeer to the Images and choose the Astrocyte algorithm. The Astrocytes pipeline is available via the download page. Install it within CyteSeer via the File --> Import Pipeline Algorithm menu item.

CyteSeer Automated Image Analysis

Each cell in the image is automatically found and measured by CyteSeer. These aren't hand traced cell edges.

Cell Counts: Characterize and Quantify Every Cell

Each cell has a unique identifying number and size, shape, intensity, location and colocalization properties.

Use Gating to Group Populations of Cells for Analysis

This example shows that a gate has been applied to show only "Red Cells" . The Cell ID column is no longer a simple 1,2,3,4. Only the "Red Cells" remain. The cell count of gated vs. non-gated populations is reported directly.

Visualize Each Cell

Report Quantitative Results

CyteSeer has broad support for hist

Summary Statistics, Student's T, Z-Prime & Gaussian Z

Vala Sciences Contact Information

For further information on products and ordering information please call or email:

Vala Sciences Inc.:
Sales and technical information:
858.461.6861
support@valasciences.com
http://valasciences.com

Links:
CyteSeer free download and trial
Kit ordering Information
Forum
Tutorials
Application Notes
Services: HCS Screening Services, IC 100 Warranty Service and Hardware Upgrades

CyteSeer is a registered trademark of Vala Sciences Inc. All rights reserved 2005-2009. Many thanks to Paige Cundiff and Alexey Terskikh at the Burnham Institute for Medical Research for their help on this project.

Image Merge with Free Form Naming Conventions

2009-11-02T22:18:26Z

CyteSeer offers two distinct ways to merge images of an arbitrary file name. These naming schemes are the easiest to use because almost any name will work. Please refer to the "Generic Naming Convention" for larger scale experiments with multiple fields of view and color on a per well basis.

Browse to Arbitrary image Set

In this example, the CyteSeer source folder is set to point to a directory with two standard 3 color RGB tiffs. The folder can hold any number of images.

Select Naming Convention

Free Form Gray Images

The Free Form Gray naming convention allows you to select which color of which image to use for analysis. This is used when combining individual wavelengths into one image. It is useful for a fast proof of concept analysis on a single image. The generic naming convention is more useful for analyzing large sets of images at once.

Free Free RGB Image

The free form RGB naming convention assumes that each image represents a given condition. The selection of colors for a given condition is carried out across all images in the directory.

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Vala Forums

2009-11-02T22:07:17Z

Vala's Forums section is another great place to request and find more information about using CyteSeer and other Vala kits in the wild. Please don't hesitate to contact us for any reason if you are having trouble.

Vala Sciences Contact Information

Measuring Caspase 3 Activation Using CyteSeer’s Protein Expression Algorithm

2009-11-02T22:00:30Z

Introduction:

Apoptosis is a programmed series of events that leads to the removal of old, unwanted, or damaged cells from the tissues of multicellular organisms [1]. In contrast to necrotic cell death, which is characterized by loss of membrane potential and the release of cellular contents into the intercellular space, apoptosis is a “clean” death distinguished by the controlled demolition of the cell. During apoptosis the cell is dismantled from within and the cellular contents are dispersed in small membrane bound vesicles which are then taken up by phagocytic cells. This organized dispersal and recycling of the cellular contents prevents the type of local inflammation and damage to surrounding cells associated with necrotic cell death [2]. Apoptosis is critical to the proper development of multicellular organisms and for maintenance of homeostasis in the mature organism. Defects in apoptotic programming are linked to major diseases such as cancer, neurodegenerative diseases, and heart disease [3-6].

Caspases are a family of cysteine proteases that are central to the apoptotic pathway, acting as both signaling molecules and the end effectors of the cell death program [7]. The family is divided into two groups, initiator caspases and effector caspases. The initiator caspases are mediators of apoptotic signaling whereas the effectors are responsible for degradation of the cellular contents. Caspase 3 is the most well studied of the effector caspases and its activation is considered one of the hallmarks of apoptotic cell death. Caspases are transcribed as precursor enzymes or pro-caspases which are activated by proteolytic cleavage. Several manufacturers have produced antibodies which only recognize the active or cleaved form of caspase 3, allowing for identification of apoptosis activation in cell culture and tissue sections by immunofluorescence. Here we demonstrate how it is possible to quantify the level of activated caspase 3 on a cell by cell basis. using CyteSeer's Protein Expression algorithm.

Figure 1: Analysis of Caspase 3 activation using CyteSeer software.

Representative images of control cells (A-C) and cells treated with 1µM staurosporine (D-F) are shown. Cells were identified by the DAPI signal (A&D). Control cells showed very low levels of staining for activated caspase 3 (B), while treatment with 1µM staurosporine produced robust activated caspase 3 staining (E). CyteSeer® applies a mask to areas of the image it recognizes as staining positive for activated Caspase 3 (C,F).

Figure 2

Increasing doses of staurosporine over a three hour incubation result in increased activation of caspase 3 as determined by CyteSeer® using the protein expression algorithm to analyze caspase 3 staining in the experimental images. Each treatment group represents eight wells in a 96 well plate with 400-500 cells analyzed per well. Analysis using Cyteseer® took, on average, less than 5 minutes to complete.

Results:

To initiate the intrinsic apoptotic pathway, HeLa cells plated in a 96 well optical plate were treated with varying concentrations of staurosporine (30nM-1µM) for three hours while control cells were treated with vehicle alone (DMSO). Following the incubation time cells were fixed and stained for activated caspase 3 (Cell Signaling Cat# 9661); DAPI stain was used to identify the nuclei. The plates were then imaged using an automated microscope (Beckman Coulter IC100) and image analysis was performed using Cyteseer’s Protein Expression Algorithm (Figure 1). Treatment with staurosporine resulted in activation of caspase 3 in a dose responsive manner (Figure 2). The data provide a Z’ statistic of 0.506 suggesting that measurement of caspase 3 activation using CyteSeer® could be used as an excellent screening assay with application in cytotoxicity monitoring, the identification of anti-cancer compounds, or the identification of anti-apoptotic molecules.

Conclusion:

The Protein Expression algorithm is just one of the many robust analysis tools contained within Cyteseer®. Here we demonstrated that the Protein Expression algorithm can be applied to apoptosis research in the analysis of caspase 3 activation. In these experiments we used a 96 well plate format and automated microscopy, however, CyteSeer is flexible and can perform similar comparative analyses on a few images collected from individual slides or can be applied to screens in larger plate formats. The excellent Z’ score achieved in these experiments suggests that staining for active caspase 3 could readily be applied to larger screens of compound libraries for cytotoxicity, anti-cancer, or anti-apoptotic properties.

References:

1.    Taylor, R.C., S.P. Cullen, and S.J. Martin, Apoptosis: controlled demolition at the cellular level. Nat Rev Mol Cell Biol, 2008. 9(3): p. 231-41.
2.    Rock, K.L. and H. Kono, The inflammatory response to cell death. Annu Rev Pathol, 2008. 3: p. 99-126.
3.    Eckert, A., et al., Increased apoptotic cell death in sporadic and genetic Alzheimer's disease. Ann N Y Acad Sci, 2003. 1010: p. 604-9.
4.    Faubel, S. and C.L. Edelstein, Caspases as drug targets in ischemic organ injury. Curr Drug Targets Immune Endocr Metabol Disord, 2005. 5(3): p. 269-87.
5.    Hamacher-Brady, A., N.R. Brady, and R.A. Gottlieb, The interplay between pro-death and pro-survival signaling pathways in myocardial ischemia/reperfusion injury: apoptosis meets autophagy. Cardiovasc Drugs Ther, 2006. 20(6): p. 445-62.
6.    Hunter, A.M., E.C. LaCasse, and R.G. Korneluk, The inhibitors of apoptosis (IAPs) as cancer targets. Apoptosis, 2007. 12(9): p. 1543-68.
7.    Li, J. and J. Yuan, Caspases in apoptosis and beyond. Oncogene, 2008. 27(48): p. 6194-206.

Vala Sciences Contact Information

IC 100 Environmental Control Upgrade Kit

2009-10-27T18:47:01Z

Upgrade your IC 100 to support precise environmental control for lice cell imaging!

CyteSeer 2 Minute Intro Video

2009-10-23T16:54:19Z

A 2 Minute Overview Video CyteSeer Image Cytometry Software

CyteSeer Image Cytometry Software works on images from any microscope system.
CyteSeer requires no drawing or tracing to identify, count and make measurements on every cell in every image.
CyteSeer combines gating concepts from traditional cytometry and plate readers.
CyteSeer measures and counts each cell based on the size, shape, intensity and colocalization visible in each image to make well defined populations of cells.
CyteSeer produces statistical comparisons across images, cells, slides, wells and plates.

CyteSeer 2.0 Release!

2009-08-27T21:49:55Z

We've just released CyteSeer 2.0 and are really excited about the big step forward in power and usability. Please take a minute to install the free download and let us know what you think.

CyteSeer 2.0 Release!

We've just released CyteSeer 2.0 and are really excited about the big step forward in power and usability. Please take a minute to install the free download and let us know what you think.

What's New?
• Much easier file navigation and measurement search
• Integrated data, cell image gallery and statistics viewer with plate map support and gating tools integrated throughout
• Updated algorithms for skeletal muscle, nuclear ploidy, lipid droplets, protein expression, co-localization, in situ hybridization and more
• Create and share custom algorithms and plug-ins with other CyteSeer users
• Broader support for bright-field and fluorescence image sets
• Launched new support forum
• Visit Vala's Articles section for more detailed application notes and step-by-step tutorials

Licensing Details:
• Free to install and use as an image & data reader
• Free 30-Day full featured demo - including the image processing engine
• CyteSeer 2.0 licenses are available at US$4595 per workstation via major credit card or purchase order at 1-888-742-5252 (VALA) or .(JavaScript must be enabled to view this email address). Volume and academic discounts available.
• If you have a valid paid or demo license for CyteSeer 1.x, CyteSeer 2.0 is a free update!

Operating Systems:
• Windows XP, Vista, 2003 Server, NT
• Mac Os 10.4+
• Linux Kernel 5.3+

Vala Sciences' technologies enable information-rich measurements for making fundamentally new insights into the cell functions critical to progress in the biological sciences.

RNA Spots Measurement Reference

2009-08-19T05:17:08Z

RNA In Situ Hybridization spot measurements for use with the Affymetrix/Panomics ViewRNA Kits. This algorithm will perform well as a general FISH analysis tool.

RNA Spots

Short Name    <-------------->    Long Name
Cell ID    <-------------->    ID of each record
1 Well    <-------------->    Name of the well
2 RNA-1 Spot Count    <-------------->    Number of RNA-1 Spots
3 RNA-1 Area    <-------------->    RNA-1 Area
4 RNA-1 Intensity    <-------------->    Total Integrated Intensity of RNA-1
5 RNA-1 Nuclear Spot Count    <-------------->    Number of RNA-1 Spots in Nucleus
6 RNA-1 Nuclear Spot Count Density    <-------------->    RNA-1 Spot Density in Nucleus
7 RNA-1 Area in Nucleus    <-------------->    RNA-1 Area in Nucleus
8 RNA-1 Area Density in Nucleus    <-------------->    RNA-1 Area Density in Nucleus
9 RNA-1 Intensity in Nucleus    <-------------->    Total Integrated Intensity of RNA-1 in Nucleus
10 RNA-1 Intensity Fraction in Nucleus    <-------------->    RNA-1 Intensity in Nucleus as Ratio of Nuclear Intensity
11 RNA-2 Spot Count    <-------------->    Number of RNA-2 Spots
12 RNA-2 Area    <-------------->    RNA-2 Area
13 RNA-2 Intensity    <-------------->    Total Integrated Intensity of RNA-2
14 RNA-2 Nuclear Spot Count    <-------------->    Number of RNA-2 Spots in Nucleus
15 RNA-2 Nuclear Spot Count Density    <-------------->    RNA-2 Spot Density in Nucleus
16 RNA-2 Area in Nucleus    <-------------->    RNA-2 Area in Nucleus
17 RNA-2 Area Density in Nucleus    <-------------->    RNA-2 Area Density in Nucleus
18 RNA-2 Intensity in Nucleus    <-------------->    Total Integrated Intensity of RNA-2 in Nucleus
19 RNA-2 Intensity Fraction in Nucleus    <-------------->    RNA-2 Intensity in Nucleus as Ratio of Nuclear Intensity
20 Cell Area    <-------------->    Cell Area
21 Nucleus Area    <-------------->    Nucleus Area
22 Nucleus Intensity    <-------------->    Total Integrated Nucleus Intensity
23 X-Nucleus    <-------------->    X-Position of Nucleus
24 Y-Nucleus    <-------------->    Y-Position of Nucleus
25 Boundary Cell    <-------------->    Boundary Cell