Vala Sciences

All CyteSeer analyses start by breaking down each image into core biological component masks for relevant cells & tissues. It then aggregates measurements as needed for experiments with many slides, wells, plates etc.

1) First, CyteSeer identifies all of the nuclei available from the nuclear images. A nuclear mask for each cell is established where the mask contains all of the pixels locations automatically identified as nuclear for a given cell.

2) Next, CyteSeer analyzes a second image – a lipid droplet image for example –such that the lipid droplets are assigned to the lipid droplet mask. A rich set of data parameters are then calculated on a “per cell basis”.

3) Third, CyteSeer tries to define the cell boundaries. This varies from assay to assay. It can be done by cell border, cell membrane or – as in this case – by assigning which droplets belong to which cell.

CyteSeer’s functionality is presented to users through three core interfaces.

•    Main window
•    Image Viewer
•    Data Viewer

The main window is what users see when CyteSeer launches. The image viewer and data are accessed from the view drop-down menu but the real goal is the simplicity of this interface. The steps required to analyze a single well, plate or arbitrarily large batch of plates are similar. In the simplest case, users select the appropriate algorithm, set the location of the images to be processed and click the ‘run’ button.

Figure 3 demonstrated how CyteSeer displays how original images from your microscope are visually combined with the masks generated by CyteSeer. The masks are the result of CyteSeer's automated cell analysis engine and all the numeric data are derived from these masks.

In this example, we're looking at a nuclear stain in blue and a lipid droplet stain in green. Masks outlining the edge of the nucleus and lipid droplets are shown in red and green respectively. The yellow lines define the cell regions and show the unique cell ID for each cell that CyteSeer finds.

No image processing is perfect over every cell in every image, but this image viewer allows you to get a feel for how well the current analysis settings are working.

Figure 4 depicts the CyteSeer Data Viewer. Automated image analysis generates a number of measurements for each cell within an image. These data are aggregated and analyzed as required by the experiment within the data viewer.

The numbers derived from the segmentation are then viewed and manipulated through the data viewer. Though hundreds of measurements are routinely available in CyteSeer, a far smaller number are relevant to any one experiment. Under this design principle of ease of use, the data viewer is designed to offer access to measurements of interest without overwhelming users. Users can create and export raw data tables, histograms, scatter plots. All of the data can be filtered through a very powerful gating interface.

Figure 4 depicts the CyteSeer Data Viewer. The numbers derived from the segmentation are then viewed and manipulated through the data viewer. These data are aggregated and analyzed as required.

The simple steps of a routine analysis are:
1) Set up gates as needed. Gating is a very powerful way to optimize many experiments but isn't required.
2) Choose the relevant set of measurements. In this example, we're looking at counting the number of lipid droplets in every cell.
3) Note that a measurement is created for each cell. i.e. Cell ID 5 has 4 lipid droplets.

Though hundreds of measurements are routinely available in CyteSeer, a far smaller number are relevant to any one experiment. The data viewer offers access measurements of interest as needed and all of the data can be filtered through a very powerful gating interface described below. Scientists have complete control of all data and graphs through standard exporting formats.

Figure 6 displays how CyteSeer offers a significant step forward in usability for image based cell gating. The CyteSeer interface is driven by drop down lists and easy to understand buttons. Additional steps within a gate are created by pressing the “+” and removed by pressing the “-“. These details seem obvious once implemented but offer a new level sophisticated analysis to a broader set of scientists.

Gating is used routinely to remove outliers from the image analysis results. When imaging hundreds of cells per well over thousands of wells, it’s important to view results based solely on relevant cells.

As the vast majority of interesting cell biology does not involve homogenous populations of cells, a powerful gating tool helps access this more subtle data.

The data viewer enables inspection of numerical data for each individual cell, well, plate and experiment. CyteSeer is designed to work with arbitrarily large sets of images and stores experimental conditions – i.e. time points or doses – for each well. All the data are stored and can be exported into commonly used text formats (csv, jpeg files).

Figure 6 shows a comparison of gated and ungated cell area from a representative well. The gated histogram on the right shows the same data with 2 percent outliers removed. All the cell, well and experiment data are easily manipulated within the CyteSeer gating, histogram and scatterplot tools. Any combination of ungated or gated numerical cell data can be viewed or exported into common accessible formats.

Figure 7 shows a graphing example from within CyteSeer.
1) Create, update or edit a plate map with information for your experiment. This can be anything that helps describe your data from concentration to time points.
2) Select the condition.
3) Click the measurement to create a bar graph to the right. Note the error bars and average values. Click a new measurement to get a new set of bar graphs.

Data analysis can be a real bottleneck with so much data created for every cell, well and plate. CyteSeer was built with this in mind and enables straightforward comparison of any by time point, dose or any other condition. The data are easily reported by cell or well and all graphs can be exported via common format to Word, Excel, PowerPoint or other program as needed.

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.
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