Figure 1: Image-Based Segmentation
Figure 2: Illustration of Contour Types
- Easy-to-Use Tabs to Quickly Set Up Data Runs
- Supports iCyte Imaging Cytometers
- Virtual Channels
- Event Data
- Well Features
- Data Reanalysis
- Repetitive Scanning
- File Merging
The iGeneration Cytometric Analysis Software allows users to configure application-specific protocols, define a carrier, and analyze the resulting data. The easy-to-use tabs allow the user to quickly and effortlessly set up experimental runs and set parameters. Using the software, it is possible to choose the lasers, laser powers, and channels being used. Users can also create virtual channels, define segmentation contours, define areas or wells to scan on a carrier, and set up complex or multiple carrier runs. In addition to the basic iGen software, we offer iBrowser Data Integration (see iBrowser tab) Software and iNovator Application Development Toolkit (see iNovator tab).
Quantitative data analysis starts with identifying events, a process similar to thresholding in flow cytometry. Values rising above the threshold value are used to identify discrete events for quantification (see Figure 1). Once the threshold level is established, the iGeneration cytometric software draws a contour around the "events".
Four types of contours may be drawn (see Figure 2):
- The threshold contour (solid red) defines the edge of the event at the threshold limit.
- The integration contour (dotted green) is a user-defined number of pixels outside of the threshold contour, ensuring that the full signal is measured. The feature data is generally based on this contour.
- The background contours (dashed pink) define an annular area around the event and are used to “background-correct” those features based on the integration contour by subtracting the mean background value from the integration contour feature value.
- The peripheral contour (dashed and dotted yellow) is used to sample and quantify signal from an area external to the integration contour.
Event Data may be displayed in multiple methods such as scattergrams, histograms, and expression maps (2-parameter frequency distribution plots, see Figure 3). Gating regions can be drawn on these graphs to isolate populations. Any event features (or ratios of event features) can be displayed in these graphs. Statistics of event features or their sub-populations may be displayed in a statistics table. Detailed Event feature information about a particular event may be displayed by selecting the event (or set of events) from a graph.
Figure 3: Sample of Event Data
In cases where the sample is not conducive to establishing boundaries between individual cellular events — such as with confluent adherent cellular samples or tumorous tissue samples — random sampling may be applied as an alternative strategy to generate quantitative expression data. Circular sampling elements of user-defined size and frequency are overlaid on the images in either a grid or random orientation. Data is reported on a per-area basis rather than a per-cell basis (see Figure 4).
Random sampling can be performed simultaneously with event segmentation so that a comparison may be made. These methods typically yield very similar results on samples conducive to event segmentation. Dissimilar results are usually an indication that one or both of the analysis strategies are flawed in some way; hence, this feature can be used as an internal quality control measure for an application.
Well Features are summary statistics derived from the event features on a per-well or per-tissue-array element basis. They can be calculated on the entire population of events or on any gated sub-population. When working with microplates, the Well Feature values can be displayed as color-coded expression levels during and after a cytometery run.
Figure 4: Random Sampling Elements Sample all Markers Across the Entire Tissue
Storage options allow saving all of the image data as a raw data image file for each of the selected hardware channels during scanning. Analysis parameters not directly related to data acquisition (such as the PMT or scatter sensor settings) can then be modified and the existing images reanalyzed using the new parameters. As a result, once data from a specimen has been acquired at optimum settings, the analysis itself (selecting threshold levels, gallery images, etc.) may be optimized by simply reanalyzing the raw data files, without rescanning the actual specimen.
iGen can be set to perform Repetitive Scans or Rescans of a given scan area, either for observing specimens at time points separated by significant time intervals or for manipulating the specimen (such as restaining or incubating) between scans.
iGen provides the ability to merge image data files so that the hardware channels from two separate runs may be treated as though they are from a single run. Segmentation may be done on a channel from either run (or both runs in the case of sub-contours or the iNovator). Virtual Channels may be defined utilizing constituents from either run.
|Area||The area of the event in μm2 based on the integration contour|
|Perimeter||The perimeter of the event in μm based on the integration contour|
|Circularity||A measure of the "roundness" of an event, calculated as the ratio Perimeter2:Area. A perfectly round event will have a Circularity of 4π.|
|Single-Level Related Features (Generated for Each Channel)|
|Integral||The sum of the pixel values within the integration contour. Integral is typically used to assess the total amount of target of interest in the event. In cell cycle analysis, the integral of the DNA-intercalating dye channel is a measure of the total DNA content of the cell.|
|Max Pixel||The value of the brightest pixel within the integration contour. In cell cycle analysis, the Max Pixel of the DNA-intercalating dye channel provides an indication of the state of chromatin condensation and is used to distinguish mitotic from G2 cells.|
|Intensity||The average pixel values for a given channel within the integration contour. Intensity is the ratio Integral:Area, essentially normalizing the signal level to the area.|
|Peripheral Integral (Max)||These features are analogous to their counterparts above, for the area enclosed by the peripheral contour.|
|Sub-contour Integral (Max)||These features are analogous to their counterparts above, for the sum of the associated sub-contour events.|
|Location and Relational Features|
|XY Position||The coordinate positions of the events on the sample carrier|
|Parent ID||The identification number of an event to which a sub-event belongs. This gives visibility to the association of sub- and parent events and allows plotting and segregating sub-events by their parents.|
|Scan Position||The position along the scan line at which an event lies. This is most frequently utilized in quality control procedures or when troubleshooting the system's response along the scan.|
|Time||The time at which data for an event was scanned. This is utilized in multi-pass or repeat scans.|
Figure 1: Use of a ridge-enhancement filter and seeded watershed in segmenting cell membranes. (A) Raw image of a DAB-stained cell membrane. (B) The outcome of applying the Frangi vesselness filter. (C) Segmentation results using the image in B and the seeded watershed technique. (D) Membrane contours overlaid on the raw image in A.
- Employ Advanced Image Processing Tools to the Segmentation and Data Analysis Process
- Control Segmentation and Data Analysis Process with Visually Oriented Macros
- Perform Multi-Scale Scanning and Analysis
- Simple and Intuitive Features:
- iNovator Workspace
- Image Processing Tools
- Multiple Analysis Paths and Components
- Two Scan Types and Two Sequenced Scan Passes
- iNovator Virtual Channels
- Tissue Microarray Scanning
iNovator Application Development Toolkit
The iNovator Application Development Toolkit, an advanced software option, provides a variety of image analysis and processing tools for image enhancement, noise removal, and segmentation.
The iNovator workspace provides multiple modules that control the various functions of data acquisition, image processing, segmentation, and event generation. By inserting these modules into the macro workspace and connecting them together, analysis pathways can be built to control the scanning and data acquisition process.
Image Processing Tools
Image filters are used to accentuate details in captured images. The image filters available in iNovator include High Gauss, High Pass, Low Pass, Vertical Edge, and Laplace (see Figure 1). These filters can emphasize certain details in the images, including partial deconvolution of the laser beam spread function. Morphological filters allow images to be more clearly visualized by turning pixels on and off according to filtering criteria. Morphological filters include erosion, dilation, opening, and closing. Finally, watershed segmentation uses Euclidian distance maps to separate closely spaced or overlapping events.
Muliple Analysis Paths and Components
Multiple analysis paths may be defined within a given macro to simultaneously contour on virtually any number of different event types or components. The iNovator workspace displays multiple paths that use different contouring criteria. For instance, if investigating green and orange fluorescence simultaneously, iNovator can display two paths. The first path contours on the Green fluorescence channel and the second path contours on the Orange channel using a different threshold. Different event components can be related to one another by using an inclusion module within the macro. For example, if an event contour of component type A is included in the event contour of component type B, then a relationship can be established between the two.
Two Scan Types and Two Sequenced Scan Passes
Not only can iNovator define multiple event components built on different contouring criteria and relate these components together, but different types of scans can be defined and sequenced within a given macro. The traditional scan, termed FieldScan in the iNovator, can be run at any resolution from 0.25 μm to 20 μm at 0.05 μm intervals. The MosaicScan, available only with the iNovator, has the same resolution options as the FieldScan with some additional options. MosaicScan first stitches together all of the contiguous scan fields in a given scan area and then segments and contours on the resultant mosaic image. This method of contouring enables the generation of contours on events that cross scan field boundaries. This technique produces more comprehensive event generation, since events bordering the scan field boundaries are now contoured. It also enables contouring on structures larger than the scan field, such as cell colonies, tissue structures, and tissue sections.
- Access, Review, and Report on Quantitative and Image Data from Multiple Samples and Carriers
- Summary Data
- Carrier Data
- Well Data
- Reports iGen System Findings
iBrowser® Data Integration Software
With the iGeneration applications, various types of data files, both numerical and image, can be defined and saved. Each type of data element may have several entities, and our iGen systems can obtain a complete set of the data elements for all the samples in an analysis, such as wells in a microtiter plate analysis or core samples in a tissue microarray. iBrowser® Data Integration Software provides a convenient method to access, review, and report on data.
Displaying Summary Data
The iBrowser software displays summary data for a specific run, including annotations for that run, the run name and ID, and a table of the well features for all wells or scan areas acquired during that run (see Figure 1). iBrowser displays the experimental annotations as well as the actual Well Feature data.
Figure 1: Viewing Results in the iBrowser® Data Integration Software
Displaying Carrier Data
Carrier data is data that is relevant to an experiment as a whole. iBrowser displays each available element in one of a series of tabs. Within each tab, the data element is laid out to approximate the physical arrangement of the wells or scan areas for the carrier used for that run [e.g., scan images (shown in CompuColor) and histograms]. It then provides a modified form of the Kolmogorov-Smirnov test in which a group of control wells is defined. The test compares each of the histograms in the data set to the control value and generates a new D-value histogram. Data generated on multiple carriers within a single run (using the iCyte robot) may be displayed one of two ways. Data may be displayed on a carrier-by-carrier basis, with the user selecting which carrier’s data to display for each data tab, or all the wells or scan areas may be displayed together. This latter option allows comparisons between wells of different carriers.
Displaying Well Data
Any of the Well Features generated in iGen systems can be graphed or displayed with histograms, scattergrams, or as 2-parameter histograms (i.e., expression maps). A group of wells or a scan area may be defined so that specified statistics may be applied to data from the entire group. Groups may be substituted for individual wells in the well-data display such that graphical data of an average of the group members (as opposed to a single well) is shown.
Reporting iCyte Findings
iBrowser can generate reports from any tab (see Figure 2). Additionally, using the Well Report feature, a report can be generated that contains multiple elements for a single well, such as well images, galleries, and scattergrams. Reports can be printed, saved as images, or saved into PDF files.