IN Cell Analyzer 1000 Analysis Modules Multi Target Analysis Training

Transcription

1 IN Cell Analyzer 1000 Analysis Modules Multi Target Analysis Training

2 Multi Target Analysis Neurite Outgrowth Membrane Translocation Analysis Object Intensity Dual Area Object Analysis Granularity Morphology Analysis Nuclear Translocation Micronuclei Cell Cycle Analysis 2 /

3 Multi Target Analysis 3 /

4 Specify protocol name. Generating an analysis protocol. Choose appropriate analysis module (Assay name) e.g. Multi Target Analysis. Define input images, wavelengths and assign objects. Segment objects e.g. define nuclei, cells, organelles, reference. Select basic measures e.g. intensity, area, Nuc / Cell intensity. Summarise measures (basic + filters + classifiers). Introduce simple filter or decision tree filter. or Add supervised classifiers. Define sub-population analysis. Data Management Timelapse / Z stack analysis. 4 /

5 Main Feature differences between Analysis modules are: Number of wavelengths / features that can be analyzed. Simple filters and classification filtering approaches (including decision tree). Sub-population analysis. Output measures. 5 /

6 Number of wavelengths / features that can be analyzed. 6 /

7 Summary of Measures in MTA Nuclei Nuc/Cell Intensity Nuc Area Nuc cg X Nuc cg Y Nuc Elongation Nuc 1/(Form Factor) Nuc Displacement Nuc Intensity Nuc Intensity CV Cell Intensity Compactness Light Flux (relative) Chord ratio Intensity (N+C) Integrated Intensity (Nuc) Integrated Intensity (Cell) Integrated Intensity (Whole Cell) Nuclei coordination Spacing (SOI) Neighbor count (SOI) Spacing (MIN) Neighbor count (MIN) Spacing (Gabriel) Neighbor count (Gabriel) Spacing (Lune) Neighbor count (Lune) Cells Nuc/Cell Intensity Cell/Bckg Intensity Cell Area Nuc/Cell Area Nuc Intensity Nuc Intensity CV Cell Gyration Radius Cell Intensity Cell Elongation Cell 1/(Form Factor) Intensity Spreading Cell cg X Cell cg Y Cell Intensity CV Background Intensity Light Flux (relative) Intensity (N+C) Integrated Intensity (Nuc) Integrated Intensity (Cell) Integrated Intensity (Whole Cell) Organelles Count Spacing Neighbor Count Mean Area 1/(Form Factor) Elongation Distance to Nuc Inclusion/Cell Intensity Intensity Total Area Inclusion/Bckg Intensity Reference Reference measures refer to individual cell measurements made in the Nuclei, Cells or Organelles channels depending on the object selected for Reference segmentation. (Nuclei) Nuc/Cell Intensity Nuc Intensity Nuc Intensity CV Cell Intensity Light Flux (relative) Intensity (N+C) Integrated Intensity (Nuc) Integrated Intensity (Cell) Integrated Intensity (Whole Cell) (Cells) Nuc/Cell Intensity Cell/Bckg Intensity Nuc Intensity Nuc Intensity CV Cell Intensity Intensity Spreading Cell Intensity CV Background Intensity Light Flux (relative) Intensity (N+C) Integrated Intensity (Nuc) Integrated Intensity (Cell) Integrated Intensity (Whole Cell) (Organelles) Inclusion/Cell Intensity Intensity Inclusion/Bckg Intensity 7 /

8 Training: Summary of steps Opening the analysis software and retrieving image data (Chapter 4). Creating an analysis protocol (Chapter 5). Filters and classification (Chapter 6). Using Supervised classification (Chapter 7). Analysing an image stack and using graphical displays (Chapter 8). Using the Batch queue manager (Chapter 10). Additional functions:- Summary Statistics/Movie mode/saving images for presentations Please refer to the Multi Target Analysis Module for IN Cell Analyzer 1000 Product User Manual for further details. 8 /

9 Opening the analysis software and retrieving image data. Creating an analysis protocol. Filters and classification. Analysing an image stack and using graphical displays. Using the Batch queue manager. 9 /

10 Opening the analysis software and retrieving image data: Opening the IN Cell Analyzer workstation launches a start up Wizard that will navigate you through the analysis set-up procedure. Select a working mode. I. Assay development mode - to define a new analysis protocol. II. Analysis mode-to use an existing protocol. 10 /

11 Opening the analysis software and retrieving image data: III. View and analyze image stack. IV. Open a data file (Retrieve data from a previously analyzed stack). 11 /

12 Opening the analysis software and retrieving image data. Creating an analysis protocol. Filters and classification. Analysing an image stack and using graphical displays. Using the Batch queue manager. 12 /

13 Creating a new analysis protocol using the analysis protocol Wizard (can also edit existing protocols via the analysis protocol editor). or Image Stack & Analysis window: edit the analysis parameters, use the Sample function to test the protocol segmentation measurements can easily be extracted from the image, population data can be generated for editing classification filters. Analysis protocol manager: view & edit the analysis parameters, use the Test function to exit the Protocol Editor and test the protocol on an image stack using the Image Stack window. the Image Stack window and the Analysis Protocol Editor cannot be open concurrently. 13 /

14 Options include importing, exporting, deleting, renaming, editing and generating New protocols 14 /

15 Wizard guides user through the following steps:- Enter a protocol name. Password protecting a protocol. Specifying assay name and microscopy type. 15 /

16 Setting the analysis parameters. Parametersdefining input images. Segmentationdefining targets. Prior to setting the analysis parameters the user is advised to optimise image displays. 16 /

17 Visuals Optimisation of image displays using the visuals display tools to clearly discern the features to be identified and measured. LUT Relates intensity to LUT (Y axis) Pixel information Mapping functions Click Visuals Histogram Gray levels (X axis) Color editor Gray levels Autocontrast 17 /

18 Multi Target Analysis worked example Apoptosis. Image stack: _iono_4hr_fav_2.xdce Hoechst used to identify the nuclei of all cells. Channel 1 (Wave 1). Blue. 360/40 460/40. Visuals: FITC-labelled Annexin V conjugate used to identify early apoptotic cells. Channel 2 (Wave 2). Green. 475/20 535/50. Visuals: Propidium iodide used to discriminate dead cells. Channel 3 (Wave 3). Red. 570/20 620/60. Visuals: /

19 Apoptosis image stack Channel 1 (Wave 1) Channel 2 (Wave 2) Channel 3 (Wave 3) Nuclear definition. Identification of early apoptotic cells. Viability definition Hoechst FITC-labelled Annexin V Propidium iodide 19 /

20 Apoptosis image stack Dead, nonviable cell. (B, G & R). Early apoptotic cell. (B & G). U-2 OS cells, 4 hrs incubation with 10 µm Ionomycin. Healthy, viable cell. (B only). Cells stained with 10 µm Hoechst 33342, 5 µm Propidium iodide and FITC-labelled Annexin V conjugate (1:1000) for 10 mins prior to imaging on the IN Cell Analyzer /

21 Segmentation Segmentation is the process of dividing an image into a number of individual objects or contiguous regions, differentiating them from each other and from the image background. For each object type, the software offers one or more segmentation methods. Accurate segmentation is essential for accurate analysis results. 21 /

22 Defining nuclei segmentation Top-hat, Global threshold, Region growing. Nuclei feature. Top-hat segmentation used in this example protocol to identify Hoechst stained nuclei. 22 /

23 Defining cells segmentation Top-hat, Multiscale top-hat, Region growing, Collar. Cells feature. Collar segmentation used in this example protocol to define a Cell region and identify early apoptotic cells. 23 /

24 Defining organelles segmentation Multiscale top-hat. Organelles feature. Not used in this example protocol. 24 /

25 Defining reference segmentation Pseudo (use objects from Nuclei, Cells or Organelles). Reference 1 feature. Pseudo segmentation using objects from Nuclei channel to discriminate dead cells (PI staining). 25 /

26 Measures Specify which basic measures will be acquired from each image. 26 /

27 Measures Specify which basic measures will be acquired from each image. 27 /

28 Filters At this stage, use the Filters Window to define simple filters that identify cells having object measures above or below threshold values. cells having objects that fall within a range of values. 28 /

29 Filters The option to add further filters for classification is available only after a basic analysis protocol has been set up. An initial analysis of sample wells should also be performed to allow the segmentation parameters to be checked and also to provide sample data to allow filter and classification thresholds to be defined. Threshold. Linear Discriminant 2D. Decision tree. Classification filters will be covered later. 29 /

30 Supervised Classifiers Develop analysis protocols with defined classifiers that automatically assign cells to a pre-defined class. The option to use Supervised Classifiers is still available within the Multi Target Analysis module. It is recommended that Supervised Classifiers and the Classification Filters are used independently. 30 /

31 Summary Window Use the Summary window to specify which measures will be included in data summary output files. choose to Include all cells in the analysis or if a filter has been defined, then the options to Include only cells where or to Exclude cells where become available. 31 /

32 Subpopulations Use the subpopulation window to specify which subpopulation measures will be reported in the subpopulation data output. The drop down menu displays the filters available for subpopulation definition. 32 /

33 Time Lapse Specify which time point should be used for analysis. 33 /

34 Z-stack analysis Specify which plane(s) are analyzed. 34 /

35 Data Management Saving data. 35 /

36 Opening the analysis software and retrieving image data. Creating an analysis protocol. Filters and classification. Analysing an image stack and using graphical displays. Using the Batch queue manager. 36 /

37 Filters and classification The ability to filter out objects based on any measure and the classification of cells into user defined populations are two functions that are achieved using the Filters available in the Multi Target Analysis: Filters Window. Filters are: defined using any available measure previously selected in the Multi Target Analysis: Measures window. used to filter or classify cells according to any fluorescence intensity or morphology based measure. available only after a basic analysis has been performed. defined when sample data has been provided. 37 /

38 Classification filters Classification filters available include: - Threshold - Linear Discriminant 2D - Decision tree 38 /

39 Threshold filter A threshold filter is used to divide a population into two subpopulations based on a single measure. Use a threshold filter to define: cells having object measures above or below threshold values. cells having object measures that fall within a range of values. 39 /

40 Threshold filter Example of a threshold filter (not used in this training protocol). 40 /

41 Linear Discriminant 2D A Linear Discriminant 2D filter is used to generate a scatter plot of any two available measures, enabling cells to be classified into up to 4 user defined populations. Use the Linear Discriminant 2D filter to: discriminant distinct sub-populations on the basis of two parameters (e.g. an assay where two different fluorescent dyes are used to mark live and dead cells). 41 /

42 Linear Discriminant 2D filter Example of a linear discriminant 2D filter (not used in this training protocol). 42 /

43 Linear Discriminant 2D filter class definition Specify the number and arrangement of classification areas required in the classification protocol in the class definition window. Option 1 - classification into 2 user-defined populations using 1 linear threshold. Option 2 - classification into 3 user-defined populations (including 1 unclassified) using 2 parallel linear thresholds. 43 /

44 Linear Discriminant 2D filter class definition 2 populations 3 populations Option 3 - classification into 2, 3 or 4 user-defined populations using 2 intersecting linear thresholds. Specify the number of populations (up to 4) and their position on the resulting scatter plot using 4 populations 44 /

45 Decision tree filter A decision tree filter is used to classify cells into multiple populations based on any available measure. The tree is designed to be multi-level and allows the cell population to be divided into two subpopulations at each decision point. The two types of filters, Threshold and Linear Discriminant 2D are available at each decision point for classification and can be used in combination. At each decision point, either one or both populations can then be classified into further subpopulations or can be reported in the Summary data. 45 /

46 Decision tree filter Start node T threshold node. Start node S scatter plot node. Node T secondary threshold node. Node S secondary scatter plot node. To set up a Decision tree, click and drag the required node type from the right hand panel to the work area on the left. The first node in a Decision tree must be a start node (threshold or scatter plot node). 46 /

47 Decision tree filter in our worked example Start node T threshold node. Node S secondary scatter plot node. 47 /

48 Decision tree filter in our worked example Start node T threshold node. Cells with compromised membranes and therefore a Nuc Intensity in the Reference 1 channel (i.e. Propidium iodide intensity in nucleus) greater than 270 GL will be classified as Dead and reported. Those cells less than the threshold are directed to the secondary node for further classification. 48 /

49 Decision tree filter in our worked example Node S secondary scatter plot node. Those cells not classified as dead in the start node are further classified using a scatter plot node with Nuc/Cell Intensity in the Nuclei channel (X-axis) and Cell intensity in the Cells channel (Y-axis) to discriminate viable and apoptotic cells. This combination of measures is required to adequately discriminate viable cells (low Nuc/Cell intensity; lower intensity of FITC- Annexin V) from apoptotic cells (higher Nuc/Cell intensity; higher intensity of FITC- Annexin V). 49 /

50 Decision tree filter in our worked example - Interactive scatter plot node. - Click on the data point to highlight the cell in the image window (with classification bitmap) and cell by cell data. - Useful for checking classification. 50 /

51 Decision tree filter in our worked example Analysis performed with the cell selection include all cells option. Ensuring that the Decision tree summary measures (% and n) are selected in the Summary window of Analysis Protocol Editor. Subpopulation measures were also selected for reporting, based on the Decision tree filter. Once a decision tree has been included in the protocol, remember to go in and check the decision tree summary measures (% and n) to see the data. 51 /

52 Opening the analysis software and retrieving image data. Creating an analysis protocol. Filters and classification. Analysing an image stack and using graphical displays. Using the Batch queue manager. 52 /

53 Analyzing an image stack and using graphical displayscolor coding of plate map, graphical analysis and viewing data in summary and cell by cell. Color coding of plate map Choose Measure 53 /

54 Graphical analysis To plot the results for analyzed wells, right click on the analyzed well(s), and select Plot Selected Data. Choose histogram or scatter plot. 54 /

55 Viewing data Summary by fields Summary by wells 55 /

56 Subpopulations (field) Subpopulations (wells) Cell by Cell 56 /

57 Opening the analysis software and retrieving image data. Creating an analysis protocol. Filters and classification. Analysing an image stack and using graphical displays. Using the Batch queue manager. 57 /

58 Using the Batch queue manager Batch Queue analysis can be used to optimise the analysis parameters for a single image stack or to run multiple image stacks through one protocol or a combination of the two Default file paths will locate the data files e.g. image stacks and analysis protocols (if image stacks and analysis protocols are to be used from a central drive file paths must be set accordingly). Select Settings and click Default File Paths. 58 /

59 Using the Batch queue manager Creating the batch queue folder: Settings/Default File Paths and type BATCH at end of Batch analysis folder file path Opening Batch Queue Manager-Application Adding image stacks and analysis protocols to the batch queue Select Protocol Browse folder Scan to load files Check Box to add files to batch queue OK 59 /

60 Using the Batch queue manager Exporting Analysis Protocols Running a batch analysis - Application and Image Stack and analysis 60 /

61 Additional Functions Movie Mode-save as AVI file All images from all channels and time points will be loaded 61 /

62 Double click on which Channel to generate movie Choose channel Define which of the time points are to be incorporated into the movie Define time interval and movie direction Play movie 62 /

63 Additional Functions Saving images for presentations Highlight image >Image>save image as Save entire or viewed image Save with and without overlay 63 /

64 Legal GE Healthcare and GE Healthcare Biosciences are trademarks of GE Healthcare companies. GE and GE Monogram are trademarks of General Electric Company. General Electric Company reserves the right, subject to any regulatory approval if required, to make changes in specifications and features shown herein, or discontinue the product described at any time without notice or obligation. Contact your GE Representative for the most current information General Electric Company - All rights reserved. The IN Cell Analyzer 1000 system is the subject of patent numbers US and and US patent application number 10/514925, together with other granted and pending family members, in the name of GE Healthcare Niagara, Inc. The IN Cell Analyzer 1000 and associated analysis modules are sold under license from Cellomics Inc. under US patent numbers US , , , , , , ; Canadian patent numbers CA , , ; Australian patent number AU ; European patent number EP ; Japanese patent number JP and other pending and foreign patent applications. Software GE Healthcare Biosciences Niagara Inc All rights reserved GE Healthcare Biosciences UK Limited All rights reserved. GE Healthcare Biosciences Niagara Inc is a wholly owned subsidiary within the GE Healthcare Biosciences group of Companies. All goods and services are sold subject to terms and conditions of sale of the company within the GE Healthcare Biosciences group, which supplies them. A copy of these terms and conditions is available on request. The IN Cell 1000 Analyzer system is for research purposes only. It is not approved for diagnosis of disease in humans or animals. GE Healthcare Biosciences UK Limited Amersham Place Little Chalfont Buckinghamshire HP7 9NA UK GE Healthcare Biosciences AB SE Uppsala Sweden GE Healthcare Biosciences Corp 800 Centennial Avenue PO Box 1327 Piscataway NJ USA GE Healthcare Biosciences Europe GmbH Munzinger Strasse 9 D Freiburg Germany 64 /