1. 2005Training A Revolution in Cell Analysis ImageStream Operator Training

2. 2005Training The ImageStream ® System ImageStream ® Imaging Flow Cytometer Brightfield, darkfield, and 4 fluorescent images at >15,000 cells/minute IDEAS ® Statistical Image Analysis Software Quantitative cellular image analysis and population statistics Novel Applications Translocation, co-localization, cell classification, cell cycle, apoptosis, etc.

3. 2005Training Just in Case … AMNIS (Latin), Stream or Torrent INSPIRE INstrument Software Processor for Imaging Research Experiments IDEAS Image Data Exploration and Analysis Software ASSIST Automated Suite of Systemwide ImageStream Tests

4. 2005Training Experimental Design Instrument Calibration with SpeedBeads ® Data acquisition Data analysis ImageStream Workflow

5. 2005Training A Revolution in Cell Analysis Experimental Design

6. 2005Training Considerations: 1. Selection of cell type 2. Selection of probes 3. Fluorescence control samples 4. Sample prep requirements ImageStream Experimental Design

7. 2005Training Experimental Design Overview Ch1 470-500nm Ch2 400-470nm Ch3 500-560nm Ch4 560-595nm Ch5 595-660nm Ch6 660-730nm 488 SSCBrightfield FITCPE7-AADPE-Cy5 PIAF488Cy-3PE-AF647 PE-AF610GFPAF546PE-AF680 PE-TRSyto GreenAF555PerCP Qdot 655Spec Green PerCP-Cy5.5 ECD YFP DRAQ5 Sybr.GreenQdot 705 1) 44  m channel width 2) Ideally 2x10 6 cells per sample 3) Less for single color fluorescent controls 4) Final Vol. = 50  l in 0.5 ml microcentrifuge tube 5) Up to 4 fluorochromes, all 488 excitable 6) 10-20 experimental samples per hour 44  m

8. 2005Training Fluorescence crosstalk control samples: Unlabeled and single color-labeled cells Cell type should be representative of experimental sample Single color labels should be identical to those used in the experimental file: the fluorochrome MUST be identical DNA control separate and run last Collected with no brightfield Used to guide automated crosstalk correction of experimental files Fluorescence Controls

9. 2005Training Sample Preparation Sample Processing Follow standard flow cytometric methods for cell harvesting, incubation, washing and staining (including reagent titration) Final concentration of 4x10 7 cells per ml will run at approximately 75 cells per second Take care to ‘balance’ fluorochrome staining intensities to avoid saturation of signal from bright stains at instrument setup conditions necessary for dim stains

10. 2005Training Experimental Design Overview Ch1 470-500nm Ch2 400-470nm Ch3 500-560nm Ch4 560-595nm Ch5 595-660nm Ch6 660-730nm 488 SSCBrightfield FITCPE7-AADPE-Cy5 PIAF488Cy-3PE-AF647 PE-AF610GFPAF546PE-AF680 PE-TRSyto GreenAF555PerCP Qdot 655Spec Green PerCP-Cy5.5 ECD YFP DRAQ5 Sybr.GreenQdot 705 1) 44  m channel width 2) Ideally 2x10 6 cells per sample 3) Less for single color fluorescent controls 4) Final Vol. = 50  l in 0.5 ml microcentrifuge tube 5) Up to 4 fluorochromes, all 488 excitable 6) 10-20 experimental samples per hour 44  m

11. 2005Training A Revolution in Cell Analysis Calibration and SpeedBeads

12. 2005Training SpeedBeads - Instrument calibration and run-time system integrity –Run-time system integrity Maintains continuous synchronization and autofocus independent of cell concentration or type –Automatic Instrument Calibrations and Tests Optical, illumination, fluidic and camera systems –Loaded at the beginning of each day and run continuously until shut down –IR laser scattering characteristics monitored –Automatically classified and not included in sample file SpeedBeads

13. 2005Training The optical system focuses on the ‘core’ stream that contains the sample Relative to the optics, the core can: move back - front (z-axis – focus) move left - right (x-axis – core tracking) speed up or slow down (y-axis – camera synchronization) SpeedBeads provide continuous feedback to update the objective stage position and camera line rate to maintain image quality optics core with cells sheath cuvette Channel Image Deviation none X Y Z SpeedBeads and Image Quality x z y

14. 2005Training Calibration using ASSIST ASSIST is a fully automated suite of instrument calibrations & tests that assures optimal performance. Tests all major subsystems using a uniform particle (SpeedBeads), and produces a report for daily quality assurance. Typically completes all calibration and tests in about 5 minutes. Automated Suite of Systemwide ImageStream Tests

15. 2005Training A Revolution in Cell Analysis Data Acquisition

16. 2005Training INSPIRE: Data Acquisition Brightfield, darkfield and fluorescent images collected in 6 channels with adjustable sensitivity. Brightfield in any channel to accommodate a variety of fluorochromes. Automated instrument calibration. On the fly images and scatter plots allow for quick sample assessment. INstrument Software Processor for Imaging Research Experiments

17. 2005Training 1.Load sample 2.Run cells with SpeedBeads 3.Establish stable core fluidics 4.Establish appropriate instrument settings 5.Choose classifiers to distinguish cells, from debris 6.Collect data 7.Return sample (optional) 8.Flush sample syringe and lines 9.Load next sample Instrument Run Sequence

18. 2005Training 1.Choose channel for Brightfield (blocked for fluorescent control) 2.Set laser power and/or camera stages to avoid camera pixel saturation by monitoring Peak Intensity plots 3.Adjust laser height to maximize dynamic range of 488 scatter intensity while still maintaining high fluorescence sensitivity 4.For samples that contain abundant debris, set squelch value to reduce sensitivity of object detection so that debris is ignored. 5.Note that the excitation & detection conditions selected for the control (laser power, camera staging) MUST be used for the experimental samples Instrument settings

19. 2005Training Choose Classifiers Detected objects can be classified in three ways: 1.Cells 2.Beads 3.Debris Only the Cells make it into your primary data file. You can save the bead and debris into separate files if you wish. Beads are automatically classified Cells can be classified based on object feature thresholds. Objects that fall outside the boundaries of any of the thresholds are classified as debris.

20. 2005Training 1.Optionally return experimental sample to tube 2.Change Sheath tank to Rinse 3.Run sterilize script: 1.Powers off illumination (all) 2.Flushes lines 3.Cleans instrument: automatically runs detergent, alcohol, bleach & water Instrument Shutdown

21. 2005Training A Revolution in Cell Analysis Data Analysis: Opening Files

22. 2005Training 200+ params/cell population statistics object values Tabular Data Image Gallery see every cell flexible viewing enhance & color tag populations virtual cell sort Workspace uni + bivariates flexible gating click dot to view cell custom parameters IDEAS ® Software Image Data Exploration & Analysis Software

23. 2005Training The ImageStream collects large numbers of digital images into a single file. Using image processing algorithms, specific features can be quantified from these images. IDEAS is the software tool used to analyze and report the image data acquired on the IS100 instrument. IDEAS Data Analysis Overview

24. 2005Training IDEAS: Cell image-based informatics 6 images per cell, 30+ standard features per image Customizable image display User definable features Features plotted on histograms or dot plots Images linked to plotted data points Populations can be created in many ways: Standard region drawing tools Tagged populations Boolean combinations of multiple populations Multiparametric filter-based Full statistics repertoire Reporting via copy to clipboard, export to stats program Batch processing to apply analysis template to all files in an experiment IDEAS Data Analysis Overview

25. 2005Training Raw Image File (RIF)--------------------------------200 MB/10,000 events –raw instrument data –collection settings Compensated Image File (CIF)-------------------200 MB/10,000 events –Corrected for spectral crosstalk –Corrected for offsets and gains from ASISST –Determination of object boundaries (segmentation) Data Analysis File (DAF)-----------------------------10 MB/10,000 events –Image Gallery –Work Area (graphs and specific images) –Calculated features and statistics –Saved state of analysis –Uses the CIF as a database: Keep track of where you store files! The DAF and related CIF must be in the same directory. Files and their Structure

26. 2005Training Opening Data Files Raw image file (.rif) Compensation matrixSave corrected image file (.cif) Select a template Data analysis file (.daf) Use “file open” in IDEAS to open a.rif Double click on a data file Cntl click on.daf R-click select open Open multiple instances of IDEAS to perform multi- sample analysis

27. 2005Training Spectral Compensation Navigate to an existing compensation matrix. Select the number of events to open. To get a quick look at the data. Opening 100 events will be faster then opening all the events. Advanced button reveals all the data file corrections that occur when creating the cif.

28. 2005Training Data File Corrections Spectral compensation removes crosstalk. Spatial alignment assures direct overlap of each channel. Camera gain correction provides uniform light response. Dark current corrections set uniform background levels. Flow speed normalization corrects for small sensitivity changes as a result of flow speed changes. EDF (extended depth of field) is used only if data is collected with the EDF element. MTF (modulation transfer function) only used for sub-pixel alignments.

29. 2005Training Corrected Image File (cif) Corrected image file has all file corrections applied and spectral crosstalk removed. Determination of object boundaries for each image have been made (segmentation). The.cif can be opened in any analysis template. Batching a.cif with an analysis template allows for quick re- analysis of archived data.

30. 2005Training Single color control samples used to calculate a 6x6 matrix. Post-acquisition compensation is applied to images on a pixel by pixel basis in IDEAS. Spectral Compensation SSC Brightfield FITC PE PE-Alexa610 Draq-5

31. 2005Training IDEAS Templates Templates contain image display settings workspace histograms user defined and experiment specific feature calculations Gating and population logic Used in batch processing to provide uniform analysis for all experimental data files.

32. 2005Training Data Analysis File (daf) The daf presents the saved state of the analysis. Uses the cif as its database for images and feature calculation. Includes all information from the template.

33. 2005Training Instrument creates Raw Image Files (rif) Corrections and segmentation are applied when a rif is opened Corrected images and segmentation masks are stored in a Corrected Image File (cif). A cif is loaded into IDEAS using a template file. Defined features are calculated for each object. Feature values and analysis results are saved in a Data Analysis File (daf). IDEAS Files Review

34. 2005Training A Revolution in Cell Analysis Data Analysis: IDEAS

35. 2005Training The IDEAS window is divided into three major areas: the image gallery, the work area, & the statistics area Image Gallery Work Area Tabular data IDEAS layout

36. 2005Training Image Display Properties Set display background and saturation levels Edit channel names Set color display properties Define masks to display and in which channels Edit gallery display views to look at combinations of channel and composite images Define composite images.

37. 2005Training Masks –Set of pixels that make up a ‘region of interest’ in an image –IDEAS automatically determines a sensitive mask for each channel of each object. This mask is best used for measuring the overall staining intensity detected in a given image. However, this mask may be less appropriate for other shape related features. IDEAS provides tools to create ‘feature- appropriate’ custom masks –The user can create custom masks in two ways: Functionalize an existing mask (erode, dilate, fill, threshold, morphology) Make complex masks through boolean combinations –These masks can then be used to build features IDEAS: Masks

38. 2005Training Masks define a region of interest for feature calculation. System masks are all inclusive. User defined masks target specific cell compartments. Complex masks use Boolean logic combine two or more masks. Masks: Region of interest

39. 2005Training Functionalize a mask: Example = threshold the system mask for the channel 6 image (nuclear stain) to constrain the region of interest to the region of dominant nuclear dye signal. IDEAS: Masks

40. 2005Training Feature Calculation Over 200 intensity and morphology based features. Feature calculator that allows for the development of novel cell classifiers. Each feature can be displayed as univariate or bivariate histograms. Population statistics are calculated for each population in the analysis.

41. 2005Training Histogram – image data linkage (‘click on a bin’) IDEAS: Plotting feature data

42. 2005Training Scatter plots – image data linkage (‘click on a dot’) IDEAS: Plotting feature data

43. 2005Training Population creation (4 ways) –Regions physically drawn on plots –Tagging cells –Boolean combinations –Filtering (‘find-like-cells’) Population display –‘Virtual sort’ in the Image Gallery –Show/Hide on existing scatter plots IDEAS: Populations

44. 2005Training Regions physically drawn on plots: rectangle, oval, polygon, line IDEAS: Creating Populations

45. 2005Training Tagging cells: population created by individually selecting objects from the image gallery and/or from scatter plots IDEAS: Creating Populations

46. 2005Training Boolean combinations: Complex populations can be created by combining existing populations with boolean operators (and, or, not) IDEAS: Creating Populations

47. 2005Training Filtering (‘find-like-cells’): Populations can be created by instructing IDEAS to find all the objects that have similar features to a given cell or existing population. IDEAS: Creating Populations

48. 2005Training ‘Virtual sort’ in the Image Gallery: IDEAS: Displaying Populations

49. 2005Training Show/Hide a population on existing scatter plots: IDEAS: Displaying Populations

50. 2005Training Statistics automatically calculated for each feature –Population stats: Count, % Total, % Gated, % Plotted –Feature stats: Mean, Median, Mode, Geometric Mean, Standard Deviation, CV, Variance Displayed under each plot and/or in the Statistics Area IDEAS: Statistics

51. 2005Training Reporting –Copy image or image gallery to clipboard –Copy graph and/or stats to clipboard –Export stats to spreadsheet IDEAS: Data Reporting

52. 2005Training Copy image or image gallery to clipboard IDEAS: Data Reporting Single image Image Gallery (composite mode)

53. 2005Training Copy graph and/or stats to clipboard IDEAS: Data Reporting Light modeDark mode

54. 2005Training Export stats or feature data to spreadsheet Export single image data to spreadsheet IDEAS: Data Reporting

55. 2005Training File tools –Batch processing Apply a compensation matrix and a saved analysis template to all files within a given experiment –Merge files into one file –Create smaller files from sub-populations –Save data in.fcs format IDEAS: File tools

56. 2005Training IDEAS Data Analysis Review IDEAS is the software tool used to analyze and report the image data acquired on the ImageStream. IDEAS allows the user to mine existing features, create new features, plot data, perform population statistical analysis and customize image display.

57. 2005Training A Revolution in Cell Analysis ImageStream Operator Training

58. 2005Training Up to 6 images per object Displayed in the Image Gallery or Work Area Customizable Image display: Linear and non-linear display transformation False color Composites Image Line and Region data IDEAS: Images

59. 2005Training Linear and Non-linear image display transformation Linear transform Non-linear transform IDEAS: Images

60. 2005Training False coloration of greyscale images and composites Bright Field and Composite IDEAS: Images Channel Images of object #2

61. 2005Training Image Line and Region data IDEAS: Images

62. 2005Training Features Are attributes related to each object image. Most of these features are derived from image processing algorithms, and most quantify morphologic aspects of the image. A large set of features is automatically calculated by IDEAS The user can create their own features through Boolean combinations and/or arithmetic operators Some feature rely on a ‘mask’ or region of interest (discussed next). The user can calculate standard features based on customized masks IDEAS: Features

63. 2005Training Plots – images linked to plotted data –Histograms and histogram overlays –Scatter plots –Linear, log, and Linear/Log transform plotting IDEAS: Plotting feature data

64. 2005Training Choice of Fluorochromes Example – 488 excitation with BF in channel 2: choose probes excitable with 488nm light Molecular probes choose probes with max emission spectra in discrete channels balance brightness levels of individual probes e.g., titrate PI such that you don’t saturate DNA detection with enough laser power to view weaker labels

65. 2005Training Cell image should fit within a camera channel width (88 pixels = 44  m diameter) bacteria = 1  m lymphocyte = 10  m cell lines = 15-40  m Cell must not clog instrument (250  m will block tubing) Resolution – 1.0  m Can be adherent or suspension cells, but must be adaptable to flow 250  m 44  m tubing channels Selection of cell type