1. ImageStream
Operator Training
A Revolution in Cell Analysis

2. 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.
Amnis has combined features of both types of cytometry into a flow-based instrument.
The ImageStream acquires up to 6 images of each cell in flow up to 300 cells per second.
brightfield, darkfield, 4 fluorescence images
There is sufficient fluorescence sensitivity and image resolution to do the job.
Designed to use the probes that you already know and love and may have spent years developing.
The successful adaptation of current labeling will ensure meaningful experiments.
Given the very large amount of data acquired, new software and algorithms are needed to measure the cells and provide statistical tests for differences among populations.
With some biological variations being large, we need to do quantitative image-based population studies.

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

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

5. Experimental Design
A Revolution in Cell Analysis

6. ImageStream Experimental Design
Considerations:
Selection of cell type
Selection of probes
Fluorescence control samples
Sample prep requirements

7. Experimental Design Overview
44 mm
1) 44 mm channel width
2) Ideally 2x106 cells per sample
3) Less for single color fluorescent controls
4) Final Vol. = 50 ml in 0.5 ml microcentrifuge tube
5) Up to 4 fluorochromes, all 488 excitable
6) experimental samples per hour
Ch1 nm
Ch2 nm
Ch3 nm
Ch4 nm
Ch5 nm
Ch6 nm
488 SSC
Brightfield
Brightfield
Brightfield
Brightfield
Brightfield
FITC PE
7-AAD
PE-Cy5
AF488
Cy-3 PI
PE-AF647
GFP
AF546
PE-AF610
PE-AF680
Syto Green
AF555
PE-TR
PerCP
Spec Green
Qdot 655
PerCP-Cy5.5
YFP
ECD
DRAQ5
Sybr.Green
Qdot 705

8. Fluorescence Controls
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
There was a note to correct for brightfield before. Is this still needed?

9. Sample Preparation Sample Processing
Follow standard flow cytometric methods for cell harvesting, incubation, washing and staining (including reagent titration)
Final concentration of 4x107 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. Experimental Design Overview
44 mm
1) 44 mm channel width
2) Ideally 2x106 cells per sample
3) Less for single color fluorescent controls
4) Final Vol. = 50 ml in 0.5 ml microcentrifuge tube
5) Up to 4 fluorochromes, all 488 excitable
6) experimental samples per hour
Ch1 nm
Ch2 nm
Ch3 nm
Ch4 nm
Ch5 nm
Ch6 nm
488 SSC
Brightfield
Brightfield
Brightfield
Brightfield
Brightfield
FITC PE
7-AAD
PE-Cy5
AF488
Cy-3 PI
PE-AF647
GFP
AF546
PE-AF610
PE-AF680
Syto Green
AF555
PE-TR
PerCP
Spec Green
Qdot 655
PerCP-Cy5.5
YFP
ECD
DRAQ5
Sybr.Green
Qdot 705

11. Calibration and SpeedBeads
A Revolution in Cell Analysis

12. SpeedBeads
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

13. SpeedBeads and Image Quality
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
core with
cells x z y
sheath
Channel
Image
Deviation none X Y Z
cuvette
optics

14. Calibration using ASSIST
Automated Suite of Systemwide ImageStream Tests
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.

15. Data Acquisition
A Revolution in Cell Analysis

16. INSPIRE: Data Acquisition
INstrument Software Processor for Imaging Research Experiments
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.

17. Instrument Run Sequence
Load sample
Run cells with SpeedBeads
Establish stable core fluidics
Establish appropriate instrument settings
Choose classifiers to distinguish cells, from debris
Collect data
Return sample (optional)
Flush sample syringe and lines
Load next sample

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

19. Choose Classifiers Detected objects can be classified in three ways:
Cells
Beads
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. Instrument Shutdown Optionally return experimental sample to tube
Change Sheath tank to Rinse
Run sterilize script:
Powers off illumination (all)
Flushes lines
Cleans instrument: automatically runs detergent, alcohol, bleach & water

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

22. Image Data Exploration & Analysis Software
IDEAS® Software
Image Data Exploration & Analysis Software
Image Gallery
see every cell
flexible viewing
enhance & color
tag populations
virtual cell sort
Tabular Data
200+ params/cell
population statistics
object values
As some of you know, I’ve spent a lot of time grappling with flow data analysis and data presentation. I’m happy to say for dealing with big data files this is a good interface. And good means, intuitive, fast, and I can get the answers I’m looking for.
It’s not only good for interactive analysis, but the range algorithms in the features, stats, and software compensation is dealt with well.
The parts of the screen layout
Image Gallery
(use screen prompts)
Workspace
clear to anyone who has work with flow data
Data Analysis summary
derived parameters: simple and complex parameters
gates, scalars, Boolean operators
Design Goals
Intuitive
Quantitative
flexible
Workspace
uni + bivariates
flexible gating
click dot to view cell
custom parameters

23. IDEAS Data Analysis Overview
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.

24. IDEAS Data Analysis Overview
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

25. Files and their Structure
Raw Image File (RIF) MB/10,000 events
raw instrument data
collection settings
Compensated Image File (CIF) MB/10,000 events
Corrected for spectral crosstalk
Corrected for offsets and gains from ASISST
Determination of object boundaries (segmentation)
Data Analysis File (DAF) 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.
Instrument creates Raw Image Files (RIFs)
Corrections and segmentation are applied when a RIF is opened. A compensation matrix (CTM) is applied.
Corrected images and segmentation masks are stored in a Compensated Image File (CIF).
A CIF is loaded into IDEAS using a template file (AST). Defined features are calculated for each object.
Feature values and analysis results are saved in a Data Analysis File (DAF).

26. Opening Data Files Raw image file (.rif) Compensation matrix
Save corrected image file (.cif)
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
Select a template
Data analysis file (.daf)

27. 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. 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. 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.
Corrected for spectral crosstalk
Corrected for offsets and gains from ASISST
Determination of object boundaries (segmentation)

30. Spectral Compensation
Post-acquisition compensation is applied to images on a pixel by pixel basis in IDEAS.
Single color control samples used to calculate a 6x6 matrix.
SSC Brightfield FITC PE PE-Alexa Draq-5
Channel imagery consists of signal from the desired channel as well as leakage from other channels due to:
Broad emission characteristics of probes
Limitations in optical filtration
Compensation removes the overlap such that channel imagery reflects only the appropriate probe

31. 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. 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.
The saved state of analysis
Image Gallery
Work Area (graphs and specific images)
Calculated features and statistics
Includes all information from the template file
Uses the CIF as its database for images and feature calculation

33. IDEAS Files Review 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).

34. Data Analysis: IDEAS
A Revolution in Cell Analysis

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

36. 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. IDEAS: Masks
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

38. Masks: Region of interest
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.

39. IDEAS: Masks 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.

40. 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.
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

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

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

43. IDEAS: Populations Population creation (4 ways) Population display
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

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

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

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

47. IDEAS: Creating Populations
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.

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

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

50. IDEAS: Statistics 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

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

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

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

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

55. IDEAS: File tools 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

56. 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. ImageStream
Operator Training
A Revolution in Cell Analysis

58. IDEAS: Images 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

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

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

61. IDEAS: Images
Image Line and Region data

62. IDEAS: Features 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

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

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