GE HealthcareLife Sciences

Toxicity Applications ManualReference protocols for use with IN Cell Analyzer

Join us for the future of predictive toxicity testing.Your vision. Our technologies.

28-9894-51 AA 01/2011 1

Contents

Preface 2

Toxicitytestingapplications 61. Micronuclei assay with online cell counting 6

2. Neurite outgrowth 10

3. Multiplexed apoptosis assay 13

4. Cell morphology classification with machine learning 17

5. Toxicity study using live cardiomyocytes 21

6. High-content hepatotoxicity screen 26

7. Cell cycle analysis using a DNA stain 30

8. Comet assay 33

9. Golgi complex disruption 36

10. Multiplexed neurite and synaptic vesicle analysis 40

Appendix 44References 44

2 28-9894-51 AA 01/2011

Preface

High-Content Analysis for toxicity testingImage-based cytotoxicity assays are an informative tool for elucidating mechanistic pathways and screening new pharmaceutical compounds for toxic effects. Early-stage drug safety testing is becoming increasingly important in driving down development costs and preventing harmful drugs from reaching the marketplace. By facilitating rapid and detailed examination of drug effects at the cellular level, high-content analysis (HCA) provides a means of identifying toxic effects early in the drug development process, when the cost of failure is relatively low.

How to use this manualThe Toxicity Applications Manual provides example protocols that will help you get started with HCA toxicity applications using the IN Cell Analyzer system. The applications have been selected to illustrate a broad range of approaches, from simple single-color assays to complex studies that monitor multiple fluorescent probes and analyze a large number of cellular measurements. Step-by-step protocols and workflows guide you through the implementation of key aspects of each application. Use the tables in the Finding an application section (page 3) to locate applications of interest. The protocols in this manual are intended for general guidance only. GE Healthcare provides training courses and materials for use of IN Cell Analyzer systems and software; these will help you get the most out of the protocols. Note that some screenshots and terminologies used in this manual may change with future updates to IN Cell Analyzer hardware and software.

IN Cell Investigator analysis levelsIN Cell Investigator is a comprehensive image analysis package that allows you to develop automated analysis protocols for a wide range toxicity applications. Designed to match your skill level and assay needs, IN Cell Investigator offers a selection of tools at three user levels. At Level 1, task-focused analysis modules are pre-configured to allow you to create an analysis routine with little or no prior experience. At Level 2, the versatile Multi-Target Analysis module offers a wider range of analysis tools and over 100 pre-developed cell measures. At Level 3 you can harness the full power of IN Cell Investigator to create your own customized measures and analysis routines. The recommended analysis level for each application in this manual is specified in the respective Image analysis section, and also in Table 2. To maximize your understanding of the analysis results, access to Spotfire™ DecisionSite™ visualization tools is embedded in the Investigator software, as illustrated in example applications (see Table 1).

Pre-configuredAnalysis

Guided Analysis Flexible Analysis

Canned AlgorithmsInvestigator 1.1-1.6 MTA Developer

Tools that support your growing experience

Fig1. IN Cell Investigator analysis levels.

28-9894-51 AA 01/2011 3

AssistanceOur Investigator Analysis Protocol service can supply information about additional predeveloped analysis solutions and provide assistance with custom analysis protocol development. Visit www.gelifesciences.com/contact for details on how to contact your local Technical Support for more information.

Finding an applicationYou can use the tables in this section to locate applications of interest quickly. In Table 1, you can look up a product or assay feature to find the applications that illustrate it. Table 2 provides an overview of the content and functionalities covered in each application.

Note that these are example applications only. The power of Investigator ensures that you can create your own analysis protocols to analyze any application of your choosing.

Table1. Locate applications by feature

Feature Application

Online cell counting 1

Plate mapping in Spotfire DecisionSite 1, 2

Decision Tree filter 3, 7

Live-cell assay 3,4,5,6

Large chip CCD camera 5, 10

Automated classifier (machine learning) 4

GE Healthcare Cardiomyocytes 5

Principal component analysis (PCA) in Spotfire DecisionSite 5

IN Cell Miner data management 5,6

Spotfire DecisionSite 2-D plots and histograms 2,5,7,8

Compound screening 5, 6

Spotfire DecisionSite profile charts 6

Dual threshold segmentation 8

3D Deconvolution 9

Spotfire DecisionSite trellis plots 9

Composed target linking 9,10

4 28-9894-51AA01/2011 28-9894-51AA01/2011 5

Table 2Summaryofapplicationcontent

No. Application Assay Example cell type Image acquisition

Image analysis level

Image analysis protocol (as defined in Investigator v1.1-1.6)

Secondary analysis and data visualization

Data management (IN Cell Miner)

1 Micronucleiassaywithonlinecellcounting

1-colorfixed-cellassayformicronucleiformation

CHO-K1 20×objective;Onlinecellcounting

1 MicronucleiAnalysisModule Dose-responsecurves,SpotfireDecisionSiteplatemap

2 Neuriteoutgrowth 2-colorfixed-cellneuriteoutgrowthassay

Neuro-2a 10×objective 1 NeuriteOutgrowthContextModule NeuriteoutgrowthSpotfireDecisionSiteGuidefacilitatescreationofplatemaps,nuclearintensityhistograms,dose-responsecurves,andneuritedensityplots

3 Multiplexedapoptosisassay

4-colorlive-cellassayusingHoechst™33342,Fluo-4AM,TMRMandTOTO-3

A549 10×objective 2 Multi-TargetAnalysisModule,includingDecisiontreefiltering

2-Dplots,Dose-Responsecurves

4 Cellmorphologyclassificationwithmachinelearning

2-colorlive-cellassayforcellmorphologychanges

U2-OS 10×objective 2 Multi-TargetAnalysisModuleincludingsupervisedmachinelearningtocreateanautomatedmorphologyclassifier

Bargraph

5 Toxicitystudyusinglivecardiomyocytes

4-colorlive-cellassayusingHoechst33342,Fluo-4AM,TMRMandTOTO-3;studyofsixtestcompoundswithknowncardiovasculareffects

GEHealthcareCardiomyocytes

40×/0.6NAobjective;large-chipCCDcamera

2 Multi-TargetAnalysisModulewiththresholdfilteringforsubpopulationidentification;

Principalcomponentanalysis(PCA);1-Dand2-Dscatterplots

Hierarchyfororganizingandbrowsingprojectdata.Platemapcoloringbyuser-selectablemeasuretoidentifyhitsanddose-dependencies

6 Hepatotoxicityscreen 4-colorlive-cellassayusingHoechst33342,Fluo-4AM,TMRMandTOTO-3;screenof42compoundswithsuspectedhepatotoxicliabilities;3-daypre-incubationwithcompounds;11-pointdose-responses

HepG2 10×objective 2 Multi-TargetAnalysisModule ToxicphenotypeclassificationusingSpotfireDecisionSiteprofilecharts

Settingupaprojecthierarchy.Platemapcoloringbyuser-selectablemeasuretoidentifyhitsanddose-dependencies

7 CellcycleanalysisusingaDNAstain

1-colorfixed-cellassayforDNAcontent

U2-OS 10×objective 3 User-definedprotocolwithDecisiontreeclassificationofcellsintoG1,S,andG2/Mphases

SpotfireDecisionSite2-Dintensityplots,dose-responsecurves,intensityhistograms

8 Cometassay Singlecellelectrophoresisonslides;fluorescentDNAdye

Jurkat 4×objective 3 User-definedprotocolwithdual-thresholdsegmentationtoidentifycomettailsandnucleoids

SpotfireDecisionSite2-Dpositionalplot,dose-responsecurveswithuser-definedmeasures

9 Golgicomplexdisruption

3-colorfixed-cellassaywithimmunostainingforCGNandTGNmarkers

HeLa 20×and100×objectives;3-Ddeconvolutiononaregionofinterest

3 User-definedprotocolidentifyingGM130,TGN46,andareasofmarkeroverlap

SpotfireDecisionSitetrellisplotsshowingdose-responsecurvesforninecellparameters.

10 Multiplexedneuriteandsynapticvesicleanalysis

3-colorfixed-cellassaywithimmunostainingforneuritesanddendriticspines(synaptophysin)

Primaryrathippocampalneurons

10×objective;large-chipCCDcamera

3 User-definedprotocolwithlinkingoftargetsetstoquantitateneuritesanddendriticspinesonaper-cellbasis

Dose-responsecurvesformeasuressuchasneuritelengthpercellandmeannumbersynapsespercell

6 28-9894-51 AA 01/2011

Toxicity testing applications

1. Micronuclei assay with online cell counting

Application overviewPerforming micronuclei assays on IN Cell Analyzer can reduce assay times from hours to minutes and remove operator bias compared to manual techniques. A critical factor in establishing robust high-content cell assays is the assurance that enough cells are imaged per treatment condition. This can be particularly challenging in toxicity assays, in which toxic compounds are likely to induce significant decreases in the total number of cells. The problem can be addressed with IN Cell Analyzer by automatically counting cells “online” as each image is acquired. In this optional acquisition mode, successive fields of view are acquired until a preset cell count threshold is achieved, at which point the system will move on to image the next well. Online cell counting has the additional advantage of reducing plate read times and the data storage burden, since no excess images are acquired once the desired number of cells has been imaged.

Micronucleus induction is a key characteristic of genotoxic compounds. The screening of new drug candidates for micronuclei formation resulting from DNA strand breakage or interference with chromosome segregation is an important element of preclinical toxicity testing. Guidelines for genotoxicity testing using in vitro micronuclei formation assays typically recommend scoring at least 2000 cells per treatment condition, or at least 1000 cells per condition if the assay employs replicates. Online cell counting ensures that the desired minimum cell count is reliably achieved even when test compound toxicity results in a cell count decrease.

Sample preparation CHO-K1 cells seeded onto 96-well MatriPlate™ microplates are incubated with mitomycin C (a clastogen) or etoposide (an aneugen) to induce micronuclei formation. After 48 h of exposure, cells are ethanol-fixed at room temperature for 30 min, and then stained with Hoechst 33342 nuclear dye to identify nuclei and micronuclei.

Image acquisitionOnline cell counting is an optional feature that is specified during creation of an image acquisition protocol. The following instructions show how to apply that feature using IN Cell Analyzer 2000. Online cell counting is also available within the IN Cell Analyzer 1000 acquisition software (see Application Note 28-9495-88 AA).

1. Switch on IN Cell Analyzer 2000 and the excitation lamp.

2. Load a sample plate into the instrument.

3. Within Assay development mode, write an acquisition protocol using the protocol designer.

4. Select the plate type from the drop down menu.

5. Choose an objective (20×/0.45 NA).

6. Select the following: Number of wavelengths (one in this example); DAPI excitation and emission channels; 1×1 binning; QUAD2 polychroic set; standard 2-D imaging mode. Start with an exposure time of 0.05 s.

7. Select Laser autofocus, click on an untreated well and optimize the focus and exposure times for each wavelength. Check the focus and exposure times in a few treated wells and then go back to an untreated well before proceeding to the next step.

8. In Acquisition options, select Horizontal serpentine and select the Count Cells option.

9. Choose the Nucleus wavelength from the drop down box (DAPI | DAPI in this example Fig 1) and enter a minimum nucleus area (50 µm2 in this example). In the Acquire until box, enter the minimum number of cells you want to image in each well (1000 in this case).

28-9894-51 AA 01/2011 7

10. Press the Sample Now button (Fig 1). The number of cells in the current field of view will be counted and displayed. The cells will have yellow outlines around them, indicating the regions identified by the online analysis algorithm as individual cells (in this case, nuclei). Optimize the minimum nucleus area setting until you are satisfied that the majority of cells in the field of view are correctly identified and that any cell debris is not being identified as cells. (Note that in cases where cells are aggregated, segmentation may be less accurate; if aggregation is extensive, consider adjusting the cell seeding density during assay optimization).

11. In Plate/slide view, select wells for acquisition and then choose the number of fields per well. The value you enter will be taken as the maximum field count per well. In this configuration, the instrument will acquire images within the well until either the Acquire until threshold or the field maximum is reached, whichever comes first. For this assay, enter the default maximum field count (100) to ensure that the software will locate 1000 cells, or image the entire well area, before moving on to the next well.

12. Run the acquisition protocol to acquire data from the plate.

A B

Fig1.OnlinecellcountingwithINCellAnalyzer2000.(A) Online cell counting is activated by selecting the Count Cells check box and entering the desired settings for Nucleus wavelength, Minimum nucleus area, and the minimum cell count. After testing the setup by clicking Sample Now, the results of online analysis appear in the Cells found box. (B) Cells identified by online cell counting are indicated by yellow outlines superimposed on the displayed image.

Image analysisInvestigatorlevel:1

Protocoltype: Micronuclei Analysis Module

Create an analysis protocol by following the steps below. The settings used to generate the example data shown here are indicated in parentheses.

1. Select Wave 1 (the nuclear channel image) for the Nuclei, Micronuclei and Cells object categories.

2. On the Segmentation page, select the Region growing method for nuclear segmentation and choose settings for Minimum area of nuclei (40 µm), shading removal (light), and noise removal (light).

3. For micronuclei segmentation, select the Multiscale top-hat method. Set minimum size (0.5 µm), maximum size (3 µm), scales (3), sensitivity (80%), and the method for detecting inclusions (In the cytoplasm). In the Advanced settings, set the sensitivity (1.4) and select Smart masking.

8 28-9894-51 AA 01/2011

4. For Cells segmentation, choose the Collar method and set the collar Radius (8 µm). The resulting collar defines the micronuclei sampling region (Fig 2B).

5. Select the desired measures, including Micronuclei count and relevant nuclear parameters. Cell measures are not relevant in this example.

6. Filters and classifiers are not used in this example.

7. On the Summary page, be sure to include the following as selected outputs: Cell Count, Micronuclei Count, Nucleus Form (both % and n), and MN (both % and n).

A B

Fig2.TreatmentofCHO-K1cellswithetoposideinducesmicronucleiformation.(A) Nuclear channel image of cells stained with Hoechst 33342; (B) Analysis overlays show detected cell nuclei (blue), micronuclei sampling boundary (green), and detected micronuclei (yellow).

Secondary analysis and data visualizationPercentage maximum cell count relative to untreated controls can be used as an index of cell proliferation (PI), which typically decreases with increasing exposure to genotoxins (Fig 3). The PI50 (concentration at half maximal PI) can be compared for different genotoxins, as can the percentage of cells presenting with micronuclei at the PI50.

A B

125

100

75

50

25

0

50

40

30

20

10

00 1 2 3 4

Log [Etoposide, nM]

% M

axim

um c

ell c

ount

% M

icronuclei

% Micronuclei% Maximum cell count

125

100

75

50

25

0

25

20

15

10

5

00 1 2 3 4

Log [Mitomycin C, nM]

% M

axim

um c

ell c

ount

% M

icronuclei

Fig3.Dose-dependencyofmicronucleiformationandcellcountfortwotestcompounds. CHO K1 cells were treated with increasing doses of (A) Etoposide, or (B) Mitomycin C. Percentage maximum cell count (left y-axis), also referred to as PI, is calculated as 100 × (mean field cell count of treated sample)/(mean field cell count of untreated control sample). The percentage of cells presenting with micronuclei is plotted on the right y-axis. Data points and error bars show the mean +/- 1 SEM, n = 8 wells per concentration. Field count data were retrieved from the IN Cell Analyzer 2000 session log.

28-9894-51 AA 01/2011 9

Plate map visualizations are useful in assessing compound effects across a plate. The plate map in Figure 4 illustrates how the online cell counting feature minimizes the total number of images (and therefore the total plate acquisition time) by collecting only the number of images required to reach the user-set cell count threshold. In wells where cells received no drug treatment, the threshold is achieved with as few as three fields of view. By contrast, at toxic concentrations of drug, up to 47 fields of view were required to reach the cell count threshold. The data points on the plate map can be colored to represent any measured parameter. In this case, the points are colored by field count, which makes the dose-dependence of the effect more apparent, as well as highlighting outliers (e.g., well H7).

Fig4.Platemapvisualizationoffieldcountperwell. Well rows and columns are plotted on the axes to create a map of the 96-well assay plate. Each point represents an acquired field, with the color of each point denoting the number of fields (tiles) acquired in the well. As the concentration of mitomycin C increases (left to right), the number of cells per well decreases due to toxic effects and therefore more image fields are acquired to achieve the cell count threshold of 1000. Field count data retrieved from the IN Cell Analyzer session log were imported into Spotfire DecisionSite software (embedded in Investigator).

10 28-9894-51 AA 01/2011

2. Neurite outgrowth

Application overviewQuantifying the effect of test compounds on neurite outgrowth provides valuable information for compound toxicity assessment and profiling. This example illustrates the use of an IN Cell Investigator Context Module to create an automated neurite analysis protocol without any prior image analysis experience. Using an integrated link in the Investigator interface, analysis results are readily exported to the embedded Spotfire DecisionSite, where a predeveloped Neurite Outgrowth Guide assists with creation of nuclear intensity histograms, neurite density plots, plate maps, and dose-response plots.

Sample preparation Mouse neuroblastoma cells (neuro-2a cell line, ATCC, CCL-131) are seeded into imaging grade 96-well plates (Greiner Bio-One) in media containing 2% FBS and incubated for 6 h. Cells are then incubated for 18 h with retinoic acid (Sigma) at a range of concentrations (5 to 50 µM) to induce neurite outgrowth. Following compound treatment, cells are fixed and indirectly immunostained for NF200 (neurofilament 200 kD subunit), which localizes to neurites. Cells are counterstained with Hoechst 33342 (Invitrogen) prior to image acquisition.

Image acquisitionImages are acquired using IN Cell Analyzer with the 10× objective, which captures a sufficient number of neurites in their entirety while providing sufficient resolution for neurite segmentation (Fig 1). Optionally, the online cell counting feature can be applied to minimize the number of fields required to achieve a sufficient neurite count, thus reducing the total acquisition time. Refer to Application 1 for an example of how to apply online cell counting. In this example, IN Cell Analyzer 2000 configured with the large chip CCD camera was used to maximize neurite capture per image

A B C

Fig1.Imagingandanalysisofneurites.(A) Image of neuronal cells indirectly stained for NF200 using a FITC-conjugated secondary antibody. (B) Colored overlays show the results of the automated analysis, detecting nuclei (yellow), cells (red), and neurites (green).(C) Colored overlays show regions of morphometric analysis (for example, to detect branch points) Only a portion of the entire field of view is shown.

28-9894-51 AA 01/2011 11

Image analysisInvestigatorLevel:1

Protocoltype:Neurite Outgrowth Context module

IN Cell Investigator provides a choice of approaches for the analysis of neurite outgrowth. These range from use of the pre-defined analysis routine presented here to the development of more complex user-defined analysis solutions (for example, see Application 10). The Neurite Outgrowth Context Module provides a quick and easy way to create an effective neurite outgrowth protocol with detailed step-by-step instructions and helpful explanatory notes to guide decision-making (Fig 1). Simply follow the instructions and then click on Analyze to run the protocol. When satisfied with the results, click Save to retain the protocol for future use. You can also access and modify the underlying analysis routine (if desired) by closing the Context Module window.

Fig2.NeuriteOutgrowthContextModuleinterface. Step-by-step instructions guide the user through protocol creation and optimization. Interactive hyperlinks (blue) allow the user to test the analysis parameters and preview the results of each step. Built-in links on a separate page of the Context Module (not shown) provide descriptions of preset measures.

12 28-9894-51 AA 01/2011

Context Module workflow1. Open an image stack in IN Cell Developer. (An example neurite outgrowth image stack is provided with the

Investigator software package on a separate DVD.)

2. From the list of available Context Modules, select Neurite Outgrowth Analysis.

3. On the first page of the Context Module, select one of the two methods for assigning neurites to cell bodies (a neurite connecting two cell bodies can be either partitioned between the two cell bodies or assigned entirely to one of them).

4. Assign the source images for nuclei and neurites and adjust the parameters to suit your image stack. Use the Preview hyperlinks to assess parameter settings throughout the protocol.

5. When satisfied with the results of individual steps, run the analysis by clicking on Analyze.

6. To save the protocol for future use, click on Save.

Secondary analysis and data visualization1. Once analysis is completed, click on the Spotfire Connect icon to transfer data to the Spotfire

DecisionSite software.

2. Within Spotfire DecisionSite, open Neurite Outgrowth within Guides.

3. Follow the instructions in the guide to quickly analyze and visualize the data. The instructions assist you in creating the following visualizations:

• Plate map: Well data are arrayed in plate format, color-coded by a user-selectable measure.

• Nuclear intensity histogram: A histogram of nuclear intensities is displayed for each well of a plate.

• Dose-response curve: A dose-response curve is plotted with a user-selectable measure on the y-axis; exponential curve fitting is applied to calculate the EC50 value.

• Neurite density: Neurite length per cell is plotted against the number of branch points per cell.

Fig3.NeuriteOutgrowthguidewithinSpotfireDecisionSite. Easy-to-follow instructions (left) assist the user in creating nuclear intensity histograms, neurite density plots, color-coded plate maps, and dose-response plots.

28-9894-51 AA 01/2011 13

3. Multiplexed apoptosis assay

Application overviewAn early hallmark of apoptosis is the loss of plasma membrane asymmetry, including translocation of phosphatidylserine to the outer leaflet of the plasma membrane. At later stages of apoptosis and in cells undergoing necrosis, plasma membrane integrity is lost. Monitoring both of these changes can enable the identification and staging of apoptotic cells in a high-content analysis assay. In this example, FITC-annexin V is used to stain exposed extracellular phosphatidylserine, propidium iodide (PI) is used to detect loss of plasma membrane integrity, and Hoechst 33342 is used to identify nuclei of all cells.

Sample preparation Human lung epithelial cells (A549) are pre-incubated with test compounds at 37°C. Following the exposure period, cells are washed gently with PBS before incubating with Hoechst 33342, FITC-annexin V, and PI in fresh culture medium for 15 min. After staining, cells may be imaged either live or following formaldehyde fixation.

Image acquisitionIn this example, a 10× objective is sufficient to capture apoptotic changes in nuclear morphology and plasma membrane staining. Since cell number is often substantially reduced in wells exposed to toxic compounds, online cell counting (optional) can ensure that a statistically significant number of cells is captured in each well; a sufficient cell count threshold of greater than 100 will help to improve assay robustness. Refer to Application 1 for an example of how to apply online cell counting.

Image analysisInvestigatorlevel:2

Protocoltype: Multi-Target Analysis Module

1. Open image stack file.

2. Create or edit an analysis protocol.

3. In the Images section of the analysis protocol, select Nuclei, Cells and Reference 1 as object types corresponding to the Hoechst 33342, FITC-Annexin V and PI image channels respectively.

4. In the Segmentation section of the analysis protocol, select the Top-hat method for nuclei segmentation and choose the minimum nuclear area and sensitivity. For segmentation of cells, select the Region growing method and choose the level of shading and noise removal required. For the image stack used in this example, enhanced levels of shading and noise removal were optimal. For segmentation of Reference 1 (detection of PI), choose to use objects from the Nuclei channel.

5. In the Measures section of the analysis protocol, select the desired measures, including Nuclei>Nuc Intensity and Cells>Nuc/Bckg Intensity.

6. If subpopulation analysis is desired (Figs 2, 3, and 4), it is helpful to run the analysis protocol once to populate the data table before setting up a Decision tree filter. To do this, proceed to the Summary section, select all measures and click Finish. Analyze data from selected wells, preferably those that contain cells likely to belong to each of the four classes, and then re-open the protocol for editing. In the Filter section of the protocol, set up a Decision tree following the example shown in Figure 2, using the interactive histogram and scatter-plot displays to assist in setting an appropriate threshold at each decision point.

14 28-9894-51 AA 01/2011

7. In the Summary section of the analysis protocol, select all of the available measures, being sure to include those for cell percentage (%) and number (n) associated with the Decision tree filter.

8. In the Data Management section of the analysis protocol, specify export of the data in Microsoft® Excel® format.

9. Finish the analysis protocol, run the analysis, and then plot the results as desired. For example, in Figure 1, intensity in the Nuclei channel is plotted against Nuc/Bckg Intensity in the Reference 1 channel. In Figure 4, the percentage of each of the four subpopulations is plotted against the dose of ionomycin.

Analysis flow chart

Open image stack file and create or edit an analysis protocol

Assign Wave indices to Nuclei (Hoechst 33342), Cells (FITC-annexin V), and Reference (PI) channels

Select segmentation methods and parameters to identify nuclei and cells (use Region growing in the FITC-annexin V channel)

Specify measures, including those for FITC-annexin V and PI intensities

Optionally, populate the data table and set up a Decision tree filter to identify subpopulations

Specify summary measures, then finish and run the analysis protocol

Open summary data file and plot results

Secondary analysis and data visualization The relative intensities of PI and FITC-annexin V can provide an indication of whether cells are healthy, early apoptotic, late apoptotic/necrotic, or late necrotic/dead (Fig 1). Using the interactive Decision tree tool integrated into the analysis protocol, the operator can easily automate assignment of cells to subpopulations based on marker intensities (Figs 2 and 3). The percentage of cells in each subpopulation can then be plotted (Fig 4) and analyzed using curve fitting tools available in most statistical analysis packages.

28-9894-51 AA 01/2011 15

Fig1.Typical2-Dplotofapoptosisassayresults. Plasma membrane integrity (PI intensity) is plotted against extracellular phosphatidlyserine (FITC-Annexin:Background Intensity) following treatment with ionomycin at doses ranging from 0 to 500 µM. Early apoptotic cells stain positive for annexin V but not for PI; late apoptotic and necrotic cells stain positive for both markers; late-stage necrotic or dead cells stain positive for PI but not annexin V. Individual points represent averaged data from a single well, with seven replicate wells per treatment condition.

Fig2.Decisiontreefilteringofsubpopulations. Using the interactive Decision tree tool integrated into the analysis protocol, the operator can easily automate assignment of cells to subpopulations based on marker intensities. The histogram tool beside each decision point shows the user-set thresholds (vertical line) adjusted to segregate the two component subpopulations. In this example, cells are initially segregated into two populations by thresholding on PI intensity. Each subpopulation is then further sub-divided into two populations based on FITC-annexin V staining. The final Decision tree routine identifies four subpopulations: healthy cells (H, green), early apoptotic (EA, magenta), dead/necrotic (DN, red), and late apoptotic (LA, yellow).

16 28-9894-51 AA 01/2011

Fig3.Automatedanalysisofamultiplexedapoptosisassay. The automated analysis protocol assigns each cell into one of the four classes created with the Decision tree tool: healthy (green outlines), early apoptotic (magenta outlines); necrotic/dead (red outlines), and late apoptotic/necrotic (yellow outlines). Arrows highlight examples of each class.

125

100

75

50

25

00 0.5 1.0 1.5 2.0 2.5 3.0

Ionomycin (log µM)

Cell

Popu

latio

n (%

)

% Healthy

% Early Apoptotic

% Late Apoptotic

% Dead Necrotic

Fig4.Dose-responseplotsofsubpopulationsidentifiedbyDecisiontreeanalysis. Ionomycin induces dose-dependent changes in the percentages of each of the four subpopulations. The percentage of healthy cells decreases as the percentages of other subpopulations initially increase. The early and late apoptotic subpopulations show a sharp decline at the highest dose, concomitant with an increase in the percentage of dead/necrotic cells.

18-1152-69 AD 08/2010 17

4. Cell morphology classification with machine learning

Application overviewDuring the early stages of death, cells exhibit changes in morphology. In particular, cells shrink in size, become more rounded, develop plasma membrane blebs, and show signs of nuclear DNA condensation and fragmentation. Such morphological changes can provide an indication of compound toxicity. To monitor cell morphology, a fluorescent reporter is required to stain the cell body. In the example shown here, a GFP fusion protein serves this purpose. Cells are counter-stained with Hoechst 33342 to capture changes in nuclear morphology and intensity. Supervised classification (including machine learning) is used to train the analysis software to automatically classify cells as dying or healthy, based on available morphology and intensity measurements. An optimizer function within the supervised classification tool helps you identify which classification method is appropriate and which parameters are most important for separating the various classes.

Sample preparationCells are seeded to 96-well plates. Following incubation with test compounds, cells are incubated with Hoechst 33342 to identify nuclei. In this example, cells express a GFP fusion protein that serves as a cell body marker. Alternatively, a cytoplasmic stain such as FITC may be added. The cells are then imaged live.

Image acquisitionIn this example, a 10× objective was sufficient to capture morphological changes associated with toxicity (Fig 1). Since cell number is often substantially reduced in wells exposed to toxic compounds, online cell counting (optional) can ensure that a statistically significant number of cells is captured in each well. A sufficient cell count threshold of greater than 100 will help to improve assay robustness. Refer to Application 1 for an example of how to apply online cell counting.

A B

Fig1.Effectofatestcompoundoncellmorphology. Cells incubated in the absence (A), or presence (B) of a toxic test compound that induces morphological changes indicative of programmed cell death (apoptosis).

18 28-9894-51 AA 01/2011

Image analysisInvestigatorLevel:2

Protocoltype:Multi-Target Analysis Module

1. Open an image stack file comprising images of healthy and dying cells (controls).

2. Create or edit an analysis protocol using the Multi-Target Analysis Module.

3. In the Parameters section of the analysis protocol, select the Wave indices that correspond to the Nuclei and Cells image channels.

4. In the Segmentation section of the analysis protocol, select the Top-hat method for nuclei segmentation, and the Multiscale top-hat method for cell segmentation. Adjust the corresponding area and sensitivity parameters to adequately detect nuclei and cell bodies. Check the segmentation settings by analyzing images from several wells and treatment conditions.

5. Save the analysis protocol and run an analysis. Click Done when the analysis is finished to save the results (*.lg3 file).

6. Use the Annotation function to create any number of desired cell classes (e.g., dying and healthy) and annotate representative cells to each class (Fig 2A). The annotated data set is used to “train” the automated classifier.

7. Use the Classification Protocol Builder (Fig 2B) to choose the method of classification and the parameters used for classifier training. Use the optimizer function to decide on the minimum number of parameters to create good separation of the classes. Save the optimized Classifier (*.xcls).

8. Open the analysis protocol (saved in Step 5) for editing, click on Supervised Classifiers and add the classifier to the analysis protocol.

9. In the Measures section of the analysis protocol, select the measures you want the software to acquire, as well as the measures you want to be reported in the Summary.

10. In the Data Management section of the analysis protocol, specify export of the data in Microsoft Excel format.

11. Test the analysis protocol on control wells to verify that the classifier works as expected. If not, re-train the classifier to suit the assay conditions.

Note that once a supervised classifier has been created and saved, it can be incorporated into any Multi-Target Analysis protocol (Level 2) that has the corresponding object types and wavelengths.

18-1152-69 AD 08/2010 19

Analysis flow chart

Open training image stack and start a new analysis protocol

Set Wave indices to correspond to Nuclei and Cells image channels

Adjust Segmentation methods and parameters

Analyze training images (positive and negative control samples) with the analysis protocol

Annotate images, and create a classification protocol (classifier)

Incorporate classifier into the analysis protocol and save

Open image stack and analyze

Open summary data file and plot results

A B

Fig2.Creatinganautomatedmorphologyclassifier. (A) Use the point-and-click annotation tool to assign cells to user-defined classes, thus building a training data set. (B) Use the classification protocol builder to help you decide the identity and number of parameters required to separate the classes. In this example, the software has automatically identified four parameters (circled in pink) that provide good separation of the two classes (represented by the green and blue points on the scatter plot). The optimized automated Classifier (*.xcls file) can then be added to any Multi-Target Analysis protocol.

20 28-9894-51 AA 01/2011

Fig3.Toxicityanalysiswithanautomatedclassifierincorporatedintotheanalysisprotocol. Screenshot shows the user interface following completion of analysis. A classifiier was created to assign cells to ‘dying’ (D, blue) or ‘healthy’ (H, pink) classes, based on multiple morphological measurements. Colors and labels in the image bitmap overlay indicate the class to which each cell has been assigned. The plate map has been colored to indicate the percentage of dying cells in each well.

Secondary analysis and data visualizationA bar graph of the results shows that compounds 1 and 2 induce significant decreases in the percentage of healthy cells, compared to the untreated sample.

90

80

70

60

50

40

3020

10

0

Compound 1

No Treatm

ent

Compound 2

% H

ealth

y

Fig4.Analysisresults.Percentageofhealthycellsisplottedforvarioustreatmentconditions. Bars represent mean % Healthy +/- 1 SD, determined from 48 replicate images (wells) per treatment condition.

18-1152-69 AD 08/2010 21

5. Toxicity study using live cardiomyocytes

Application overviewCardiomyocytes derived from human embryonic stem cells (hESC) provide a physiologically relevant model system for toxicity testing of drug compounds. In this example, GE Healthcare hESC-derived cardiomyocytes are used to screen a panel of six test compounds (plus controls) for toxic effects.

Sample preparationCryopreserved cardiomyocytes are thawed and plated into 96-well microplates, allowed to mature, and then incubated with test compounds at 37°C. A prepared mix of four different fluorescent probes is then added directly to the cells. After incubating at 37°C for a further hour, the cells are imaged with IN Cell Analyzer 2000. The multi-fluor probe mix provides information on DNA status (Hoechst 33342), calcium mobilization (Fluo-4 AM), mitochondrial status (TMRM), and plasma membrane integrity (TOTO-3 iodide).

Image acquisitionImages of live cells are acquired on IN Cell Analyzer using a 40×/0.6 NA objective. In this example we used IN Cell Analyzer 2000 configured with the large chip CCD camera, imaging four fields of view in each of three replicate wells. The Quad 2 polychroic mirror was used in conjunction with the following excitation (x) and emission (m) filter combinations: 350/50x, 455/50m for Hoechst 33342; 490/20x, 525/20m for Fluo-4 AM; 579/34x, 624/40m for TMRM; and 645/30x, 705/72m for TOTO-3.

Image analysis InvestigatorLevel:2

Protocoltype: Multi-Target Analysis Module

Nuclei are identified (segmented) on the basis of Hoechst 33342 staining. A dilated collar around the nuclear region is used to sample Fluo-4 intensity. Mitochondria (“organelles”) are segmented on the basis of TMRM staining and sampled in a “dilated cell” region (consisting of the nuclear region and surrounding extended collar region). TOTO-3 signal is detected by setting up a “reference” object type, specifying Nuclei as the source of the segmentation mask, and then applying the mask to the TOTO-3 channel. Using images from control and sample wells, the segmentation parameters should be adjusted to ensure that nuclei are accurately identified, that the mitochondrial sampling region is large enough to identify the majority of mitochondria, and that mitochondria are accurately segmented. A threshold filter is set up to identify viable and non-viable cells on the basis of TOTO-3 intensity.

22 28-9894-51 AA 01/2011

Analysis workflow

Open image stack file and create or edit an analysis protocol

Define input images by selecting object types (Nuclei, Cells, Organelles, and Reference 1), and specifying the corresponding image channel (Wave): Hoechst, Fluo-4, TMRM and TOTO-3, respectively

Select and adjust segmentation methods for Nuclei, Cells, and Organelles. Specify that Reference 1 objects are from Nuclei

Select all available measures, collect some sample analysis data, and use it to set up a threshold filter that identifies TOTO-3-positive and -negative subpopulations.

Select all Summary measures, including those for number and percentage of cells in the defined subpopulation

Specify spreadsheet format for data export

Save and run the analysis protocol

Secondary analysis and data visualizationA variety of approaches for secondary analysis and data visualization may aid understanding and interpretation of the results. For the study described here, data transformation and clustering tools were informative, including Principal Component Analysis (PCA), hierarchical clustering, and k-means clustering. Secondary analysis and data visualization were conducted using Spotfire DecisionSite tools, which facilitate generation of 2-D and 3-D scatter plots, bar charts, heat maps, dose-response plots, and profile plots.

PCA is a powerful data reduction tool used to describe the dataset using fewer variables (principal components), aiding identification of similarities and differences within a complex dataset. The following instructions guide you through PCA and plotting of the results in one or two dimensions.

1. Import analysis results into Spotfire DecisionSite.

2. Select Data>Add Columns to add extra columns to the Spotfire data table to incorporate information relating to the compound name, concentration, incubation time, etc..

3. Select Data > Clustering > Principal Component Analysis to open the PCA dialog.

4. Add the required columns from the Available columns list and select All records. Select Replace empty values with Column average.

5. For the Calculation options, select 1 principal component, use the default column name (PCA) and select Overwrite. Check the Create Scatter Plot box and 2D. The principal component is then calculated and a new column containing the results is added to the data set. (The number of principal components is the number of dimensions to which you wish to reduce the original data. The PCA tool calculates the n best components, where n is the number of dimensions you have selected.)

18-1152-69 AD 08/2010 23

6. Create a scatter plot for one principal component, selecting PCA1 for the y-axis and compound for the x-axis (Fig 1). Right click on the plot, select Properties to open the Scatter Plot dialog. Select Color By compound, check Categorical, and select Size By viable %.

7. To produce a scatter plot for two principal components, repeat the above steps in the PCA dialog box, selecting 2 principal components. This automatically generates a 2-D scatter plot of PCA1 on the x-axis and PCA2 on the y-axis. Right click on the plot and select Properties to open the Scatter Plot dialog. Color the data points by compound. Connect lines by compound, ordering by concentration and selecting arrows indicate direction.

Fig1.Identificationoftoxiccompoundsonthebasisofthefirstprinciplecomponent(PCA1). Each point represents the well-averaged PCA1 value for a particular treatment (three replicates per dose, with doses ranging from 0 to 300 µM). Points are colored by compound and sized by relative percentage cell viability, as indicated in the legend. Each vertical axis shows the PCA1 results for one of the test compounds or controls over the dose range (three replicates per dose). Thresholding can be applied to identify toxic compounds on the basis of a selected parameter. For example, acutely toxic compounds (circled in pink) could be defined as those having PCA1 values less than -0.27 (as indicated by the broken pink line).

24 28-9894-51 AA 01/2011

Fig2.2-DPCAanalysisrevealsdifferentialphenotypicresponsestotestcompounds. Each point represents the well-averaged PCA2 value for a particular treatment on the y-axis and the PCA1 value on the x-axis (three replicates per dose, with doses ranging from 0 to 300 µM). Points are colored by compound and connected by concentration, as indicated in the legend

MiningFor further management and investigation of the results, import acquisition and analysis files into IN Cell Miner HCA data management system (see Application 6 for more detail). Using IN Cell Miner, you can organize data hierarchically into Project, Screen, Run, and Plate aspects. A graphical view of the project hierarchy (Figure 3) allows you to browse and access each aspect by clicking on the respective node. From the plate aspect (Figure 4), you can assess summary results at a glance by coloring the wells according to any acquired measure . IN Cell Miner allows you to capture the full value of your data by enabling you to organize, find, share, compare and publish your data—quickly and easily.

18-1152-69 AD 08/2010 25

Fig3.INCellMinerdataorganizationfortheproject. Data are organized into hierarchical levels that can be specified by the system administrator.

Fig4.INCellMinerPlateViewrevealsadose-dependenteffectoncellcount. Wells of the plate map are colored by the selected measure (in this case cell count). As compound dose increases from left to right across the plate, total cell count decreases, as indicated by the well color (only a portion of the entire Plate Aspect window is shown here).

26 28-9894-51 AA 01/2011

6. High-content hepatotoxicity screen

Application overviewLiver toxicity has led to ‘black box’ warnings for a number of drugs and is a common reason for withdrawal of drugs from the market. Detecting potential hepatotoxic compounds early in the drug development process may reduce overall project costs and lower the risk of adverse effects during clinical trials or after launch. High-content screening, which enables detailed profiling of drug effects at the cellular level, has the potential to identify hepatotoxic compounds at an early stage in the development process when the cost of failure is relatively low. In addition, a high content screening approach can provide deeper insight into the mechanisms of drug action. In this example, a collection of compounds is screened for toxic effects on Hep G2 cells (a human hepatocyte cell line). Cells are exposed to compounds for three days, and then imaged live after adding a mixture of four fluorescent probes to report on mitochondrial membrane potential (TMRM), intracellular free calcium (Fluo-4 AM), plasma membrane permeability (TOTO-3), and nuclear status (Hoechst 33342). Live-cell imaging captures both time- and dose-dependent effects of test compounds. High-content analysis facilitates robust multiparametric cell profiling to identify toxic compounds, rank the degree of toxicity, and gain mechanistic insights. IN Cell Miner high-content data management system provides a powerful means of storing, retrieving, and interpreting the vast quantity of data generated using this approach.

IN Cell InvestigatorSpotfire Decision Site

IN Cell InvestigatorLevel 2 (MTA)

bulk cultureassay design

96 or 384-well plates

IN Cell Analyzer

optimization

visualizationanalysis

data management

IN Cell Miner

executiondesign

Fig1.Processflowchartforhigh-contenthepatotoxicityscreening. The IN Cell Analyzer high-content analysis system and associated software provide an integrated solution for high-content screen execution, analysis, and data management.

Sample preparationHep G2 cells (GE Healthcare) are thawed, suspended in complete growth medium and seeded at 9000 cells per well to 96-well plates precoated with poly-D-Lysine. After a 24 h incubation to allow the cells to attach, test compounds are added (50 µl of 3× stock) and the cells are incubated for a further three days. Assay plates are then transferred to IN Cell Analyzer 2000. Onboard liquid handling is used to add fluorescent probe solution (50 µl of 3× stock) to all test wells. Final concentrations of the fluorescent probes are 0.8 µM Hoechst 33342, 20 nM TMRM, 1 µM Fluo-4 AM, and 1 µM TOTO-3 (all sourced from Invitrogen).

In this example, the set of 42 test compounds included both severely and moderately hepatotoxic compounds. Also included were non-toxic compounds and compounds known to be toxic to other organs. Compounds were tested at concentrations up to 100 µM, or 30 times the maximum total concentration of the drug reported circulating at the therapeutic dose (30× Cmax). The final DMSO concentration for all test wells was 0.5%. Assay setup allowed for seven drug compounds per plate (11 doses per compound) for a total of six plates. Control compounds (0.5% DMSO, 6.25 µM FCCP, 0.63 µM ionomycin, and 0.0063% Triton X-100) were present on all assay plates.

18-1152-69 AD 08/2010 27

Image acquisitionCells are imaged using IN Cell Analyzer and a 10× objective. Ten fields of view are acquired from each well at 1h, 2h and 3h time points. In this example, IN Cell Analyzer 1000 1.1 kinetic instrument was used with the following excitation (x) and emission (m) filters: D360/40x and HQ460/40m for Hoechst 33342; HQ570/20x and HQ620m for TMRM; HQ475/20x and HQ535m for Fluo-4 AM; HQ620/60x and HQ700/75m for TOTO-3 iodide.

Image analysisInvestigatorLevel:2

Protocoltype:Multi-Target Analysis Module

Nuclei are segmented using the Hoechst 33342 image. The degree of nuclear fragmentation or condensation is quantified from the Hoechst 33342 image as the number of inclusions in the defined nuclear region. A cell-sampling region is defined by creating a collar around each nucleus. Mitochondria membrane potential is quantified from the TMRM image as the number of punctate inclusions in the cytoplasm (defined by the collar). Intracellular free Ca2+ is quantified from the Fluo-4 AM image as the average intensity in the defined cell region. This is achieved by setting up a reference channel, and specifying Cells as the source of the segmentation mask. Likewise, a Reference channel is used to quantify plasma membrane permeability from the TOTO-3 image by measuring average intensity in the nuclear region. The analysis workflow is summarized below.

Open image stack file and create or edit an Analysis Protocol

Define input images by selecting object types and corresponding image channel (Wave):

Select and adjust a segmentation method for each object type:

Select and include in the Summary all available measures

Specify spreadsheet format for data export

Save and run the analysis protocol

Object type Wave ProbeNuclei 1 Hoechst 33342Cells 2 TMRMOrganelles 2 TMRMOrganelles 1 1 Hoechst 33342Reference 1 3 Fluo-4Reference 2 4 TOTO-3

Object type Segmentation Objects fromNuclei Top-hat -Cells Collar (10 um) -Organelles Multi-scale top-hat -Organelles 1 Multi-scale top-hat -Reference 1 Pseudo CellsReference 2 Pseudo Nuclei

28 28-9894-51 AA 01/2011

Secondary analysis and data visualizationTransfer analysis results to Spotfire DecisionSite by clicking on the Spotfire Connect icon. Profile charts are a useful means of displaying the normalized results for a set of key parameters. For example, Figure 2 shows the profile plots for seven drugs at 11 doses (77 plots in all). Toxic responses can easily be identified by automatic coloring of the plots based on a user-set threshold.

Fig2.Toxicresponseidentificationusingprofilecharts.Profilechartsareshownforsevendrugsat11doses. Each chart has six vertical axes, each representing a different measured parameter (normalized well-averaged result). In this example, parameters 1 to 6 are: Nuclear area, TMRM organelle count, Nuclear inclusions, Fluo-4 intensity, TOTO-3 intensity, and Cell count. Profiles showing toxic indications for fewer than two of the six parameters are categorized as non-toxic responses (green), while those showing toxic indications for two or more parameters are categorized as toxic responses (red).

Data management with IN Cell MinerUse IN Cell Miner to store, retrieve, and further assess data from the toxicity screen.

1. Open the IN Cell Miner HCM software and from the homepage create a new project; Project Aspect will automatically open, displaying the project icon.

2. Set up multiple levels of the project (corresponding to screen, run and plate) by right clicking on each icon in turn and adding the required information. Create multiple runs and plates corresponding to time points or compound sets as required. Save the project before importing data.

3. Right click on a plate icon and select Import image stack. Select the correct plate destination and then select the corresponding source image stack file (*.xdce) to import.

4. In Project Aspect, right click on a run icon and select Transfer to plate aspect. All plates in the run will then be displayed and different parameters can be selected for comparison.

Toxic Non-toxic

18-1152-69 AD 08/2010 29

Fig3.INCellMinerHCMPlateAspect. Each plate is represented as a heat map, which can be colored by an acquired measure (in this case, cell count). Compounds having dose-dependent effects on cell count are readily apparent.

Fig4.EC50/IC50determination. The plate map is colored to show the effect of lovastatin on total mitochondrial area. The corresponding dose-response plot and summary data (including EC50/IC50 and Hill slope) are displayed.

30 28-9894-51 AA 01/2011

7. Cell cycle analysis using a DNA stain

Application overviewThe DNA content of cell nuclei varies through the cell cycle in a predictable fashion. Cells in G2 and M phases have twice the DNA content of cells in G1 phase, while cells undergoing DNA synthesis in S phase have an intermediate amount of DNA. Consequently, the relative proportion of cells in G1, S, and G2/M phases can be determined by quantifying the intensity of nuclear staining with DNA-binding dyes such as Hoechst 33342. Cell-by-cell fluorescence intensity measurements can be acquired by flow cytometry, imaging flow cytometry, or high-content analysis systems such as IN Cell Analyzer. Of these three approaches, only HCA allows the cells to be examined in situ as adherent monolayers, and is therefore amenable to higher-throughput applications and detailed morphological analysis of adherent cells. To study the effects of chemical perturbation of the mammalian cell cycle, we have treated human osteosarcoma cells (U2-OS) with colchicine, which binds tubulin and acts as a mitotic inhibitor by preventing microtubule polymerization.

Sample preparation and image acquisition1. Plate U2-OS cells overnight in 96-well plates and treat for a further 24 h with colchicine.

2. After incubation, remove the medium and incubate the cells for 30 min at room temperature with 20 µM Hoechst 33442 in the presence of 4% formalin in PBS. This serves to stain and fix the cells in one step and in our hands gives the most reliable cell cycle discrimination.

3. Wash the cells with PBS and image on IN Cell Analyzer using a 10× objective with a 350/50 nm excitation filter and a 455/50 nm emission filter

Image analysisInvestigatorlevel:2or3

Protocoltype: Multi-Target Analysis Module or user-defined protocol

1. Create an analysis protocol that identifies cell nuclei and reports all available nuclear measures.

2. Collect some sample data and use it to create a Decision tree filter similar to the one shown in Figure 1, which classifies cells as G1, S, or G2M phase based on integrated nuclear intensity (density × area). Histograms accessed within the tool help you set an appropriate threshold at each decision point.

3. Incorporate the Decision tree filter into the analysis protocol.

4. In Multi-target analysis, select the Summary measures associated with the Decision tree filter (number and percentage of cells in each subpopulation).

5. Save and run the analysis protocol.

18-1152-69 AD 08/2010 31

A B

Fig1.SubpopulationanalysisusingDecisiontreefiltering. (A) A data filter Decision tree classifying cells into subpopulations on the basis of integrated nuclear intensity (density × area) at each decision point. Initially, cells are segregated into G1 and S/G2/M populations. The S/G2/M population is then further subdivided into S and G2M populations as shown. (B) Intensity histogram colored to show the results of Decision tree classification of cells into G1 (blue), S (red), and G2M (green) subpopulations.

Secondary analysis and data visualizationThe benefits of HCA for study of the cell cycle are shown in Figure 2. In addition to cell cycle classification, nuclear morphology can be quantified and the assay can be readily combined with fluorescent markers for other end-points, such as cell viability and apoptosis.

Fig2.Scatterplotsofsinglecelldatarevealtheeffectsofcolchicinetreatment. Spotfire DecisionSite scatter plots show data from cells cultured in (A) the absence, or (B) the presence of 625 nM colchicine. Images show the analysis overlays of cell cycle phase classification. The symbols on the plots are colored according to the cell cycle phase classification. The G2/M phase nuclei, in particular, are characterized by brighter staining with the dye, indicative of a higher DNA content, and many are larger.

A

B

32 28-9894-51 AA 01/2011

The results presented in Figure 3 show the expected dose-dependent block of the cell cycle in the G2/M phase by colchicine.

70

60

50

40

30

20

10

Cell

Cycl

e Ph

ase

(as

% to

tal n

ucle

i)

Cell CydePhase:

1 10 100 1000 10000

Colchicine conc. (nM)

G1SG2M

Fig3.Colchicineinducesdose-dependentchangesincellcyclephase. (A) Dose-response plots of cell cycle status versus colchicine concentration. (B) Spotfire trellis plot of cell cycle distribution for the various concentrations of colchicine. Data points on the graph are means of four replicate wells (+/- 1 SD).

18-1152-69 AD 08/2010 33

8. Comet assay

Application overviewMeasurement of DNA damage by single cell electrophoresis, commonly referred to as the comet assay, is a widely used technique that detects damaged DNA by subjecting cells embedded in an agarose gel to lysis and DNA denaturation, followed by electrophoresis. Automated analysis through HCA enables a significantly higher throughput than manual techniques, and decreases assay times from hours to minutes.

Sample preparation and image acquisition1. Following electrophoresis, fix comet assay slides in 70% ethanol for 5 min and dry overnight at room temperature.

2. Stain DNA with SYBR™ Green dye (20 µl/sample) at 4°C for 5 min and remove excess stain by blotting before drying at room temperature.

3. Place slides in a slide holder and acquire images on IN Cell Analyzer using a 4× objective with 480–40 nm excitation and 535–50 nm emission filters.

Image analysisInvestigatorLevel:3

Protocoltype: User-defined protocol

Analyze images (Fig 1) using a dual-threshold segmentation approach to identify comet tails and nucleoids (Fig 2). Construct user-defined measures for commonly used comet assay parameters (Fig 5), for example:

%DNAinTail: Intensity measures for nucleoid DNA (nucleoid-only fluorescence) and comet DNA (total fluorescence) are used to construct a user-defined measure expressing the % intensity of DNA staining in comet tails as:

% DNA = [comet intensity - nucleoid intensity]/comet intensity × 100

TailLength: Center of gravity positions (expressed as x, y coordinates in micrometers from the lower left corner of each image) for linked nucleoids and comets are used to calculate the distance between paired objects using Pythagoras’ theorem.

Tail Length = √ [(x - x)2 + (y - y)2]

TailMoment: The two previously calculated values for % DNA in Tail and Tail Length are combined to formulate a third commonly used measure of DNA damage in comet assays:

Tail Moment = [% DNA in Tail] × [Tail Length]

Contact your local Technical Support for information about availability of a user-defined protocol for comet assay analysis.

34 28-9894-51 AA 01/2011

Secondary analysis and data visualizationDefining commonly used comet assay metrics allows data to be automatically generated and reported within the Investigator software interface (Fig 3) and exported directly into Spotfire DecisionSite analytics software for data visualization (Fig 4). Examples of user-defined comet assay measures reported by the analysis protocol are shown in Figure 5.

A B

Fig1.CometassayimagedonINCellAnalyzer1000. Jurkat cells were incubated in the absence (A) or presence (B) of 100 µM H2O2, processed for electrophoresis, and stained with SYBR Green dye for imaging.

Image

Segmentation 1

Intensity (RFU)

Center of gravity(x,y) µm

Nucleoid:comet linking

% DNA in tail*

100 x [comet intensity – nucleoid intensity]/[comet intensity]

Tail length*

v [(x-x)2 + (y-y)2]

Comet moment*

[%DNA in tail] x [Tail length]

Segmentation 2

Intensity (RFU)

Center of gravity(x,y) µm

Target setComets

Target setNucleoids

Fig2. Scheme for a user-defined comet assay analysis protocol. *User-defined measures.

18-1152-69 AD 08/2010 35

Fig3.CometassayanalysiswithINCellInvestigator. Data for comet tail moment (% DNA in Tail × Tail Length) are shown for two H2O2 treated cells with high and low levels of DNA damage (arrowed).

Fig4.VisualizationofcometassaydatainSpotfireDecisionSite. Comet data were imported directly from IN Cell Investigator Developer into Spotfire DecisionSite and overlaid on the analyzed image. Colored circles indicate the position of the center of gravity of comet tails with circle color indicating the percentage of DNA in the comet tails (numerical values are shown next to each comet) and the size of each circle indicating the tail moment value (% DNA in Tail × Tail Length). Axes are scaled in micrometers.

Fig5.User-definedcometassaymeasures. Jurkat cells were treated with H2O2 over a range of concentrations from 0 to 100 mM and analyzed for Tail Length, % DNA in Tail, and Tail Moment.

36 28-9894-51 AA 01/2011

9. Golgi complex disruption

Application overviewCentral to the secretory pathway, the Golgi complex is actively involved in sensing stress stimuli and triggering apoptosis when stress thresholds are exceeded. Consequently, morphological changes in the Golgi complex can be an early indication of cytotoxicity. Using high-content analysis, compounds that alter or disrupt the Golgi apparatus can be detected and differentiated from each other on the basis of their effects on Golgi compartment markers. In this example, compartment-specific markers are used to distinguish the cis-Golgi network (CGN) from the trans-Golgi network (TGN).

Sample preparationHeLa cells are seeded into 96-well plates and incubated at 37ºC overnight. After incubation for 4 h with test compounds, cells are fixed with 2% formalin prior to permeabilization with 0.2% Triton™ X-100. Golgi functional regions are detected by indirect immunostaining for GM130 (CGN marker) and TGN41 (TGN marker). Cell nuclei are counterstained with Hoechst 33342.

Fig1.CGNandTGNmarkersarelocalizedtodistinctbutcloselyjuxtaposedlocations.Mainimage: pseudocolored image of untreated HeLa cells acquired with the 20×/0.45 NA objective in 2-D deconvolution mode shows distribution of the CGN marker GM130 (green) and the TGN marker TGN46 (red) in characteristic ribbon-like structures. Inset: acquisition with the 100×/0.9 NA objective in 3-D Deconvolution mode reveals that the two markers are located in non-overlapping compartments (only a portion of the entire image is shown).

Image acquisitionImages are acquired using IN Cell Analyzer 2000; the Quad2 polychroic mirror was chosen in conjunction with the following excitation (x) and emission (m) filter sets: 350/50x and 455/50m for Hoechst 33342 (nuclei); 490/20x and 525/36m for FITC-conjugated secondary antibody (CGN marker); 579/34x and 624/40m for Texas Red-conjugated secondary antibody (TGN marker). High magnification objectives (40×/0.6 NA up to 100×/0.9 NA) are recommended for detailed analysis of Golgi phenotype, particularly if image restoration (deconvolution) modes are applied, as detailed below. For screening configurations, the 20×/0.45 NA objective provides an optimal cell count while generating images with enough resolution for feature extraction and analysis of compartment overlap.

IN Cell Analyzer 2000 onboard deconvolution modes can be applied during image acquisition to improve contrast and resolution of CGN and TGN compartments. 2-D Deconvolution mode is the most rapid image restoration option, and is suitable for screening configurations. For more detailed and quantitatively accurate analysis of Golgi morphology and marker localization, a 3-D Deconvolution mode is recommended.

18-1152-69 AD 08/2010 37

The following protocol outlines the procedure for performing 3-D deconvolution on a region of interest. Settings used to generate the inset in Figure 1 are indicated in parentheses.

1. Load the plate into the instrument and ensure lamp is on

2. Within Assay development mode, write an acquisition protocol using the protocol designer.

3. Select the plate type from the drop down menu.

4. Choose the objective (in this case 100×/0.9 NA).

5. In the Microscope card select:

a. Number of wavelengths (3)

b. Excitation and emission filters for each wavelength (as above)

c. 1×1 binning

d. Polychroic (QUAD 2)

e. Imaging mode for each wavelength (3-D Deconvolution for all)

6. Move to the Focus card, click in a well containing all three stains, and select the focus strategy (SWAF).

7. Find a region of interest either by performing a preview scan or by using the manual microscope mode. It may be helpful to start with a lower magnification to survey the sample and then swap up to the 100× objective.

8. Drag the field of view over the selected region of interest and then optimize the focus and exposure times for each wavelength.

9. Determine depth of the sample by manually stepping through the Z dimension in 2 µm increments, to determine the Z heights corresponding to the top and bottom of the sample, and then calculating the difference.

10. Return to the Microscope card and select the desired Z step size (for example ½ depth of field) and an appropriate Z slice number to cover the depth of the sample. With a positive Z step, the instrument will start the 3-D scan at the top of the section moving down through the slices, with the original focus position being the central slice.

11. In the Processing card, select the deconvolution method (Enhanced Ratio - aggressive) and under More Options adjust settings as desired (the default settings were used in this example).

12. Save the protocol and run the imaging experiment.

13. When 3-D deconvolution has been completed, the deconvolved image stack can be examined in the data review mode. Pseudo-color and contrast at each wavelength can be adjusted as required. The deconvolved stack can be viewed as a movie by selecting the movie icon and adjusting the control settings (e.g., Z slice range, frame rate, swing).

Compounds known to disrupt the Golgi complex have distinct effects on Golgi marker localization. BFA inhibits guanine nucleotide exchange factors involved in membrane trafficking, and is often used to dissect the Golgi, since it is known to have differential effects on localization of Golgi compartment markers. Consistent with literature reports, BFA causes the putative Golgi structural protein GM130 to redistribute to discrete punctate fragments while the TGN marker becomes more widely dispersed throughout the cytoplasm and sometimes concentrates in a juxta-nuclear region characteristic of the microtuble organizing center (Fig 2A). By contrast, nocodazole, which disrupts microtubule polymerization, is known to disperse the Golgi compartment into discrete ministacks, with CGN and TGN markers localizing to adjacent regions of the ministacks (Fig 2B).

38 28-9894-51 AA 01/2011

A B

Fig2.EffectsoftestcompoundsonGolgimarkerlocalization. (A) Following BFA treatment, the CGN marker (green) localizes to discrete punctate fragments, while the TGN marker (red) is more diffuse and often concentrates in the juxtanuclear region of the microtubule organizing center (arrow).(B)Nocodazole treatment disperses the Golgi into ministacks, with the CGN (green) and TGN (red) markers localizing to distinct but adjacent regions of each ministack. This image was acquired in 3-DDeconvolution mode using the 100x/0.9NA objective.

Image analysis Investigatorlevel:3

Protocoltype: User-defined

Write a user-defined protocol to identify nuclei, CGN (GM130 staining) and TGN (TGN41 staining) on a per cell basis. Construct user-defined measures for parameters of interest, for example:

• Cell count

• Total area of the CGN per cell

• Total area of the TGN per cell

• Total area per cell of both compartments combined (CGN plus TGN)

• Average area per cell of both compartments combined

• Percentage of the combined region comprising CGN [100 × CGN/(CGN + TGN)] per cell

• Total area of CGN and TGN overlap (“co-localization”) per cell

• Integrated intensity (DxA) of signal in regions of overlap (both CGN and TGN channels) per cell

• Average area per cell of CGN-TGN overlap

• Percentage of cells exhibiting any CGN-TGN overlap

Contact your local Technical Support for information about availability of a user-defined protocol for this assay.

18-1152-69 AD 08/2010 39

A B

Fig3.AnalysisofCGNandTGNlocalizationafterGolgidisruption. (A) Cropped image acquired using the 40×/0.6 NA objective in 2-D deconvolution imaging mode. HeLa cells were treated with 1.85 µM nocodazole before staining for nuclei (blue), GM130 (green), and TGN46 (red). (B) Same region of interest with analysis results superimposed to show areas identified as CGN (green outlines), TGN (red outlines), regions of marker overlap (cyan outlines), and cell body (yellow outline).

Secondary analysis and data visualizationTransfer analysis results to Spotfire DecisionSite by clicking on the Spotfire Connect icon. Dose-response plots for nine measured parameters reveal differential effects of nocodazole and BFA (Fig 4).

A B

Fig4.Drugeffectsonnineanalysisparameters. (A) Results for nocodazole treatment; vesicle count increases for both the CGN (green box) and TGN (pink box). (B) Results for BFA treatment; CGN vesicle count (green box) increases with dose, while TGN vesicle count (pink box) is relatively static.

40 28-9894-51 AA 01/2011

10. Multiplexed neurite and synaptic vesicle analysis

Assay overviewQuantifying the effect of test compounds on neurite outgrowth and synaptic vesicles can provide valuable information for compound toxicity assessment and profiling. This example gives an overview of the strategy for developing a user-defined protocol for multiplexed analysis of neurites and associated synaptic vesicles.

Sample preparationCultured primary hippocampal neurons are treated with test compounds and then stained to detect nuclei, cell bodies, neurites and synaptic vesicles. ββIII-Tubulin is a suitable marker for neuronal cell bodies and neurite extensions, while synaptophysin can be used as a marker of synaptic vesicles. The example images shown here were acquired from stained cell samples kindly provided by Dr. Janet Anderl, Millipore Corporation; the cells were indirectly immunostained for βIII-tubulin (FITC-conjugated secondary antibody) and synaptophysin (Cy™3-conjuaged secondary antibody) using a kit for neurite outgrowth and synaptic activity (HCS226, Millipore). Cells were counterstained with Hoechst 33342 dye (also provided in the kit) to identify nuclei.

Image acquisitionImages are acquired using IN Cell Analyzer with a 10× objective. Three replicate wells are imaged for each treatment condition, with four fields of view captured from each well. In this example, IN Cell Analyzer 2000 configured with large chip CCD camera was used to maximize the number of neurites captured per image.

A B

Fig1.Identificationofneuritesandsynapticvesiclesinaprimaryrathippocampalculture.(A) Portion of an image acquired with the 10× objective shows cells indirectly immunostained for βIII-tubulin (green) and synaptophysin (red), which localizes to synaptic vesicles. (B) Segmentation outlines resulting from the user-defined analysis routine show the identification of neurites (blue outlines) and synaptic vesicles (yellow outlines). Samples for imaging were provided courtesy of Janet Anderl, Millipore Corporation.

18-1152-69 AD 08/2010 41

Image analysis Investigatorlevel:3

Protocoltype: User-defined

IN Cell Investigator provides a choice of approaches for analysis of neurite outgrowth, ranging from the relatively simple Level 1 protocol presented in Application 2 to more complex user-defined protocols, such as the Level 3 analysis solution described here. In the protocol outlined below, target sets are created to identify nuclei, neurites, cell bodies, and synaptic vesicles. In primary cultures such as those imaged here, densely packed and often overlapping structures must be distinguished from each other, requiring advanced image processing tools accessible through the macro facility. Once preprocessing, segmentation, and post-processing steps have been defined for the various targets, composed linking is used to associate synaptic vesicles, neurites, and cell bodies. Desired measures can then be constructed and incorporated into the protocol

1. Open an image stack in IN Cell Developer.

2. Start a new user-defined protocol using the analysis protocol wizard.

3. Select corresponding source images for analysis of nuclei, neurites, and synapses.

4. Identify nuclei from the Hoechst image (DAPI channel):

a. Segment nuclei using the Nuclear segmentation method (or any other suitable method).

b. Erode the nuclear outlines and apply a sieve to exclude small fragments.

5. Identify the whole neuronal network from the tubulin image (FITC channel):

a. Create an image-preprocessing step to enhance the neuronal network in the image. If adequate feature definition is not achievable with the standard menu of preprocessing options, advanced transform filters are accessible through the macro facility. Use the Transform window to assess the effects of various filters (e.g., Target Accent). Any transform filter of choice can then be built into a macro, which can be incorporated into the user-defined protocol.

b. Segment the whole neuronal network from the image.

c. Apply a sieve to exclude small fragments.

d. Save the segmented network bitmap image in a spare channel to be used in Step 7.

6. Identify cell bodies (soma) in the tubulin image (FITC channel):

a. Create a preprocessing macro to recall the neuronal network bitmap.

b. Select the Intensity segmentation method.

c. Erode the neuronal network bitmap until only cell body outlines remain.

d. Apply Clump Breaking using nuclei as a seed.

e. Apply a sieve to remove small fragments and save output of this step (segmented cell bodies bitmap image) in a spare channel by selecting the output value in the All images panel.

f. Dilate the cell bodies bitmap slightly to assure overlap of cell bodies and neurites in Target linking (Step 9).

7. Identify neurites in the tubulin image (FITC channel):

a. Create a preprocessing macro to subtract the cell bodies bitmap from the whole neuronal network bitmap, leaving only the neurite extensions.

b. Apply Intensity segmentation.

8. Identify synaptic vesicles in the synaptophysin image (Cy3 channel) using the Vesicle segmentation method.

9. Create a Composed link to associate cell bodies with neurites and neurites with synaptic vesicles.

10. Construct user-defined measures for key parameters, such as number of neurites per cell, average neurite length per cell, and average number of synaptic vesicles per cell. Apply measures to linked targets.

42 28-9894-51 AA 01/2011

Analysis flowchart

Image sourceβIII-tubulin-stained neurons

Image sourceHoechst-stained

nuclei

Image sourceSynaptophysin-stained

synaptic vesicles

Target setNuclei

Segmentation(Nuclear)

Postprocessing (Erosion, sieve)

Seed for clump-breaking

Target setNeuronal network

Segmentation(Intensity)

Postprocessing (Sieve)

Target setCell body

Segmentation(Intensity)

Postprocessing(Erosion, clump breaking, sieve,

dilation)

Target setNeurites

Segmentation(Intensity)

Postprocessing (Sieve)

Target linkCell body- Neurites

Composed Target linkCell body-

Neurites-Synapses

Measures(as required)

Target setSynapses

Segmentation(Vesicle)

Target linkNeurites-Synapses

Preprocessing(Add macro to subtract

Cell body from Neuronal network)

Preprocessing(Add macro to bring

network bitmap)

Preprocessing(Add macro – Target Accent)

18-1152-69 AD 08/2010 43

Data visualization and interpretationCompound effects on neurite morphology and synaptic vesicles can be examined by plotting key measures as a function of dose, as illustrated in Figure 2.

A B

5000 200

150

100

50

0

2000

3000

2000

1000

0

Mea

n N

euri

tele

ngth

per

cel

l

Acrylamide (mM)

Mean num

bersynapses per cell

1.0 x 10 -6

1.0 x 10 -5

1.0 x 10 -4

1.0 x 10 -3

1.0 x 10 -2

1.0 x 10 -1

1.0 x 10 0

1.0 x 10 1

1.0 x 10 2

1.0 x 10 3

300

200

100

0

Sum

cel

l bod

ies

per

4 fie

lds

Acrylamide (mM)

1.0 x 10 -6

1.0 x 10 -5

1.0 x 10 -4

1.0 x 10 -3

1.0 x 10 -2

1.0 x 10 -1

1.0 x 10 0

1.0 x 10 1

1.0 x 10 2

1.0 x 10 3

Fig2.Acrylamide-inducedlossofneuritesandsynapses. (A) Both mean neurite length per cell and number of synapses per cell decrease in a dose-dependent manner, while the total number of cells (B) is relatively constant across the dose range. For both plots, each data point represents the mean of 3 replicate wells, with error bars showing +/- 1 SEM.

44 28-9894-51 AA 01/2011

Appendix

References

Toxicology(includingapoptosis)1. Rodriguez, R A., et al. 297 distinct cell lines: a high-content analysis assay

and a full-automation design solely using noncontact liquid dispensing. Journal of the Association for Laboratory Automation 12, 318–326 (2007).

2. Takahashi, M. et al., Clk-1 deficiency induces apoptosis associated with mitochondrial dysfunction in mouse embryos. Mechanisms of Ageing and Development 129, 291–298 (2008).

3. Edward, J. et al. High-content screening as a universal tool for finger printing of cytotoxicity of nanoparticles. ACSNANO 2(5), 928–938 (2008).

4. Pinto, S. et al. High-content analysis of oxygen reactive species and oxidative stress for toxicology in vitro, Toxicology Letters 196, S147–S147 (2010).

5. Rawlinson, L. A. High-content analysis of cytotoxic effects of pDMAEMA on human intestinal epithelial and monocyte cultures. J. Control Release 146(1), 84–92 (2010).

6. Beeson, C. C., et al. A high-throughput respirometric assay for mitochondrial biogenesis and toxicity. Analytical Biochemistry404, 75–81 (2010).

7. Gorenstein, J. et al. Reducing the multidimensionality of high-content screening into versatile powerful descriptors, BioTechniques, 48(3), 663–665 (2010).

Neuronalstudies(includingneuriteoutgrowth)8. Ramm, P. et al. Automated screening of neurite outgrowth. Journal of

Biomolecular Screening 8, 7–18 (2003).

9. Battle, D. J.et al. The Gemin5 protein of the SMN complex identifies snRNAs. Molecular Cell 23, 273–279 (2006).

10. Danzer, K. M. et all. Different species of {alpha}-synuclein oligomers induce calcium influx and seeding. Journal of Neuroscience 27, 9220–9232 (2007).

11. Li, F. et al. High-content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles. BMC. Biotechnol. 7, 66 (2007).

12. Rainey-Smith, S. et al. Neuroprotective effects of hesperetin in mouse primary neurones are independent of CREB activation. Neuroscience Letters 438, 29–33 (2008).

13. Li, F. et al. High-content image sequence analysis for quantifying calcium signals inside cells with mutant presenilin-1 of familial Alzheimer disease. Life Science Systems and Applications Workshop 2007, (2008).

14. MacInnes, N. et al. Proteasomal abnormalities in cortical Lewy body disease and the impact of proteasomal inhibition within cortical and cholinergic systems. Journal of Neural Transmission 115, 869–878 (2008).

15. Oien, D.B. et al. Clearance and phosphorylation of alpha-synuclein are inhibited in methionine sulfoxide reductase a null yeast cells. Journal of Molecular Neuroscience 39, 323–332 (2009).

16. Zhao, H. et al. Screening platform for glioma growth and invasion using bioluminescence imaging. Journal of Neurosurgery 111(2), 238–246 (2009).

17. Anderl, J. L. et al. A neuronal and astrocyte co-culture assay for high-content analysis of neurotoxicity, Journal of Visualized Experiments, 5(27), 1173 (2009)

18. Matsumoto, K. et al. Stimulation of neuronal neurite outgrowth using functionalized carbon nanotubes, Nanotechnology 21(11), 115101 (2010).

19. M. Gerard, M. et al. Inhibition of FK506 binding proteins reduces alpha-synuclein aggregation and Parkinson’s disease-like pathology. Journal of Neuroscience 30 (7), 2454–2463 (2010).

Cellcycleanalysis20. Thomas, N. and Goodyer, I. D. Stealth Sensors: real-time monitoring of the

cell cycle. Targets Innovations in Genomics & Proteomics 2, 26–33 (2003).

21. Thomas, N. Lighting the circle of life: fluorescent sensors for covert surveillance of the cell cycle. Cell Cycle2, 545–549 (2003).

22. Thomas, N. et al. Characterization and gene expression profiling of a stable cell line expressing a cell cycle GFP sensor. Cell Cycle4, 191–195 (2005).

23. Stubbs, S. and Thomas, N. Dynamic green fluorescent protein sensors for high-content analysis of the cell cycle. Methods Enzymol. 414, 1–21 (2006).

24. Padfield, D, et al. Spatio-temporal cell cycle phase analysis using level sets and fast marching methods. Medical Image Analysis 13(1), 143–55 (2009).

18-1152-69 AD 08/2010 45

Cancerbiology(includingDNArepair)25. Das, A., et al. Non–small cell lung cancers with kinase domain mutations

in the epidermal growth factor receptor are sensitive to ionizing radiation. Cancer Research 66, 9601–9608 (2006).

26. Bin, Z,et al. High-content fluorescent-based assay for screening activators of DNA damage checkpoint pathways. Journal of Biomolecular Screening 13, 538–543 (2008).

27. Katz, D., et al. Increased efficiency for performing colony formation assays in 96-well plates: novel applications to combination therapies and high-throughput screening. BioTechniques 44, S9–S14 (2008)

28. Prevo, R,et al. Class I PI3 kinase inhibition by the pyridinylfuranopyrimidine inhibitor PI-103 enhances tumor radiosensitivity. Cancer Research 68, 5915–5923 (2008).

RelatedGEHealthcarepublications32. Application note: Identification of cells in S phase using the Cell Proliferation

Fluorescence Kit and IN Cell Analyzer 1000. GE Healthcare, 11-0011-45 Edition AA, (2004).

33. Application note: The measurement of DNA condensation and mitochondrial changes during apoptosis and necrosis in cell based assays. GE Healthcare, 11-0032-63 Edition AA, (2004).

34. Application note: Single-color mitotic index analysis using the IN Cell Developer Toolbox. GE Healthcare, 11-0036-52 Edition AA, (2005).

35. Application note: Micronucleus formation analysis on the IN Cell Analyzer 1000. GE Healthcare, 28-4070-46 Edition AA, (2006).

36. Application note: Online cell counting in a micronuclei formation assay using the IN Cell Analyzer 1000. GE Healthcare, 28-9495-88 Edition AA (2009).

37. Application note: Automated analysis of apoptosis and cytotoxicity using the IN Cell Analyzer 1000. GE Healthcare, 28-9037-99 Edition AA, (2006).

38. Application note: Detecting drug-based intracellular organelle perturbation using the IN Cell Analyzer 1000. GE Healthcare, 28-9038-47 Edition AA, (2006).

39. Application note: Multiparameter analysis of cell cycle related events using a GFP sensor. GE Healthcare, 28-9039-48 Edition AA, (2006).

40. Application note: Quantitating responses of subpopulations in cellular assays using the IN Cell Analyzer 1000 Multi Target Analysis Module. GE Healthcare, 28-9193-76 Edition AA, (2007).

29. Yamaguchi, H. et al. Rapid screening of antineoplastic candidates for the human organic anion transporter OATP1B3 substrates using fluorescent probes. Cancer Letters, 260, 163–169 (2008).

30. A.F. Santos, et al. Angiogenesis: An improved in vitro biological system and automated image-based workflow to aid identification and characterization of angiogenesis and angiogenic modulators, ASSAY and Drug Development Technologies,6, 693–710 (2008).

31. Norton, J.T. et al. Automated High-Content Screening for Compounds that Disassemble the Perinucleolar Compartment, J. Biomol. Screening (2009).

41. Brochure: Cell integrity assays-High-content analysis of essential cell integrity and toxicity parameters using the IN Cell Analysis System. GE Healthcare, 28-4087-16 Edition AA, (2006).

42. Brochure: Cell Proliferation-Fluorescence Assay Direct quantitation of cell proliferation without affecting cell morphology. GE Healthcare, 11-0026-22 Edition AA, (2004).

43. Application note: Use of on-line cell counting for micronucleus and neurite outgrowth assays on IN Cell Analyzer 2000. GE Healthcare, 28-9673-96 Edition AA, (2009).

44. Application note: Characterization of drug action on the Golgi complex using high-content analysis on IN Cell Analyzer 2000. GE Healthcare, 28-9676-17 Edition AA, (2010)

45. Application note: High content analysis of a live multiplexed cytotoxicity study using cardiomyocytes. GE Healthcare, 28-9859-16 Edition AA (2010).

46. Application note: Neurite outgrowth Cell-by-Cell Analysis using the IN Cell Developer Toolbox. GE Healthcare, 14-0005-35 Edition AA, (2005).

47. Application note: A high-content assay for neurite outgrowth using the IN Cell Analyzer 2000. GE Healthcare, 28-9538-32 Edition AA, (2009).

48. IN Cell Analyzer Analysis Applications Manual, GE Healthcare, 25-8098-21UM Rev-A (2004).

For local office contact information, visitwww.gelifesciences.com/contact

GE Healthcare Bio-Sciences ABBjörkgatan 30751 84 UppsalaSwedenwww.gelifesciences.com

28-9894-51 AA 01/2011

GE, imagination at work, and GE monogram are trademarks of General Electric Company.MatriPlate is a trademark of GE Healthcare Companies.All third party trademarks are the property of their respective owners.The IN Cell Analyzer system and the IN Cell Investigator software are sold under use license from Cellomics Inc. under US patent numbers US 5989835, 6365367, 6416959, 6573039, 6620591, 6671624, 6716588, 6727071, 6759206, 6875578, 6902883, 6917884, 6970789, 6986993, 7060445, 7085765, 7117098, 7160687, 7235373, 7476510; Canadian patent numbers CA 2282658, 2328194, 2362117, 2381344; Australian patent number AU 730100; European patent numbers EP 0983498, 1095277, 1155304, 1203214, 1348124, 1368689; Japanese patent numbers JP 3466568, 3576491, 3683591, 4011936 and equivalent patents and patent applications in other countries. GE Healthcare Cardiomyocytes are sold under licence from Geron Corporation and Wisconsin Alumni Research Foundation (WARF) under US patent and publication numbers: US 7,425,448, US 2009/0017465, US 6,800,480, US 5,843,780, US 6,200,806, US 7,029,913, US 7,582,479, US 7,413,902; US 7,297,539, US 2009/0047739 and US 2007/0010012 and equivalent patent and patent applications in other countries.© 2011 General Electric Company—All rights reserved.First published Jan. 2011All goods and services are sold subject to the terms and conditions of sale of the company within GE Healthcare that supplies them. A copy of these terms and conditions is available on request. Contact your local GE Healthcare representative for the most current information.GE Healthcare UK Ltd, Amersham Place, Little Chalfont, Buckinghamshire, HP7 9NA, UKGE Healthcare Bio-Sciences Corp, 800 Centennial Avenue, P.O. Box 1327, Piscataway, NJ 08855-1327, USAGE Healthcare Europe GmbH, Munzinger Strasse 5, D-79111 Freiburg, GermanyGE Healthcare Japan Corporation, Sanken Bldg. 3-25-1, Hyakunincho, Shinjuku-ku, Tokyo 169-0073, Japan