A Bit About Us
Strength in Vertical Manufacturing Vertically integrated product development and manufacturing has been a cornerstone of Thorlabs since it was founded in 1989. Our product development, manufacturing, and testing are all done in house, as is our marketing, web development, and software package design. Doing so has allowed us to control costs, ensure quality, maintain speed to market, and build upon product complexity. This same philosophy was adopted by Thorlabs Imaging Systems (our VA group) from its infancy. Realizing the benefit of the vertically integrated model in the early months of establishing his new business unit, Jeff Brooker, General Manager of the Imaging Systems group, arranged a shipment of underutilized machining equipment to his location in Sterling, Virginia. In 2013, the VA team expanded their machine shop capabilities by investing in a pallet pull system that can manufacture complex components lights out. As it can be difficult to find a facility that The pallet pull system at our Sterling, VA facility. properly executes intricate machining, Brooker decided instead to leverage extensive in-house design and fabrication capabilities. “By keeping this type of manufacturing in-house, it enables us to minimize design trade-offs, develop targeted solutions to challenging problems, and customize each individual system to the customer’s specifications,” said Brooker.
Thorlabs Imaging Systems’ Machine Shop
Our software, ThorImageLS, is also developed in house for similar reasons. The software is considered to be just as important as the hardware in making an imaging system purchase. For this reason, we wanted the flexibility to control the design from beginning to end so that it could be tailored to the customer’s needs as opposed to needing to fit other manufacturers’ components into an application that they weren’t intended to be integrated into. Application programming interfaces (APIs) and a software development kit (SDK) are also included for the development of custom applications by OEMs and developers in third-party software packages such as MATLAB and μManager / ImageJ.
It is this ability to custom tailor all aspects of a system from the time of conception that sets Thorlabs apart from the competition and drives our success in producing cuttingedge products.
Thorlabs Imaging Systems’ Machine Shop
248
Imaging Cytometry
Selection Guide
Cytometry Overview
Software & Analysis
Page 250
Pages 255 - 265
iCyteÂŽ Imaging Cytometer
References & Publications
Pages 251 - 254
Pages 266 - 267
249
Imaging Cytometry Cytometry Overview
Laser Scanning Cytometry
iCyte Imaging Cytometer
Laser Scanning Cytometry (LSC) combines the advantages of flow and image cytometry into a single system that provides unparalleled flexibility and power for rapid measurements of biochemical constituents or evaluation of cell morphologies.
Software & Analysis References & Publications
LSC instruments are non-confocal systems that utilize laser-spot scanning illumination. The detection system, which combines PMTs for fluorescence detection and photodiodes for absorbance and scatter detection, offers excellent quantitative cytometric performance and imaging capabilities. The systems provide fully automated or interactive operation for analysis of cellular and tissue samples that have been stained with fluorescent or chromatic dyes. Fluorescent excitation, laser light absorbance by chromatic dyes, and scatter signals may all be acquired simultaneously.
Features n Biochemistry-Like
Analysis of Heterogeneous Cell Populations via Total Signal Quantification in Cellular and Tissue Specimens n Analysis of Chromatically and Fluorescently Stained Samples Simultaneously or Sequentially n Visualize Specimens Conventionally with Either LaserScanning Imaging Techniques or a Microscope n View Individual Events in the Population Data n Reanalyze the Same Cells Under Varying Conditions n Measure and Localize Cellular Constituents n Detect Molecular Constituents in the Surrounding Environment and Correlate their Presence to Cell Processes n Simultaneously Study Cultured Cells at the Individual and Colony Level n Perform Automated Analysis on Tissue Sections and Microarrays
Since the cells in LSC are on slides, they can be measured repeatedly over time, which is ideal for the study of time-resolved processes such as enzyme kinetics. Data analysis and image processing can be performed either in real time as the scan is progressing or at the post-data acquisition stage, allowing the researcher to modify analysis strategies or explore alternative scenarios. Numerical data are displayed as traditional cytometric histograms or bivariate scattergram Scanning Scan Autofocus Scatter ComputerMirror Lens Optics Sensor Controlled plots and can be Stage related in a variety of ways to the image • data from which 405 nm Diode Laser they are derived. 488 nm Diode Laser 633 nm HeNe Laser 532 nm Diode Laser
•
Dichroic PhotoMirrors and multipliers Optical Filters Simplified iCyte® Diagram
250
Objective Lenses
Imaging Cytometry Cytometry Overview
iCyte® Automated Imaging Cytometer The iCyte® Automated Imaging Cytometer combines digital microscopy with image processing and real-time population data analysis of analytical cytometry to produce high levels of information content with minimal investigation time.
iCyte Imaging Cytometer Software & Analysis References & Publications
This quantitative image analysis system enables walk-away, highthroughput applications (e.g., cell cycle screens on a 96-well plate in under ten minutes) as well as highcontent analysis (where scanning iCyte® Automated Imaging Cytometer and analysis can take longer depending on the amount of multiplexing required, the desired resolution, and the size Features of the scan area). It automatically segments n High-Content, Cell-Based Analysis for Morphological and quantifies cellular and subcellular and Quantitative Features events. The automated analysis allows for n Complete Laser and PMT/PD System for Flexible the study of morphometric and fluorescent Dye Combinations measurement correlations on thousands of ® n Compatible with Microtiter Plates, Microscope Slides, cells per specimen. The iBrowser software and Other Carriers (see pages 258 - 259 for details) automates the analysis of the measurement for quick n Automated or Manual Operation Modes and easy-to-understand analysis. n Analyzes Cellular or Tissue Specimens
SPECIFICATIONS Lasers (up to 4 Lasers in 5 Available Wavelengths)
Detectors
Emission Detection Options
Data Channels
Violet (405 nm) Blue (488 nm) Green (532 nm) Yellow (561 nm) Red (633 nm) 4 Photomultiplier Tube Fluorescence Detectors (Interchangeable Filter Blocks) 2 Photodiode Light-Loss/Scatter Dectectors Blue (445 – 485 nm) Green (515 – 585 nm) Orange (565 – 585 nm) Red (600 – 635 nm) Crimson (650 -700 nm) Near-Infrared (750 – 800 nm) 6 Data Channels per Pass Plus Unlimited Programmable Virtual Channels (Multiple-Pass Scans Extend the Number of Acquired Data Channels)
Microscope
Olympus IX 51-Series Microscope Base
Autofocus
Variable-Resolution Laser Scan Imaging (4X, 10X, 20X, 40X, or 60X Objective Lens Magnification and User-Selectable Rate of Stage Motion)
Visualization Specimen Carriers Computer
See Pages 252 - 254 for Optional Upgrades
To learn more about our iCyte Automated Imaging Cytometer or to request a quote, please contact ImagingSales@ thorlabs.com.
High-Resolution Laser Scan Imaging with CompuColor™ and Laser Scatter Brightfield Imaging Glass or Plastic Microtiter Plates, Microscope Slides, Petri Dishes, Chamber Slides, and Proprietary Carriers Core i7 2600k Processor, 8 GB RAM, 1 TB Hard Drive, LCD Monitor, Windows® 7 Operating System
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Imaging Cytometry Cytometry Overview
iCyte® Automated Imaging Cytometer: Options and Upgrades
iCyte Imaging Cytometer
1) Fourth Laser Upgrade
Software & Analysis References & Publications
The option to integrate a fourth laser into our iCyte Laser-Scanning Cytometry system significantly expands the variety of compatible dyes. The fourth laser option comes with either a 532 nm or 561 nm laser.
Features n Replaces
All Gas Lasers (Except HeNe) with Solid-State Lasers n Improved Design of the Main Dichroic Filter for Wider Emission Spectral Band Detection n New Optics and Mounting Hardware n Upgraded Software
To accommodate the additional laser, the existing gas lasers have been exchanged for solid-state lasers (except for the HeNe laser). Additionally, the standard emission filter cubes have been reformulated to allow for efficient detection across the emission bands located between the laser lines. This opens up the option for the researcher to utilize various combinations of laser lines and emission bands in single- or multiple-pass scanning. The filtering of absorption and forward scatter detection channels has also been extended. This yields the ability to detect the effect of chromatic stains and sample morphology on each laser line in both single- or multiple-pass scanning without the need for user intervention.
Configuration A: 405 nm, 488 nm, 532 nm, and 633 nm LASER LINE
405 nm
488 nm
532 nm
633 nm
Diode
DPSS
DPSS
HeNe
Standard Emission Filter
425 - 455 nm
500 - 521 nm
550 - 600 nm
650 - 800 nm
Optional Emission Filter
N/A
500 - 530 nm
565 - 595 nm
N/A
532 nm Shortpass
488 nm Bandpass or 532 nm Shortpass
532 nm Shortpass
633 nm Bandpass
561 nm
633 nm
Laser Type
Standard Absorption Filter
Configuration B: 405 nm, 488 nm, 561nm, and 633 nm LASER LINE Laser Type
488 nm
Diode
DPSS
DPSS
HeNe
Standard Emission Filter
425 - 455 nm
515 - 545 nm
575 - 625 nm
650 - 800 nm
Optional Emission Filter
N/A
500 - 521 nm or 500 - 532 nm
565 - 595 nm
N/A
561 nm Shortpass
488 nm Bandpass or 532 nm Shortpass
561 nm Shortpass
633 nm Bandpass
Standard Absorption Filter
HER2/Neu – DAB & Hematoxylin
252
405 nm
HER2/Neu – Alexa 488 & Propidium Iodide
Imaging Cytometry
iCyte® Automated Imaging Cytometer: Options and Upgrades 2) Environmental Chamber for Live-Cell Analysis
Cytometry Overview iCyte Imaging Cytometer Software & Analysis
The Environmental Chamber for Live-Cell Analysis provides a temperature- and CO2-controlled environment that allows scanning of live cells for extended periods of time. This chamber is ideal for certain assays such as live-cell phagocytosis assay or live HeLa cell growth studies. The incubation chamber is maintained at a temperature between 36.5 °C and 37 °C by the incubator control unit, which maintains the gas environment within the chamber at a 5% CO2, 95% air mixture. To maintain precise atmospheric control, 100% CO2 input to the Control Unit is required (CO2 gas tank and tank regulator are not included with the kit). Additionally, multiple sample carriers may be run in the environmental chamber: 35 mm petri dishes, 50/60 mm petri dishes, and chambered cover glass.
3) Enhanced Blue Cube
References & Publications
Environmental Chamber on Laser Scanning Cytometry Stage
Features n Provides
Temperature- and AtmosphereControlled Environment for iCyte Laser Scanning Cytometry n Supports Multiple Carrier Types • 35 mm Petri Dishes • 50/60 mm Petri Dishes • Chambered Cover Glass
The Enhanced Blue Cube allows scans using the 405 nm (Violet Diode) and 488 nm (Argon) lasers to be conducted in a single pass, cutting in half the time required for scanning assays using these lasers individually. The enhanced blue cube is ideal for cell cycle assays where DNA content and chromatin condensation are used to determine the cell cycle phase, and other biomarker expression levels are also assessed. The table below shows examples of some dye combinations that may be used with the enhanced blue cube. Combining the Dual-Channel Absorption/Scatter Detector (shown in the image to the right) with the Enhanced Blue Cube allows the acquisition of 488 shaded relief during a two-laser scan by filtering out the 405 nm component. Either 488 nm or 633 nm light-loss may be acquired along with the 488 shaded relief.
The blue cube within the cytometer. The insert depicts the light path.
Features n Slightly
LASER
DETECTOR
DYE
TARGET
405 nm 405 nm
Blue
DAPI
DNA - Fixed Cells
Blue
Hoechst
488 nm
DNA - Live Cells
Green
Alexa 488
Secondary
488 nm
Orange
Phycoerytherin
Secondary
488 nm
Long Red
Pe/Cy5
Secondary
Reduced Bandwidth (430 – 470 nm) Eliminates 488 nm Laser Line Interference n Enables 405 and 488 nm Lasers to be Run Simultaneously n Compatible with Dual-Channel Absorption/ Scatter Detector 253
Imaging Cytometry Cytometry Overview
iCyteÂŽ Automated Imaging Cytometer: Options and Upgrades
iCyte Imaging Cytometer
4) Dual-Channel Laser Light Scatter and Laser Light-Loss Detector
Software & Analysis References & Publications
PD Sensor #1 (Shown in Shaded Relief Position)
Features n Quantify
The optional Dual-Channel Assembly allows for the simultaneous capture by two channels of scatter and/or light-loss data. The result is decreased throughput time by more than 50% for certain assays and enhanced data sets. When the two channels are used for lightloss measurements, it is possible to quantify chromatic dye absorption for two dyes in a single pass. When one channel is set for scatter and the other for light-loss, the chromatic dye levels and morphology information can be quantified.
488 nm Filter PD Sensor #2
Chromatic Dye Light-Loss with Two Chromatic Stains in Half the Time n Obtain Richer Data in a Single Pass n Corrects for Spectral Overlap n Combines Fluorescence and Chromatic Analysis
50/50 Beamsplitter 488 nm Filter Specimen Objective Lens
Dual-Channel Detector Diagram. Sensor #1 is set for measuring scatter, while Sensor #2 is set for light loss.
This dual-channel assembly is also capable of correcting for spectral overlap for a single pass scan since both channels of data can be acquired simultaneously. When appropriate, corrections of the chromatic dye images for autofluorescence is also possible because the absorption measurement uses dedicated detectors that are separate from the fluorescencemeasuring photomultiplier tubes. This allows chromatic and fluorescence measurements to be made simultaneously. CompuColor image combining the 488 nm and 633 nm light-loss images
5) Optional Carriers We offer several additional carrier types for use with our laser scanning cytometer and any stage with a microtiter plate footprint. These carriers are easily installed to further accommodate experimental demands. The Chamber Slide Holder is for scanning specimens in chamber slides. This carrier is suitable for live cell analysis. The Petri Dish Holder can scan up to 3 petri dishes in a single run, making it suitable for both fixed- and live-cell analysis. The 4-Slide Holder accommodates up to 4 standard microscope slides simultaneously. This carrier is ideal for applications such as automated tissue and TMA analysis. 254
Compatible Carriers for iCyte Cytometer
Imaging Cytometry Cytometry Overview
Software iGeneration Cytometric Analysis Software The iGeneration Cytometric Analysis Software allows users to configure application-specific protocols, define a carrier, and analyze the resulting data. The easy-to-use tabs allow the user to quickly and effortlessly set up experimental runs and set parameters. Using the software, it is possible to choose the lasers, laser powers, and channels being used. Users can also create virtual channels, define segmentation contours, define areas or wells to scan on a carrier, and set up complex or multiple carrier runs. Segmentation Quantitative data analysis starts with identifying events, a process similar to thresholding in flow cytometry. Values rising above the threshold value are used to identify discrete events for quantification. Once the threshold level is established, the iGeneration cytometric software draws a contour around the “events.”
iCyte Imaging Cytometer
Features n Easy-to-Use
Tabs to Quickly Set Up
Data Runs n Capabilities • Segmentation • Virtual Channels • Event Data Capture • Well Features • Data Reanalysis • Repetitive Scanning • File Merging
Software & Analysis References & Publications
Four types of contours may be drawn: • The threshold contour (solid red) defines the edge of the event at the threshold limit. • The integration contour (dotted green) is a user-defined number of pixels outside of the threshold contour, ensuring that the full signal is measured. The feature data is generally based on this contour.
Image-Based Segmentation
• The background contours (dashed pink) define an annular area around the event and are used to “background-correct” those features based on the integration contour by subtracting the mean background value from the integration contour feature value. • The peripheral contour (dashed and dotted yellow) is used to sample and quantify signal from an area external to the integration contour. Event Data Event Data may be displayed in multiple methods such as scattergrams, histograms, and expression maps (2-parameter frequency distribution plots). Gating regions can be drawn on these graphs to isolate populations. Any event features (or ratios of event features) can be displayed in these graphs. Statistics of event features or their sub-populations may be displayed in a statistics table. Detailed Event feature information about a particular event may be displayed by selecting the event (or set of events) from a graph.
Illustration of Contour Types Sample of Event Data
Area
Integral
Max Pixel
Peripheral Integral
255
Imaging Cytometry Cytometry Overview iCyte Imaging Cytometer Software & Analysis References & Publications
Software
Random Sampling In cases where the sample is not conducive to establishing boundaries between individual cellular events — such as with confluent adherent cellular samples or tumorous tissue samples — random sampling may be applied as an alternative strategy to generate quantitative expression data. Circular sampling elements of user-defined size and frequency are overlaid on the images in either a grid or random orientation. Data is reported on a per-area basis rather than a per-cell basis. Random sampling can be performed simultaneously with event segmentation so that a comparison may be made. These methods typically yield very similar results on samples conducive to event segmentation. Dissimilar results are usually an indication that one or both of the analysis strategies are flawed in some way; hence, this feature can be used as an internal quality control measure for an application. Well Features Well Features are summary statistics derived from the event features on a per-well or per-tissue-array element basis. They can be calculated on the entire population of events or on any gated sub-population. When working with microplates, the Well Feature values can be displayed as color-coded expression levels during and after an iCyte run. Data Reanalysis Storage options allow saving all of the image data as a raw data image file for each of the selected hardware channels during scanning. Analysis parameters not directly related to data acquisition (such as the PMT
or scatter sensor settings) can then be modified and the existing images reanalyzed using the new parameters. As a result, once data from a specimen has been acquired at optimum settings, the analysis itself (selecting threshold levels, gallery images, etc.) Random Sampling Elements Sample all Markers Across the Entire Tissue may be optimized by simply reanalyzing the raw data files, without rescanning the actual specimen. Repetitive Scanning The iCyte can be set to perform Repetitive Scans or Rescans of a given scan area, either for observing specimens at time points separated by significant time intervals or for manipulating the specimen (such as restaining or incubating) between scans.
File Merging The iCyte provides the ability to merge image data files so that the hardware channels from two separate runs may be treated as though they are from a single run. Segmentation may be done on a channel from either run (or both runs in the case of sub-contours or the iNovator). Virtual Channels may be defined utilizing constituents from either run.
MORPHOLOGICAL FEATURES The area of the event in µm2 based on the integration contour
Area Perimeter
The perimeter of the event in µm based on the integration contour
Circularity
A measure of the “roundness” of an event, calculated as the ratio Perimeter2:Area. A perfectly round event will have a Circularity of 4π.
Signal-Level Related Features (Generated for Each Channel) Integral
The sum of the pixel values within the integration contour. Integral is typically used to assess the total amount of target of interest in the event. In cell cycle analysis, the integral of the DNA-intercalating dye channel is a measure of the total DNA content of the cell.
Max Pixel
The value of the brightest pixel within the integration contour. In cell cycle analysis, the Max Pixel of the DNA-intercalating dye channel provides an indication of the state of chromatin condensation and is used to distinguish mitotic from G2 cells.
Intensity
The average pixel values for a given channel within the integration contour. Intensity is the ratio Integral:Area, essentially normalizing the signal level to the area.
Peripheral Integral (Max)
These features are analogous to their counterparts above, for the area enclosed by the peripheral contour.
Sub-contour Integral (Max)
These features are analogous to their counterparts above, for the sum of the associated sub-contour events.
Location and Relational Features XY Position Parent ID Scan Position
256
Time
The coordinate positions of the events on the sample carrier The identification number of an event to which a sub-event belongs. This gives visibility to the association of sub- and parent events and allows plotting and segregating sub-events by their parents. The position along the scan line at which an event lies. This is most frequently utilized in quality control procedures or when troubleshooting the system’s response along the scan. The time at which data for an event was scanned. This is utilized in multi-pass or repeat scans.
Imaging Cytometry Cytometry Overview
Software iNovator Application Development Toolkit The iNovator Application Development Toolkit, an advanced software option, provides a variety of image analysis and processing tools for image enhancement, noise removal, and segmentation.
iCyte Imaging Cytometer Software & Analysis
A: Raw DAB Image
B: Application of Frangi’s Vesselness Filter
C: Segmentation: Seeded Watershed
D: Segmentation Results Overlaying Raw Image
iNovator Workspace The iNovator workspace provides multiple modules that control the various functions of data acquisition, image processing, segmentation, and event generation. By inserting these modules into the macro workspace and connecting them together, analysis pathways can be built to control the scanning and data acquisition process.
Image Processing Tools
Use of a ridge-enhancement filter and seeded watershed in segmenting cell membranes. (A) Raw image of a DAB-stained cell membrane. (B) The outcome of applying the Frangi vesselness filter. (C) Segmentation results using the image in B and the seeded watershed technique. (D) Membrane contours overlaid on the raw image in A.
Image filters are used to accentuate details in captured images. The image filters available in iNovator include High Gauss, High Pass, Low Pass, Vertical Edge, and Laplace. These filters can emphasize certain details in the images, including partial deconvolution of the laser beam spread function. Morphological filters allow images to be more clearly visualized by turning pixels on and off according to filtering criteria. Morphological filters include erosion, dilation, opening, and closing. Finally, watershed segmentation uses Euclidian distance maps to separate closely spaced or overlapping events.
Multiple Analysis Paths and Components
References & Publications
Features n Employ
Advanced Image Processing Tools to the Segmentation and Data Analysis Process n Control Segmentation and Data Analysis Process with Visually Oriented Macros n Perform Multi-Scale Scanning and Analysis n Simple and Intuitive Features • iNovator Workspace • Image Processing Tools • Multiple Analysis Paths and Components • Two Scan Types and Two Sequenced Scan Passes • iNovator Virtual Channels • Tissue Microarray Scanning
Multiple analysis paths may be defined within a given macro to simultaneously contour on virtually any number of different event types or components. The iNovator workspace displays multiple paths that use different contouring criteria. For instance, if investigating green and orange fluorescence simultaneously, iNovator can display two paths. The first path contours on the Green fluorescence channel and the second path contours on the Orange channel using a different threshold. Different event components can be related to one another by using an inclusion module within the macro. For example, if an event contour of component type A is included in the event contour of component type B, then a relationship can be established between the two.
Two Scan Types and Two Sequenced Scan Passes Not only can iNovator define multiple event components built on different contouring criteria and relate these components together, but different types of scans can be defined and sequenced within a given macro. The traditional scan, termed FieldScan in the iNovator, can be run at any resolution from 0.25 μm to 20 μm at 0.05 μm intervals. The MosaicScan, available only with the iNovator, has the same resolution options as the FieldScan with some additional options. MosaicScan first stitches together all of the contiguous scan fields in a given scan area and then segments and contours on the resultant mosaic image. This method of contouring enables the iCyte to generate contours on events that cross scan field boundaries. This technique produces more comprehensive event generation, since events bordering the scan field boundaries are now contoured. It also enables contouring on structures larger than the scan field, such as cell colonies, tissue structures, and tissue sections.
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Imaging Cytometry Cytometry Overview
Software
iCyte Imaging Cytometer
iBrowser® Data Integration Software
Software & Analysis References & Publications
With the iGeneration applications, various types of data files, both numerical and image, can be defined and saved. Each type of data element may have several entities, and the iCyte can obtain a complete set of the data elements for all the samples in an analysis, such as wells in a microtiter plate analysis or core samples in a tissue microarray. iBrowser® Data Integration Software provides a convenient method to access, review, and report on data.
Features n Access,
Review, and Report on Quantitative and Image Data from Multiple Samples and Carriers n Displays • Summary Data • Carrier Data • Well Data n Reports iCyte Findings
Displaying Summary Data The iBrowser software displays summary data for a specific run, including annotations for that run, the run name and ID, and a table of the well features for all wells or scan areas acquired during that run. iBrowser displays the experimental annotations as well as the actual Well Feature data.
Increasing drug concentration
DNA content histograms
KS test differences in cell cycle distribution compared to controls
Viewing Results in the iBrowser® Data Integration Software
Displaying Carrier Data Carrier data is data that is relevant to an experiment as a whole. iBrowser displays each available element in one of a series of tabs. Within each tab, the data element is laid out to approximate the physical arrangement of the wells or scan areas for the carrier used for that run [e.g., scan images (shown in CompuColor) and histograms]. It then provides a modified form of the Kolmogorov-Smirnov test in which a group of control wells is defined. The test compares each of the histograms in the data set to the control value and generates a new D-value histogram. Data generated on multiple carriers within a single run (using the iCyte robot) may be displayed one of two ways. Data may be displayed on a carrier-by-carrier basis, with the user selecting which carrier’s data to display for each data tab, or all the wells or scan areas may be displayed together. This latter option allows comparisons between wells of different carriers.
Displaying Well Data Any of the Well Features generated with the iCyte can be graphed or displayed with histograms, scattergrams, or as 2-parameter histograms (i.e., expression maps). A group of wells or a scan area may be defined so that specified statistics may be applied to data from the entire group. Groups may be substituted for individual wells in the well-data display such that graphical data of an average of the group members (as opposed to a single well) is shown. 258
Imaging Cytometry Cytometry Overview
Software Reporting iCyte Findings
iCyte Imaging Cytometer
iBrowser can generate reports from any tab. Additionally, using the Well Report feature, a report can be generated that contains multiple elements for a single well, such as well images, galleries, and scattergrams. Reports can be printed, saved as images, or saved into PDF files.
Software & Analysis References & Publications
Apoptosis and DNA Damage Applications LSC Advantages n Multiple
Probes (e.g., DNA Content, Phospho-H2AX, and Caspase-3 Cleavage) can be Analyzed in a Single Sample while Obtaining Widefield Images n Combines the Advantages of Flow Cytometry and Image Analysis in a Single System n Inverted Format Supports a Diversity of Carrier Types, Allowing Flexibility in Matching Application Needs with the Appropriate Carrier Type
LSC technology is particularly well suited for multiplexed quantitative analysis. It is commonly used with high-content assays incorporating multiple markers [e.g., detection of DNA content and DNA double-strand breaks (DSBs) by labeling cells with antibodies to phosphorylated histone H2AX and p53 Binding Protein 1 (53BP1)]. In this example, an analysis is performed to compare A549 cells treated with gamma radiation to untreated controls. Individual cellular events are identified based on DNA (DAPI) staining. Scattergrams of DAPI vs. H2AX or 53BP1 fluorescence measure the intensity and number of individual foci. Individual foci as well as pan-nuclear staining can be quantified and related to their cell cycle phase.
iBrowser Interface
A
B
C
D
(A) Laser scan image of A549 cells showing pH2AX and 53BP1 expression foci. (B) Contours on DNA of entire cell as well as pH2AX foci (top), 53BP1 foci (middle), and both foci (bottom). (C) Scattergram of total expression vs. foci count and histogram of foci count for control sample: 53BP1 left, pH2AX right, and (D) 3GY irradiated sample.
259
Imaging Cytometry Cytometry Overview
Cell Cycle and DNA Content
iCyte Imaging Cytometer
LSC Advantages
Software & Analysis References & Publications
n Direct G2
Mitotic
Chromatin Condensation vs. DNA Content G1
Post-Mitotic
DNA Content
Cell cycle analysis using direct, highly precise measurement of DNA content and chromatin condensation is one of the best examples to highlight the imaging, quantification, and multiplexing capabilities of the LSC technology. For simple cell cycle analysis, LSC utilizes a single DNA dye. Laser scan images of nuclear fluorescence or laser light-loss are generated, and segmentation contours are drawn around the nuclei to determine the total nuclear fluorescence (fluorescence integral) or laser light absorption integral, which is proportional to the DNA content. LSC generates DNA histograms with sharp G1 peaks, a definite S phase, and a clearly separated G2 population. All of these are classic and robust measures of the quality of DNA content analysis.
Stoichiometric Measurement with Precision on Par with Flow Cytometry n High Sensitivity, Low Background, and High Dynamic Range n Any Number of Other Stoichiometric Nuclear Dyes (Both Chromatic and Fluorescent) can be Used n Maintain Relationships Between Stoichiometric Measurements and Image Data (Morphology) n “Relocation� from Multivariate Analysis to Image Data
LSC-Generated Histograms Showing Resolved Peaks and Separated Populations
Max Pixel - Chromatin Condensation
The Max Pixel feature provides a direct measurement of chromatin condensation, allowing mitotic cells to be separated from G2 cells independent of a specific mitotic marker. Since cell cycle data may be determined directly from DNA content using a single dye, other data acquisition channels can be used to explore cell-cycle related expression of other proteins of interest. Color-Coded Gates Match Coloration on the Image for Sub-Population Sorting
260
Imaging Cytometry
DNA Damage Applications (Micronucleus Assay) Features n Optional
Image of DNA Damage Measurement
iNovator Application Development Toolkit Makes it Possible to Identify Closely Spaced Objects such as Nuclei in a Binucleated Cell, as well as the Associated Micronuclei n Multiple Probes can be Analyzed in a Single Sample while Obtaining Widefield Images n The LSC Inverted Format Supports a Diversity of Carrier Types, Allowing Flexibility in Matching the Needs of Your Application with the Appropriate Carrier Type
Cytometry Overview iCyte Imaging Cytometer Software & Analysis References & Publications
LSC technology provides a novel approach for automated scoring of micronuclei (MN) in different types of mammalian cells, serving as a biomarker of genotoxicity and mutagenicity. The technology’s flexibility fosters a diversity of MN applications: simultaneous measurement of multiple features such as DNA content and cytoplasmic and nuclear area; mono- and multi-nucleated cells; nuclear texture and intensity of nuclear and MN staining; protein content and cytoplasm density, and additional features using relevant molecular probes. These investigations can be carried out in cell lines, buccal cells, erythrocytes, dermal model systems, and a number of other specimen types.
Intercellular Translocation/Compartmentalization Applications LSC Advantages n Using
Nuclear–Cytoplasmic Translocation. Green: High Detection in the Cytoplasm. Orange: High Detection in the Nucleus.
Advanced Data Processing, Subcellular Compartmentalization, and Translocation can be Monitored and Quantified n Multiple Probes can be Analyzed in a Single Sample while Obtaining Widefield Images, which may be Correlated to the Quantitative Data in Galleries or Field Images n Combines the Advantages of Flow Cytometry and Image Analysis in a Single System n Inverted Format Supports a Diversity of Carrier Types, Allowing Flexibility in Matching the Needs of Your Application with the Appropriate Carrier Type
Many areas of research in cell biology, such as apoptosis or signal transduction cascades, revolve around the ability to detect and identify specific, individual cellular organs and structures. Our LCS systems are ideal for this investigation and identification. For instance, using the Transfluor® assay, our system can identify and track the translocation of NFkB from cytoplasm to nucleus. The LCS systems can distinguish nuclear regions by detecting a DNA-binding dye, and the surrounding region is monitored by defining a ring around the nucleus using peripheral contours. The data, displayed in a scattergram, will show the resolved cells based on their measured nuclear fluorescence and populations that have high levels of NFkB (for this example) can be identified. 261
Imaging Cytometry Cytometry Overview iCyte Imaging Cytometer
Cell Surface Immunophenotyping E
A
n LSC
Software & Analysis References & Publications
LSC Advantages
B
C
D
(A) Custom carrier. (B) Lymphocytes are isolated from leukocytes based on cell area and CD45 expression. (C) Each “lane” of the Clatch slide is plotted in its own scattergram (D), where the quadrants are defined for FITC+/ PE+, FITC-/PE+, FITC+/PE-, and FITC-/PE-. (E) Cell galleries allow an examination of cell morphology for any of the sub-populations.
Analysis Requires Fewer Cells and Allows Full-Panel Analysis of Samples that Cannot be Analyzed by Other Platforms n Easily Links Cell Immunophenotype and Morphology n Instrument Stability Ideal in Laboratories where Assay is Performed Intermittently n Simple Standardized Assay Requires Few Decisions by Technologist n Cells in situ After Data Acquisition for Possible Re-Analysis n Allows Recovery of DNA from the Analyzed Sample – FISH Assays
Immunophenotyping, a common application in flow cytometry, can be difficult when only a small number of cells are available. In these cases, LSC analysis is a methodology of choice because of its low sample requirements. Immunophenotyping by LSC is based on a custom-made carrier. Specimens derived from fine-needle aspirates, body fluid, tissue or blood are introduced into 12 “lanes” or wells and stained with different antibody-conjugate combinations. The lymphocytes are gated from other leukocytes based on cell area and CD45 expression. Data for each “lane” of the slide is plotted in its own scattergram, dividing the cells into four sub-populations. Cell immunophenotype can be linked to cell morphology, based on the images generated during analysis. Because the cells are left in situ during LSC analysis, they can be reprocessed afterwards for cytoplasmic, immunological or genetic markers.
Live Cell Studies LSC Advantages n Multiple
Lasers and Detectors Allow Study of Complex Assays Requiring Multiple Markers n Cells may be Identified for Analysis Using Either Fluorescence Characteristics or the Cells’ Ability to Absorb or Scatter Light n Multiple Probes can be analyzed in a Single Sample while Obtaining Widefield Images, which may be Correlated to the Quantitative Data in Field Images Scan Field images of Hoechst-stained (blue) live THP-1 cells and phagocytized CypHer5E-labeled beads (red) at t = 0 hours (left) and t = 7 hours (right)
Our LSC platforms with environmental chamber are ideal for live-cell analysis, making it easy to accomplish with carriers such as petri dishes and chambered cover glass. The precision-controlled environment enables time-sensitive investigation of various live-cell assays. For example, live-cell phagocytosis studies on THP-1 cells assays can be studied to show the time evolution of Hoechst-stained live THP-1 cells and phagocytized CypHer5E labeled beads by using the repetitive scan feature. For the images shown above, scans were taken every 30 minutes over a 17.5 hour period. This ability to repeatedly scan the same sample over long periods of time is also particularly useful in live HeLa cell growth studies, as well as other time-sensitive dynamics. 262
Imaging Cytometry Cytometry Overview
Tissue Section Analysis LSC Advantages n Quantitative
Unbiased Analysis with High Precision, Dynamic Range, and Sensitivity – Tissue Cytometry n Analyze Fluorescent and Chromatically Stained Samples n Laser Scatter Morphology with in situ Quantification n Multiple Lasers and Detectors Allow for Complex Assays Requiring Multiple Markers n Variable Resolution Scanning Allows Throughput and Image Resolution Optimization n Analysis may be Done Using Identification of Individual Cells or Using a Random-Sampling Approach n Optional Robot Feature Allows Walk-Away Operation A wide range of tissue analysis applications are performed on the iGeneration LSC instruments, from routine enumeration of cellular events expressing marker(s) of interest to cutting-edge quantitative in situ protein expression analysis and multi-color co-localization studies. Automated “scoring” of protein expression in tissue specimens is a typical application for tissue analysis. In a typical two-scale automated workflow, samples are rapidly scanned
iCyte Imaging Cytometer Software & Analysis References & Publications
for a quick overview of morphology and any staining artifacts. Regions for high-resolution scanning are then selected either manually or in automated fashion. Compensation is applied to isolate the dye signals. The Linear Unmixing method of compensation reassigns spectral overlap signals to the compensated channels for each dye, retrieving what would otherwise be “lost” signal. Data from these high-resolution scans is plotted and exported for further analysis.
Tissue Microarray (TMA) Analysis on LSC Technology A
B
LSC Advantages n Chromatically and Fluorescently
iBrowser display of breast tissue microarray stained with anti-Her2/Alexa 647 and counterstained with DAPI. (A) Region images of each core presented in the same array pattern as the core elements. Her2-positive areas appear as red. (B) Kolmogorov-Smirnov test comparing the Her2 expression to the Her2-negative control cores (highlighted with red rectangles in both panels). The magnitude and breadth of the downward peak indicates the level of Her2 expression.
Stained Samples may be Analyzed Sequentially or Simultaneously Utilizing a Combination of Fluorescent and Chromatic Stains on the Same Slide n Multiple Fluorescent Dyes can be Used, Allowing Improved Quantification of Immunohistochemistry (ISH) and Fluorescence in situ Hybridization (FISH) TMAs n Combination of Highly Quantitative and Qualitative (Image) Data n Rapid and Unbiased Automated Analysis with the Supporting Image Data Archived in an Accessible Format
LSC instrumentation provides researchers with unprecedented flexibility and quality of quantitative automated analysis of fluorescently and chromatically stained TMAs. The excellent performance characteristics and extensive analysis capabilities of LSC technology make it the technology of choice for TMA analysis.
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Imaging Cytometry Cytometry Overview iCyte Imaging Cytometer Software & Analysis References & Publications
Multiplexed Tissue Analysis Laser scanning cytometry is a unique platform that provides high-resolution morphometric imaging data with multi-parameter cytometric analysis of tissue specimens. In this example, iCyte analysis was performed on rat pancreas tissue sections stained for four separate markers: Insulin, Glucagon, Mitochondria developed with immunofluorescence labels, and KI-67 developed with chromagenic dye DAB; nuclear counterstaining was performed with a DNA-binding dye DAPI. Image acquisition was performed in two steps with a fast overview scan of the entire sample followed by high-resolution scanning of islets selected to generate images from which quantitative data were extracted for analysis.
LSC Advantages n Quantitative
Unbiased Analysis with High Precision, Dynamic Range, and Sensitivity n Analyze Fluorescent and Chromatically Stained Samples n Laser Scatter Morphology with in situ Quantification n Multiple Lasers and Detectors Allow for Complex Assays Requiring Multiple Markers n Variable Resolution Scanning Allows Throughput and Image Resolution Optimization n Produce Full Mosaic-Tiled Regions by Imaging Multiple Structures Under Different Fluorescence Wavelengths
DNA (DAPI) Blue Fluorescence
Glucagon (AF532) Yellow Fluorescence
Mitochondria (AF488) Green Fluorescence
Insulin (AF647) Long Red Fluorescence
Ki67 (DAB) 488 Absorbance
Composite Colored Image
High-Resolution Field Images by Channel and CompuColor Composite 40 Objective (Ø0.5 µm Step size) Overview Scan 20: Objective at 20 µm Step Size
Automated Image analysis was performed using the iCyte Cytometric Analysis Software in combination with iNovator and iBrowser, consisting of spectral overlap compensation, segmentation, and data extraction.
Nuclei (DAPI)
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Proliferating Cells (Ki67)
Pancreatic Islets (Cluster of Beta Cells Insulin)
Glucagon (Clusters of Alpha Cells)
Imaging Cytometry Cytometry Overview
Multiplexed Tissue Analysis
iCyte Imaging Cytometer Software & Analysis References & Publications
Regions of Interest are Color-Coded and Matched to Displayed Image
Populations are Color-Coded to Images and Matched to Regions of Interest
A variety of morphological features are available in the iGeneration Cytometric Software in addition to those based on signal intensity. Some features, such as the peripheral contour features or texture, take into account both signal and location of signal and hence can be considered hybrid features. Parameters analyzed in this protocol included number of cells per sample, average size of islets, insulin per sample, insulin per islet, percentage of insulin-positive cells, glucagon per sample, glucagon per islet, percentage of glucagon-positive cells, mitochondria per sample, mitochondria per islet, mitochondria per insulinpositive beta cell, mitochondria per glucagonpositive alpha cell, percentage of proliferating cells,
mitochondria per proliferating cell, percentage of insulin-positive proliferating cells, and percentage of glucagon-positive proliferating cells. Based on the desired outputs to study, appropriate scattergrams, histograms, and statistics to measure these assay endpoints can be generated. Within the scattergrams and histograms, regions are drawn to identify the subpopulations of interest. Gating regions can be verified by using the Gallery function to view cells in specific regions or by highlighting the events of specific regions within the field images. A combination of cytometric and imaging data significantly increases the accuracy of analysis.
A combination of cytometric and imaging data significantly increases the accuracy of tissue analysis. LSC technology enables a unique and highly efficient quantitative tissue analysis because images are generated and data analysis is performed for both fluorescent and chromagenic labels simultaneously.
Data for Any Event can be Easily Displayed
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Imaging Cytometry Cytometry Overview iCyte Imaging Cytometer Software & Analysis References & Publications
References Published 2013 Mollova M, Bersell K, Walsh S, Savla J, Tanmoy Das L, Shin-Young Park, Silberstein LE, dos Remedios CG, Graham D, Colan S and Kühn B. “Cardiomyocyte Proliferation Contributes to Post-natal Heart Growth in Humans.” PNAS (2013); 110 (4): 1446-51. Nombela-Arrieta C, Pivarnik G, Winkel B, Canty KJ, Harley B, Mahoney JE, Shin-Young Park, Lu J, Protopopov A, Silberstein LE. “Quantitative Imaging of Haematopoietic Stem and Progenitor Cell Localization and Hypoxic Status in The Bone Marrow Microenvironment.” Nature Cell Biology (2013); 15: 533-543. Published online April 28, 2013; DOI: 10.1038/ncb2730. Shin-Young Park, Wolfram P, Canty K, Harley B, Nombela-Arrieta C, Pivarnik G, Manis J, Beggs HE, and Silberstein LE. “Focal Adhesion Kinase Regulates the Localization and Retention of Pro-B Cells in Bone Marrow Microenvironments.” J Immunol(2012); 190:1094-1102; Prepublished online 21 December 2012.
Published 2012 Chandra T, Kirschner K, Thuret JY, Pope BD, Ryba T, Newman S, Ahmed K, Samarajiwa SA, Salama R, Carroll T, Stark R, Janky R, Narita M, Xue L, Chicas A, Nunez S, Janknecht R, Hayashi-Takanaka Y, Wilson MD, Marshall A, Odom DT, Babu MM, Bazett-Jones DP, Tavare S, Edwards P, Lowe SW, Kimura H, Gilbert DM and Narita M. “Independence of Repressive Histone Marks and Chromatin Compaction during Senescent Heterochromatic Layer Formation.” Molecular Cell July 27, 2012, 47, 203-214. Chea J, Zhang S, Zhao H, Zhang Z, Lee EY, Darzynkiewicz Z, Lee MY. “Spatiotemporal recruitment of human DNA polymerase delta to sites of UV damage.” Cell Cycle (2012) 1:11 (15):2885-95. Chouinard JA, Rousseau JA, Beaudoin JF, Vermette P and Lecomte R. “Positron emission tomography detection of human enothelial cell and fibroblast monolayers: effect of pretreament and cell density on FDG uptake.” Vascular Cell, 2012, 4:5. Coelho C, Tesfa L, Zhang J, Rivera J, Gonçalves T, Casadevall A. “Analysis of cell cycle and replication of mouse macrophages after in vivo and in vitro Cryptococcus neoformans infection using laser scanning cytometry.” Infect Immun (2012) 80(4):1467-78. Fediuk J, Gutsol A, Nolette N, Dakshinamurti S. “Thromboxane-induced actin polymerization in hypoxic pulmonary artery is independent of Rho.” Am J Physiol Lung Cell Mol Physiol (2012) 1;302(1):L13-26. Fueldner C, Mittag A, Knauer J, Biskop M, Hepp P, Scholz R, Wagner U, Sack U, Emmrich F, Tárnok A, Lehmann J. ”Identification and evaluation of novel synovial tissue biomarkers in rheumatoid arthritis by laser scanning cytometry.” Arthritis Research & Therapy 2012, 14:R8. Halicka HD, Zhao H, Li J, Traganos F, Studzinski GP, Darzynkiewicz Z. “Attenuation of constitutive DNA damage signaling by 1,25-dihydroxyvitamin D3.” (2012) Aging 4(4):270-8. Halicka HD, Zhao H, Li J, Lee YS, Hsieh TC, Wu JM and Darzynkiewicz Z. “Potential anti-aging agents suppress the level of constitutive mTOR- and DNA damage signaling.” AGING, December 2012, 4 No.12, 952-965. Hu J, Hwang SS, Liesa M, Gan B, Sahin E, Jaskelioff M, Ding Z, Ying H, Boutin AT, Zhang H, Johnson S, Ivanova E, Kost-Alimova M, Protopopov A, Wang YA, Shirihai OS, Chin L and DePinho RA. “Antitelomerase Therapy Provokes ALT and Mitochondrial Adaptive Mechanisms in Cancer.” Cell, February 17, 2012 148, 651-663.
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Jacobetz MA, Chan DS, Neesse A, Bapiro TE, Cook N, Frese KK, Feig C, Nakagawa T, Caldwell ME, Zecchini HI, Lolkema MP, Jiang P, Kultti A, Thompson CB, Maneval DC, Jodrell DI, Frost GI, Shepard HM, Skepper JN and Tuveson DA. “Hyaluronan impairs vascular function and drug delivery in a mouse model of pancreatic cancer.” Published online March 30, 2012, doi: 10.1136/gutjnl-2012-302529. Lin Y, Richards FM, Krippendorff BF, Bramhall JL, Harrington JA, Bapiro TE, Robertson A, Zheleva D and Jodrell DI. “Paclitaxel and CYC3, an aurora kinase A inhibitor, synergise in pancreatic cancer cells but not bone marrow precursor cells.” British Journal of Cancer (2012) 107, 1692-1701. May R, Akbariyeh S, Li Y. “Pore-Scale Investigation of Nanoparticle Transport in Saturated Porous Media Using Laser Scanning Cytometry.” Environmental Science Technology (2012) 46 (18), pp 9980-9986, doi: 10.1021/es301749s. Epub 2012 Sep 7. Maruyama T, Yamamoto S, Qiu J, Ueda Y, Suzuki T, Nojima M, Shima H. “Apoptosis of bladder cancer by sodium butyrate and cisplatin.” J Infect Chemother (2012) 18(3):288-95. McKenna E, Traganos F, Zhao H, Darzynkiewicz Z. ”Persistent DNA damage caused by low levels of mitomycin C induces irreversible cell senescence.” Cell Cycle (2012) 11(16):3132-40. Svensson L, Stanley P, Wllenbrock F and Hogg N. “The Gaq/11 Proteins Contribute to T Lymphocyte Migration by Promoting Turnover of Integrin LFA-1 through Recycling.” PloS ONE 7(6): e38517. doi: 10.1371/journal.pone.0038517. Takahashi H, Ruiz P, Ricordi C, Delacruz V, Miki A, Mita A, Misawa R, Barker S, Burke GW, Tzakis AG, Ichii H. “Quantitative in situ analysis of FoxP3+ T regulatory cells on transplant tissue using laser scanning cytometry.” Cell Transplant (2012) 21(1):113-25. Thon JN, Macleod H, Begonja AJ, Zhu J, Lee K, Mogilner A, Hartwig JH & Italiano JE Jr. “Microtubule and cortical forces determine platelet size during vascular platelet production.” Nature Communications (2012) 3:852, DOI: 10.1038/ncomms1838. Tsiper MV, Sturgis J, Avramova LV, Parakh S, Fatig R, Juan-Garcia A, Li N, Rajwa B, Narayanan P, Qualls CW Jr., Robinson JP and Davisson VJ. “Differential Mitochondrial Toxicity Screening and Multi-Parametric Data Analysis.” PLOS ONE, October 2012, 7 (10) e45226. Xu J and Amiji M. “Therapeutic Gene Delivery and Transfection in Human Pancreatic Cancer Cells using Epidermal Growth Factor Receptor-targeted Gelatin Nanoparticles.” The Journal of Visualized Experiments (JoVE), (2012) (59), e3612, DOI:10.3791/3612. Zhao H, Rybak P, Dobrucki J, Traganos F, Darzynkiewicz Z. “Cytometry A. Relationship of DNA damage signaling to DNA replication following treatment with DNA topoisomerase inhibitors camptothecin/topotecan, mitoxantrone, or etoposide.” Cytometry A (2012) 81(1):45-51.
Published 2011 Ananthanarayanan V, Deaton RJ, Amatya A, Macias V, Luther E, Kajdacsy-Balla A, Gann PH. “Subcellular localization of p27 and prostate cancer recurrence: automated digital microscopy analysis of tissue microarrays.” Hum Pathol (2011) PMID: 21292307. Darzynkiewicz Z, Pozarowski P, Lee BW, and Johnson GL. “Chapter 9: Fluorochrome-Labeled Inhibitors of Caspases: Convenient In Vitro and In Vivo Markers of Apoptotic Cells for Cytometric Analysis” in DNA Damage Detection In Situ, Ex Vivo, and In Vivo: Methods and Protocols, Methods in Molecular Biology, 682, Springer Science, Ed. Vladimir V. Didenko. DOI 10.1007/978-1-60327-409-8_9.
Imaging Cytometry Cytometry Overview
References Darzynkiewicz Z, Smolewski P, Holden E, Luther E, Henriksen M, François M, Leifert W, Fenech M. “Laser scanning cytometry for automation of the micronucleus assay.” Mutagenesis (2011) 26(1):153-61. Darzynkiewicz Z, Traganos F, Zhao H, Halicka HD, Skommer J, and Wlodkowic D. “Chapter 6: Analysis of Individual Molecular Events of DNA Damage Response by Flow- and Image-Assisted Cytometry” in Recent Advances in Cytometry, Part B: Advances in Applications Fifth Edition, (2011) Methods in Cell Biology, Vol. 103, Eds. Zbigniew Darzynkiewicz, Elena Holden, William Telford, and Donald Wlodkowic, pp. 115 - 147. Darzynkiewicz Z and Zhao H. “Chapter 8: Detection of DNA Strand Breaks in Apoptotic Cells by Flow- and Image-Cytometry” in DNA Damage Detection In Situ, Ex Vivo, and In Vivo: Methods and Protocols, Methods in Molecular Biology, Vol. 682, Springer Science, Ed. Vladimir V. Didenko. DOI 10.1007/978-1-60327-409-8_8. Fenech M, Holland N, Zeiger E, Chang WP, Burgaz S, Thomas P, Bolognesi C, Knasmueller S, Kirsch-Volders M, Bonassi S. “The HUMN and HUMNxL international collaboration projects on human micronucleus assays in lymphocytes and buccal cells--past, present and future.” Mutagenesis (2011) 26(1): 239-45. Gosens R, Stelmack GL, Bos ST, Dueck G, Mutawe MM, Schaafsma D, Unruh H, Gerthoffer WT, Zaagsma J, Meuers H, Halayko AJ. “Caveolin-1 is required for contractile phenotype expression by airway smooth muscle cells.” J Cell Mol Med (2011) DOI 10.1111/j.15824934.2010.01246.x. Hao S, Zhao H, Darzynkiewicz Z, Battula S, Ferreri NR. “Differential regulation of NFAT5 by NKCC2 isoforms in medullary thick ascending limb (mTAL) cells.” AM J Physiol Renal Physiol (2011) DOI 10.1152/ ajprenal.00408.2010. Henriksen M, Miller B, Newmark J, Al-Kofahi Y, and Holden E. “Chapter 7: Laser Scanning Cytometry and Its Applications: A Pioneering Technology in the Field of Quantitative Imaging Cytometry” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 161 - 205. Jaskelioff M, Muller FL, Paik JH, Thomas E, Jiang S, Adams AC, Sahin E, Kost-Alimova M, Protopopov A, Cadinanos J, Horner JW, Maratos-Flier E, Depinho RA. “Telomerase reactivation reverses tissue degeneration in aged telomerase-deficient mice.” Nature (2011) 469(7328): 102-6. Jonasch E, McCutcheon IE, Waguespack SG, Wen S, Davis DW, Smith LA, Tannir NM, Gombos DS, Fuller GN and Matin SF. “Pilot trial of sunitinib therapy in patients with von Hippel-Lindau disease.” Annals of Oncology, 2011 22: 2661-2666. Juan G, Zoog SJ, and Ferbas J. “Chapter 12: Leveraging Image Cytometry for the Development of Clinically Feasible Biomarkers: Evaluation of Activated Caspase-3 in Fine Needle Aspirate Biopsies” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 309 - 322. Kovalenko EI, Ranjbar S, Jasenosky LD, Goldfeld AE, Vorobjev IA, Barteneva NS. “The use of HaloTag-based technology in flow and laser scanning cytometry analysis of live and fixed cells” BMC Res Notes (2011) 9;4:340.
Leifert WR, Francois M, Thomas P, Luther E, Holden E, and Michael Fenech. “Chapter 13: Automation of the Buccal Micronucleus Cytome Assay Using Laser Scanning Cytometry” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 321 - 339. Lowes LE, Goodale D, Keeny M, and Allan AL. “Chapter 10: Image Cytometry Analysis of Circulating Tumor Cells” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 261 - 290.
iCyte Imaging Cytometer Software & Analysis References & Publications
McGrath MA, Morton AM, and Harnett MM. “Chapter 9: Laser Scanning Cytometry: Capturing the Immune System In Situ” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 231 - 260. Park M, Youn BS, Zheng XL, Wu D, Xu A, and Sweeny G. “Globular Adiponectin, Acting via AdipoR1/APPL1, Protects H9c2 Cells from Hypoxia/Reoxygenation-Induced Apoptosis.” PLoS one (2011) 6(4):e19143. Reinert A, Mittag A, Reinert T, Tárnok A, Arendt T, and Morawski M. “On the quantification of intracellular proteins in multifluorescencelabeled rat brain slices using slide-based cytometry.” (2011) Cytometry A 79A:485-491. Sahin E, Colla S, Liesa M, Moslehi J, Müller FL, Guo M, Cooper M, Kotton D, Fabian AJ, Walkey C, Maser RS, Tonon G, Foerster F, Xiong R, Wang YA, Shukla SA, Jaskelioff M, Martin ES, Heffernan TP, Protopopov A, Ivanova E, Mahoney JE, Kost-Alimova M, Perry SR, Bronson R, Liao R, Mulligan R, Shirihai OS, Chin L, and DePinho RA. “Telomere dysfunction induces metabolic and mitochondrial compromise.” (2011) Nature 470(7334):359-65. Soubeyran I, Mahouche I, Grigoletto A, Leste-Lasserre T, Drutel G, Rey C, Pedeboscq S, Blanchard F, Brouste V, Sabourin J, Bécouarn Y, Reiffers J, Ichas F, De Giorgi F. “Tissue Microarray Cytometry Reveals Positive Impact of Homeodomain Interacting Protein Kinase 2 in Colon Cancer Survival Irrespective of p53 Function.” The American Journal of Pathology (2011) 178, No. 5, 1986-1999. Stefan T and Jacobberger JW. Chapter 14: Laser Scanning Cytometry of Mitosis: State and Stage Analysis” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 341 - 372. Tao M, Ma D, Li Y, Zhou C, Li Y, Zhang Y, Duan W, Xu X, Wang R, Wu L, and Liu H. “Clinical significance of circulating tumor cells in breast cancer patients.” Breast Cancer Res Treat (2011) PMID:21512769. Wu E, Menon V, Geddie W, Sun Y. “An automated microfluidic sample preparation system for laser scanning cytometry.” Biomed Microdevices (2011) PMID:21243437. Zhao H, Dobrucki J, Rybak P, Traganos F, Dorota Halicka H, Darzynkiewicz Z. “Induction of DNA damage signaling by oxidative stress in relation to DNA replication as detected using ‘click chemistry’.” Cytometry A (2011) 79(11):897-902.
Krull DL and Peterson RA. “Chapter 11: Preclinical Applications of Quantitative Imaging Cytometry to Support Drug Discovery” in Recent Advances in Cytometry, Part A: Instrumentation Fifth Edition, (2011) Methods in Cell Biology, Vol. 102, Eds. Zbigniew Darzynkiewicz, Elena Holden, Alberto Orfao, William Telford, and Donald Wlodkowic, pp. 291-308. 267