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Journal of Histochemistry and Cytochemistry logoLink to Journal of Histochemistry and Cytochemistry
. 2012 Oct;60(10):723–733. doi: 10.1369/0022155412453052

Imaging Flow Cytometry

Coping with Heterogeneity in Biological Systems

Natasha S Barteneva 1,2,3,4,5,✉, Elizaveta Fasler-Kan 1,2,3,4,5,✉, Ivan A Vorobjev 1,2,3,4,5
PMCID: PMC3524563  PMID: 22740345

Abstract

Imaging flow cytometry (IFC) platforms combine features of flow cytometry and fluorescent microscopy with advances in data-processing algorithms. IFC allows multiparametric fluorescent and morphological analysis of thousands of cellular events and has the unique capability of identifying collected events by their real images. IFC allows the analysis of heterogeneous cell populations, where one of the cellular components has low expression (<0.03%) and can be described by Poisson distribution. With the help of IFC, one can address a critical question of statistical analysis of subcellular distribution of proteins in a cell. Here the authors review advantages of IFC in comparison with more traditional technologies, such as Western blotting and flow cytometry (FC), as well as new high-throughput fluorescent microscopy (HTFM), and discuss further developments of this novel analytical technique.

Keywords: imaging flow cytometry, flow cytometry, biological heterogeneity, fluorescence, cellular morphology, Poisson distribution, single cell, high-throughput fluorescent microscopy

Several types of high-throughput instrumentation for analyzing and quantifying different aspects of cell biology are now available. They include plate readers, sequencing platforms, DNA, RNA and protein microarrays, Western blotting, flow cytometers, and so on. However, many platforms allow readouts only on the population level. Technologies that allow readout at the single-cell level include flow cytometry (FC), imaging cytometry, and different automated microscopy setups. These platforms are essential for assaying diversity within cell populations and searching for rare cells with specific features (stem cells, etc.).

Cytometry assays are sensitive, fluorescence-based methods aimed at determining a molecular phenotype of single cells. They can be multiparametric, multiplexed, quantitative, and qualitative. They can also be extracted as a result of kinetic or single end-point measurements. FC allows for the simultaneous quantification of multiple fluorescent emissions and the scattered light of single cells acquired in the laminar flow of cell suspension. FC is a technology that measures the cellular properties (protein expression, siRNA expression, etc.) in a snapshot of the entire population (Shapiro 2005; Huang S 2009). Imaging cytometry (IC) is represented by two different types of technology: 1) high-throughput microscopy and laser scanning cytometry, which interrogates cells or tissue specimens in situ positioned on the microscope slide or in the microplate wells (Kamentsky and Kamentsky 1991; Darzynkiewicz et al. 1999; Gerstner et al. 2004; Terjung et al. 2010; Henriksen et al. 2011; Rimon and Schuldiner 2011), and 2) imaging flow cytometry (IFC), which interrogates cells and cellular aggregates in the laminar flow (McGrath et al. 2008).

The most important difference between FC and IFC depends on whether the fluorescence data of the cell suspension are obtained with cell morphology or from fluorescence pulse-analysis. IFC allows for the acquisition and identification of tens of thousands of cellular events based on their fluorescent and morphological parameters. The first and unique IFC instrument—Imagestream 100 (IS100)—was introduced in 2005, and the next generation of Imagestream imaging flow cytometers (IS-X) was recently launched by Amnis Corp. (Seattle, WA) (Basiji et al. 2007).

IFC in the Evaluation of Cellular Heterogeneity

IFC allows for the evaluation of morphological and fluorescent data at a single-cell as well as at a population level (Figure 1). IFC combines the statistical advantage of FC with the ability to identify each event based on a real image, which allows it to analyze protein expression in single cells in heterogeneous cell populations, where the level of expression of one of the proteins is low and could be described by Poisson distribution (rare cell subpopulations with <0.01% of expression). The multiple applications of IFC include analysis of nuclear-cytoplasmic translocation (Arechiga et al. 2005; Fanning et al. 2006; Danis et al. 2008), quantification of apoptosis based on the changes in nuclear morphology (George et al. 2004; Henery et al. 2008; Khuda et al. 2008), and quantitative analysis of internalized bacteria and protozoan parasites (Muskavitch et al. 2008; Bisha and Brehm-Stecher 2009; Ploppa et al. 2011). In recent years, IFC was also employed for the evaluation of asymmetric cell division (Filby et al. 2011), internalization of CypHer5E-conjugated antibodies and PKH-labeled exosomes (Xu et al. 2010; Vallhov et al. 2011), intercellular communication by exchange of cytoplasmic material (Domhan et al. 2011), analysis of cell interactions and immune synapse (Ahmed et al. 2009; Ouk et al. 2011), and some other experimental applications (Ponomarev et al. 2011).

Figure 1.

Figure 1.

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A typical workflow for image flow cytometry.

The cell populations are heterogeneous with respect to cell cycle phase, size, volume, physiological state, and their individual development history (Lloyd et al. 2000; Kaern et al. 2005; Pilborough et al. 2009). In a clonal population, large variations in phenotype may be the result of fluctuating gene expressions (Kim et al. 1998; Pilborough et al. 2009; Dietmair et al. 2012). Emerging fundamental research on bacteria (Elowitz et al. 2002; Ozbudak et al. 2002; Yu et al. 2006) and, more recently, on yeast and mammalian cells (Newman et al. 2006; Raj et al. 2006; Sigal et al. 2006; Chang et al. 2008) shows that protein expressions can have significant variations inside clones of genetically identical cells (intraclonal variation).

The important advantage of cytometric methods over Western blotting and gel-shift assay is that they efficiently overcome the heterogeneity drawback, allowing the data collection of a number of cell populations without averaging the signal intensities. When measuring the average signal intensity, information regarding cell subpopulations of heterogeneous populations can be missed (Huang S 2009). For example, when a 25% decrease in signal intensity is observed with Western blotting, it is impossible to tell, if this results from a 25% reduction in 100% of the cell population or a 50% reduction in only 50% of the population. To overcome this drawback, a combination of Western blotting and cell sorting is used.

Also, the amount of cells needed for Western blotting analysis is in the range of 5 × 105–106 cells per sample, making it practically useless for the analysis of small and rare subpopulations (a rare population being <0.03%). Microarrays and two-dimensional gels are also biased toward more abundant genes (Lu and King 2009). Although FC can identify and sort rare cell populations with high speed, dealing with rare cell detection using this approach is plagued by the contamination of false-positive events due to autofluorescence, nonspecific immunostaining, and cell aggregates (Radbruch and Recktenwald 1995). For rare cell detection, the following parameters are considered important: 1) the ability of the instrument to process large numbers of cells, 2) the number of cells analyzed by instrument per unit of time, 3) the sensitivity of the instrument, 4) the specificity of the assay, and 5) the consistency of the instrument performance.

We had difficulties analyzing the large, 100K files using the IDEAS software that came with the IS-100. However, IFC is very helpful in identifying and characterizing small populations of cells, such as the low percentage population of cells that forms an immune synapse in our research of CTL-macrophage interaction of an HIV infection (Ahmed et al. 2009).

Recently, IFC, combined with cell sorting preenrichment, was successfully employed for the identification and characterization of a novel pluripotent population of very small embryonic-like stem cells (VSELs) in human and animal tissues (Kucia et al. 2006; Zuba-Surma, Klich, et al. 2009; Zuba-Surma, Kucia, Rui, et al. 2009). According to Zuba-Surma, Kucia, Ratajczak, et al. (2009), the frequency of VSELs in murine adult spleen (1 × 108 cells) was 3.9 × 103, which corresponds to approximately one cell per 2.5 × 104 (0.25% of cell population) and even lower in other adult murine organs. IFC is also helpful for the analysis of rare cell populations (<0.01%) after cell sorting.

IFC in Signal Transduction Research

Strategies to monitor and manipulate individual signaling pathways and the modification of target protein properties by reversible phosphorylation can offer valuable tools to investigate the precise role of signal transduction pathways in the pathogenesis and progression of human diseases. The phosphorylation of a protein is the principal cellular mechanism regulating protein function and is controlled by protein kinases and phosphatases. Therefore, monitoring kinase and phosphatase activities is crucial to understanding signal transduction pathways. The Western blot is currently regarded as the most versatile and convenient assay for quantifying signaling activities, partly due to the successful use of short synthetic peptides to produce epitope-targeted antibodies for this platform. However, using Western blot, multiple signaling activities cannot be simultaneously quantified, and the signal intensities of various subpopulations can not be differentiated. Other drawbacks of the Western blotting approach are the necessity of cell lysis and solubilization of the samples. The solubilization step is required to release the protein of interest from its membrane environment and may lead to artificial associations and oligomerization of proteins and other artifacts. Also, Western blotting requires a significant amount of cells (5 × 105–106 per line), and in many cases, preliminary immunomagnetic purification and/or fluorescent-activated cell sorting (FACS) is needed. Many proteins involved in signal transduction pathways are expressed at low levels and cannot be evaluated by standard Western blotting techniques. Recently, new approaches, such as antibody co-localization microarrays and reverse protein assays, have been developed (Kornblau et al. 2009; Brennan et al. 2010; Pla-Roca et al. 2012). Reverse protein arrays can determine the levels of multiple phosphoproteins in cell lysates in a high-throughput fashion on chips, but it is impossible to differentiate signal intensities of individual cells and discrete cell subsets using these techniques (Wu et al. 2010).

An increasingly common technique to characterize signal transduction pathways relies on FC. FC has been shown to provide quantitative data for measuring signaling activities similar to the Western blot (Krutzik and Nolan 2003; Krutzik et al. 2004, 2011) and is also capable of measuring multiple signaling activities in single cells on a population level. FC has been most commonly used for the analysis of multiple kinases (such as the JAK-STAT pathway) and phosphatases in response to different signaling (Perez et al. 2004; Heller et al. 2006; Teofili et al. 2007; Kotecha et al. 2008; Oh et al. 2010). The major drawback in using FC to characterize different signal transduction pathways is the lack of a reliably wide panel of antibodies with high specificity. Antibodies are frequently cross-reactive, but in FC, there is no way to filter out “cross-reactivity” (Oberprieler and Tasken 2011). Currently, numerous antibodies against phosphoepitopes are under development in several laboratories (Kotecha et al. 2008; Lee et al. 2008; Oberprieler et al. 2010).

The signal-induced nuclear-cytoplasmic translocation of key signal transduction molecules (such as NF-κB, FOX-p3, Smad, and others) is a common phenomenon in many pathways. FC is unable to provide information about the subcellular localization of the fluorescent signal. To obviate the requirement for determining nuclear localization for molecules in the NF-κB pathway, the FC approach relies on the detection of phospho-specific intermediaries (Phospho-p65) (Armstrong et al. 2006). Also, FC is not capable of providing information about the interrelationship of signaling activities at the intracellular level. The FC study of signaling in adhesive cells is at a disadvantage in that cells have to be detached into a single suspension before fixation. Also, the Western blotting of adherent cells in many cases requires preliminary cell sorting to enrich for a certain cell population; this situation is quite common in the field of signal transduction.

Traditionally, transport between the nucleus and the cytoplasm has been studied using subcellular fractionation followed by Western blotting and/or fluorescent microscopy (Shakulov et al. 2000; Zhou et al. 2006) and, recently, by automated high-throughput microscopy systems (Rimon and Schuldiner 2011). For quantitative measurements of protein distribution, the fluorescent intensities of the nucleus and cytoplasm in the fluorescent microscope are usually limited to 50 to 100 cells per experiment due to the small frame size. However, the major advantage of the light microscopy approach is spatial precision, which is being further developed with the implementation of super-resolution techniques (reviewed by Huang B et al. 2009). Also, measurements can be made in live, functioning cells and directly correlated with single molecules’ trajectories in real time (Tu and Musser 2011).

IFC allows for the quantitation of bidirectional trafficking of transcriptional regulators from the nucleus to the cytoplasm inside the cell, with a maximum 60× magnification (for IS-X; 40× for IS-100). The approach of image analysis to nuclear-cytoplasmic translocation used in IFC includes the initial identification and segmentation of single cells, and then the signal intensities are summed in the nuclear and cytoplasmic regions; the latter is defined as the whole cell body minus the nuclear region. This is important for the precise identification of individual cells in nuclear translocation assays. On the other hand, it is different from conventional high-throughput imaging, where signal intensities are summed in the nuclear regions plus the cytoplasmic regions, defined by an annulus set around each cell (Ding et al. 1998; Ozaki et al. 2010). IDEAS software, developed by Amnis Corp, is capable of calculating nuclear-cytoplasmic translocation based on the degree of co-localization of DNA markers and the protein of interest (George et al. 2006). The similarity score is derived from Pearson’s correlation coefficient (pixel-by-pixel correlation of the NF-κB and nuclear dye-image pair within the nuclear morphology mask dilated by one pixel), and the cross-correlation methods have been used to estimate the translocation of NF-κB and other transcription factors (FOX-p3, p65, interferon regulatory factor-7 [IRF-7]) (Arechiga et al. 2005; Fanning et al. 2006; Danis et al. 2008; Maguire et al. 2011). IFC is particularly valuable for signal transduction analysis in 1) experiments employing cells with low levels of expression of the protein of interest and 2) cellular populations with a particularly wide range of responses to a signal (George et al. 2006).

Elimination of False-Positive and False-Negative Events with IFC

A limitation of FC as a method is that it can create “false-positive” heterogeneity due to staining and sample preparation artifacts resulting from cross-reactivity of antibodies, as well as the absence of morphological information (Lambert and Desbarats 2007; Kuonen et al. 2010). Dead cell exclusion and multiple-event discrimination using gating on light-scatter dual-parameter dot blots alleviate but do not completely eliminate the problem. IFC should be a method of choice for the validation of antibody specificity and cross-reactivity. Ouk et al. (2011) used IFC to demonstrate that adhesion of platelets on the surface of leukocytes is likely to be responsible for false-positive expressions of platelet markers on leukocytes determined by FC. Standard gating exclusions of multiaggregates in FC analyses (FSC-W/FSC-H) do not work in this case because of the relatively small size of the platelets attached (2–3 µ).

Most false-positive events are known to be associated with apoptosis, cytotoxicity, and cell differentiation, including kinases and protein phosphatases (Crisman et al. 2007). During apoptosis, detection of false-positive events can occur due to several factors: 1) Apoptotic bodies and microparticles can adhere to the surface of live, non-apoptotic cells; 2) cells stained with an apoptotic marker could adhere to a normal cell; 3) cells can phagocytize a fluorescently labeled apoptotic body and/or cell fragments; and 4) apoptotic bodies and/or cell fragments can be counted as apoptotic cells (Bedner et al. 1998, 1999; Darzynkiewicz et al. 2001). Because IFC includes real imaging of each fluorophore distribution in the cell, it is possible to determine if fluorophores are located outside the area of the cell or at the expected location (membrane for Annexin V assay, nucleus for terminal deoxynucleotidyl transferase dUTP nick end labeling [TUNEL] assay, cytoplasm for caspase-3 substrate) (Henery et al. 2008). Another example of false- negative events is that some cells in the population can lose the apoptotic marker due to the loss of membrane integrity. It is also important that most if not all FC techniques for the detection of apoptosis (such as TUNEL method, conventional apoptosis assays using propidium iodide, subG1 DNA content determination, etc.) also detect necrosis and other cellular processes (Ansari et al. 1993; Li et al. 1995; Frankfurt et al. 1996; Rieger et al. 2010; Bucur et al. 2012). IFC is capable of discriminating false events on the basis of cell images and providing morphological information on these cells (nuclear fragmentation, membrane blebbing, etc.).

Advantages of IFC

The FC statistical advantage comes at the price of missing information because FC measures total fluorescence of individual cells selected by sophisticated gating from a cell population. As a result, fluctuations in the protein and gene expression may be due to a difference in cell volumes (Newman et al. 2006; Volfson et al. 2006; Tsuru et al. 2009). A common method to reduce cell variability relies on “gating” (i.e., creating sequential gates in the FSC-A/SSC-A and FSC-H/FSC-W dot blots and discarding all the cells that fall outside the chosen gates, hence reducing variability in the cellular optical properties). In practice, FC does not allow for the discrimination between multiple events and large cells, masking potentially relevant cells and biasing results. Excluding events outside scattered light gates can drastically reduce the analyzed sample size, particularly in the analysis of detached or digested cells, enzymatically treated with trypsin or collagenase, and ignore the ungated cell population by masking potentially relevant cellular events. Recently, Knijnenburg et al. (2011) introduced a correction for phenotypic variability based on the regression model that uses all the cells in the sample to normalize cell size and granularity effects on fluorescence intensity. On the other hand, IFC offers an alternative to gating on the basis of light scattering such that gating uses an aspect ratio of cells (a ratio of cell diameters), diameter and cell volume, and other morphological features.

The pool of antibodies that can be used for IFC includes FC-quality antibodies, as well as polyclonal antibodies used for immunoprecipitation and immunohistochemistry assays (such as rabbit polyclonal NFκB p65 antibody and anti-IRF-7 [Santa Cruz Biotechnology, Santa Cruz, CA]) (Fanning et al. 2006; Tibrewal et al. 2008).

Image processing needs to be adapted to each new assay developed and therefore is the bottleneck of high-throughput imaging technology (Starkuviene and Pepperkok 2007). The IS-instruments analyze cells flowing in suspension and collect dark field, bright field, and multiple fluorescent parameters (Basiji et al. 2007). The greatest advantage of IFC compared with the FC is its unique ability to identify collected events by their real images: For each event displayed on a two-dimensional dot as a “dot,” IDEAS software retrieves a real image of the event. The reverse identification of cellular events on a dot blot is also possible. This capability is crucial for characterization and identification of cellular subpopulations and for identifying false-positive and false-negative events. IFC allows for simultaneous analysis of fluorescent and morphological parameters, which is important for monitoring different processes together such as apoptosis and autophagy (De la Calle et al. 2011). The resolution of images is limited by 0.5-µ pixels in IS-100 and 0.3-µ pixels in IS-X (when using 60× objective), which allows for the quantification of many cellular parameters, such as size, volume, shape, contrast, spot count, and texture. The IDEAS software gives researchers the capability to analyze acquired cellular images while using bivariate plots and histograms, as well as a sequential gating strategy. It is also possible to create new, complex features using Boolean logic and the combination of two-dimensional masks (such as morphology masks, intensity threshold masks, spot masks, and others). The detailed description of individual masks and features can be found on the company site (www.amnis.com). Moreover, the IDEAS software has the additional option of defining populations using a gallery of individual images. Overall, the IDEAS software used in IS instruments provides a unique approach allowing the investigator to quickly change critical parameters during the analysis of cellular populations. It is more user-friendly than other currently available image analysis programs that are used in microscopy (Metamorph, ImageJ, etc.), and analysis performed by IDEAS does not require creating scripts or using special programming skills.

Because of the ability to collect real images, IFC also has an advantage in the statistical analysis of not only single cellular events but also cell aggregate content and the study of cell-cell interactions. IFC allows for the analysis of tens of thousands of cellular events versus high-throughput automated fluorescent microscopy, which is usually limited by parallel analysis of several hundred to a few thousand events (Terjung et al. 2010). For example, quantitative analysis of microaggregates of leukocytes and platelets (Goetz et al. 2005) achieved with light microscopy was limited by analysis of 50 aggregates per experiment. FC analysis of aggregates, the most widely used method of aggregate detection, provides information only about the level of fluorescent intensity of the aggregate subpopulation defined by the coexpression of platelet and leukocyte (or monocyte) markers (Harding et al. 2007; Izzi et al. 2007; Alberti et al. 2009; Singh et al. 2012). FC is not able to provide information about morphology and quantitative cellular content of mixed aggregates. Ouk et al. (2011) used IFC to quantitatively assess the number of adherent platelets to neutrophils and monocytes, to visualize and further confirm mixed aggregates forming during different experimental conditions. The IFC approach could be a method of choice in the study of platelet-leukocyte interactions leading to the formation of mixed aggregates.

Analysis of cell interactions, such as forming immunological synapses with the help of IFC, also allows overcoming limitations of current imaging techniques, such as difficulty in objectively finding rare events, limited statistics, and sample photobleaching during fluorescent microscopy sessions (Ahmed et al. 2009). Using IFC, we were able to analyze thousands of T cell–macrophage conjugates for each experimental point (Ahmed et al. 2009). The same methodology could also be used to evaluate the co-localization and movement of molecules in the interactions between antigen-specific cells and target cells and/or between pathogen and immune cells.

Challenges Associated with IFC Studies

The IFC research discussed in the previous sections provided a lot of new information about protein expression, signaling pathways, and cellular interactions. Nevertheless, there appears to be a number of technical and experimental limitations, all of which should be considered when planning IFC experiments.

Traditional microscopic methods have many advantages compared with IFC; they have better spatial resolution and allow analysis of spatial-temporal organization of the samples, time-lapse experiments with single cells, and so on (Rimon and Schuldiner 2011). However, a limited number of cells are evaluated, and until recent introduction of automated systems, operator bias always had to be considered. IFC has common traits with high-throughput microscopic systems, such as moderate resolution (0.5–1.0 µ) and the capability of analyzing thousands of cellular events (Table 1). On the other hand, high-throughput fluorescent microscopy (HTFM) has advantages of time-lapse analysis of cells and parallel analysis of hundreds of markers, and no perturbation is used for adherent cells. The major advantages of IFC include a hardware that is oriented to analysis without perturbation of cells into suspension and well-developed software that does not require programming skills and is not limited by a few prearranged analysis algorithms. The choice between the currently available microscopic imaging software is influenced mainly by the programming expertise and the hardware resources (Terjung et al. 2010).

Table 1.

Summary of Imaging Flow Cytometry (IFC) and Other Common Techniques for Cell Population Analysis

Feature Western Blotting IFC Flow Cytometry Laser Scanning Cytometry High-throughput Fluorescent Microscopy
Capable of analyzing heterogeneous cell population No Yes Yes Yes Yes
Light scatter No Yes (side scatter) Yes (forward and side scatter) Yes No
Brightfield No Yes No Yes Yes
Capable of analyzing rare subpopulation (<0.01%) No Yes Yes Capable of analyzing 1:104 cells (Megyeri et al. 2004) No
Needs spectral compensation NA Yes Yes No No
Cell morphology data No Yes No Yes Yes
Statistics (representative experiment) Averaged data from 5 × 105–106 cells per slot 103–104 events Up to 106–107 events 102–105 cells 102–104 cells
Speed of acquisition 5 × 105–106 cells per slot up to 101–3 × 101 slots/hr 300–1000 events/sec 3000–10,000 events/sec Up to 100 cells/sec (5000 cells/min) (Pozarowski et al. 2006) Parallel acquisition; rate depends on the exposure time
Single-cell analysis (time lapse) No No No Yes Yes
Cell population analysis (time lapse) No Yes Yes Yes Yes
Operator bias Not significant Not significant Not significant Not significant Not significant
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NA, not applicable.

Although technical limitations continue to be addressed through constant developments, the multicolor experiments developed for flow cytometers or microscopes are not immediately transferable to IS instruments (IS-X or early model IS-100). Some differences need to be taken into account when you are planning the multicolor assays using an imaging flow cytometer. Two major considerations are the following: 1) wide bandpass of the optical filters and related significant spectral compensation and 2) optimal speed that relies on cell concentrations and is relatively low. For example, maximal speed (up to 300 events/sec for IS-100 and up to 1000 events/sec for IS-X) can only be achieved with a relatively high sample concentration that is about 107 cells per milliliter (500,000 cells per 50 µl of volume for IS-100 and 3× less for IS-X). Another problem that stems from the contamination of the sample is a large number of non-target fluorescent particles and microparticles, which are acquired by IS systems as separate events.

The acquisition timescale in IFC ranges in minutes, all the way from tenths of a minute to 100 min needed to push through tens of thousands of events from samples with a low cellular concentration. Several issues arise when trying to automatically image multiple live cells with the help of the IS series in IFC, such as the lack of availability of nutrients and oxygen over long time periods, because samples presumably are run in phosphate buffer solution (PBS). Focusing is a large contributor to the length of time required for analysis, because a large percentage of cells will be excluded from analysis due to focusing problems. Moreover, if fluorescence labeling with cell tracking and vital dyes is used, additional experiments are required to exclude phototoxicity as a reason for observed differences between the experimental and control cells.

Postacquisition image analysis of data from IS instruments requires pixel-by-pixel spectral compensation, described in detail in the study published by Ortyn et al. (2006). The IS instruments have wide, non-interchangeable optical filters with the width in ranges from 25 to 85 nm (channel 3, 500–560 nm; channel 4, 560–595 nm; channel 5, 595–660 nm; channel 6, 660–730 nm for IS-100) (see Suppl. Table S1 for IS-X). Many dyes may require a significant spectral compensation (e.g., dihydroethidium [DHE] may require 73.9% compensation in channel 4 and 40.6% compensation with DRAQ 5; cited by Ploppa et al. 2011 for the IS-100). This leads to the low detection signal of quantum dots (QDs) and such standard violet dyes as Pacific Blue and Pacific Orange. These dyes are practically undetectable in the multiparameter panels partly due to the significant spectral compensation (Chattopadhyay et al. 2012; our unpublished data). The preferential violet fluorochromes for antibody conjugates imaged with IS instruments are Brilliant Violet–based tandem dyes (Chattopadhyay et al. 2012). The QD combinations for IS instruments provide a good staining only in the absence in the panel overlapping bright fluorochromes with wide spectra; that is, only as a combination of different QDs together (such as QD525 and QD605 in the study by Domhan et al. 2011).

It is useful to perform IFC in combination with other methodologies (Western blotting, FC, fluorescent microscopy) and verify, if the different platforms can give comparable results. Consequently, it will help to overcome drawbacks and ambiguities associated with data interpretation.

Conclusions

Recent advances in fluorescent technologies have made the collection of multiparametric imaging files routine, but efficient analysis of these data remains a challenge. IFC offers a solution by allowing a collection of tens of thousands of cellular events and the choice of gated statistical analysis on the basis of fluorescent and morphological features combined with the capability of identifying collected objects on the basis of real images. The principal enhancements of IFC over the past 5 years have been the following: 1) the introduction of a higher resolution (60× objective); 2) using variable objectives, allowing the throughput options to be tailored to the requirements of a specific application (the speed of acquisition can be increased up to 1000 events/sec); 3) introducing additional functionality to the instrumentation (switching between FC and IFC modes); and 4) increasing laser line versatility (to enhance reagent compatibility and multiplexing capability). The advantage of IFC compared with high-throughput microscopy and laser scanning cytometry systems designed to work with adherent cells is its well-developed experiment-approached software and its ability to image cells in suspension. Thus, IFC takes its own unique place in biomedical research. Also, as the interest in performing IFC systems grows, the necessity of combining this technique with cell sorting becomes evident.

Supplementary Material

Supplemental Material
supp_60_10_723__index.html (883B, html)

Acknowledgments

We are grateful to Aleksandra Gorelova for help with the preparation of the manuscript. Space considerations limited us to a selected list of available literature on IFC.

Footnotes

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work was supported in part by NIH S10 RR023459 grant and the Immune Disease Institute to NSB and RFBR grant 11-04-01749a to IAV.

Supplementary material for this article is available on the Journal of Histochemistry & Cytochemistry Web site at http://jhc.sagepub.com/supplemental.

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