Flow Cytometry in Drug Discovery and Development

This book covers the unique application of flow cytometry in drug discovery and development. The first section includes two introductory chapters, one on flow cytometry and one on biomarkers, as well as a chapter on recent advances in flow cytometry. The second section focuses on the unique challenges and added benefits associated with the use of flow cytometry in the drug development process. The third section contains a single chapter presenting an in depth discussion of validation considerations and regulatory compliance issues associated with drug development.

• Language: English
• Publisher: Wiley
• Release date: Apr 20, 2011
• ISBN: 9780470922781

Book preview

Part I

Introduction

Philip Marder and Virginia Litwin

The discovery and development of novel therapeutic compounds is a lengthy, difficult, and expensive process with recent estimates of more than 1.2 billion dollars required for each new drug brought to market. As a result, the standard processes of the pharmaceutical industry are being reevaluated and modified in order to increase efficiencies in the drug development process. One approach in process transformation is to promote more informed decision making by incorporating advanced technologies such as flow cytometry.

A wide variety of flow cytometric methods are employed during various stages of the drug development life cycle. This book explores many of the benefits and complexities associated with this unique application of the technology. Part I is intended to provide the reader with essential background information regarding both flow cytometry (Chapters 1 and 2) and drug development (Chapter 3).

Chapter 1

Introduction to Flow Cytometry

Elizabeth Raveche, Fatima Abbasi, Yao Yuan, Erica Salerno, Siddha Kasar, and Gerald E. Marti

1.1 Introduction

This chapter presents, in basic terms, the concepts and principles of flow cytometry. Numerous books and articles describing flow cytometers and their use in a clinical and biomedical research setting have been published [1–7]. In this chapter, flow cytometers will be discussed from their infancy arriving at the current instrumentation that allows for detection of numerous features of individual cells or particles, including determination of size and granularity, surface marker expression, DNA content, intracellular protein expression, and function. The key to flow cytometers is that the analysis is done on cells in suspension [8–10]. The analysis of individual cells (or particles) rather than the whole population allows for detection of multiple properties measured on the same cell. The detection is rapid (as fast as the cell in the fluid sheath passes through the laser beam). In addition to analysis of individual cells, some types of flow cytometers can physically sort cells based on signals associated with the parameters being detected. The term fluorescence-activated cell sorter or FACS has been adopted to refer to this type of analysis [11]. Flow cytometry is a very useful tool for both clinical diagnosis and scientific research. The history of flow cytometers has been the subject of numerous reviews [12–20]. The first flow cytometers were introduced in the mid-1970s and first used for DNA analysis and leukemia immunophenotyping [7, 21–25]. A further impetus to bring flow cytometers to the forefront of clinical labs came in the early 1980s with the discovery that individuals infected with the HIV virus developed AIDS, which could be monitored by enumerating the number of CD4+ T cells by flow cytometric analysis [26–30]. Currently, there are emerging areas with flow cytometric applications including the enumeration of CD34+ hematopoietic stem cells [29, 31, 32], detection of circulating metastatic tumor cells [33–37], determination of antigen-specific T cells [38–40], and identification of pathogens [41–45], to list a few. Combination of sorting with molecular analysis represents an important use of the sorting aspects of flow cytometers. There are over 100,000 flow cytometers in use and the employment of this instrument in clinical diagnostics has increased dramatically, particularly with the increase in FDA-approved fluorochrome reagents for in vitro diagnostics (fluorochrome-conjugated antibodies). However, in third-world countries, access to clinical flow cytometers is not optimal [46, 47]. The use of flow cytometers and the impact of this instrument on biomedical and clinical studies can be appreciated by looking at the increase in publications in which the word flow cytometry appeared in the abstract or title with time (Figure 1.1).

Figure 1.1 A bar graph showing the number of publications having flow cytometry in their title/abstract since 1970 to present. There is almost a 150% increase since 1980–1989.

Improvements in instrumentation and computer-assisted analysis have made the flow cytometer a critical instrument in biomedical research, clinical diagnostics, and drug discovery. Herzenberg was honored for his work in flow cytometry by the American Association for Clinical Chemistry with the Ullman Award in 2002 and some of the history described in this chapter comes from his lecture and the accompanying article [19]. The original description of the first flow cytometer was provided in Scientific American [48]. This instrument consisted of one laser and two light detectors, one for forward scatter to measure cell size and the other for fluorescence. This meant that one was restricted to measuring a single marker. When one of the authors of this article used that prototype instrument, the LASL, we were measuring the DNA content of individual cells. This was one of the first uses of these early flow cytometers since reagents were available that not only bound specifically to DNA (e.g., ethidium bromide developed by Dittrich and Gohde in 1969 [49]) but also emitted fluorescence when excited with a laser. Much of the essentials of the modern-day FACS are the same as those in the early flow cytometers. However, these early flow cytometers were cumbersome and required an on-site engineer. The laser was water cooled and alignment issues were critical. In addition, no computer was attached to these early flow cytometers, nor were programs available for data analysis [50]. At one point, we took Polaroid pictures of oscilloscopes and sent data to a DEC10 supercomputer and wrote our own programs for cell cycle analysis.

Although the development of FACS depended on many advances in various disciplines including dye chemistry, electronics, and computers, one important breakthrough that was critical for the development of flow cytometers was the principle of measuring cells or particles in liquid suspension. Advances in the flow principle began in 1940 with Crosland-Taylor using the flow principle and light scatter to measure blood cells [51]. The breakthrough technology was first developed by Coulter and the Coulter principle describes changes in the electrical conductivity of a small saline-filled orifice as a cell passes through it. In 1953, Wallace Coulter and his brother Joe obtained a U.S. patent for the Coulter counter that automated counting of particles, particularly cells in the blood [52]. The use of a liquid stream (or a sheath) to which a sample is introduced allows individual cells to be distributed in the sheath that then passes through a nozzle (detecting electrical conductivity changes) to generate a trigger, which indicates the presence of a signal that exceeds the threshold level.

Many of the applications for FACS analysis involve the identification of membrane markers via the use of fluorochrome-tagged antibodies, which recognize these markers. Many of these membrane markers are surface proteins or surface antigens, which help to define the cell. These antigens are used to classify the cells and are often assigned a cluster of differentiation number or a CD number. Antibodies (which are normally produced by B lymphocytes) can be made that specifically bind to these CD molecules. There are more than 200 CD molecules that have been identified and specific antibodies have been produced that recognized these CD markers [53–55]. In addition, many of these antibodies are commercially available as labeled antibodies with different fluorochromes.

1.2 Basic Principles of How a Flow Cytometer Works

The basic components of a flow cytometer (Figure 1.2) consist of (1) a flow cell that forces single cells into the middle of a fluidic sheath, (2) a laser source of light, (3) optical components to focus light of different wavelengths (colors) onto a detector, (4) a photomultiplier to amplify the signal, and (5) a computer.

Figure 1.2 Diagrammatic representation of a basic flow cytometer. The fluorescently labeled cells are hydrodynamically focused into a single file in the flow cell. Individual cells are excited by the laser light source and the fluorescence emissions, FSC, and SSC are detected. The cells can then be given a particular charge based on their fluorescence profile and deflected toward the oppositely charged plates. In the figure, light grey cells and dark grey cells are given negative and positive charges, respectively, and are thus deflected toward two different tubes.

In a basic flow cytometer, the sample (containing the cells tagged with fluorochromes in a liquid) is drawn up and pumped into the flow cell through tubing. The cells flow through the flow chamber rapidly and singly and are passed through one or more laser light beams. As the laser beam hits the cells, the light beam is scattered in a forward direction and a side direction. Fluorescence emission can also be detected. Scatter or fluorescence is captured, filtered (based on the wavelength), and directed to the appropriate photodetectors for conversion to electronic signals. The electronics in the flow cytometer amplify the signal and convert the analog data to digital data, which can then be analyzed by computer software programs.

1.3 Fluidics

1.3.1 Flow Cells

In order to perform flow cytometric analysis, the sample must be in a suspension and the cell in the sample stream must be centered in the laminar flow [49]. Hydrodynamic focusing induces cells to orient with their long axis parallel to the flow. The end result is that the introduced sample passes by the laser with each cell oriented in the center of the sample stream in a particular manner in three dimensions.

1.4 Optics

Flow cytometers depend on the laws of optics, such as reflection, refraction, and other principles, which are not new but based on works established centuries ago [56]. Optics are present on both the excitation and the emission side. The excitation optics encompass the lasers and the lenses that focus the laser beam. The emission optics are involved in collecting the emission following excitation. These involve lenses to collect emitted light and mirrors and filters to route specified wavelengths of the collected light to designated optical detectors. Light coming out of a laser may be considered a beam but fluorescence must be considered as a photon.

1.4.1 Light Scatter

Due to differences between the refractive indices of cells and the surrounding sheath fluid, light impinging upon the cells is scattered. The forward light scatter (FSC) provides empirical information on cell size. Light scattered in an orthogonal direction or side scatter (SSC), which is collected by a different detector, provides information about granularity.

1.4.2 Types of Lasers

Laser stands for light amplification by stimulated emission of radiation. Gas lasers have mirrors at each end of a cylinder or plasma tube filled with an inert gas. The gas is ionized to a higher energy state by a high-voltage electric current. When these excited atoms return to the ground state, they give off photons of a characteristic wavelength. The photons can be reflected by the mirrors and the excitation of the atoms in the plasma can be amplified but the wavelengths of the emission still are the characteristic wavelengths for that gas [57]. In the front of the laser there is a small optic that allows the transmitted light to form a laser beam of desired output wavelengths. The light from lasers is a stimulated emission and it has uniform characteristics. For current stream-in-air instrumentation, it is desirable to have at least 50 mW of power for each laser line in use, since the fluorescence signal (and thus sensitivity) increases with laser power. Cytometers use multiple lasers that are positioned spatially such that there is a time delay for each laser beam intercept with the cell. Newer solid-state diode lasers [58–60] are becoming prevalent and these are significantly cheaper than the older gas ion lasers. Diode lasers are pumped by input of electric current. A partial list of different lasers is presented in Table 1.1.

Table 1.1 Partial List of Laser with Their Excitation Wavelength Line and the Fluorochromes which Can be Detected.

The most common lasers for flow cytometers are the argon ion lasers that run at 488 nm. The lasing medium in an ion laser is plasma. A high-voltage pulse is used to ionize the gas to start the plasma. Ion lasers require a high current to maintain the plasma discharge. In addition to the 488 nm emission, argon ion lasers also emit at 515 nm (green) and 457 nm (violet-blue). Other emissions can be obtained using specially coated mirrors. The new low-power, air-cooled argon laser gives out 25 mW at 488 nm. To obtain other lines of emission, large lasers capable of giving 100 mW in UV must be used.

Krypton lasers can give out strong blue-green lines and UV and violet lines. Krypton lasers need to be water cooled and optimized and the alignment is very difficult. Another type of laser is a dye laser and the lasing medium in a dye laser is a fluorescent dye. The selection of dye depends on the wavelength at which the operation is desired. Helium–neon (He–Ne) lasers are also small, air cooled, and stable. The most common lasers emit at 633 nm and have power outputs ranging from 1 to 50 mW. He–Ne lasers are available at 633, 543, 594, and 611 nm. Helium–cadmium (He–Cd) lasers emit 5–200 mW in blue (441 nm) and 1–50 mW in UV (325 nm). They plug into the wall and do not require water cooling.

1.4.3 Filters for Emission

All signals that are emitted from fluorochromes that are excited as the cells to which they are bound are interrogated by the laser beams are routed to detectors via a system of mirrors and optical filters. In addition, beam splitters direct light of different wavelengths in different directions. The most commonly used filters are short-pass filters (which transmit wavelengths of light equal to or shorter than the specified wavelength), long-pass filters (which transmit wavelengths of light equal to or larger than the specified wavelength), and band-pass filters (which allow a narrow range of wavelengths to reach the detector). An example of these types of filters is presented in Figure 1.3. Because each fluorochrome has an emission spectrum, the choice of filters optimizes detection of the specific fluorochrome by one detector or photomultiplier tube (PMT).

Figure 1.3 An example of fluorescence emission of various wavelengths (top) as it passes through different types of optical filters (bottom).

Detection of fluorochromes requires selection of appropriate filters that are placed before each detector or PMT. The type of filter selected must collect as much emitted light from the primary fluorochrome for high sensitivity, but as little as possible from other fluorochromes to reduce the compensation required. A partial list of filters is presented in Table 1.2.

Table 1.2 Partial List of Filters Typically Employed with Various Fluorochromes.

1.5 Types and Choice of Fluorochromes

A fluorochrome is a fluorescent marker that emits a particular wavelength when a laser light hits it. Fluorescence occurs when a molecule, which is excited by light from a laser at one wavelength, loses its energy and emits light of a longer wavelength. The emitted wavelength is what is detected. The excited and emitted light are of different wavelengths. The fluorescence intensity that is emitted is proportional to the quantity of binding sites for the fluorescent compound on the cell. Therefore, the more the fluorescence that is emitted the more the binding sites on the cell. For instance, for an antibody tagged with FITC (fluorescein isothiocyanate, which is excited by a 488 nm argon laser but emits in the 520 nm (green) range) that recognizes and binds to CD4, the more the 520 nm emission the more the CD4 on the cell (Figure 1.4).

Figure 1.4 Excitation and emission spectra of FITC and phycoerythrin (PE). Fluorescent molecules absorb light of a characteristic wavelength and emit light of a longer wavelength. FITC and PE that are commonly used for flow cytometry absorb at 488 and 488–560 nm, respectively, but emit at 520 and 590 nm, respectively. Thus, they can be excited by the same laser line and used together in the same tube [10].

The fluorochrome label for a reagent depends on instrument configuration (type and number of lasers and type of optical filters and detectors), which determines if a given instrument can excite a given fluorochrome and detect the emission. While it is not possible to uniformly state the best fluorochrome combination, there are a few guidelines that can help in this choice. The first issue is to determine what is the reagent brightness, which takes into account the resolvable signal associated with the presence of the marker being detected by comparing a negative and a positive sample. The negative population emission is the background emission. Background is signal (emission) due to electronic noise (dark current), cell autofluorescence, nonspecific staining, and background emission that is a spillover from another fluorochrome [61, 62]. The rule of thumb is to use the brightest reagents possible [63, 64]. There is a caveat to this statement. The spillover problems increase as the number of colors to be resolved (different emissions) increases. Compensation can help prevent the spillover contribution, but as a rule of thumb, one should use fluorochromes whose emissions have the least amount of spectral overlap [65, 66]. In addition, logically the markers with the least amount of expression on a given population should be detected with a reagent that is labeled with the brightest fluorochrome. However, this weakly expressed antigen should be stained with a fluorochrome-tagged reagent that does not have spillover issues with another fluorochrome reagent recognizing a cell marker that is highly expressed [64]. A final word of caution is to take into account that a fluorochrome as a single reagent may give different results when employed in a multicolor reagent cocktail and this is a fidelity issue. To determine if this is a problem, one can compare the antibody–fluorochrome conjugate by itself and compare the results with the results obtained for this reagent when it is in the multicolor reagent cocktail. One should try and use reagent combinations that have good fidelity when used in a multicolor reagent cocktail.

1.6 Compensation

Due to overlap in the emission spectra of different dyes, it is often not possible to choose emission filters that uniquely measure only one of the dyes in a multicolor experiment. Due to this spectral overlap, one fluorochrome can contribute a signal to several detectors; therefore, the contribution in detectors not assigned to that fluorochrome must be removed from the total signal in those detectors. Compensation is an artificial means of eliminating spectral overlap between two different fluorochromes by mathematical means and is not just a subtraction process [65, 66]. Compensation between detectors can be performed either by hardware, after signal detection but before logarithmic conversion and/or digitization, or by uncompensated data that are analyzed post-collection by software (Figure 1.5).

Figure 1.5 Compensation controls (a–c) human PBMCs stained with a single FITC-labeled antibody. (a) Cells are gated on the lymphoid gate based on forward scatter (x-axis) and low side scatter (y-axis). (b) Uncompensated data demonstrating that the FITC signal is spilling into the PE channel with 65.4% of the cells demonstrating dual positivity incorrectly. (c) Data after compensation with only single positive cells expressing FITC and no PE. Note that the PE mean for the FITC negative and FITC positive cells is nearly identical. (a′–c′) Two-color data of human PBMCs stained with anti-CD19 labeled with PerCP-Cy5.5 (B-cell marker) and anti-CD3 labeled with APC (T-cell marker). There should be no dual positive cells. Data were collected as listmode and compensation was added after data collection: (a′) lymphoid gate; (b′) uncompensated data; (c′) data after compensation. (See the color version of the figure in the Color Plates section.)

1.7 Threshold and Gates

An electronic threshold is defined as a gate for acquiring signals. Only events with intensity greater than the threshold will be processed and analyzed. Thresholding is most often used to eliminate debris. A gate is a boundary that can be used to identify subpopulations and this limits the number of events that are analyzed (note that these events are acquired in the listmode data file but not analyzed). Gates are often used to identify the lymphoid cells for analysis.

1.8 Analysis

The electric pulses that are detected by the PMTs are amplified (log amplification is most often used to measure fluorescence). These amplified signals are converted from analog to digital. Data can be stored as a listmode file, which consists of a complete listing of all events and parameters that were measured [67]. One can take a listmode file and subject the data to analysis such as regions and gating but one cannot adjust amplification or fluorescence [68].

1.8.1 Flow Histogram

For single-color analysis, the events can then be plotted as a single parameter such as a histogram, in which the x-axis is the measurement and the y-axis is the number of events. Usually the x-axis corresponds to channels (typically, 1024 channels); the brighter the specific fluorescence the higher the channel number. A new Logicle display method (also known as biexponential method) when analyzing flow data enables the close to zero signals to be shown on the plot graph that combines both the logarithmic and linear scales, providing a more complete way of interpretation of data [11, 69, 70]. Multicolor flow analysis is often displayed as two-color analysis. In Figure 1.6, an idealized phenotype of cells in two-color analysis is shown.

Figure 1.6 Idealized two-parameter quadrant analysis. A population of cells is stained for two markers labeled with PE indicated with a diamond (y-axis) and FITC indicated with a solid small circle (x-axis). Lower left quadrant: cells lacking both the markers, and hence double negative. Lower right quadrant: cells FITC and shown as cells with solid circles. Upper left quadrant: cells PE and shown as cells with diamonds. Upper right quadrant: cells expressing both markers, also called double positive, and shown as individual cells (diamonds and solid circles) together.

1.8.2 Thresholding and Doublet Discrimination

The flow cytometer parameters can be set such that only events whose intensity is greater than a particular threshold value are recorded. This is called thresholding and it can be used to eliminate debris (Figure 1.7a), that is, cells having a very low FSC and SSC. Cells of interest can be gated based on fluorescence parameters; for example, expression of CD45 with low side scatter predominantly identifies lymphoid cells (Figure 1.7b and c). Although hydrodynamic focusing streams the cells into a single file, occasionally two cells stick together. A doublet made of two single positive cells (each one positive for a different fluorochrome) can be erroneously recorded as a double positive cell. Hence, doublet discrimination is crucial. Doublets will have a higher FSC height/FSC area ratio as compared to singlets. One can set a gate around the events in which there is a linear relationship between FSC-H and FSC-A (Figure 1.7d) and these cells can then be analyzed for different markers (Figure 1.7e–h).

Figure 1.7 Typical gating for lymphoid cells. (a) The different populations of cells present in the sample are visualized based on their size (FSC) and complexity (SSC). Thresholding was performed to remove debris (arrow). (b) The cells are stained with a FITC-labeled antibody that recognizes CD45, a hematopoietic lineage marker (referred to as CD45-FITC). The circled population indicates lymphoid gate (high FSC but very low SSC). (c) CD45-gated lymphoid cells are reevaluated to exclude large CD45+ cells. (d) Doublets that are distinguished from singlets on the basis of FSC-H/FSC-A ratio are excluded from the area of interest indicated with the circle. Detection of macrophages (CD45+CD14+) (e), B cells (CD45+CD19+) (f), and T cells (CD45+CD3+) (g). (h) Two subpopulations, B and T cells, can be easily visualized on the same dot plot.

1.8.3 Two-Color Dot Plot Versus Contour Plot

The fluorescence of two different markers can be represented in 2D using the two-color dot plot (Figure 1.8a–d) or a contour plot (Figure 1.8b′–d′). There are advantages to data displayed in either mode and data are amenable to either type of analysis. In Figure 1.8, identical data are displayed as a dot plot and a contour plot. In a dot plot, each dot represents one or more events (events are usually cells that have passed the criteria of thresholding and gating) that are determined by the user. The density of events can be color coded (e.g., red implying highest density). The events are gated on the lymphoid gate on the basis of FSC and SSC (Figure 1.8a) with a second gate to include only lymphoid cells that are CD3 positive (Figure 1.8c′). There exists a small population of cells positive for both CD4 and CD8, which is visible in the dot plot (Figure 1.8d). Contour plots show the same data in which rings represent a defined percentage of total events with a particular combination of fluorescence intensities (Figure 1.8b′–d′). This type of data display removes the outliers allowing one to clearly see subpopulations of cells. In addition, one can smoothen the contour plots making it even more difficult to visualize the rare population (Figure 1.8d′, CD4+/CD8+ cells). Using contour plots as a formatting option, other display options include linear density and log density contour plots in which the contour lines are defined as a percentage of the maximum numbers. There is an additional option in which one can combine a contour plot and a dot plot in which dots are displayed below the lowest contour line allowing the observation of the rare population in a contour plot. Despite different display options, the data quadrant statistics would be identical in both the dot plot and the contour plot.

Figure 1.8 Dot plot versus contour plot of the same data. Cells were stained with CD3-FITC, CD16 and CD56-PE, CD4-APC, and CD8-PerCP-Cy5.5. Plots (b)–(d) and (b′)–(d′) show similar results by both dot plot and contour plot. However, the rare population of double positive cells can be visualized in the dot plot (d) but not in the contour plot (d′). For details refer to the text.

1.9 Sorting

Cells (or particles) of interest (expression of desired markers) can be purified or sorted [71]. In most flow cytometers equipped with sorting capabilities, the liquid sheath stream is regularly broken into droplets by the vibration of the piezoelectric crystal attached to the flow chamber. A cell passing through the laser meeting the selection criteria based on the fluorescence pattern is electrically charged in the droplet. These droplets containing the charged cells are then deflected and collected into awaiting tubes/wells or onto slides. Depending on the further downstream applications of these sorted cells, they may have to be collected in appropriate buffers or under sterile conditions. The temperature of the sheath fluid and sample collection tubes may need to be controlled. In the example shown, mouse spleen cells to be sorted are shown on the left (Figure 1.9a–d) and analysis of the cells following sorting is shown on the right (Figure 1.9 a′–d′). The live cell gate (Figure 1.9a and a′) indicates that following sorting the cells that were gated on CD4 and the live gate resulted in 99% of the gated cells meeting the gating criteria (live, singlets, and CD4+).

Figure 1.9 Sorting of CD4+ cells from cells stained with CD4-APC and CD25-PE. (a) After thresholding, the live cells were gated. (b) Doublet discrimination was performed on the live cells. (c) Single-color histogram showing the CD staining before sorting (left side). (d) Two-color dot plot representing the different subpopulations present after thresholding and gating but before sorting. The indicated subpopulation was sorted. (a′–d′) The sorted population was reacquired to assess the efficiency of sorting. The FSC and SSC of the sorted cells (a′) were limited to the cells present in the gate region of (a). (b′) The sorted cells were predominantly singlets. (c′) The single-color histogram of CD4 showed predominantly CD4+ population after sorting. (d′) Two-color dot plot representing the different subpopulations present after sorting. The quadrant statistics indicate that the sorted cells are a 99% pure population of CD4+CD25− cells.

1.10 Conclusion

Since their introduction in the 1970s, the design and applications of flow cytometers have undergone tremendous change. Current flow cytometers are rapid, use multiple lasers (five lasers on several instruments), and can detect more than 20 different fluorochrome tags. Some of the novel applications include fluorescence in situ hybridization (FISH) using flow cytometer and Amnis ImageStream, which is a blend of flow cytometry and microscopy and allows the visualization of single cells. The field of flow cytometry holds great promise for research and clinical diagnostics.

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