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Correspondence to: T. Ryan Gregory, Dept. of Zoology, University of Guelph, Guelph, Ontario, Canada N1G 2W1. E-mail: rgregory@uoguelph.ca
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Summary |
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The study of genome size variation is important from a number of practical and theoretical perspectives. For example, the long-standing "C-value enigma" relating to the more than 200,000-fold range in eukaryotic genome sizes is best studied from a broad comparative standpoint. Genome size data are also required in detailed analyses of genome structure and evolution. The choice of future genome sequencing projects will be dependent on knowledge regarding the sizes of genomes to be sequenced, and so on. To date, genome size data have been acquired primarily by Feulgen microdensitometry or flow cytometry. Each has several advantages but also important limitations. In this review, we provide a practical guide to the new technique of Feulgen image analysis densitometry. The review is designed for those interested in genome size measurements but not extensively experienced in histochemistry, densitometry, or microscopy. Therefore, relevant historical and technical background information is included. For easy reference, we provide recipes for required reagents, guidelines for cell staining, and a checklist of steps for successful image analysis. We hope that the accuracy, rapidity, and cost-effectiveness of Feulgen image analysis demonstrated here will stimulate further surveys of genome sizes in a variety of taxa. (J Histochem Cytochem 50:735749, 2002)
Key Words: C-value, DNA content, Feulgen densitometry, genome size, image analysis
THE FIRST DETAILED MEASUREMENTS of nuclear DNA contents were made by
In 1950, Hewson Swift developed the concept of the "C-value" in reference to the haploid "class" of DNA in plants, and 1 year later
Eukaryotic genome sizes vary more than 200,000-fold, with this entire range found among protists. In animals, the range is roughly 2500-fold and in vertebrates it is more than 350-fold (
Knowledge of species' genome sizes not only is relevant to a host of important general biological questions but it may also be useful in the classification of organisms in the way that chromosome numbers have been (e.g.,
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DNA Quantification: Past and Present |
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Several methods have been employed to quantify nuclear DNA. Some of the earliest studies involved bulk biochemical DNA extraction techniques to estimate the total DNA content of a preparation, which was then divided by an estimate of the number of nuclei present. Although imprecise, these methods were sufficient to demonstrate the constancy of DNA across tissues and among conspecific individuals, and to hint at the pronounced variation of genome sizes in comparisons of different species (e.g.,
Densitometry
Densitometric methods were first employed for relative nucleic acid quantifications by Torbjörn Caspersson in the 1930s, although actual genome size measurements were not made with these techniques until some time later. The landmark surveys of both
Two issues complicate the quantification of stain molecules bound to DNA. The first is that it is not possible to measure absorbance directly; absorbance is the lack of emitted light, and therefore represents non-information. Instead, absorbance (optical density, OD) must be calculated indirectly from measurements of the amount of light passing through the object (transmittance, T). Transmittance, in turn, is measured as the difference between the intensity of incident light entering the object and that of the transmitted light leaving it. In Feulgen DNA densitometry, measurements are taken both within the nucleus and outside the nucleus in a clear area of the slide. The difference in light intensity between the two areas represents the transmittance. Optical density and transmittance are related to one another as follows:
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(1) |
Because the relationship is not simple, this calculation is usually done automatically by the densitometry equipment.
In the simplest physical terms, the absorbance of monochromatic light by a uniform solution is proportional to both its concentration (Beer's law) and its thickness (Lambert's law). These rules hold for measurements of stained nuclear DNA, but whereas the absorbances of solutions can be ascertained by a single measurement (e.g., with a spectrophotometer), the heterogeneous nature of DNA stain in the nucleus means that any single point measurement will not be representative of the nucleus as a whole. Moreover, a single density measurement ignores variation in the sizes of individual nuclei. To solve these problems, it is necessary to take a series of point densities covering the entire nuclear area. The sum of these individual optical densities is the integrated optical density (IOD) of the nucleus:
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(2) |
It is the mean (or modal) IOD of all nuclei measured for an unknown species that is compared against the IOD of nuclei from a known "standard" (more on choice of standards below). Historically, these individual point densities have been obtained in two ways: by moving a narrow beam of light through the nucleus and taking density measurements at each point (e.g., with a "flying spot" densitometer) or by moving the nucleus itself through a narrow beam (e.g., with a "scanning stage" densitometer). Both approaches have been successfully employed in traditional Feulgen densitometry methods. However, in each case the cumulative measurement of individual point densities substantially slows the analysis and limits measurements to a single nucleus at a time. As discussed below, image analysis-based methods suffer neither of these constraints.
Fluorometry
As an alternative to measurements of stain absorbance, it is also possible to quantify the fluorescence of a DNA-specific stain. This is accomplished by using an appropriate light source to stimulate the emission of light of a specific wavelength by the stain molecules. In some cases, fluorescence studies have used Feulgen staining similar to densitometric studies ("static fluorescence cytophotometry"; e.g.,
Developed in the late 1970s primarily as a means of detecting the anomalous DNA contents of cancer cells, flow cytometry has since become a staple of genome size research. Briefly, this method involves treating samples of nuclei in suspension with a DNA-specific fluorochrome (e.g., propidium iodide or DAPI) and measuring their fluorescence against that of a known standard included in the sample. This is accomplished by passing the stained nuclei through the path of a laser of a specific wavelength, which stimulates the emission of light by the fluorochrome. The technique is rapid and accurate but it has some important limitations related to the large number of nuclei required for analysis and the need to place them in suspension.
When leaf nuclei or blood cells are used, very large cell populations can be sampled from a single individual. However, when small organisms are to be studied, the large cell numbers required for flow cytometry can present a problem. For example, a recent study of genome sizes in the crustacean genus Daphnia used 20160 individuals for each measurement (
Although flow cytometry is currently the most efficient and accurate method available for genome quantification, the cost of equipment is a major barrier to its broad use. The constraints of standard densitometric approaches make them even less appealing. However, a method that combines the advantages of Feulgen densitometry (e.g., permanent and easily prepared specimens, tissue-specific measurements, multiple standards, low cost) without the immense time consumption of traditional densitometric techniques would provide an attractive alternative to flow cytometry. Fortunately, advances in computing and imaging technology have facilitated the development of such a method.
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Image Analysis Densitometry |
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As with flow cytometry, the use of image analysis technology in DNA quantification began in cancer diagnosis (e.g.,
Basic Concepts
The heterogeneity of DNA staining within nuclei is a problem faced by any densitometric technique, and Feulgen image analysis is no exception. However, the means by which this difficulty is overcome differs crucially in image analysis vs flying spot or scanning densitometry. In image analysis densitometry, the microscope field is captured by a microscope-mounted CCD (charge-coupled device) or digital camera connected to a computer via a "frame-grabber" board. As with all digital images, these photos are displayed as a series of pixels, each of which is of a specific color and intensity. The different intensities of the various nuclear pixels represent ready-made point intensities that can be converted to absorbance values by the image analysis software.
A color image of stained nuclei can be made into a single linear scale of pixel intensities by converting the image to grayscale, by using monochromatic incident light (as with an interference filter in front of the light source), or by analyzing only one of the three constituent "channels" (red, green, or blue) that make up the color pixel. In each case, pixel intensities vary along a scale from 0 (black) to 255 (white). A measurement of a section of the slide lacking nuclei provides the measure of incident light, just as it does in traditional Feulgen densitometry methods. However, in this case integrated optical densities are calculated from pixel values along the 256-value scale:
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(3) |
where n = total number of pixels in the nucleus, IFi = intensity of "foreground" (nuclear) pixel, and IBi = intensity of "background" (clear area) pixel. Thus, the image analysis approach uses individual pixel values to instantaneously calculate IOD from the image as a whole. This approach not only avoids the necessity of acquiring individual point densities one at a time, as in standard methods, but it also allows the simultaneous tabulation of IODs for all of the nuclei within a microscope field. By way of comparison, it may take more than an hour to measure 50 nuclei with a scanning stage or flying spot densitometer, but an image analysis system can measure 500 nuclei in less than five minutes.
Outline of Methodology
Hardware and Software.
The technique outlined below is based on measurements of Feulgen-stained animal nuclei (primarily nucleated vertebrate erythrocytes) conducted with the Bioquant True Color Windows 98 v3.50.6 image analysis software package (R&M Biometrics; Nashville, TN). Hardware consisted of an Optronics DEI-750 CE three-chip CCD camera connected via a BQ6000 frame-grabber board to a Pentium II 300 MHz PC running Windows 98. To reinforce the cost-effectiveness of the image analysis approach, we employed an inexpensive Leica DM LS compound microscope in this study. Higher-quality optics do improve the measurements slightly but are not generally necessary for accurate genome size estimates.
The particular imaging equipment used in this study represents just one of a large number of hardware and software packages available. Choice of equipment will be dependent on price, preference for user interface, and other features of the package in addition to densitometric tools (e.g., fluorescence or morphometric capability). In any case, the key components of the system are a camera with a linear response (e.g., a doubling of density registers as such) and a software package capable of accurate IOD measurements. Linearity of camera response can be tested with the use of stepped density filters ("density wedges"), which are available from optics suppliers (e.g., Edmund Industrial Optics; Burlington, NJ). Any system should be tested for accuracy using a broad series of known genome size standards before purchase. A second system (Hitachi HV-C20 three-chip CCD camera, Pro Series Capture 128 frame-grabber, and Image-Pro Plus 3.0 image analysis software) was tested, and did not perform satisfactorily. Caveat emptor!
The Feulgen Reaction.
In its initial formulation, the histochemical reaction developed by Robert Feulgen was used simply for the detection of DNA in the nucleus (
It is important to note that "Feulgen" is an ordered series of chemical reactions, not a stain. The most commonly employed stain in the Feulgen reaction is Schiff's reagent, developed by Hugo Schiff in the 1860s (
Schiff reagent has been prepared in a variety of ways, including bubbling SO2 gas through a solution of dissolved fuchsin, but many of these proved difficult to standardize (for review see
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Almost every step in the Feulgen reaction procedure has been varied among studies, and each can affect the efficacy of staining. Notable examples include sensitivity to the choice of fixative and fixation time, concentration and temperature of acid and hydrolysis time, preparation of Schiff reagent and staining time, and so on. In previous studies, hydrolysis conditions have varied greatly, from "hot hydrolysis" in relatively weak acid (e.g., 1 N HCl at 60C) to "cold hydrolysis" in strong acid (e.g., 5 N HCl at 2030C). The recommended duration of hydrolysis has also varied greatly, and must be altered according to cell type, fixative used, and so on (e.g.,
Once a Feulgen protocol is decided upon (see Appendix 2 and below for reasons to vary it slightly), material on prepared microscope slides can be stained in groups. Traditionally, slides have been stained in small numbers, usually not more than 20 at a time. With slow measurement techniques these small batches were not a problem. However, with the measurement lag eliminated by the rapidity of image analysis techniques, it is desirable to stain slides in large collections to allow multiple standards to be included in each staining run and to reduce the error associated with comparisons across runs. With the use of 1L glass staining boats and large-capacity metal racks (both available from Fisher), up to 100 slides can be stained simultaneously (and in fact, we routinely perform three such runs together for a total of 300 slides per batch). If stored in the dark (e.g., in a slide box), stained slides remain measurable for long periods of time, although different mounting media and exposure to intense light shorten their lifespan (
Microscope Set-up. The first step in the measurement of DNA contents by image analysis involves setting up the microscope (Appendix 3). Lenses must be clean (because even small marks will appear in the image). Proper Köhler illumination should be established according to the microscope manufacturer's instructions (briefly, this involves ensuring that the light path is direct from source to ocular and that contrast is optimized). The microscope should be placed on a microscopy bench to minimize vibrations. The microscope light source, camera, and computer should all be plugged into a voltage regulator to eliminate fluctuations in the current, which degrade the image quality. Standard surge suppressors and "line conditioners" are not sufficient for this purpose; a proper voltage regulator is required, such as the Sola Minicomputer Regulator (MCR) (Elk Grove Village, IL) employed in this study.
It is customary in Feulgen densitometry to use monochromatic light of a wavelength near the absorption maximum for Schiff reagent (560 nm). Flying spot densitometers (e.g., Vickers M85) can have their light source set to a specific wavelength, whereas scanning densitometers employing modified compound microscopes have traditionally been equipped with interference filters to produce the desired wavelength of incident light. This is not strictly necessary, because measurements at off-peak wavelengths give the same ratios as those performed around 560 nm (
The most significant optically based source of error is glare (i.e., the loss of light caused by refraction), and cell membranes represent the primary source of unwanted glare in an image. In some cases, as with very old slides or when nonspecific background staining has occurred, it will be impossible to eliminate the cell membranes from the captured image. Under most conditions, however, a properly matched refractive oil placed between the specimen and a coverslip can remove this problem. Once the appropriate refractive liquid is determined, it should be consistently applicable to cells of the same type. For example, most vertebrate erythrocytes can be examined with the use of oil of nD = 1.540. Kits containing oils of several different refractive indices are available (e.g., from Cargille Laboratories; Cedar Grove, NJ). The following steps can be taken to select an oil with the appropriate refractive index for the cell type being analyzed:
Before capturing an image (and indeed, before setting up Köhler illumination), it is necessary to choose the magnification level that will be used. Most animal nuclei are too small to measure with objectives lower than x40. The use of a x40 objective (actually x400, since there is normally an additional x10 lens in the body of the microscope) eliminates the need for a second immersion oil and also provides more individual nuclear measurements per field, and is therefore considerably faster than higher magnification lenses. However, lower magnifications mean smaller individual nuclear images, i.e., fewer pixels (points densities) per nucleus. This generally produces higher coefficients of variation and less accurate IOD ratios, and can also result in greater eye strain because fewer pixels in each nucleus makes it more difficult to define the appropriate thresholds (see below). In general, x100 oil immersion objectives should be used unless prohibitively large nuclei are being measured.
Image Capture and Analysis. Image analysis software packages and CCD cameras differ somewhat in their use, but their principles are shared. Details of how to carry out these general instructions should be available in the user's manual. In any case, the first step involves establishing the appropriate conditions for capturing an image. First, a field containing nuclei must be located and brought into focus (on the computer screen, not the ocular). Once focus is optimized, an area of the slide should be selected that is free of nuclei. This blank area can be used to adjust the brightness and color balance of the microscope and camera. The exposure level of the camera can be used to increase or decrease the brightness of the image, but it is best to make this adjustment with the microscope light source. In the present study, we ordinarily used an exposure time of 1/125 sec (longer exposures provide brighter but "shaky" images, and very short exposures are susceptible to the effects of high-frequency oscillations in microscope light output). Even at a suitable exposure, repeated measurements of the same nucleus can show minor variations in IOD, although these are usually small (<1%).
After a choice of exposure, the camera should be "white balanced" on a blank area of the slide. If an interference filter is used, this should be done before the filter is added (the brightness will have to be increased after the filter is put into place). Once completed, a field containing nuclei for measurement can be relocated. Final adjustments to brightness should then be performed; a maximal pixel intensity on the field of 190 (in the green channel) is a good general guideline for how bright the image should be (a histogram of pixel intensities in the entire image can usually be generated by image analysis programs).
Some image analysis packages allow measurements to be performed from "live" images, whereas others require images to be saved to the hard drive before analysis. In either case, the green channel should be used for the IOD measurements because it includes the absorption peak for the FeulgenDNA dye complex, and therefore gives the highest IODs and the most accurate estimations of genome size. Some packages allow the green channel to be measured directly from the image, but others require it to be extracted from the color image to give a derived grayscale image. Once a measurable image is obtained, it is necessary to select the threshold of pixel values that are to be included in the measurement. In most cases it will be possible to zoom in on a nucleus to carefully select the pixels within it. It is not strictly necessary to highlight all pixels within a nucleus (this is especially true if there are very light areas within it, which could cause large non-nuclear areas of the image to be selected). The threshold is used only for outlining the objects to be measured; so long as the nuclei are properly outlined, all pixels within them will be included in the measurement. As a rule, it is less problematic to over-select pixels (i.e., to include pixels outside the perimeter of the nucleus) than to under-select, because these extra pixels will not contribute to the resulting IOD measurement when the background pixel value is subtracted from them.
Once thresholding is complete, all objects falling within the density of the thresholds should be highlighted. Any non-nuclear objects that are highlighted should be omitted, as should misshapen, broken, overlapping, or otherwise anomalous nuclei. Most software packages include data filters to automatically eliminate objects touching the edge of the field or falling outside a specified size range. Consistency in the criteria employed for exclusion is important for minimizing variability in results obtained by different investigators (e.g.,
To measure the IOD of green-channel pixels, it is necessary to provide a measure of background (incident) light for comparison. This is not the same as a "background correction," which must not be used in IOD measurements. Most software packages allow a non-nuclear area of the field to be selected manually, and the background intensity (IBi in Equation 3) is taken as the average of all pixels in the selected area. This single value is then compared against all foreground (nuclear) pixels. Therefore, it is important that an area free of nuclei and located as centrally as possible be used in determining the background. Estimating specific backgrounds (e.g., individually next to each nucleus) does not greatly improve results and greatly slows data acquisition. So long as the illumination is homogeneous and the field does not otherwise contain light and dark areas, a single background measurement per field is sufficient. Using even an inexpensive microscope, we find that the repeated measurement of the same nucleus captured in different areas of the field produces an error of only 1% (a maximal CV of 3% is recommended for this test;
An important consideration in the choice of field to measure is the number of nuclei contained within it. Obviously, a very dense field with overlapping nuclei is not suitable, and very scanty fields with only a few nuclei each will substantially slow measurement times (although in smears from many amphibians, sparse nuclei are a fact of life). This problem can be largely alleviated, with vertebrate blood samples at least, by using a "flame tip" method of smear preparation (Fig 1). However, as shown in Fig 2, there is a strong negative relationship between the mean IOD of nuclei and their number in a given field. This variance can be explained by a combination of factors, including slower drying and/or crowding in dense areas of the slide that prevents nuclei from flattening out and later reduces their uptake of stain, darker background measurements that result in lower calculated IODs, and increased glare from membranes. In our analysis of varying the number of nuclei per field by more than an order of magnitude, the error in mean IOD exceeded 10%. Field density should therefore be kept as constant as possible, including across slides. When nuclei of different sizes are used, a measure of percent field covered by nuclear pixels can be employed in place of a simple count of nuclei present. Thus, a typical field density used in our analyses at x100 would be 2030 nuclei for chicken and 1020 nuclei for trout. With larger nuclei and smaller nuclear counts per field, the variance among fields becomes higher relative to within-field variance (ANOVA, 20 fields each for chicken and trout), although in each case the maximal error in mean IOD among fields is only about 4%.
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Some variation among individual nuclei is also to be expected, based on slight differences in staining, orientation, and local background conditions. In cancer detection applications, a coefficient of variation (CV = 100% x standard deviation/mean) of 6% or less is considered acceptable (e.g.,
Calculations of Genome Size
As with all densitometric and fluorometric methods, image analysis-based techniques involve the conversion of unitless IOD values to absolute genome sizes by the comparison of ratios with standards of previously estimated DNA contents. In some recent image analysis studies, the peak IOD values of standards and unknowns have been used for these ratios, as is typically done with flow cytometry (e.g.,
The choice of standards used in the calculation of absolute genome sizes is crucial. The rapidity of image analysis and the feasibility of staining large numbers of slides make it possible to use several (five to ten) standard species. It has sometimes been suggested that standard cells (e.g., chicken blood) should be placed on the same slide as the unknown specimen, a protocol that would limit the number of standards that could be used. However, this is unnecessary so long as the series of standard slides is included in each staining run. It is desirable to choose standards of the same cell type as the unknowns, although this convention has often been ignored. As will be seen below, differences in the characteristics of standard vs unknown cells can represent a substantial source of error.
It is best to include a range of standards broader in both directions than the expected range of unknowns. Wherever possible, standards should be of commonly used species (e.g., chicken) or ones that have previously been measured using a reliable method such as flow cytometry.
Before calculation of mean IOD, it is good practice to use a spreadsheet to "sort" the data in ascending/descending order. This will enable extremely anomalous values (such as those caused by small bits of debris overlooked during manual exclusion) to be identified and excised. In addition, it is advisable to clip the top and bottom 5% of the measured IOD values (e.g., five values on either end of a 100-value sample), because the extremes of the range may reflect improperly oriented nuclei or other minor problems with staining and/or analysis.
Genome sizes can be calculated in two ways. In the simplest approach, a standard curve (IOD vs known C-value) is generated and used primarily as a "check" that the stain was accurate across the range of standards included. A single primary standard (preferably chicken at 1 C = 1.25 pg) can then be used with confidence to calculate genome sizes:
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where CVu = C-value of unknown, CVs = C-value of standard, IODu = mean IOD of unknown, and IODs = mean IOD of standard.
Alternatively, the regression equation of the standard curve can itself be used to calculate genome sizes:
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where CVu = C-value of unknown, IODu = mean IOD of unknown, with y-intercept (y0) and slope referring to those of the regression of mean IODs of standards vs C-values of standards. It is worth noting that although this may help to distribute the error more evenly among standards, most of the C-values used in the regression will have been measured against a chicken standard, so that they may in fact introduce an additional level of error. Moreover, this approach is statistically problematic if the unknown genome size falls outside the range of the standards included. In either case, a standard curve is important because it demonstrates that the staining procedure was successful and that the analysis equipment is performing in a properly linear fashion. Fig 3A presents a very broad standard curve covering a roughly 400-fold range in DNA contents. It is evident from this figure that the imaging system and staining protocol are accurate across this range in DNA contents, as well as within the more limited range encountered in most animal genome size estimates (Fig 3B).
Additional Sources of Error
By following the procedures outlined above and in Appendix 2, accurate and rapid measurements can be made on large numbers of nuclei. However, no amount of care can provide accurate genome size estimates if there are fundamental discrepancies in the amount of stain present in the nuclei. The inclusion of several standards can help to identify such problems, but they must ultimately be addressed before the cells are stained. Surprisingly, each of the sources of error discussed below has been largely overlooked by previous densitometric studies of animal genome sizes. It is our hope that they will be properly addressed in future surveys.
Staining Protocol.
Not all stains are created equal. For one, even certified dye lots of "basic fuchsin" are invariably mixtures of pararosaniline along with traces of rosaniline, magenta II, and other impurities (
It is common practice to re-use Schiff reagent, but its efficacy diminishes with each use (i.e., intensity of staining fades but ratios of standards may not change). If Schiff reagent is to be re-used, it should be stored in a refrigerator, because cooling increases the solubility of SO2 and prevents its dissociation from the stain molecules. The storage container should also be filled completely and capped tightly to prevent dissolution of SO2. Eventually a white precipitate forms, and the reagent should then be discarded. Under ideal circumstances, Schiff reagent is best prepared fresh before each staining run and used only once. Recall, however, that stain preparation should begin a few days before an intended staining run to allow the recommended period of decolorization (see Appendix 1). Other minor sources of variation in stain quality are addressed in Appendix 1.
Age of Slides.
Several fixatives are routinely employed in the preparation of blood smears. The most common in the genome size literature include methanolglacial acetic acid (3:1 MeOH:GAA), methanolformalinglacial acetic acid (85:10:5 MFA), and formalin (usually 10%). Cells are typically fixed immediately after smear preparation, or (as in this study) are stored after air-drying and then postfixed before hydrolysis. In our experiences, these two protocols have little effect on the resulting IOD measurements of nucleated blood cells, although the latter is much more convenient when smears are prepared in the field. "Wet-fixing" before air-drying can significantly affect densitometric measurements in tissues such as liver (e.g.,
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This error is best and most simply corrected by using slides of approximately the same age. When this is not possible, error can be reduced (to an extent) by using lengthier fixation regimens. We have tested fixation times ranging from 30 min to 1 week and have determined that a 24-hr fixation is the most suitable for use with erythrocytes. However, long fixations can pose their own problems if an inappropriate fixative is used (different fixatives also tend to alter the kinetics of Feulgen hydrolysis; e.g.,
Cell Types. A hydrolysis curve (i.e., IOD vs hydrolysis time) should be prepared when nuclei of different sizes are compared. Even if cells of the same type and age are being used, there can still be significant differences among cells in the optimal time between full DNA hydrolysis and subsequent depolymerization and loss of DNA. In some cases, no fixation or hydrolysis regimen can correct for differences in the affinity of nuclei for stain. This is particularly true when comparisons are conducted across cell types. Again, this is a source of error that has not generally been addressed in previous genome size studies, but it is one that can affect the validity of the resulting estimates.
The main difficulty in comparisons across cell types (and indeed, with different cell ages) lies in differences in the level of DNA compaction. This problem has long been recognized in Feulgen densitometry and can even be seen among different types of white blood cells taken from the same individual (e.g.,
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In vertebrates, the most reliable way to correct for these errors is to simply be consistent in the types of cells selected for measurement. For example, a comparison of mouse liver cells against chicken erythrocytes gave an inflated mouse genome size estimate of 3.45 pg, whereas a comparison of mouse liver vs chicken liver gave a C-value of 3.23 pg, or approximately 0.5% lower than the most recent flow cytometric estimate (
The suitability of a cell type for use as a standard can be demonstrated only through independent confirmation by other techniques. For example, if insect sperm or hemocytes measured densitometrically against chicken erythrocytes give the same values as insect neural tissue measured by flow cytometry (or better yet, complete genome sequencing), then the chicken standard can be considered reliable for this type of analysis. In cases such as these, it may be advisable to use only the proven standard in the calculation of genome size, although a standard curve should still be included as a check of staining accuracy, as outlined above. What should no longer be considered acceptable is the use of a single standard of one cell type compared against an unknown of another cell type, a protocol employed in many previous studies. With the use of rapid and accurate methodologies such as that described here, there is no reason to use small numbers of untested and potentially unreliable standards.
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Concluding Remarks |
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Although the accurate measurement of genome sizes is not simple, it is of enough practical and theoretical importance to justify the effort. Recent advances in computing and image processing technology, combined with proven methods of histochemical staining, allow the reliable and rapid estimation of nuclear DNA contents. Image analysis densitometry has been accepted as an accurate means of quantifying DNA for clinical applications (
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Footnotes |
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1 DCH and TRG should be considered joint first authors.
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Acknowledgments |
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Supported by a University of Guelph Alumni Doctoral scholarship to TRG, Natural Sciences and Engineering Research Council of Canada (NSERC) postgraduate scholarships to TRG and DCH, and an NSERC research grant to PDNH.
We wish to thank all those who provided samples or technical advice: Marc Freeman, Jean Joss, Lloyd Kinzer, Denis Lynn, John Phillips, Kate Sheridan, Adrian Sumner, Phil Wiebe, Tony Wood, and the staff of the Arkell Poultry Research Facility. Our most sincere thanks to Ellen Rasch and Grace Wyngaard for their invaluable input and generous hospitality.
Received for publication November 21, 2001; accepted January 16, 2002.
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Appendix 1 |
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Appendix 2 |
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Appendix 3 |
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