A highly important, though not regularly addressed topic in 3D imaging is the method of imaging. Most publications cited in this introduction were obtained by applying either light or electron microscopy with different types of microscopes. However, it is important to know about the limitations of each of the microscopes as well as about the pitfalls occurring, when working with images. Obligate processing steps preceding electron microscopy (EM), for example, include dehydration, resin embedding, ultra-cutting and a facultative contrast- or immuno-staining. EM recordings can reach a resolution level of several nanometers. However, each of the mentioned processing steps may introduce artifacts and potentially affect the morphology of nuclear material in scales considerably larger than nanometers. Light microscopes are much more limited in resolution compared to EM (<300 nm in lateral and <500 nm in axial direction). Nevertheless there are certain advantages, especially in the specimen preparation. Physical sectioning is not demanded and water, the major component of all biological specimen, is not hindering successful imaging like in EM. Nevertheless besides the limited resolution, additional problems, elucidated below, need to be considered when dealing with data gained by light microscopy.
Fluorescence light microscopy is widely used to investigate the topography and function of cellular organelles and substructures. The development of a wide range of fluorochromes differing in their spectral signatures enabled the simultaneous visualization of various fluorescent objects in the nucleus. The output of a confocal microscope typically consists of a series of 2D images that make up the complete fluorescent 3D object of interest, with each image taken from consecutive focus planes (Conchello and Lichtman, 2005). These images are built up as matrices of voxels (=pixels in 3D data sets) with gray values within a range set by the digital data format of the image (e.g. gray value ranges from 0 to 255 for 8-bit images). Quantification of these fluorescent objects, e.g. based on distance measurements or gray value ratios between the voxels of the objects, is necessary for determining their size, topography or position. Evaluation software usually require the user to set a threshold value (TH) above which a voxel is included in the analysis and below which it is assigned to background and therefore not considered in subsequent calculations. Although TH-independent evaluation algorithms that analyze variations in gray value or evaluations that consider TH ranges are being developed (Albiez et al., 2006; Stadler et al., 2004), most of the currently used software tools still demand the input of a single TH value as key parameter. Several algorithms for automated TH determination based on gray value
histograms of the 3D data sets or on pattern or edge recognition have been developed (Hu et al., 2006; Vasilic and Wehrli, 2005; Yi and Coppolino, 2006). However, a sampling of recent publications using confocal microscopy imaging in the field of cell biology reveals that researchers still prefer to perform thresholding themselves (Bacher et al., 2006; Branco and Pombo, 2006), suggesting that automated procedures for TH determination are still not working satisfactorily.
One of the difficulties that hinders the choice of an optimal TH is that recorded images not only comprise signals derived from the focused optical plane, but also exhibit signals from fluorescent objects above and/or below this plane. This out-of-focus light leads to blurred (i.e. lower contrasted) data sets which can not be segmented using a single TH value because either additional out-of-focus light is at least to some extent included (low TH value) or true signals would be excluded (high TH value).
The use of monochromatic laser lines for excitation and the introduction of a pinhole in the pathway of light detection in order to reduce the amount of out-of-focus light (Pawley, 2006) allowed the development of confocal microscopes, a significant advance in the field of fluorescence microscopy. However, even confocal images suffer from blurred light (Pawley, 2006). This blurring occurs according to the point-spread function (PSF), which is the mathematical description of how a point-like light source would appear in a microscope data set (Shaw and Rawlins, 1991). Fluorescence microscopes can be understood as linear systems, where any two different light signals coming from two different points of the object do not interfere with each other and do contribute additively to the total image. Therefore, for a given object, the expected image can be obtained by breaking the object down into a set of points and adding a copy of the PSF to the image at the location of each point. The PSF depends mainly on the objective that is used to acquire the image. Since a point-like light source is blurred to a greater extent in the axial direction, it appears elongated rather than spherical (Pawley, 2006; Shaw and Rawlins, 1991; see Fig. 2). One way to correct for this mathematically-defined distortion is to apply deconvolution (McNally et al., 1999). Deconvolution is a mathematical procedure that applies the reverse PSF on the raw images obtained from the microscope. The results are images which are less blurred and therefore more contrasted (Pawley, 2006). Deconvolution was shown to increase the resolution of confocal data sets (Dey et al., 2006; Pawley, 2006; van der Voort and Strasters, 1995) and to emhance image quality more than could be achieved by image filtering techniques, as proven by co-localization analysis (Landmann, 2002; Sedarat et al., 2004). The PSF can be measured by imaging fluorescent spheres with diameters much smaller than the optical resolution limit of the microscope, thereby serving as a point-like light source (Hiraoka et al., 1990) which can subsequently be converted into a PSF by appropriate software. Specialized deconvolution programs, additionally allow sensible PSF estimates to be extracted from
slightly larger objects (e.g. 175 nm beads), which have a considerable advantage with respect to the signal-to-noise ratio (SNR).
Nevertheless, although deconvolution is routinely and successfully used in the processing of wide-field microscopy images, its usefulness is considered with skepticism when it comes to confocal data sets. While some scientists consider raw confocal images as the best possible resolved and therefore not improvable, others fear that the 'black-box'-procedure deconvolution could add artifacts to confocal images (Kriete et al., 2001; Markham and Conchello, 2001; McNally et al., 1999; Wallace et al., 2001) and therefore prefer to evaluate unprocessed raw data sets (Sadoni and Zink, 2004). Accordingly, the first aim of this study was to establish a working protocol for deconvolution of confocal images to provide 3D data sets of a quality sufficient for subsequent studies on nuclear architecture.