Signal to noise ratio
This value is judged on the image quality and can adopt values from <10 (=noisy), 10<20 (=moderate noise) and >20 (=low noise). The data sets, investigated in this study were all set to 30.
Background estimation
Background values were always adopted by Huygens estimation on the input raw images with the recommended “lowest-value”-tool
Maximum iterations
Deconvolution by the iterative Maximum-Likelihood Estimation alogorithm is a in principal endless preocedure. A stopping criterion has to be assigned to stop the calculation and to avoid obvious artifacts by deconvolution. Based on experience and on comparison between raw data and deconvoluted images we did not apply more than 10 iterations. Ahigh number of iterations can produce artifacts. These are probably caused by over- or underestimations of some of the values of deconvolution parameters (Conchello and Lichtman, 2005; Markham and Conchello, 2001).
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Quality threshold
Another quality characteristic is the quality level at which deconvolution is stopped. As soon as the quality between the previous image and the subsequent one is not more increasing as predetermined in this criterion, the whole procedure stops. The quality threshold was set in all cases at 0.1.
3.13.3
The impact of deconvolution on image restoration
Figure 13
Single sections of a 5-colour immuno-FISH experiment before (column A1-A5) and after deconvolution (column B1-B5):
A1,B1 shows DAPI-counterstain,
A2,B2 H3K27me3 antibody staining, which marks
the inactive X but displays also a foci like pattern throughout the nucleus.
A3,B3 shows X-chromosome paints
The active X-chromosome appears much more bigger and decondensed than the inactive X- chromosome.
Examples for distinct changes in the patterns after deconvolution are highlighted by white arrows. After deconvolution sub-structures emerge (holes in the paints, arrows in A3,B3)
A4 and B4 show BACs with weakly expressed genes
A5 and B5 show BACs with mid-expressed genes
The impact of deconvolution is clearly visible in all channels. Background blur and noise are reduced to a minimum and contrast is enhanced thereby highlighting smaller structures that can be seen hardly in raw images.
Substructures which are hardly to identify in the raw images, emerge more clearly and with sharpened edges after deconvolution (figures 13 and 14). Examples are shown for a five- color immuno-FISH experiment in figures 13. In figure 14 a magnification of the inactive X- chromosome before and after deconvolution is delineated to exemplify the power of this tool. This makes it much more easy for the user to set thresholds which often turned out to be the most critical step in evaluation programs like co-localization analysis (figure 15).
The grainy signals shown for DAPI-counterstaining, H3K27me3 staining and chromosome paints (figure 15) make it difficult to set an appropriate threshold (figure 15, A1-A3). After application of deconvolution signals appear as well defined structures which facilitates significantly to distinguish between signals and background (figure 15, B1-B3). Through this clear separation it is much easier to determine thresholds.
Figur 14
The figure shows a typical H3K27me3 staining in a HFb-nucleus. The inactive X is magnified and structures that appear after deconvolution are marked by white arrows.
A H3K27me3 before deconvolution: in the raw image blur and higher backround is obvious.
B H3K27me3 after deconvolution: blur and background noise are clearly reduced. In contrast to the raw image
the consistency of substructures can be seen.
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Figure 15
The upper row (A1-A3) shows raw data of mid sections of HFbs with thresholds which I would set to separate signals from background. Raw signals appear more grainy in all three channels compared to the deconvolved images (B1-B3). For the DAPI channel (A1) a threshold 40 grey values higher than for the deconvolved picture
has to be set to achieve a similar signal indicating noise reduction after deconvolution. The raw pictures of H3K27me3 (A2) and X-chromosomal paints (A3) show clear graining. The same picture after deconvolution
shows the chromosome territories more distinct and with sharpened edges.
3.13.4
The consistency of evaluation results at different thresholds
To test whether the results of the co-localization analysis were reliable co-localization analysis with deconvolved images was performed over a wide threshold range in steps of five (figure 16). The analysis was exemplified for H3K4me3 with paints (example for a big object) on the one hand and for BAC signals (small object) on the other hand. The applied thresholds were relatively low because deconvolution of images already took a considerable amount of signal intensity. The absolute co-localization values of H3K4me3 with #18 paints on the one hand and BACs containing highly expressed genes on the other hand deviated from each other because of their different properties (see chapter results figure 40 and 43). Despite co-localization values for thresholds 30 and 60 are very different for both CT#18 and BACs one should keep in mind that values divergate not too much from one threshold step to
the next (between 30 and 60 not more than 6% in all cases). Given that thresholds 30 and 60 are not appropriate it is convincing that if a threshold is not set totally wrong the achieved results are acceptable. However it is important for the user to get experience with histone methylation patterns to decide what is the appropriate threshold. Using a reasonable amount of nuclei a statistical error can be minimized.
To summarize the findings described above, taking the Manders coefficient for evaluation in co- localization analysis was an acceptable approach.
Figure 16
Test of the consistency of co-localization analysis results over a range of thresholds for BAC signals and chromosome 18 paints with H3K4me3. The Manders coefficient M reflects the percentage of overlapping volume of the chromosome territories HSA #18 and highly expressed genes respectively with the H3K4me3 staining pattern. All images were deconvolved before co-localization analysis. Despite drastic changes concerning the amount of voxel in the H3K4me3 signal that contributes to co-localization analysis, the deviation in per cent from one threshold step to the next is reasonable and proofs the choice of this evaluation method right for these kind of experiments.
Bar indicates 5µm
Note: The applied thresholds in the displayed images was 50 for CT #18 and 70 for highly expressed genes in all cases.