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Image Analysis of Gene Locus Positions within
Chromosome Territories in Human Lymphocytes
Karel Štěpka1 and Martin Falk2
1
2
Centre for Biomedical Image Analysis,
Faculty of Informatics, Masaryk University,
Brno, Czech Republic
[email protected]
Department of Cell Biology and Radiobiology,
Institute of Biophysics of ASCR,
Brno, Czech Republic
[email protected]
Abstract. One of the important areas of current cellular research with
substantial impacts on medicine is analyzing the spatial organization of
genetic material within the cell nuclei. Higher-order chromatin structure
has been shown to play essential roles in regulating fundamental cellular
processes, like DNA transcription, replication, and repair. In this paper,
we present an image analysis method for the localization of gene loci with
regard to chromosomal territories they occupy in 3D confocal microscopy
images. We show that the segmentation of the territories to obtain a
precise position of the gene relative to a hard territory boundary may lead
to undesirable bias in the results; instead, we propose an approach based
on the evaluation of the relative chromatin density at the site of the gene
loci. This method yields softer, fuzzier “boundaries”, characterized by
progressively decreasing chromatin density. The method therefore focuses
on the extent to which the signals are located inside the territories, rather
than a hard yes/no classification.
1
Introduction
The study of the spatial organisation of the genetic material within the nuclei
of eukaryotic cells is one of the most important avenues of current intracellular
research. ToDo: mention what can be studied
Three-dimensional images of the genetic material can be acquired using confocal fluorescence microscopy. The objects of interest (e.g. the whole nucleus,
individual chromosomes, their parts or individual gene loci) are fluorescently
stained, so that they appear as bright areas or spots in the acquired images.
The fluorescent staining can be performed using fluorscent proteins or antibodies attached to proteins, or, in the case of individual gene loci, using bacterial
artificial chromosomes (BAC). These DNA fragments are first prepared to match
the target DNA, fluorescently tagged, and then hybridized with the target in a
process called fluorescence in situ hybridization (FISH). [1]
Human chromosome 5 (HSA5) is one of the human autosomes. Conditions
related to the chromosomal aberrations of HSA5 include the cri du chat syndrome, familial adenomatous polyposis, myelodysplastic syndromes, or Crohn’s
disease [2], [3], [4], [5]. Understanding of the spatial arrangement of the chromosome and the ways it can interact with other chromosomes is therefore of high
interest.
2
Image Data
ToDo: describe the cells we have
The chromosomes are actually tangled and looped strands of chromatin, i.e.,
DNA and proteins. In some places, mostly at the center of the territory occupied
by the chromosome, these loops are densely packed. In other places, chromatin
may be more decondensed, and the chromatin strand may be arranged more
loosely.
However, due to the fact that the width of the strand is below the diffraction
limit for optical microscopes, we cannot examine (or even reliably detect) the
individual loops of the strand – when passing through the optical system, the
light gets distorted by the point spread function (PSF) of the system, and the
resulting image does not contain areas thinly or thickly populated by the loops of
the chromatin, but only areas of low or high total fluorescence. The image areas
with higher total fluorescence intensity correspond to the regions containing
more chromatin loops, more densely packed.
The 3D images of a sample cell can be seen in Fig. 1. The three images show
the individual channels: the cell nucleus, the two HSA5 territories, and the two
gene loci, one per each chromosome territory. All three channels were aligned. We
can see that the nucleus is partially visible even in the channels reserved for the
territories and the loci, as a result of fluorescence bleed-through (also referred to
as crosstalk or crossover), a common problem in fluorescence microscopy. We can
also note that while the cell nucleus has a relatively well-defined boundary, and
the gene loci appear as point-like particles that can be sufficiently represented
by their center of mass, the chromosome territories are of more irregular shapes,
and do not have a definite boundary that would allow for a clear segmentation.
There have been methods introduced specifically to segment chromosome
territories, such as [6] or [7]. However, as noted in [8], when used to help determine the relative positions of gene loci within the territories, these methods
suffer from the bias introduced by arbitrarily selecting the threshold value for
the hard territory boundary.
It has been shown that genes positioned more peripherally within the territory are more active than those closer to the center [8], [9]. Therefore, to avoid
the bias caused by a binary “inside boundary/outside boundary” classification
of genes that are of such high interest, we propose an approach in which the gene
loci are related to the spatial density of the chromatin loops, with higher density
usually located at the territory center, and lower density at the periphery. This
will remove the arbitrary thresholds between the loci deep inside the chromo-
Fig. 1. The individual channels of the acquired images. From left to right: cell nucleus,
chromosome territories, gene loci. (xy-, xz-, and yz-planes; the ticks at the image borders
indicate the position of the cutting planes)
some territories, the loci in the areas of lesser density chromatin, and the loci
which seem to be completely outside the territories (in such cases, it is assumed
that the gene locus is on a chromatin strand that extends relatively far from the
center of the territory, and whose fluorescence is not high enough for the strand
to be detectable on its own).
3
3.1
Analysis Method
Nucleus Segmentation
As the basis for the further analysis, in each image, the cell nucleus was segmented. The cell nuclei in the data set were counterstained with DAPI, and
they were approximately spherical and relatively regular, with very few to none
non-convex areas. Segmenting cell nuclei is a common task in biomedical image
analysis, and most of the common approaches are reliable when used on regularly
shaped cells with enough contrast.
For our study, we selected the method described by Gué et al. in [10]. This
approach first median-filters the image to suppress noise, then determines an
intensity threshold using the ISODATA algorithm [11]. Finally, the nucleus mask
is smoothed using a 3D mathematical morphologic closing, followed by opening.
3.2
Gene Loci Detection
To detect small, point-like particles in fluorescent images, several methods have
been proposed, whose properties and performance have been discussed in comparison studies such as [12].
From these methods, we selected the one proposed by Matula et al. in [13],
based on the 3D morphological extended maxima (EMax) transform. After suppressing the noise with a 3D Gaussian filter, a morphological HMax transform
is computed; this transform identifies those local intensity maxima whose height
exceeds a specified threshold. The EMax image is then defined as the regional
maxima of the result. After the computation of the EMax transform, the components whose size does not fall within the range allowed for fluorescence spots
can be discarded.
An advantage of this method is the straightforward relationship between its
result and its HMax height parameter. Since the number of fluorescent spots
present in each nucleus in our data set is expected to be equal to 2, the height
threshold can be automatically adjusted for each image, so that the spots are
detected even in those images whose contrast deviates from the average contrast of the data set. This helps with non-supervised processing of large data
sets, in which the images acquired later during the session may be affected by
photobleaching.
3.3
Chromosome Territory Processing
ToDo:
In order to process the chromosome territories, it is necessary to suppress
the bleed-through from the nucleus channel, i.e., the part of the signal emitted
not by the marked chromosomes, but by the whole counterstained nucleus.
To do this, we first take that part of the territory channel which corresponds
to the areas masked by the nucleus segmentation, as obtained in section 3.1.
Within this region, we compute an intensity threshold using the Otsu algorithm [14]. This value is then subtracted from the territory channel, clamping
the lowest intensities at 0. This removes the background fluorescence caused by
the bleed-through. Following this, the territory image intensities were normalized
to h0; 1i.
For noise suppression, we used Gaussian blurring with σ = 1 voxel. Apart
from suppressing the noise, this also smoothed the territories proper, replacing
the need for averaging the intensity values around the gene loci positions detected
in section 3.2. The influence of any possible imprecisions in the localization of
the loci has also been reduced.
For each gene locus, the normalized territory intensity at its position was
obtained. Being already normalized, this value would represent the location of
the gene locus in respect to the chromosome territory – a value of 0 would mean
the locus is completely “outside”, on an otherwise invisible chromatin strand
extending from the the territory; conversely, a value of 1 would correspond to the
locus being situated in the area of the highest fluorescence, and, consecutively,
the highest chromatin density (which may sometimes, but not always, correspond
to the center of a hard segmentation of the territory).
However, in some of the images, the maximum intensities of the two chromosome territories differed significantly, and relating both gene loci to the same
maximum intensity might not have revealed all important information. Therefore, for each locus, we also calculated the ratio of the territory intensity at its
location to the maximum intensity of the territory to which this particular locus
belonged.
To determine which locus belonged to which territory, we computed rough
segmentations of the territories by searching for the lowest intensity threshold yielding two connected components. These hard masks did not necessarily
represent the ideal segmentations that would be comparable across all images.
However, within a single image (and therefore coming from the same thresholding operation), they made it possible to determine whether a gene locus was
closer to one territory or the other. To do this, we calculated the Euclidean distance transform (DT) of the rough territory mask. In the DT image, every voxel
value either corresponded to its distance from the territory mask (for voxels
outside the mask), or had the value of 0 (for voxels inside the mask). From the
DT images, it was then possible to determine which locus was closer to which
terrritory.
We can see an example of these results in Fig. 2. The line running from top to
bottom is the boundary between the influence zones of the two territories, whose
rough segmentations are also shown. The cross marks correspond to the positions
of the two gene loci; note that the left locus appears to be positioned just at the
border of the territory mask. If the threshold for the hard segmentation changed,
the position of the locus might change from “inside” to “outside” or vice versa.
Because of this, the hard classification is prone to bias related to the threshold
value.
Fig. 2. The boundary between the influence zones of the two chromosome territories,
overlaid over the original territory channel. The small closed curves around the territories show the rough boundaries, from which the DT was computed. The crosses mark
the positions of the gene loci; the loci themselves are not visible in this channel. Note
the left locus lying just at the border of the hard segmentation. (xy-plane)
However, the assignment of the loci to the territories is not negatively influenced by the fact that the segmentation may not be precise. This is illustrated in
Fig. 3. The figure shows that with different thresholds (all of them yielding two
chromosome territories), the influence zones undergo changes much less rapid
than the territory masks themselves, thus still allowing reliable assignment.
Fig. 3. Stability of the influence zones. X-axis: different intensity thresholds yielding
two territories. Y-axis: the amount of voxels which are different when compared to using
the first threshold, as percentage of the whole image. We can see that even though the
difference between the masks taken at higher thresholds grows, as the masks shrink,
the difference between the influence zones is much more stable, keeping the assignment
of the loci to the territories largely independent of the exact territory segmentation
4
Results
The results measured can be seen in Fig. 4. The figure shows the histograms of
the chromatin intensities at the gene loci, relative to the maximum intensity of
the territories assigned to each locus. In each nucleus, a pair of gene loci was
present, one locus per each of the chromosome territories. The grey bars show
the data for the less intensive loci of each pair, the black bars show the data for
the more intensive loci.
We can clearly see that the two populations are different, suggesting than
in each nucleus, one of the two copies of the gene tends to be located more
centrally, while the other is located more peripherally, possibly allowing the
gene more interaction with its surrounding.
To investigate the relationship between the chromatin intensities at the gene
loci, and the distances of the loci from the rough segmentation boundaries of
the chromosome territories, we calculated the Pearson’s coefficient according to
ρX,Y =
cov(X, Y )
,
σX σY
(1)
Fig. 4. Histogram of the chromatin intensities at the gene loci, relative to the maximum intensity of the assigned chromosome territory. The grey bars represent the less
intensive loci of each pair, the black bars represent the more intensive ones
where cov is the covariance, and σX is the standard deviation of X. The value of
the correlation coefficient was below 0.39, which suggests that there is indeed a
positive relationship between the values, but the distance from the hard segmentation boundary does not capture all details of the chromatin structure inside
the territory (such as in the cases when the territory contains interior areas with
lower chromatin density).
5
Conclusion
We have studied ToDo: some cells, chromosomes and genes, which were fluorescently stained somehow.
After a relatively straightforward segmentation of the cell nuclei and the
detection of the gene loci, we focused on the analysis of the locus positions in
relation to their chromosome territories.
As noted in the literature, segmentation of chromosome territories is difficult,
mainly because of the fact that they have no definite, hard boundary. To avoid
the bias caused by the arbitrary selection of such boundary, we analyzed the
gene loci not in relation to the territory segmentation, but rather to the fluorescence intensity, corresponding to the density of the chromatin at the specified
location. As an additional benefit of this, we were able to take into account the
variations of the chromatin density in the areas that would otherwise fall inside
the hard segmentation boundary. These areas would then be counted as being
“hidden” in the deep interior, while in reality, the chromatin strands may be
more decondensed there, allowing for more interaction with their surrounding.
Our approach enabled us to determine the intensities at the gene loci and
observe that in each nucleus, one gene locus of the pair tends to be located in
an area of high fluorescence, while the other locus of the pair is located more
peripherally. From the medicine and biology point of view, this is related to
the amount of interaction with the neighboring chromosomes that is possible
for such locus, and may be of high interest to further studies focusing e.g. on
chromosomal breakpoints.
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