Quantifying the tissue shrinkage caused by sample
preparation for micro-CT and LSFM
J. Goyens1,2, J. Buytaert1, D. De Greef1, P. Aerts2,3 and J. Dirckx2
1
University of Antwerp, Laboratory of BioMedical Physics, Groenenborgerlaan 171, B-2020
Antwerp, Belgium
2
University of Antwerp, Laboratory of Functional Morphology, Universiteitsplein 1, B-2610
Antwerp, Belgium
3
University of Ghent, Department of Movement and Sport Sciences, Watersportlaan 2, 9000
Ghent, Belgium
Aims
Micro Computed Tomography (micro-CT) and light sheet fluorescence microscopy (LSFM) are
often used tomographic methods for imaging macroscopic samples with histological detail. It is
even possible to image multiple tissue types simultaneously (e.g. bone, muscle, nerve and fat
tissue). However, micro-CT requires staining with heavy elements (and thus fixation and
sometimes dehydration) to distinguish soft tissue. LSFM also involves fixation, dehydration
and staining, and additionally requires decalcification and clearing. In both preparation
methods, the sample is prone to shrinkage, which is often not mentioned, let alone quantified.
Therefore, our aim is to quantify and compare tissue shrinkage of 3 micro-CT stains (PTA, IKI
and I2E) and the LSFM preparation in bone, muscle and brain tissue.
Method
To quantify sample shrinkage, we had to scan the specimens twice: once before (fresh, as a
reference), and once after specimen preparation. Obviously, imaging fresh tissues with microCT is difficult, hence the need for staining. Therefore, we chose to scan single-tissue samples.
With careful optimization of the micro-CT scan parameters, we were able to visualize the outer
boundaries of the soft tissue samples. This is sufficient to determine sample volume. Since
LSFM is simply impossible on unprepared samples, these specimens were visualized by
micro-CT as well, both before and after specimen preparation.
In total, we collected 96 micro-CT datasets: 2 scans (pre and post staining) of 4 preparations
methods on 4 tissue „types‟, which we repeated on 3 different animals to acquire some
statistics.
Tissue samples
Bone, muscle, nerve and fat are the four main animal tissue types. We excluded fat from this
study, as it is not compatible with the specimen preparation methods. The other tissues were
harvested from 10 male New Zealand White rabbits (12 weeks old). All animal manipulations
in this work were performed in accordance with Belgian legislation and the directives set by
the Ethical committee on Animal Experimentation of our institution (University of Antwerp,
Belgium). They were housed in cages with food and water ad libitum in our animal facility.
We collected muscle samples of about 7x7x7mm from the hamstring posterior thigh muscles.
Next, a brain sample, a bone shard of the middle ear bulla wall and the incus (the second and
middle of the middle ear ossicles) were harvested. We included incus bones in the study
because of their small size – which may make them prone to shrinkage – and because they
are part of our main line of research. However, since the incus is very fragile, we also included
a more rigid bone shard of the middle ear bulla.
Micro-CT imaging
The samples were scanned with a new SkyScan 1172 X-ray micro-computer tomograph
weeks at the facilities at the VUB (Vrij Universiteit Brussel, Belgium). For each sample, we
manually optimized the scan parameters (voltage, current, filter and magnification) for good X-
ray contrast while keeping the scanning time short. The latter was necessary to minimize
shrinkage inside the scanner, as the temperature easily reaches 30°C. To further avoid – or at
least minimize – dehydration and shrinkage during scanning, we placed the samples in a
closed-off custom-made Plexiglass container. In this small container, the specimen was
placed on a synthetic foam island, surrounded by water to saturate the atmospheric humidity.
Micro-CT staining
Immediately after micro-CT scanning of the fresh samples, the samples were fixated in 10%
formalin. To visualize soft tissue with micro-CT, it has to be doped by heavy chemical
elements, that enhance the X-ray absorption. We chose for three widely accepted and
implemented micro-CT stains: phosphotungstic acid (PTA), iodine in absolute ethanol (I2E)
and aqueous iodine with potassium iodide (IKI, one formulation of Lugol‟s solution) 1. PTA has
been used already for many years in histological staining and electron microscopy 2.
Traditionally, it is used in 100% ethanol, however, we used a 100% aqueous solution to
minimize possible additional shrinkage caused by dehydration, which also works well
(Metcher, personal communication). For I2E, we first dehydrated the sample with a graded
ethanol series (30%, 60%, 90%, 100% and 100%, each for at least a day) before staining. The
aqueous IKI staining was used in a 3% (1% iodine I2 + 2% potassium iodide) instead of the
0.3% mentioned in Metscher et al., for a better accordance with other literature3,4 and the I2E
mass concentration (1%).
LSFM staining
The fixated samples were made transparent and fluorescent using the standard preparation
procedure for LSFM5, consisting of decalcification by 10% EDTA, dehydration by a graded
ethanol series (30%, 60%, 90%, 100% and 100%, the same series as for the I2E staining),
clearing in a graded Spalteholtz fluid series and staining by immersion in a fluorescent dye
(Rhodamine B isothiocyanate 5x10−4 mg/ml for at least a day).
Segmentation
We derived sample volumes from the micro-CT datasets in the dedicated software package
Amira 5.3.2 (FEI Visualization Sciences Group). First, we selected the voxels belonging to the
sample with automatic grey-scale thresholding. Subsequently, we visually inspected and
improved this automatic segmentation manually. Although very time consuming, the additional
effort results in more realistic results. This is especially the case in difficult, noisy, blurred
and/or deformed image data6,7, such as those of our fresh and LSFM-prepared soft tissue
samples. Finally, a 3D model of the selected voxels was generated (see Fig. 1) from which the
volume could be calculated.
The manual correction possibly introduces a human (operator) bias in the segmentation
process. To assess this inter-operator variability, 4 samples (one incus, one bone shard, one
brain and one muscle) were analyzed by three operators.
Figure 1: Three-dimensional mesh models of the unstained micro-CT scans of incus bone (A),
bulla bone shard (B), hamstring muscle sample (C) and brain tissue sample (D).
Results and discussion
Though micro-CT imaging of unstained (fresh) tissue is difficult, the optimization of the scan
parameters for single-tissue samples enabled us to determine the outer boundaries (see Fig.
1).
(Average) volume shrinkage
The average volume shrinkage and standard deviation per preparation method and tissue type
is presented in Figs. 2 and 3. Figure 2 shows that the water-based stainings (PTA and IKI)
cause the least amount of shrinkage. Further, PTA outperforms IKI and is therefore the
preferred staining method to perform morphometric measurements, though it still causes
substantial shrinkage on soft tissues: average volume shrinkage of (10 ± 3)% for muscle
(corresponding to 3.6% isometric linear shrinkage) and (27 ± 2)% for brain tissue
(corresponding to 10.1% isometric linear shrinkage). However, the PTA molecule is larger
than those of IKI, and therefore its penetration depth is limited8.
Figure 2: The average volume shrinkage per preparation method and per tissue type is
presented with error bars for the standard deviation.
Independent of the preparation method, the soft tissue samples are prone to shrinkage, while
calcified (bone) tissue does not shrink (see Fig. 3). Within the soft tissues, brain is more
delicate than muscle. The excessive shrinkage in the muscle and brain samples in the I2E and
LSFM methods (> 55%) is probably caused by dehydration by ethanol. Both tissue types
contain lipids that may get dissolved by ethanol9-11. Therefore, although LSFM has its benefits
(histological quality, real-time sectioning, high-resolution etc.), one should be aware of the
major volume (and thus linear) shrinkage. Micro-CT thus offers a powerful alternative, when
combined with the appropriate stain.
Segmentation: variability between operators
The accuracy and operator dependency of (manual) segmentations are often debated. We
compared segmentations of the same sample by three operators. Over the four tissue types,
the average relative standard deviation is only 0.8%. The segmented volumes of the bone
shards show the highest variation, but still the relative standard deviation is only 1.4%.
Figure 3: The average volume shrinkage per preparation method and per tissue type is
presented with error bars for the standard deviation.
Conclusion
Staining is necessary when imaging soft tissue simultaneously with bone with tomographic
techniques. Specimens treated with stains and accompanying preparation steps (e.g. fixation
and dehydration) are prone to shrinkage and deformations. However, this shrinkage is often
neglected or underestimated. To our knowledge, we are the first to quantify shrinkage caused
by stains for micro-CT and LSFM. These results, accepted for publication in Microscopy and
Microanalysis, show that substantial shrinkage is to be expected. All methods cause
considerable shrinkage of soft tissues, but aqueous stains perform better than ethanol-based
stains. Sample shrinkage should therefore be taken into account when making morphometric
measurements or models based on stained specimens. The best performing stain tested, is
aqueous PTA, though its penetration depth should be considered.
Furthermore, the results showed to be independent of the operator.
Acknowledgements
Jan Buytaert and Daniel De Greef are both financially supported by the Research Foundation
Flanders with a fellowship. Jan Buytaert furthermore acknowledges VOCATIO for its support.
Jana Goyens is financially supported by a grant (ID BOF UA 2011-445-a) from the Research
Council of the University of Antwerp. The use of the SkyScan 1172 system, located at the
VUB facilities, was made possible by the support of the Hercules Foundation. Brian Metscher
and Egon Heiss are thanked for their welcome insights
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