Conventional histology of paraffin embedded tissue specimens has been the gold standard for the analysis of tissue samples for decades. While countless staining protocols and the use of microscopes allow very specific analysis down to (sub-)cellular level, histology falls short when it comes to acquiring 3D information of the tissue. Serial sectioning marginally circumvents this problem, but it is very time consuming, labour intensive and fails to deliver a real 3D dataset1,2. Therefore, techniques that can correlate cellular and morphological information within the 3D context of the entire tissue volume are in great demand.
MicroCT imaging can deliver high resolution 3D datasets3. Soft tissue specimens, however, require additional contrast commonly achieved using heavy ion-based staining methods. Our group, among others, has used different CT staining protocols to improve the specificity of microCT imaging. While phosphotungstic acid or iodine-based protocols can greatly increase the CT contrast of tissue, and therefore allow for a 3D assessment on a micrometer scale4–8, they do not increase the specificity to identify certain cells or tissue structures. Other approaches involve using reagents known from classical histological stainings. Eosin can be used to stain cytoplasm9, while hematein (led-III-acetate) functions as a nucleus specific staining agent10,11. While these approaches seem to take the specificity of microCT based virtual histology to a new level, they are hindered by limited sample sizes as well as by the fact that only a few applicable protocols exist to this day.
Metscher et al. showed that a specific antibody staining of chick embryos for microCT analysis is possible using silver labelled antibodies12. Due to the limited tissue penetration depth of the metal labelled antibodies this approach is limited to very small samples with relatively low tissue densities and can therefore not substitute classical immunohistochemistry.
However, staining can be entirely avoided by the use of free propagation-based phase contrast imaging, which can be performed using synchrotron light sources (SRµCT). This method has shown to be well suited deliver very detailed and three-dimensional information about the anatomical structure of the tissue samples and thereby depict the 3D architecture of unstained soft tissue specimens6,13−17. However, so far microCT cannot compete with histological approaches when it comes to the specific visualisation of cellular structures and tissues. Therefore, it would be of great interest to combine 3D microCT imaging and classical staining-based histology to take advantage of both imaging modalities.
In an earlier study we showed already that histological sections from paraffin embedded samples experience some non-uniform deformations during their preparation caused by the mechanical sectioning with a microtome, as well as the deparaffinisation and the staining procedure7. Similar findings were stated previously18–20. Furthermore, we demonstrated that this problem can be circumvented by using resin embedded samples. However, resin embedding also offers many disadvantages when compared to paraffin. The embedding itself takes much longer, the cutting requires special equipment, and the diversity of available staining methods is much smaller21,22. Thus, an improvement of the cross-method analysis of microCT and classical paraffin histology would benefit from an approach which compensates for the deformation caused by the histological processing is necessary.
One way to achieve this is to use image co-registration techniques. The typical approach entails one image that is kept unchanged and another image which is modified, optimising the registration quality. Modification can be simple translation and rotation if the object is considered to be rigid, otherwise affine and non-uniform transformations are also possible23,24. Optimising the parameters of the transformation requires a cost function which approaches its extreme when a perfect match is reached. Depending on the content of the two different images such a cost function can be cross-correlation or cross-entropy7 among others. Finally, an interpolation function is needed to apply the calculated transformation onto the second image. The entire approach is usually performed in a loop to iteratively optimise the parameters.
An illustrative example for an application of such a registration pipeline can be found in oncological nanomedicine. The depiction of nanoparticles (NPs) in their local tissue environment is an important matter for the characterisation of nanomaterial-based theranostics. As an example we used metal-based NPs to assess their radiotherapeutic enhancement in tumours, as has been explored before25–27. Achieving a homogenous distribution of NPs within the tumour is crucial. Optimisation of the NP delivery strategy, assessing their distribution and, more importantly, their correlation to necrotic areas and immune cells, is key. These parameters cannot be obtained through classical microscopy methods since NPs are usually below the resolution limit of optical microscopy and the tumours are too large for electron microscopy. At the same time, tumours possess many different types of tissue like the tumour tissue itself, fibrous areas, a tumour capsule and areas of different degrees of necrosis28,29, making them suitable for comparative histological analysis. In addition, this heterogeneous appearance increases the likelihood of non-uniform deformations during the sample preparation.
Using this model, we show that the combination of label-free 3D virtual histology and classical 2D paraffin-based histology can achieve a precise overlay of microCT and histology and/or immunohistochemistry by compensating for the unavoidable non-uniform deformations that occur during the sectioning and staining process. This enables the precise co-localisation of specifically stained cells or tissue structures into their three-dimensional anatomical context. As an example, we demonstrate that the approach can be implemented in the development of nanoparticle based therapeutic strategies by assessing the nanoparticle distribution in tumour tissue, which was not possible using a single imaging modality.