Spectral Imaging: Methods, Design, and Applications - Biomedical Optical Imaging Technologies: Design and Applications - page 153

An example of such an application is spectral karyotyping or SKY [ 39 , 68 , 69 ].
SKY is based on fluorescence in situ hybridization (FISH) and uses five different
fluorochromes (Fig. 4.21 a) to label each one of the 24 human chromosomes (or other
species). Each chromosome is labeled with a different combination of few out of
the five fluorochromes. As an example, chromosome 2 is labeled with Cy5.5 and
chromosome 3 is labeled with FITC, Rhodamine, Cy5, and Cy5.5, and with five
fluorochromes, there are 2 5 -1 D 31 different possible combinations. Spectral
images are usually acquired with a 63 or 100 objective lens with high NA
(1.3-1.4) in the range of 450-780 nm and a spectral resolution of 10 nm at 500 nm
peak. The spectral images are processed and analyzed with spectral and imaging
algorithms. The spectrum at each pixel is classified based on the known spectra of
the five fluorochromes by using linear decomposition algorithms as described in
Sect. 4.5.3.1 . The spectrum of each chromosome is rather complex, as shown in
Fig. 4.21 b.
The high specificity of the acquired spectral data enables a successful classifica-
tion (Fig. 4.22 ), and it is used intensively for both diagnostics and research.
Similar approaches such as M-FISH and COBRA-FISH have been developed by
using a set of matched filters instead of measuring the spectral images and provide
similar information [ 70 , 71 ]. Further works have shown a combination of even more
colors simultaneously [ 72 - 75 ].

4.6.2 Observation of Mixed Stained Entities with Known Stains

The application described above is demanding, but the method enjoys a priori
assumptions that simplify the procedure, the fact that there are no combinations
of few species in one measurement. If, for example, few chromosomes could be co-
localized, it would make the task much more complex, if solvable at all. In addition,
once there are combinations of species, the quantity of each can vary, which again
makes the analysis more complex.
Therefore, in cases where the mix of species is unknown, a different approach
should be taken, and as a result, much fewer species can be detected simultaneously.
Although complex, these applications are still based on a limited number of known
stains. Therefore, their spectra can be measured, stored in a library, and be used
during the analysis process.
An example of such a case appears in bright-field measurements of tissue
sections or cells that are stained for different proteins that can be co-localized at
different concentration ratios. In bright-field measurements, the full spectral range
is available for detection, and it is not necessary to block parts of the excitation
or emission ranges like in fluorescence. On the other hand, mixtures of stains may
have a complex spectrum, and the more stains that are added, the more “brown”
the sample gets. This is the case in histological staining when multiple features
have to be detected. For such an application, spectral un-mixing algorithms can be
used as described in Sect. 4.5.3.3 . It allows one to separate the complex color image