Raman and FTIR microscopic imaging of colon tissue: a comparative study

Abstract

Colon tissue constitutes a valid model for the comparative analysis of soft tissue by Raman and Fourier transform infrared (FTIR) imaging because it contains four major tissue types such as muscle tissue, connective tissue, epithelium and nerve cells. Raman microscopic images were recorded in the mapping mode using 785 nm laser excitation and a step size of 10 microm from three regions within a thin section that encompassed mucus, mucosa, submucosa, and longitudinal and circular muscle layers. FTIR microscopic images that were composed of 4, 8 and 9 individual images of 4096 spectra each were recorded from the same regions using a FTIR spectrometer coupled to a microscope with a focal plane array detector. Furthermore, Raman microscopic images were recorded at a step size of 2.5 microm from three ganglia that belong to the myenteric plexus. The results are discussed with respect to lateral resolution, spectral resolution, acquisition time and sensitivity of both modalities.

Label‐free Raman spectroscopic imaging to extract morphological and chemical information from a formalin‐fixed, paraffin‐embedded rat colon tissue section

“…However, the selected spatial resolution was significantly lower than that in our study at a step size of 10 μm at the mucosal surface, compared to our step size of 2.8 μm (Krafft et al . 2008). Our results clearly indicate that Raman microspectroscopy can discern the biochemical difference from within each layer following extensive chemical processing – resulting in superior contrast to the currently universally used H&E stain (Figure 5).…”

“…This has previously been observed from frozen then passively dried colon tissue sections (Krafft et al . 2008). PCA shows that some muscle‐like signals were also present across the luminal edge of the mucosa (Figure 4b); however, the reference linear fit suggests that these signals are of lipid origin and are likely detected from the cytoplasmic components of the luminal epithelial cells (Figure 5b).…”

“…Furthermore, many chemometric data analysis approaches have now been devised which allow cell/tissue classification to be carried out in a number of different ways (Krafft et al . 2008; Lloyd et al . 2012; Mavarani et al .…”

Raman spectroscopic mapping for the analysis of solar radiation induced skin damage

“…It is one of the simplest unsupervised learning algorithms that solves the well known clustering problem and is often used for spectral image analysis 24 . In general, its is used to group or classify spectra that are similar, therefore forming clusters represented by the spectral mean which identify groups or regions of an image that have the same molecular properties.…”

Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

“…It has also been used in conjunction with other multivariate methods such as Linear Discriminant Analysis to derive clinically relevant estimates of the sensitivities and specificities of the diagnostic protocols [69] and in comparative analyses of the spectral content in various types of human cancer cell lines [70]. Hierarchical cluster analysis is similarly often employed for example in the intercomparison of spectral classes in discrimination of malignant and non-malignant tissues [71], and for visualising the spatial distribution of identified components in spectral maps [72,73]. Notably, however, for diagnostic applications, precise information on the chemical determinants of the differentiation of tissue types or sample regions is not required.…”