Aim 3:
In 2016, metabolite information was first investigated from formalin fixed paraffin embedded samples, as it was believed that FFPE tissue processing could chemically modify and affect the metabolite content, potentially leading to metabolite loss. Because clinical tissue banks can store FFPE at room temperature for years, clinical respositories for a variety of diseases exist in the form of FFPE tissues, including biopsies, surgical resection specimens and tissue microarrays. Biopsies are particularly important because they are highly relevant in terms of determining tumor susceptibility to drug for clinical decision-making.
Tissue microarrays (TMAs) are blocks containing tissue cores from different sources, that are organized in an array.
It was believed that small moleculars are lost during the fixation steps and processing. In fact, it seems counterintitutiive that formaldehye fixed cross-links
However, Ly et al. demonstrated that mass spectrometry imaging
no ideal fixative has been found, i.e., a fixative that perfectly preserves cellular morphology and yet does not modify the specimen composition so as not to change the reactivity of the chemical moieties therein for subsequent detection
Presences of tyrosine rings, in proteins, have been identified as an important factor for the affinity of the protein to formaldehyde. In its absence, the presence of arginine residue, phenylalanine or tryptophan as a conserved substitution (tyrosine to a phenylalanine or tryptophan) would help to create the formalin affinity.
Owing to this, the commercial formalin is a 2-phase fixative, with an initial alcohol fixation phase, followed by a cross-linking phase mediated by aldehyde. The alcohol initially causes dehydration in the process, hardening the tissues and membrane. Formalin, when stored for longer periods, gets oxidized to form formic acid. Hence in stored formaldehyde, presence of unknown formic acid (also reacts with blood to form a birefringent crystal called formalin pigments) is expected
Owing to this, the commercial formalin is a 2-phase fixative, with an initial alcohol fixation phase, followed by a cross-linking phase mediated by aldehyde. The alcohol initially causes dehydration in the process, hardening the tissues and membrane. Formalin, when stored for longer periods, gets oxidized to form formic acid. Hence in stored formaldehyde, presence of unknown formic acid (also reacts with blood to form a birefringent crystal called formalin pigments) is expected
\cite{Thavarajah_2012}
e used gas chromatography followed by mass spectrometry (GC/MS approach) to identify about 80 metabolites (including amino acids, saccharides, carboxylic acids, fatty acids) present in such archive material. Importantly, about 75% of identified compounds were detected in all three types of specimens. Moreover, we observed that fixation with formalin itself (and their duration) did not affect markedly the presence of particular metabolites in tissue-extracted material, yet fixation for 24h could be recommended as a practical standard. \cite{26348873}
While conservation of metabolites detected between different preparations was evaluated between fresh frozen, formalin-fixed paraffin embedded and formalin fixed, the spatial conservation of relative intensities was not evaluated in context of spatial or intensity of the conservation using mass spectrometry imaging. Additionally, the authors used different tissue specimens, although the same disease type, to evaluate whether or not the metabolites of interest are also identified in another sample preparation to denote conservation of metabolite markers. While important that the metabolite can be detected, this analysis does not enable to us to make practical considerations about whether to actually use mass spectrometrying to evaluate a metabolite in formalin fixed paraffin embedded tissues. In order to analyze this group of biomolecules in biologically relevant context, it is important to know that metabolite distribution and intensity is not modified by the fixation and paraffin embedding processes. While preliminary results demonstrate that a range ofmetabolite can indeed be detected in formalin-fixed paraffin embedded tissues , it is important for us to explore distributions and intensities that while detected, are not conserved. Even if the same metabolite is detected, intensity and spatial distributions of the metabolite, but also be concerned
Here, we describe image processing to statistically evaluate the spatial and intensity conservation of species between different sample preparations, including formalin fixed paraffin embedded, formalin fixed, and flash frozen. Additionally, we use in vitro pancreatic cancer samples to compare sample preparations with 81 biological replicates per a single analysis. This sample, while not clinical tissue, is ideal for conservation analysis, as the sample can be reproducibily grown using all explored sample preparation mechanisms, and therefore sample to sample variability is not an issue. Additionally, 81 biological replicates/condition/analysis ensures that the signals we see are not a chance of error. Experimental variability between spheroids will be evaluated for each analysis, and then between analyses. Using our microarray spheroid platform, where we grow 81 spheroids, each 800 micron in diameter in a crosslinked-gelatin hydrogel.
Our analysis strategies is to perform a Pearson's Correlation Coefficient Analysis to evaluate how similar the spatial distributions are between two images. This assigns correlations based on intensity. It uses a scale from -1 to 1, where 1 is a perfect correlation, 0 for no correlation, and -1 for anti-correlation. This is used to evaluate how well the spatial distributions overlap.
The other implemented analysis strategy is to perform a Manders split coefficient is proportionally to intensity of the colocalizing pixels or voxels in each color channel. Here values, range from 0 to 1, thus expressing the fraction of the intensity in a channel that is located in pixels where there is above zero intensity.
Testing for statistical significance: The Costes analyses can tell us if the pearsons r and split Mander's coefficients are better than pure chance or not. This is done by shuffling the pixels in one images, and re-performing the Costes and Mander's analysis. Then it calculates the
Pearson's correlation coefficient (r) between the randomized image of 1 and the original image of image 2. A Costes P-Value is calculated by comparing the Pearson's correlation coefficient of the first image.
A P-value of 1.00 means that none of the randomized images had better correlation. 0.95 is the normal statistical confidence limit of 95%. Anything lower than that, and the correlation / colocalisation that you measure in the real images is not likely to be better than random chance, and thus is probably not interesting.
Additionally, in a scatterplot, the plots the two intensity values for each pixel are plotted against each other.
These coefficients measure the amount or degree of colocalization.
Costes automatic threshold
To calculate this automatic threshold, limit values for each channel are initialised to the maximum intensity of the each channel and progressively decremented. The Pearson's coefficient is concomitantly calculated for each increment. The final thresholds are then set to values which minimize the contribution of noise (i.e. Pearson's coefficient under the threshold being null or negative).
Things outputted by
Manders' overlap coefficient is based on the Pearson's correlation coefficient with average intensity values being taken out of the mathematical expression (Manders 1992). This new coefficient will vary from 0 to 1, the former corresponding to non-overlapping images and the latter reflecting 100% co-localisation between both images. M1 is defined as the ratio of the "summed intensities of pixels from the green image for which the intensity in the red channel is above zero" to the "total intensity in the green channel" and M2 is defined conversely for red. Therefore, M1 (or M2) is a good indicator of the proportion of the green signal coincident with a signal in the red channel over its total intensity, which may even apply if the intensities in both channels are really different from one another.
Intensity correlation quotient (ICQ) is defined as the ratio of positive (Ai-a)(Bi-b) products divided by the overall products subtracted by 0.5. As a consequence, the ICQ varies from 0.5 (co-localisation) to -0.5 (exclusion) while random staining and images impeded by noise will give a value close to zero.