Jürgen Popp

Handling Different Spatial Resolutions in Image Fusion by Multivariate Curve Resolution-Alternating Least Squares for Incomplete Image Multisets

Data fusion of different imaging techniques allows a comprehensive description of chemical and biological systems. Yet, joining images acquired with different spectroscopic platforms is complex because of the different sample orientation and image spatial resolution. Whereas matching sample orientation is often solved by performing suitable affine transformations of rotation, translation, and scaling among images, the main difficulty in image fusion is preserving the spatial detail of the highest spatial resolution image during multitechnique image analysis. In this work, a special variant of the unmixing algorithm Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) for incomplete multisets is proposed to provide a solution for this kind of problem. This algorithm allows analyzing simultaneously images collected with different spectroscopic platforms without losing spatial resolution and ensuring spatial coherence among the images treated. The incomplete multiset structure concatenates images of the two platforms at the lowest spatial resolution with the image acquired with the highest spatial resolution. As a result, the constituents of the sample analyzed are defined by a single set of distribution maps, common to all platforms used and with the highest spatial resolution, and their related extended spectral signatures, covering the signals provided by each of the fused techniques. We demonstrate the potential of the new variant of MCR-ALS for multitechnique analysis on three case studies: (i) a model example of MIR and Raman images of pharmaceutical mixture, (ii) FT-IR and Raman images of palatine tonsil tissue, and (iii) mass spectrometry and Raman images of bean tissue.

Multiplex coherent anti-Stokes Raman scattering microspectroscopy of brain tissue with higher ranking data classification for biomedical imaging

Abstract. Multiplex coherent anti-Stokes Raman scattering (MCARS) microscopy was carried out to map a solid tumor in mouse brain tissue. The border between normal and tumor tissue was visualized using support vector machines (SVM) as a higher ranking type of data classification. Training data were collected separately in both tissue types, and the image contrast is based on class affiliation of the single spectra. Color coding in the image generated by SVM is then related to pathological information instead of single spectral intensities or spectral differences within the data set. The results show good agreement with the H&E stained reference and spontaneous Raman microscopy, proving the validity of the MCARS approach in combination with SVM.

Raman spectroscopic profiling of intracellular lipid compositions of macrophages induced in the vicinity of cancer cells (Conference Presentation)

Lipidomics is a vast field of intracellular pathways of lipids and their biochemical functions. In analogy to genomics and proteomics it contributes to the overall comprehension of system biology. The field elucidates the role of lipids as a subset of the major biological components. Within this family of molecules, often referred to as metabolic lipidome, lipid mediators (LMs) are currently under detailed investigations. Being part of lipid signaling events, which are unique in a sense that they are produced “on demand” at the site of action, LMs fulfill important roles in receptor and enzyme regulated processes. Furthermore, LMs along with phospholipids (PL) are known to have pro- and anti-tumoral properties, and cancer cells exhibit aberrant LM and PL profiles. Typical cells that produce a broad variety of LMs are monocytes and macrophages, which also use these chemical mediators to influence the communication between monocytes and macrophages with cancer cells. As lipidomics research involves the identification and quantification of the thousands of cellular lipid molecular species and their interactions with other lipids, proteins, and other metabolites, comparably fast analytical techniques that detect the overall lipid composition of individual cells are highly advantageous. Several types of analytical methodologies are applied for characterization of the lipidome of cells. By far most commonly used is mass spectrometry in combination with separation techniques that can provide a profile of the variety of lipids present, as well as their identification. Similar information can be obtained utilizing NMR spectroscopy. Meanwhile also well established for profiling biological samples is Raman spectroscopy. As Raman micro-spectroscopy can be used to image individual cells and depict subcellular components based on their spectroscopic fingerprints, it appears as an ideal label-free technique to investigate intracellular alterations noninvasively. minute spectral changes, due to compositional alterations can be reproducibly detected. In this context Raman micro-spectroscopy has for instance been applied to typing of bacteria or the differentiation between cancerous and normal cells. Raman spectroscopy can provide an OMIC-like view of the chemical status of individual cells and metabolism and has been suggested for lipidomic profiling.(1,2) The obtained data sets of were subjected to common statistical data evaluation, such as hierarchical cluster (HCA) and principal component analysis (PCA), in order to relate spectroscopic alterations to the compositional changes associated with the presence of a cancerous environment. Here we present first results obtained from M1 and M2 macrophages cocultured in vitro with cancer cells in order to evaluate the potential of Raman spectroscopy for lipid profiling. Acknowledgements: Financial support from the Carl Zeiss Foundation is highly acknowledged. References 1. Huang W, Spiers A. Consideration of Future Requirements for Raman Microbiology as an Examplar for the Ab Initio Development of Informatics Frameworks for Emergent OMICS Technologies OMICS: A Journal of Integrative Biology 2006;10:238-41. 2. Wu H, Volponi J, Oliver A, Parikh A, Simmons B, Singh S. In vivo lipidomics using single-cell Raman spectroscopy. Proc Natl Acad Sci USA 2011;108:3809-14.