Numerical experiments with MALDI Imaging data

This article does not present new mathematical results, it solely aims at discussing some numerical experiments with MALDI Imaging data. However, these experiments are based on and could not be done without the mathematical results obtained in the UNLocX project. They tackle two obstacles which presently prevent clinical routine applications of MALDI Imaging technology. In the last decade, matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) has developed into a powerful bioanalytical imaging modality. MALDI imaging data consists of a set of mass spectra, which are measured at different locations of a flat tissue sample. Hence, this technology is capable of revealing the full metabolic structure of the sample under investigation. Sampling resolution as well as spectral resolution is constantly increasing, presently a conventional 2D MALDI Imaging data requires up to 100 GB per dataset. A major challenge towards routine applications of MALDI Imaging in pharmaceutical or medical workflows is the high computational cost for evaluating and visualizing the information content of MALDI imaging data. This becomes even more critical in the near future when considering cohorts or 3D applications. Due to its size and complexity MALDI Imaging constitutes a challenging test case for high performance signal processing. In this article we will apply concepts and algorithms, which were developed within the UNLocX project, to MALDI Imaging data. In particular we will discuss a suitable phase space model for such data and report on implementations of the resulting transform coders using GPU technology. Within the MALDI Imaging workflow this leads to an efficient baseline removal and peak picking. The final goal of data processing in MALDI Imaging is the discrimination of regions having different metabolic structures. We introduce and discuss so-called soft-segmentation maps which are obtained by non-negative matrix factorization incorporating sparsity constraints.     

Energy dependence of multiplicity in proton-nucleus collisions and models of multiparticle production

This is a continuation of our earlier investigation (Gurtuet al 1974Phys. Lett. 50 B 391) on multiparticle production in proton-nucleus collisions based on an exposure of emulsion stack to 200 GeV/c beam at the NAL. It is found that the ratioR _em = 〈n _s〉/〈n _ch〉, where 〈n _ch〉 is the charged particle multiplicity in pp-collisions, increases slowly from about 1 at 10 GeV/c to 1·6 at 68 GeV/c and attains a constant value of 1·71 ± 0·04 in the region 200 to 8000 GeV/c. Furthermore,R _em = 1·71 implies an effectiveA-dependence ofR _A =A ^0.18,i.e., a very weak dependence. Predictions ofR _em on various models are discussed and compared with the emulsion data. Data seem to favour models of hadron-nucleon collisions in which production of particles takes place through adouble step mechanism,e.g., diffractive excitation, hydrodynamical and energy flux cascade as opposed to models which envisage instantaneous production.     

Activation of the pre-supplementary motor area but not inferior prefrontal cortex in association with short stop signal reaction time – an intra-subject analysis

Background  

Our previous work described the neural processes of motor response inhibition during a stop signal task (SST). Employing the race model, we computed the stop signal reaction time (SSRT) to index individuals' ability in inhibitory control. The pre-supplementary motor area (preSMA), which shows greater activity in individuals with short as compared to those with long SSRT, plays a role in mediating response inhibition. In contrast, the right inferior prefrontal cortex (rIFC) showed greater activity during stop success as compared to stop error. Here we further pursued this functional differentiation of preSMA and rIFC on the basis of an intra-subject approach.     

Influencing receptor-ligand binding mechanisms with multivalent ligand architecture

Multivalent ligands can function as inhibitors or effectors of biological processes. Potent inhibitory activity can arise from the high functional affinities of multivalent ligand-receptor interactions. Effector functions, however, are influenced not only by apparent affinities but also by alternate factors, including the ability of a ligand to cluster receptors. Little is known about the molecular features of a multivalent ligand that determine whether it will function as an inhibitor or effector. We envisioned that, by altering multivalent ligand architecture, ligands with preferences for different binding mechanisms would be generated. To this end, a series of 28 ligands possessing structural diversity was synthesized. This series provides the means to explore the effects of ligand architecture on the inhibition and clustering of a model protein, the lectin concanavalin A (Con A). The structural parameters that were varied include scaffold shape, size, valency, and density of binding elements. We found that ligands with certain architectures are effective inhibitors, but others mediate receptor clustering. Specifically, high molecular weight, polydisperse polyvalent ligands are effective inhibitors of Con A binding, whereas linear oligomeric ligands generated by the ring-opening metathesis polymerization have structural properties that favor clustering. The shape of a multivalent ligand also influences specific aspects of receptor clustering. These include the rate at which the receptor is clustered, the number of receptors in the clusters, and the average interreceptor distance. Our results indicate that the architecture of a multivalent ligand is a key parameter in determining its activity as an inhibitor or effector. Diversity-oriented syntheses of multivalent ligands coupled with effective assays that can be used to compare the contributions of different binding parameters may afford ligands that function by specific mechanisms.     

In situ isobaric lipid mapping by MALDI–ion mobility separation–mass spectrometry imaging

The highly diverse chemical structures of lipids make their analysis directly from biological tissue sections extremely challenging. Here, we report the in situ mapping and identification of lipids in a freshwater crustacean Gammarus fossarum using matrix‐assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) in combination with an additional separation dimension using ion mobility spectrometry (IMS). The high‐resolution trapped ion mobility spectrometry (TIMS) allowed efficient separation of isobaric/isomeric lipids showing distinct spatial distributions. The structures of the lipids were further characterized by MS/MS analysis. It is demonstrated that MALDI MSI with mobility separation is a powerful tool for distinguishing and localizing isobaric/isomeric lipids.     

A Caged In-Source Laser-Cleavable MALDI Matrix with High Vacuum Stability for Extended MALDI-MS Imaging

Insufficient vacuum stability of matrix chemicals is a major limitation in matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) of large tissue sample cohorts. Here, we designed and synthesized the photo-cleavable caged molecule 4,5-dimethoxy-2-nitrobenzyl-2,5-dihydroxyacetophenone (DMNB-2,5-DHAP) and employed it for lipid MALDI-MSI of mouse brain tissue sections. DMNB-2,5-DHAP is vacuum-stable in a high vacuum MALDI ion source for at least 72 h. Investigation of the uncaging process suggested that the built-in laser (355 nm) in the MALDI ion source promoted the in situ generation of 2,5-DHAP. A caging group is used for the first time in designing a MALDI matrix that is vacuum-stable, uncaged upon laser irradiation during the measurement process, and that boosts lipid ion intensity with MALDI-2 laser-induced postionization.     

Quantitative analysis of the role played by poly(vinylpyrrolidone) in seed-mediated growth of Ag nanocrystals

This article presents a quantitative analysis of the role played by poly(vinylpyrrolidone) (PVP) in seed-mediated growth of Ag nanocrystals. Starting from Ag nanocubes encased by {100} facets as the seeds, the resultant nanocrystals could take different shapes depending on the concentration of PVP in the solution. If the concentration was above a critical value, the seeds simply grew into larger cubes still enclosed by {100} facets. When the concentration fell below a critical value, the seeds would evolve into cuboctahedrons enclosed by a mix of {100} and {111} facets and eventually octahedrons completely covered by {111} facets. We derived the coverage density of PVP on Ag(100) surface by combining the results from two measurements: (i) cubic seeds were followed to grow at a fixed initial concentration of PVP to find out when {111} facets started to appear on the surface, and (ii) cubic seeds were allowed to grow at reduced initial concentrations of PVP to see at which concentration {111} facets started to appear from the very beginning. We could calculate the coverage density of PVP from the differences in PVP concentration and the total surface area of Ag nanocubes between these two samples. The coverage density was found to be 140 and 30 repeating units per nm(2) for PVP of 55,000 and 10,000 g/mol in molecular weight, respectively, for cubic seeds of 40 nm in edge length. These values dropped slightly to 100 and 20 repeating units per nm(2), respectively, when 100 nm Ag cubes were used as the seeds.