Hans Lohninger

Application of gold thin-films for internal standardization in LA-ICP-MS imaging experiments

LA-ICP-MS imaging experiments are of growing interest within the field of biosciences. Revealing the distributions of major components as well as trace elements in biological samples can help to understand fundamental biological processes. However, highly variable sample conditions and changing instrumental parameters during measurement time aggravate reliable quantification especially in biological tissues. Normally matrix matched standards used for calibration are scarcely available and the manufacturing process thereof is rather complicated. Thus most experiments reported in the literature only delivered qualitative information on the analyte distributions. The use of appropriate internal standards facilitates the preparation of calibrations even without the utilization of matrix-matched standards. In the presented work an approach for providing reliable quantitative bio-images is proposed using gold thin-layers as an internal standard and patterns printed with commercially available inkjet printers as standards. The method development is based on copper from blue ink as the element of interest. It could be shown that gold standardization compensates instrumental drifts, matrix related ablation differences and day-to-day signal changes. Not only was the quality of the obtained images improved by gold standardization; while the relative standard deviation of the measurements was around 15% before standardization it could be decreased to less than 5% by gold standardization. Also quantitative information could be obtained for samples with unknown analyte concentrations. Depending on the used beam diameter limits of detection in the range of some hundreds ng g(-1) were achieved. The presented method is a promising and easy-to-handle alternative to matrix matched standards for signal quantification.

Quantitative LA-ICP-MS imaging of platinum in chemotherapy treated human malignant pleural mesothelioma samples using printed patterns as standard

LA-ICP-MS has often been applied for the analysis of trace elemental distributions in biological tissues. However, the strong matrix dependence of LA-ICP-MS analyses and highly variable matrix conditions aggravate reliable qualitative distribution analyses and thus for obtaining quantitative information elaborate quantification strategies have to be applied. In this work printed patterns on paper with thin gold layers as pseudo-internal standard have been proposed as an alternative approach to the commonly used matrix-matched tissue standards. Besides a major reduction of the workload for standard preparation the presented method allows for compensation of instrumental drifts during measurement as well as a reduction of matrix related effects. The developed method has been verified using matrix-matched tissue standards (deviations from the actual metal content less than 5% with relative standard deviations of less than 7%) and applied to platinum imaging on human malignant pleural mesothelioma samples after administration of individuals with platinum containing cytostatic drugs.

Optimisation of a sample preparation procedure for the screening of fungal infection and assessment of deoxynivalenol content in maize using mid-infrared attenuated total reflection spectroscopy

A sample preparation procedure for the determination of deoxynivalenol (DON) using attenuated total reflection mid-infrared spectroscopy is presented. Repeatable spectra were obtained from samples featuring a narrow particle size distribution. Samples were ground with a centrifugal mill and analysed with an analytical sieve shaker. Particle sizes of <100, 100–250, 250–500, 500–710 and 710–1000 µm were obtained. Repeatability, classification and quantification abilities for DON were compared with non-sieved samples. The 100–250 µm fraction showed the best repeatability. The relative standard deviation of spectral measurements improved from 20 to 4.4% and 100% of sieved samples were correctly classified compared with 79% of non-sieved samples. The DON level in analysed fractions was a good estimate of overall toxin content.

Chemometric Analysis of Multisensor Hyperspectral Images of Precipitated Atmospheric Particulate Matter

The chemometric analysis of multisensor hyperspectral data allows a comprehensive image-based analysis of precipitated atmospheric particles. Atmospheric particulate matter was precipitated on aluminum foils and analyzed by Raman microspectroscopy and subsequently by electron microscopy and energy dispersive X-ray spectroscopy. All obtained images were of the same spot of an area of 100 × 100 μm(2). The two hyperspectral data sets and the high-resolution scanning electron microscope images were fused into a combined multisensor hyperspectral data set. This multisensor data cube was analyzed using principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex component analysis. The detailed chemometric analysis of the multisensor data allowed an extensive chemical interpretation of the precipitated particles, and their structure and composition led to a comprehensive understanding of atmospheric particulate matter.

GC/MS and chemometrics in archaeometry

Summary100 mg amounts of glue on two copper-age arrowheads have been investigated by means of gas-chromatography/mass spectrometry and chemometrics. The samples have been identified as birch bark derivatives, but the distribution patterns of triterpenoids and steroids show significant differences to similar samples from younger archaeological periods that can be explained by the use of a different species of birch and a different method of preparation.

Validation of Chemometric Models for the Determination of Deoxynivalenol on Maize by Mid-Infrared Spectroscopy

Validation methods for chemometric models are presented, which are a necessity for the evaluation of model performance and prediction ability. Reference methods with known performance can be employed for comparison studies. Other validation methods include test set and cross validation, where some samples are set aside for testing purposes. The choice of the testing method mainly depends on the size of the original dataset. Test set validation is suitable for large datasets (>50), whereas cross validation is the best method for medium to small datasets (<50). In this study the K-nearest neighbour algorithm (KNN) was used as a reference method for the classification of contaminated and blank corn samples. A Partial least squares (PLS) regression model was evaluated using full cross validation. Mid-Infrared spectra were collected using the attenuated total reflection (ATR) technique and the fingerprint range (800–1800 cm−1) of 21 maize samples that were contaminated with 300 – 2600 µg/kg deoxynivalenol (DON) was investigated. Separation efficiency after principal component analysis/cluster analysis (PCA/CA) classification was 100%. Cross validation of the PLS model revealed a correlation coefficient of r=0.9926 with a root mean square error of calibration (RMSEC) of 95.01. Validation results gave an r=0.8111 and a root mean square error of cross validation (RMSECV) of 494.5 was calculated. No outliers were reported.

Intact Cell/Spore Mass Spectrometry of Fusarium Macro Conidia for Fast Isolate and Species Differentiation

The focus of this paper is the development of an approach called intact cell mass spectrometry (ICMS) or intact spore mass spectrometry (ISMS) based on the technique matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the rapid differentiation and identification of Fusarium species. Several parameters, which are known to affect the quality of IC mass spectra, have been investigated in detail by varying the MALDI matrix as well as the solvent system, in which the matrix has been dissolved, the solvent system for sample purification and the type of sample/MALDI matrix deposition technique. In the end characteristic as well as highly reproducible IC or IS mass spectra or peptide/protein fingerprints of three Fusarium species (F. cerealis, F. graminearum and F. poae) including 16 Fusarium isolates derived from different hosts and geographical locations have been obtained. Unscaled hierarchical cluster analysis based on ICMS data of eight selected Fusarium isolates of two species F. graminearum and F. poae revealed significant difference among the peptide/protein pattern of them. The results of the applied cluster analysis proved that, ICMS is a powerful approach for the rapid differentiation of Fusarium species. In addition, an on-target tryptic digestion was applied to Fusarium macro conidia spores to identify proteins using MALDI post source decay (PSD) fragment ion analysis. Two kinds of trypsin, namely bead-immobilized − to favor cleavage of surface-associated proteins − and non-immobilized trypsin were applied and compared. The results showed that the latter is more suitable for generating sequence tags by PSD fragment ion analysis.

Image-Based Chemical Structure Determination

Chemical imaging is a powerful tool for understanding the chemical composition and nature of heterogeneous samples. Recent developments in elemental, vibrational, and mass-spectrometric chemical imaging with high spatial resolution (50–200 nm) and reasonable timescale (a few hours) are capable of providing complementary chemical information about various samples. However, a single technique is insufficient to provide a comprehensive understanding of chemically complex materials. For bulk samples, the combination of different analytical methods and the application of statistical methods for extracting correlated information across different techniques is a well-established and powerful concept. However, combined multivariate analytics of chemical images obtained via different imaging techniques is still in its infancy, hampered by a lack of analytical methodologies for data fusion and analysis. This study demonstrates the application of multivariate statistics to chemical images taken from the same sample via various methods to assist in chemical structure determination.

High throughput inclusion body sizing: Nano particle tracking analysis

The expression of pharmaceutical relevant proteins in Escherichia coli frequently triggers inclusion body (IB) formation caused by protein aggregation. In the scientific literature, substantial effort has been devoted to the quantification of IB size. However, particle-based methods used up to this point to analyze the physical properties of representative numbers of IBs lack sensitivity and/or orthogonal verification. Using high pressure freezing and automated freeze substitution for transmission electron microscopy (TEM) the cytosolic inclusion body structure was preserved within the cells. TEM imaging in combination with manual grey scale image segmentation allowed the quantification of relative areas covered by the inclusion body within the cytosol. As a high throughput method nano particle tracking analysis (NTA) enables one to derive the diameter of inclusion bodies in cell homogenate based on a measurement of the Brownian motion. The NTA analysis of fixated (glutaraldehyde) and non-fixated IBs suggests that high pressure homogenization annihilates the native physiological shape of IBs. Nevertheless, the ratio of particle counts of non-fixated and fixated samples could potentially serve as factor for particle stickiness. In this contribution, we establish image segmentation of TEM pictures as an orthogonal method to size biologic particles in the cytosol of cells. More importantly, NTA has been established as a particle-based, fast and high throughput method (1000-3000 particles), thus constituting a much more accurate and representative analysis than currently available methods.

How salt lakes affect atmospheric new particle formation: A case study in Western Australia

New particle formation was studied above salt lakes in-situ using a mobile aerosol chamber set up above the salt crust and organic-enriched layers of seven different salt lakes in Western Australia. This unique setup made it possible to explore the influence of salt lake emissions on atmospheric new particle formation, and to identify interactions of aqueous-phase and gas-phase chemistry. New particle formation was typically observed at enhanced air temperatures and enhanced solar irradiance. Volatile organic compounds were released from the salt lake surfaces, probably from a soil layer enriched in organic compounds from decomposed leaf litter, and accumulated in the chamber air. After oxidation of these organic precursor gases, the reaction products contributed to new particle formation with observed growth rates from 2.7 to 25.4nmh-1. The presence of ferrous and ferric iron and a drop of pH values in the salt lake water just before new particle formation events indicated that organic compounds were also oxidized in the aqueous phase, affecting the new particle formation process in the atmosphere. The contribution of aqueous-phase chemistry to new particle formation is assumed, as a mixture of hundreds of oxidized organic compounds was characterized with several analytical techniques. This chemically diverse composition of the organic aerosol fraction contained sulfur- and nitrogen-containing organic compounds, and halogenated organic compounds. Coarse mode particles were analyzed using electron microscopy, energy dispersive X-ray spectroscopy and Raman spectroscopy. Ultra-high resolution mass spectrometry was applied to analyze filter samples. A targeted mass spectral analysis revealed the formation of organosulfates from monoterpene precursors and two known tracers for secondary organic aerosol formation from atmospheric oxidation of 1,8-cineole, which indicates that a complex interplay of aqueous-phase and gas-phase oxidation of monoterpenes contributes to new particle formation in the investigated salt lake environment.