MALDI Data Research Articles

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19.

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

NECTAR: A New Algorithm for Characterizing and Correcting Noise in QToF-Mass Spectrometry Imaging Data.

A typical mass spectrometry imaging experiment yields a very high number of detected peaks, many of which are noise and thus unwanted. To select only peaks of interest, data preprocessing tasks are applied to raw data. A statistical study to characterize three types of noise in MSI QToF data (random, chemical, and background noise) is presented through NECTAR, a new NoisE CorrecTion AlgoRithm. Random noise is confirmed to be dominant at lower m/z values (∼50-400 Da) while systematic chemical noise dominates at higher m/z values (>400 Da). A statistical approach is presented to demonstrate that chemical noise can be corrected to reduce its presence by a factor of ∼3. Reducing this effect helps to determine a more reliable baseline in the spectrum and therefore a more reliable noise level. Peaks are classified according to their spatial S/N on the single ion images, and background noise is thus removed from the list of peaks of interest. This new algorithm was applied to MALDI and DESI QToF data generated from the analysis of a mouse pancreatic tissue section to demonstrate its applicability and ability to filter out these types of noise in a relevant data set. PCA and t-SNE multivariate analysis reviews of the top 4000 peaks and the final 744 and 299 denoised peak list for MALDI and DESI, respectively, suggests an effective removal of uninformative peaks and proper selection of relevant peaks.

Sequencing intact membrane proteins using MALDI mass spectrometry

Membrane proteins are key players in many cellular events and represent crucial drug targets. Matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) is a valuable approach to investigate them. To our knowledge, there are only a few reports of sequencing small membrane proteins using MALDI in-source decay (ISD). We report the successful fragmentation and sequencing of membrane proteins up to 46 kDa by MALDI-ISD. We have 1) investigated key MALDI parameters that influence the sequencing of a soluble protein; 2) used atomic force microscopy to observe our samples and correlate their topological features with MALDI data, which allowed us to optimize fragmentation conditions; 3) sequenced N- and C-termini of three membrane proteins (SpoIIIAF, TIM23, and NOX), solubilized in three different ways. Our results indicate that detergent and buffer type are of key importance for successful MALDI-ISD sequencing. Our findings are significant because sequencing membrane proteins enables the unique characterization of challenging biomolecules. The resulting fragmentation patterns provide key insights into the identity of proteins, their sequences, modifications, and other crucial information, such as the position of unexpected truncation.

Supervised topological data analysis for MALDI mass spectrometry imaging applications

BackgroundMatrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) displays significant potential for applications in cancer research, especially in tumor typing and subtyping. Lung cancer is the primary cause of tumor-related deaths, where the most lethal entities are adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). Distinguishing between these two common subtypes is crucial for therapy decisions and successful patient management.ResultsWe propose a new algebraic topological framework, which obtains intrinsic information from MALDI data and transforms it to reflect topological persistence. Our framework offers two main advantages. Firstly, topological persistence aids in distinguishing the signal from noise. Secondly, it compresses the MALDI data, saving storage space and optimizes computational time for subsequent classification tasks. We present an algorithm that efficiently implements our topological framework, relying on a single tuning parameter. Afterwards, logistic regression and random forest classifiers are employed on the extracted persistence features, thereby accomplishing an automated tumor (sub-)typing process. To demonstrate the competitiveness of our proposed framework, we conduct experiments on a real-world MALDI dataset using cross-validation. Furthermore, we showcase the effectiveness of the single denoising parameter by evaluating its performance on synthetic MALDI images with varying levels of noise.ConclusionOur empirical experiments demonstrate that the proposed algebraic topological framework successfully captures and leverages the intrinsic spectral information from MALDI data, leading to competitive results in classifying lung cancer subtypes. Moreover, the framework’s ability to be fine-tuned for denoising highlights its versatility and potential for enhancing data analysis in MALDI applications.

Analytical Method Cluster Development for Comprehensive Characterisation of Suberinic Acids Derived from Birch Outer Bark.

Suberin is a complex polyester biopolymer, and it is practically impossible to estimate the real content of suberin in suberised plant tissues. This indicates the importance of the development of instrumental analytical methods for the comprehensive characterisation of suberin derived from plant biomass for the successful integration of suberinic products into biorefinery production chains. In this study, we optimised two GC-MS methods-one with direct sylilation, and the second with additional depolymerisation, using GPC methods with RI detector and polystyrene calibration and with a three-angle light scattering detector and an eighteen-angle light scattering detector. We also performed MALDI-Tof analysis for non-degraded suberin structure determination. We characterised suberinic acid (SA) samples obtained from birch outer bark after alkaline depolymerisation. The samples were particularly rich in diols, fatty acids and their esters, hydroxyacids and their corresponding esters, diacids and their corresponding esters, as well as extracts (mainly betulin and lupeol) and carbohydrates. To remove phenolic-type admixtures, treatment with ferric chloride (FeCl3) was used. The SA treatment with FeCl3 allows the possibility to obtain a sample that has a lower content of phenolic-type compounds and a lower molecular weight than an untreated sample. It was possible to identify the main free monomeric units of SA samples by GC-MS system using direct silylation. By performing an additional depolymerisation step before silylation, it was possible to characterise the complete potential monomeric unit composition in the suberin sample. For the molar mass distribution determination, it is important to perform GPC analysis. Even though chromatographic results can be obtained using a three- laser MALS detector, they are not fully correct because of the fluorescence of the SA samples. Therefore an 18-angle MALS detector with filters was more suitable for SA analysis. MALDI-Tof analysis is a great tool for the polymeric compound structural identification, which cannot be done using GC-MS. Using the MALDI data, we discovered that the main monomeric units that makes up the SA macromolecular structure are octadecanedioic acid and 2-(1,3-dihydroxyprop-2-oxy)decanedioic acid. This corresponds with GC-MS results, showing that after depolymerisation hydroxyacids and diacids were the dominant type of compounds found in the sample.

Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks.

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.

MO435MULTIMODAL IMAGING FOR MOLECULAR TISSUE ANALYSIS

Abstract Background and Aims MALDI mass spectrometric imaging (MALDI MSI) is a powerful histologic tool for the analysis of biomolecules in tissue samples. MALDI MSI measurements result in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples. For more detailed analysis of MALDI MSI data and correlation between the molecular and microscopic levels, a combination of MALDI MSI data and histological staining is essential. By combining MALDI MSI data and histological data, much more information are obtained than by analyzing both methods individually. Therefore, MALDI MSI datasets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provides more information than a single tissue section. We have developed, established and validated an algorithm for an automatic registration of MALDI data with different histological image data for cross-process evaluation of multimodal datasets to create 3D models. This multimodal imaging approach simplifies and improves molecular analyses of tissue samples in clinical research and diagnosis. Method The datasets for fusion and creation of a 3D model consist of mass spectrometric data, histological and immunohistochemical staining methods. Histological tissue sections of a whole mouse kidney were prepared. For MALDI MSI data, organ sections were analyzed by using a Rapiflex mass-spectrometer. Results A mathematical registration was used to achieve a perfect superposition of the individual histological sections of mass spectrometric data. It is feasible to combine mass spectrometric data, histological and immunohistochemical datasets in high numbers and reconstruct the measured mouse kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distributions can be obtained. The fusion of the data also offers a virtual incision of the organ from arbitrary angle and level. The algorithms are adapted to take the data fusion automatically offering a high-throughput approach for clinical diagnostics and the possibility to involved artificial intelligence in its interpretation in research. Conclusion A successful fusion of MALDI MSI data and different histological and immunohistochemical staining datasets of a whole organ is performed.

Non-covalent binding and selective fluorescent sensing of dipyrone with a carbocyanine dye and cetyltrimethylammonium bromide

The work is aimed at the search of selective fluorescent sensors without using specific artificial receptors, antibodies, enzymes etc. With this end in view, methods based on non-covalent binding of target analytes are sought. We observed dramatic changes in the emission spectrum of a carbocyanine dye in a micellar surfactant solution (cetyltrimethylammonium bromide, CTAB) in the presence of dipyrone (metamizol, analgin): the 480 nm band intensity increases with a simultaneous decrease in intensity in near-IR region (720 nm). MALDI and NMR-1H data show the intact molecules of dipyrone and the dye. The detection can be performed in the presence of other organic species and inorganic salts. Dipyrone testing is feasible within 5 × 10–7–5 × 10–4 M with RSDs of 3.5% by using a visualizer instead of a spectrofluorimeter.

P0659MULTIMODAL IMAGING OF MALDI MSI DATA

Abstract Background and Aims MALDI mass spectrometric imaging (MALDI MSI) is a powerful histologic tool for the analysis of biomolecules in tissue samples. MALDI MSI measurements results in a high sensitivity and accuracy of spatial distribution of biomolecules in tissue samples The resolution information of MALDI MSI is in the range of 5-10 µm in the spatial distribution and has the ability to identify proteins, peptides, lipids and small biomolecules directly in tissue samples in one analytical step..For a more detailed analysis of MALDI MSI data and a correlation between the molecular and microscopic level, a combination of MALDI MSI data and histological staining is essential. By combining MALDI MSI data and histological data, much more information are obtained than from a single analysis of both methods. Therefore, MALDI MSI data sets and histological staining were fused to a 3D model presenting a biomolecule distribution of the whole organ and provide more information than a single tissue section. We developed, established and validate an algorithm for an automatic registration of MALDI data with different histological image data for the cross-process evaluation of multimodal data sets for creating 3D models. This multimodal image approach simplifies and improves molecular analyses of tissue samples clinical research and diagnosis. Method The data sets for the fusion and creating of a 3D model consist of mass spectrometric data as well as histological and Immunohistochemical staining methods. Histological tissue sections of a whole mice kidney were prepared. For MALDI MSI data the organ sections were coated and incubated with a trypsin solution were performed by using a sprayer for MALDI imaging. As matrix, α-cyano-4-hydroxycinnamic acid was used. MALDI MSI was performed using the Rapiflex. For histological staining the hematoxylin-eosin and Gomori staining were chosen. For Immunohistochemical double staining and immunofluorescence, were used for the detection of Collagen type I, smooth muscle actin and the cell nuclei. Results By using a mathematical registration, a perfect superposition of the individual histological sections mass spectrometric data was achieved. It is possible to combine mass spectrometric data, histological and Immunohistochemical data sets in a high number and to reconstruct the measured mice kidney. By using different imaging methods, a variety of information about tissue structure as well as tissue changes and protein distribution can be obtained. The fusion of the data also offers a virtual incision of the organ from any angle and level. The algorithms are adapted to take the data fusion automatically offering a high-throughput approach for clinical diagnostics and the possibility to involved artificial intelligence in its interpretation in research. Conclusion There is a successful fusion of MALDI MSI data and different histological and Immunohistochemical staining data sets of a whole organ

Fractionation of Aschalcha extra heavy oil resins and studying their structure

In this paper we isolated resins from a sample of extra heavy oil and from the products of catalytic aquathermolysis in the presence of a cobalt based catalyst and studied their structure. In fact, an oil fraction and 4 resin fractions (R1-R4) have been extracted by liquid adsorption chromatography method in the presence of pure solvents and their binary mixtures in various ratios. Adding the catalyst to the system during heat and steam exposure for 6 hours leads to a significant improvement of the structural-group composition. Thus, the aliphaticity index of R3 decreased as a result of the detachment of alkyl substituents in aromatic fragments under the influence of catalyst and passes into lighter fractions of resins, as well as towards saturated and aromatic hydrocarbons. From another hand, the oxidation coefficient also manifests itself in fractions R1 and R2 as a result of oxidative catalytic cracking processes, which also indicates a redistribution of the group composition. MALDI data have showed that the average molecular weight of resins for each of the fractions has decreased whereas IR spectroscopy has highlighted a change in their structural group composition.

Quantifying biological samples using Linear Poisson Independent Component Analysis for MALDI-ToF mass spectra.

MotivationMatrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI) facilitates the analysis of large organic molecules. However, the complexity of biological samples and MALDI data acquisition leads to high levels of variation, making reliable quantification of samples difficult. We present a new analysis approach that we believe is well-suited to the properties of MALDI mass spectra, based upon an Independent Component Analysis derived for Poisson sampled data. Simple analyses have been limited to studying small numbers of mass peaks, via peak ratios, which is known to be inefficient. Conventional PCA and ICA methods have also been applied, which extract correlations between any number of peaks, but we argue makes inappropriate assumptions regarding data noise, i.e. uniform and Gaussian.ResultsWe provide evidence that the Gaussian assumption is incorrect, motivating the need for our Poisson approach. The method is demonstrated by making proportion measurements from lipid-rich binary mixtures of lamb brain and liver, and also goat and cow milk. These allow our measurements and error predictions to be compared to ground truth.Availability and implementationSoftware is available via the open source image analysis system TINA Vision, www.tina-vision.net.Supplementary information Supplementary data are available at Bioinformatics online.

Wine yeasts identification by MALDI-TOF MS: Optimization of the preanalytical steps and development of an extensible open-source platform for processing and analysis of an in-house MS database

Saccharomyces cerevisiae is the most important yeast species for the production of wine and other beverages. In addition, nowadays, researchers and winemakers are aware of the influence of non-Saccharomyces in wine aroma complexity. Due to the high microbial diversity associated to several agro-food processes, such as winemaking, developing fast and accurate methods for microbial identification is demanded. In this context, MALDI-TOF MS mass fingerprint provides reliable tool for fast biotyping and classification of microorganisms. However, there is no versatile and standardized method for fungi currently available. In this study, an optimized sample preparation protocol was devised for the biotyping of yeasts of oenological origin.Taking into account that commercially available reference databases comprise almost exclusively clinical microorganisms, most of them bacteria, in the present study a database of yeasts isolated from vineyards and wineries was created, and its accuracy was tested using industrial and laboratory yeast strains. In addition, the implementation of a program for MALDI-TOF MS spectra analysis has been developed as an extensible open-source platform for MALDI data processing and analysis with statistical techniques that has arisen from our previous experience working with MALDI data. The software integrates two R packages for raw MALDI data preprocessing: Continuous Wavelet Transform (CWT)-based algorithm and MassSpecWavelet. One of the advantages of the CWT is that it can be directly applied to a raw spectrum, without prior baseline correction.Mass fingerprints of 109 S. cerevisiae strains and 107 non-Saccharomyces isolates were generated by MALDI-TOF MS upon optimized sample preparation and instrument settings and analyzed for strain, species, and genus-level differentiation. As a reference method, for S. cerevisiae differentiation at strain level, the analysis of the polymorphism in the inter-delta region was chosen. The data revealed that MALDI-TOF MS can be used for the rapid and accurate identification of S. cerevisiae and non-Saccharomyces isolates at genus and species level. However, S. cerevisiae differentiation at strain level was not successfully achieved, and the differentiation among Metschnikowia species was also difficult.

Design and synthesis of hyperstructured molecules based on cyclophosphazene core for multiphoton absorption

Cyclophosphazene based hyperstructured molecules were synthesized through simple nucleophilic substitution reactions. All these molecules were characterized by multinuclear NMR, MALDI and HRMS spectral data. Third order nonlinear optical properties of the hyperstructured molecules were measured using Z-scan technique with 532 nm, picosecond (ps) laser and 800 nm, femtosecond (fs) laser pulses. The molecules showed reverse saturable absorption on excitation at both 532 nm and 800 nm, which could be attributed to the two-photon absorption (2 PA) and three-photon absorption (3 PA), respectively. The 2 PA and 3 PA cross section values exhibited by the molecules based on cyclophosphazene are as high as 527 GM and 1.86 × 10−76 cm−6 s2 photon−1, respectively. The 2PA, 3PA coefficients and optical limiting properties make them suitable candidates for nonlinear optical devices in the visible and near IR range.

Application of MALDI Mass Spectrometry in Natural Products Analysis.

This article presents the utility of mass spectrometry with a MALDI ionization source in natural products analysis. The advantages and drawbacks of this technique for natural products analyses will be presented and discussed. In addition, the structural determination of secondary metabolites using MALDI-MS/MS will be explored, which can guide MALDI experimental methods and stimulate new research in this area. Finally, several important approaches for MALDI data processing will be discussed.

Tissue MALDI Mass Spectrometry Imaging (MALDI MSI) of Peptides.

Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a technique to visualize molecular features of tissues based on mass detection. This chapter focuses on MALDI MSI of peptides and provides detailed operational instructions for sample preparation of cryoconserved and formalin-fixed paraffin-embedded (FFPE) tissue. Besides sample preparation we provide protocols for the MALDI measurement, tissue staining, and data analysis. On-tissue digestion and matrix application are described for two different commercially available and commonly used spraying devices: the SunCollect (SunChrom) and the ImagePrep (Bruker Daltonik GmbH).

Characterization of Shea Butter Using a Combination of MALDI-TOFMS, SPME-GC-MS and Multivariate Data Analysis

Lipids and volatile compounds of shea butter samples from different African countries (Burkina Faso, Chad, Mali, and Uganda) and different production technologies have been analyzed using an approach based on MALDI-TOF-MS and SPME-GC-MS combined with exploratory multivariate data analysis. By applying MALDI-MS analysis total DAGs were found in the range of 7.6- 22.6% and TAGs in the range of 77.7-92.4% of the shea butter samples. Thereby, major DAGs were found PO (7.3-21.9%), PL (0.0-26.3%), SO (16.3-42.1%), OO (15.1-57.4%), LO (9.1-17.9) and major TAGs were identified POO (2.7-7.8%), POS (4.9-8.3%), SOS (13.8-45.3%), SOO (18.9-39.5%), OOO (1.0-24.3%), AOO (1.4-2.5%) and AOS (1.4-3.7%), respectively. TAG/DAG ratios were found to be dependent on production conditions indicating for inactivation of the lipases by boiling the shea kernels before fat extraction. Volatiles pattern of shea samples detected by headspace-SPME-GC-MS contained more than 100 different compounds, among them many aldehydes, ketones and organic acids. PCA of MALDI data showed a dominating influence of geographic origin on TAG composition characterized mostly by a different saturation degree of the fatty acyl residues declining from West to East Africa. Similar geographic relationships could be visualised by exploratory PLS-DA of the volatiles data. Mainly lipid peroxidation products (in particular unsaturated aldehydes) were identified to be responsible for this sample distribution. Volatile patterns are also influenced by producing methods. For example, methylketones - probably of microbial origin - have been found as marker compounds for "heaping" of the shea kernels before fat extraction. Keywords: Butyrospermum parkii (C.F. Gaertn.), MALDI-TOF-MS, Partial least squares discriminant analysis (PLS-DA), Principal component analysis (PCA), Shea butter, Triacylglycerols, Vitellaria paradoxa (C.F. Gaertn.), Volatiles.

A qualitative examination of the GST proteome of the blow fly, Lucilia cuprina: use of cross-database matching of MALDI data.

This study was aimed at determining whether, in the absence of a full genetic database for the Sheep Blowfly (Lucilia cuprina) glutathione transferases from this insect could be characterized by cross-database matching of MALDI TOF data with the database for other metazoan organisms. Glutathione transferases of L. cuprina were partially purified by the sequential use of affinity chromatography media; first on glutathione immobilized on epichlorohydrin-activated Sepharose 6B and subsequently on dinitrophenyl-glutathione immobilized on the same matrix. The Proteins obtained were separated by 2D SDS-PAGE and tentatively characterized by MALDI-TOF analysis of tryptic peptides. The mass fragments were matched against the NCBInr "Other metazoa" database. The GSTs matched to other insect species were identified as coming from the Sigma, Delta and Epsilon classes. The relative abundance of most of these GSTs appeared to vary little during development, or across bodily segments, an exception being one group, (Zone E) tentatively identified as Epsilon class, which was most prominent in eggs and absent from adults and which is therefore assumed to play a specific role in development.

Retraction Note: Heterologous expression, purification and characterization of nitrilase from Aspergillus niger K10

The authors have retracted the article Heterologous expression, purification and characterization of nitrilase from Aspergillus niger published in BMC Biotechnology (2011, 11:2). After publication of the article, the authors realized that MALDI data analysis of the wild-type enzyme in Figure 1 prepared by Karel Bezouska is erroneous. Specifically, the protein sequence of the nitrilase purified from Aspergillus niger K10 (designated Nit-ANigWT) is incorrect. Hence, the two enzymes in Figure 1 are not variants of the same enzyme as hypothesized in the original article. In light of this, the original conclusions are no longer valid and further investigation of this protein is in progress. The Ethical Committee of the Academy of Sciences of the Czech Republic and the Charles University in Prague has found evidence of scientific misconduct on the part of Karel Bezouska. We apologize to all affected parties.

Global Relative Quantification with Liquid Chromatography–Matrix-assisted Laser Desorption Ionization Time-of-flight (LC-MALDI-TOF)—Cross–validation with LTQ-Orbitrap Proves Reliability and Reveals Complementary Ionization Preferences

Quantitative LC-MALDI is an underrepresented method, especially in large-scale experiments. The additional fractionation step that is needed for most MALDI-TOF-TOF instruments, the comparatively long analysis time, and the very limited number of established software tools for the data analysis render LC-MALDI a niche application for large quantitative analyses beside the widespread LC-electrospray ionization workflows. Here, we used LC-MALDI in a relative quantification analysis of Staphylococcus aureus for the first time on a proteome-wide scale. Samples were analyzed in parallel with an LTQ-Orbitrap, which allowed cross-validation with a well-established workflow. With nearly 850 proteins identified in the cytosolic fraction and quantitative data for more than 550 proteins obtained with the MASCOT Distiller software, we were able to prove that LC-MALDI is able to process highly complex samples. The good correlation of quantities determined via this method and the LTQ-Orbitrap workflow confirmed the high reliability of our LC-MALDI approach for global quantification analysis. Because the existing literature reports differences for MALDI and electrospray ionization preferences and the respective experimental work was limited by technical or methodological constraints, we systematically compared biochemical attributes of peptides identified with either instrument. This genome-wide, comprehensive study revealed biases toward certain peptide properties for both MALDI-TOF-TOF- and LTQ-Orbitrap-based approaches. These biases are based on almost 13,000 peptides and result in a general complementarity of the two approaches that should be exploited in future experiments.

Matrix‐assisted laser desorption/ionization mass spectrometric investigation of pollen and their classification by multivariate statistics

A fast and reliable online identification of pollen is not yet available. The identification of pollen is based mainly on the evaluation of morphological data obtained by microscopic methods. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS) was applied to the analysis of extracts and milled pollen samples. The obtained MALDI data were explored for characteristic peak patterns which could be subjected to a multivariate statistical analysis. Two sample preparation methods are presented, which require only minimal or no chemical extraction of the pollen. MALDI pollen spectra could be recorded showing various peak patterns. A multivariate statistics approach allowed the classification of pollen into clusters indicating similarities and differences between various species. These results demonstrate the potential and the reliability of MALDI-TOF MS for the identification and, in combination with multivariate statistics, also for the classification of pollen.