Stephan Seifert

Identification of aqueous pollen extracts using surface enhanced Raman scattering (SERS) and pattern recognition methods

Aqueous pollen extracts of varying taxonomic relations were analyzed with surface enhanced Raman scattering (SERS) by using gold nanoparticles in aqueous suspensions as SERS substrate. This enables a selective vibrational characterization of the pollen water soluble fraction (mostly cellular components) devoid of the spectral contributions from the insoluble sporopollenin outer layer. The spectra of the pollen extracts are species-specific, and the chemical fingerprints can be exploited to achieve a classification that can distinguish between different species of the same genus. In the simple experimental procedure, several thousands of spectra per species are generated. Using an artificial neural network (ANN), it is demonstrated that analysis of the intrinsic biochemical information of the pollen cells in the SERS data enables the identification of pollen from different plant species at high accuracy. The ANN extracts the taxonomically-relevant information from the data in spite of high intra-species spectral variation caused by signal fluctuations and preparation specifics. The results show that SERS can be used for the reliable characterization and identification of pollen samples. They have implications for improved investigation of pollen physiology and for allergy warning.

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

RATIONALE 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. 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. RESULTS 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. CONCLUSIONS 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.

Monitoring of plant–environment interactions by high‐throughput FTIR spectroscopy of pollen

Summary Fourier transform infrared (FTIR) spectroscopy enables chemical analysis of pollen samples for plant phenotyping to study plant–environment interactions, such as influence of climate change or pathogens. However, current approach, such as microspectroscopy and attenuated total reflection spectroscopy, does not allow for high-throughput protocols. This study at hand suggests a new spectroscopic method for high-throughput characterization of pollen. Samples were measured as thin films of pollen fragments using a Bruker FTIR spectrometer with a high-throughput eXTension (HTS-XT) unit employing 384-well plates. In total, 146 pollen samples, belonging to 31 different pollen species of Fagaceae and Betulaceae and collected during three consecutive years (2012–2014) at locations in Croatia, Germany and Norway, were analysed. Critical steps in the sample preparation and measurement, such as variabilities between technical replicates, between microplates and between spectrometers, were studied. Measurement variations due to sample preparation, microplate holders and instrumentation were low, and thus allowed differentiation of samples with respect to phylogeny and biogeography. The spectral variability for a range of Fagales species (Fagus, Quercus, Betula, Corylus, Alnus and Ostrya) showed high-species-specific differences in pollens chemical composition due to either location or year. Statistically significant inter-annual and locational differences in the pollen spectra indicate that pollen chemical composition has high phenotypic plasticity and is influenced by local climate conditions. The variations in composition are connected to lipids, proteins, carbohydrates and sporopollenins that play crucial roles in cold and desiccation tolerance, protection against UV radiation and as material and energy reserves. The results of this study demonstrate the value of high-throughput FTIR approach for the systematic collection of data on ecosystems. The novel FTIR approach offers fast, reliable and economical screening of large number of samples by semi-automated methodology. The high-throughput approach could provide crucial understanding on plant–climate interactions with respect to biochemical variation within genera, species and populations.

Taxonomic relationships of pollens from matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry data using multivariate statistics

RATIONALE Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has been suggested as a promising tool for the investigation of pollen, but the usefulness of this approach for classification and identification of pollen species has to be proven by an application to samples of varying taxonomic relations. METHOD MALDI-MS in combination with hierarchical cluster analysis (HCA) and principal component analysis (PCA) was used to delineate taxonomic relations between plants based on pollen biochemistry. To assess the robustness of the approach, pollen of 74 species of the plant orders Fagales and Coniferales were probed. RESULTS Discrimination at the levels of plant order and genus were achieved using the whole spectral range. In many cases, different species of the same genus could be distinguished. The sources of the spectral/chemical differences at the genus level can be understood using PCA. Specifically, typical mass regions for exact genus detection were identified. CONCLUSIONS Our results indicate that the chemical information represented by MALDI-TOFMS data is useful for reconstructing taxonomic relationships and is complementary to other chemical information on pollen from other spectroscopic data.

Physiological influence of silica on germinating pollen as shown by Raman spectroscopy

The process of silicification in plants and the biochemical effects of silica in plant tissues are largely unknown. To study the molecular changes occurring in growing cells that are exposed to higher than normal concentration of silicic acid, Raman spectra of germinating pollen grains of three species (Pinus nigra, Picea omorika, and Camellia japonica) were analyzed in a multivariate classification approach that takes into account the variation of biochemical composition due to species, plant tissue structure, and germination condition. The results of principal component analyses of the Raman spectra indicate differences in the utilization of stored lipids, a changed mobilization of storage carbohydrates in the pollen grain bodies, and altered composition and/or structure of cellulose of the developing pollen tube cell walls. These biochemical changes vary in the different species.

Simplifying the Preparation of Pollen Grains for MALDI-TOF MS Classification

Matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) is a well-implemented analytical technique for the investigation of complex biological samples. In MS, the sample preparation strategy is decisive for the success of the measurements. Here, sample preparation processes and target materials for the investigation of different pollen grains are compared. A reduced and optimized sample preparation process prior to MALDI-TOF measurement is presented using conductive carbon tape as target. The application of conductive tape yields in enhanced absolute signal intensities and mass spectral pattern information, which leads to a clear separation in subsequent pattern analysis. The results will be used to improve the taxonomic differentiation and identification, and might be useful for the development of a simple routine method to identify pollen based on mass spectrometry.

Evaluation of variable selection methods for random forests and omics data sets

Abstract Machine learning methods and in particular random forests are promising approaches for prediction based on high dimensional omics data sets. They provide variable importance measures to rank predictors according to their predictive power. If building a prediction model is the main goal of a study, often a minimal set of variables with good prediction performance is selected. However, if the objective is the identification of involved variables to find active networks and pathways, approaches that aim to select all relevant variables should be preferred. We evaluated several variable selection procedures based on simulated data as well as publicly available experimental methylation and gene expression data. Our comparison included the Boruta algorithm, the Vita method, recurrent relative variable importance, a permutation approach and its parametric variant (Altmann) as well as recursive feature elimination (RFE).  In our simulation studies, Boruta was the most powerful approach, followed closely by the Vita method. Both approaches demonstrated similar stability in variable selection, while Vita was the most robust approach under a pure null model without any predictor variables related to the outcome. In the analysis of the different experimental data sets, Vita demonstrated slightly better stability in variable selection and was less computationally intensive than Boruta. In conclusion, we recommend the Boruta and Vita approaches for the analysis of high-dimensional data sets. Vita is considerably faster than Boruta and thus more suitable for large data sets, but only Boruta can also be applied in low-dimensional settings.

Multivariate analysis of MALDI imaging mass spectrometry data of mixtures of single pollen grains

AbstractMixtures of pollen grains of three different species (Corylus avellana, Alnus cordata, and Pinus sylvestris) were investigated by matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI-TOF imaging MS). The amount of pollen grains was reduced stepwise from > 10 to single pollen grains. For sample pretreatment, we modified a previously applied approach, where any additional extraction steps were omitted. Our results show that characteristic pollen MALDI mass spectra can be obtained from a single pollen grain, which is the prerequisite for a reliable pollen classification in practical applications. MALDI imaging of laterally resolved pollen grains provides additional information by reducing the complexity of the MS spectra of mixtures, where frequently peak discrimination is observed. Combined with multivariate statistical analyses, such as principal component analysis (PCA), our approach offers the chance for a fast and reliable identification of individual pollen grains by mass spectrometry. Graphical Abstractᅟ