Multivariate Imaging for Fast Evaluation of In Situ Dark Field Microscopy Hyperspectral Data

Dark field scattering microscopy can create large hyperspectral data sets that contain a wealth of information on the properties and the molecular environment of noble metal nanoparticles. For a quick screening of samples of microscopic dimensions that contain many different types of plasmonic nanostructures, we propose a multivariate analysis of data sets of thousands to several hundreds of thousands of scattering spectra. By using non-negative matrix factorization for decomposing the spectra, components are identified that represent individual plasmon resonances and relative contributions of these resonances to particular microscopic focal volumes in the mapping data sets. Using data from silver and gold nanoparticles in the presence of different molecules, including gold nanoparticle-protein agglomerates or silver nanoparticles forming aggregates in the presence of acrylamide, plasmonic properties are observed that differ from those of the original nanoparticles. For the case of acrylamide, we show that the plasmon resonances of the silver nanoparticles are ideally suited to support surface enhanced Raman scattering (SERS) and the two-photon excited process of surface enhanced hyper Raman scattering (SEHRS). Both vibrational tools give complementary information on the in situ formed polyacrylamide and the molecular composition at the nanoparticle surface.Caroline von Humboldt Professorship of HUPeer Reviewe

Analysis of data from multimodal chemical characterizations of plant tissues

Die Vorverarbeitung und Analyse von spektrometrischen und spektroskopischen Daten von Pflanzengewebe sind in den unterschiedlichsten Forschungsbereichen wie der Pflanzenbiologie, Agrarwissenschaften und Klimaforschung von großer Bedeutung. Der Schwerpunkt dieser Arbeit liegt auf der optimierten Nutzung von Daten von Pflanzengeweben, insbesondere der Daten gewonnen durch Matrix–Assistierte Laser–Desorption–Ionisierung Massenspektrometrie, Raman-Spektroskopie und Fourier-Transform-Infrarotspektroskopie. Die Klassifizierungsfähigkeit mit diesen Methoden wird insbesondere nach Kombination der Daten untereinander und mit zusätzlichen chemischen und biologischen Informationen verglichen. Die diskutierten Beispiele befassen sich mit der Untersuchung und Einordnung innerhalb einer bestimmten Pflanzenart, beispielsweise der Unterscheidung von Proben aus unterschiedlichen Populationen, Wachstumsbedingungen oder Gewebeunterstrukturen. Die Daten wurden mit sowohl mit explorativen Werkzeugen wie der Hauptkomponentenanalyse und der hierarchischen Clusteranalyse, als auch mit Methoden des maschinellen Lernens wie die Diskriminanzanalyse oder künstliche neuronale Netzwerke umfassten. Konkret zeigen die Ergebnisse, dass die Kombination der Methoden mit zusätzlichen pflanzenbezogenen Informationen in einer Konsensus-Hauptkomponentenanalyse zu einer umfassenden Charakterisierung der Proben führt. Es werden verschiedene Strategien zur Datenvorbehandlung diskutiert, um nicht relevante spektrale Information zu reduzieren, z.B. aus Karten von Pflanzengeweben oder eingebetteten Pollenkörnern. Die Ergebnisse dieser Arbeit weisen auf die Relevanz der gezielten Nutzung spektrometrischer und spektroskopischer Daten hin und lassen sich nicht nur auf pflanzenbezogene Themen, sondern auch auf andere analytische Klassifizierungsprobleme übertragen.The pre-processing and analysis of spectrometric and spectroscopic data of plant tissue are important in a wide variety of research areas, such as plant biology, agricultural science, and climate research. The focus of the thesis is the optimized utilization of data from plant tissues, which includes data from Matrix-Assisted-Laser Desorption/Ionization time of flight mass spectrometry, Raman spectroscopy, and Fourier transform infrared spectroscopy. The ability to attain a classification using these methods is compared, in particular after combination of the data with each other and with additional chemical and biological information. The discussed examples are concerned with the investigation and classification within a particular plant species, such as the distinction of samples from different populations, growth conditions, or tissue substructures. The data were analyzed by exploratory tools such as principal component analysis and hierarchical cluster analysis, as well as by predictive tools that included partial least square-discriminant analysis and machine learning approaches. Specifically, the results show that combination of the methods with additional plant-related information in a consensus principal component analysis leads to a comprehensive characterization of the samples. Different data pre-treatment strategies are discussed to reduce non-relevant spectral information, e.g., from maps of plant tissues or embedded pollen grains. The results in this work indicate the relevance of the targeted utilization of spectrometric and spectroscopic data and could be applied not only to plant-related topics but also to other analytical classification problems

Spectroscopic Discrimination of Sorghum Silica Phytoliths

Grasses accumulate silicon in the form of silicic acid, which is precipitated as amorphous silica in microscopic particles termed phytoliths. These particles comprise a variety of morphologies according to the cell type in which the silica was deposited. Despite the evident morphological differences, phytolith chemistry has mostly been analysed in bulk samples, neglecting differences between the varied types formed in the same species. In this work, we extracted leaf phytoliths from mature plants of Sorghum bicolor (L.) Moench. Using solid state NMR and thermogravimetric analysis, we show that the extraction methods alter greatly the silica molecular structure, its condensation degree and the trapped organic matter. Measurements of individual phytoliths by Raman and synchrotron FTIR microspectroscopies in combination with multivariate analysis separated bilobate silica cells from prickles and long cells, based on the silica molecular structures and the fraction and composition of occluded organic matter. The variations in structure and composition of sorghum phytoliths suggest that the biological pathways leading to silica deposition vary between these cell types.Peer Reviewe

Video_2_Deposition of silica in sorghum root endodermis modifies the chemistry of associated lignin.mp4

Silica aggregates at the endodermis of sorghum roots. Aggregation follows a spotted pattern of locally deposited lignin at the inner tangential cell walls. Autofluorescence microscopy suggests that non-silicified (-Si) lignin spots are composed of two distinct concentric regions of varied composition. To highlight variations in lignin chemistry, we used Raman microspectroscopy to map the endodermal cell wall and silica aggregation sites in sorghum roots grown hydroponically with or without Si amendment. In +Si samples, the aggregate center was characterized by typical lignin monomer bands surrounded by lignin with a low level of polymerization. Farther from the spot, polysaccharide concentration increased and soluble silicic acid was detected in addition to silica bands. In -Si samples, the main band at the spot center was assigned to lignin radicals and highly polymerized lignin. Both +Si and -Si loci were enriched by aromatic carbonyls. We propose that at silica aggregation sites, carbonyl rich lignin monomers are locally exported to the apoplast. These monomers are radicalized and polymerized into short lignin polymers. In the presence of silicic acid, bonds typically involved in lignin extension, bind to silanols and nucleate silica aggregates near the monomer extrusion loci. This process inhibits further polymerization of lignin. In -Si samples, the monomers diffuse farther in the wall and crosslink with cell wall polymers, forming a ring of dense lignified cell wall around their export sites.</p

Video_1_Deposition of silica in sorghum root endodermis modifies the chemistry of associated lignin.mp4

Silica aggregates at the endodermis of sorghum roots. Aggregation follows a spotted pattern of locally deposited lignin at the inner tangential cell walls. Autofluorescence microscopy suggests that non-silicified (-Si) lignin spots are composed of two distinct concentric regions of varied composition. To highlight variations in lignin chemistry, we used Raman microspectroscopy to map the endodermal cell wall and silica aggregation sites in sorghum roots grown hydroponically with or without Si amendment. In +Si samples, the aggregate center was characterized by typical lignin monomer bands surrounded by lignin with a low level of polymerization. Farther from the spot, polysaccharide concentration increased and soluble silicic acid was detected in addition to silica bands. In -Si samples, the main band at the spot center was assigned to lignin radicals and highly polymerized lignin. Both +Si and -Si loci were enriched by aromatic carbonyls. We propose that at silica aggregation sites, carbonyl rich lignin monomers are locally exported to the apoplast. These monomers are radicalized and polymerized into short lignin polymers. In the presence of silicic acid, bonds typically involved in lignin extension, bind to silanols and nucleate silica aggregates near the monomer extrusion loci. This process inhibits further polymerization of lignin. In -Si samples, the monomers diffuse farther in the wall and crosslink with cell wall polymers, forming a ring of dense lignified cell wall around their export sites.</p

Multimodal Imaging of Silicified Sorghum Leaves

The plant cell wall is a complex composite material made of polysaccharides, polyphenols, proteins, and minerals. In this work, a multimodal imaging approach was taken, using Raman and Fourier-transform infrared (FTIR) microspectroscopy along with fluorescence imaging, scanning electron microscopy (SEM), and elemental mapping by energy dispersive X-ray spectroscopy (EDX). We characterized the chemical composition of sorghum leaf cross-sections extracted from fresh tissue as well as after paraffin embedding. The complementary vibrational information of Raman and FTIR spectra related a silica deposition to a specific organic composition in the epidermis, specifically with respect to lignin. Moreover, the data enable in situ correlation of autofluorescence with a specific lignin structure. Our results showed that lignin 5–5’ linkages that produce biphenyl structures are important determinants of the cell wall fluorescence properties. The reported multimodal approach will help to clarify the process of biosilica formation and related questions regarding cell wall biochemistry.Peer Reviewe

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) shows adaptation of grass pollen composition

MALDI time-of-flight mass spectrometry (MALDI-TOF MS) has become a widely used tool for the classification of biological samples. The complex chemical composition of pollen grains leads to highly specific, fingerprint-like mass spectra, with respect to the pollen species. Beyond the species-specific composition, the variances in pollen chemistry can be hierarchically structured, including the level of different populations, of environmental conditions or different genotypes. We demonstrate here the sensitivity of MALDI-TOF MS regarding the adaption of the chemical composition of three Poaceae (grass) pollen for different populations of parent plants by analyzing the mass spectra with partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA). Thereby, variances in species, population and specific growth conditions of the plants were observed simultaneously. In particular, the chemical pattern revealed by the MALDI spectra enabled discrimination of the different populations of one species. Specifically, the role of environmental changes and their effect on the pollen chemistry of three different grass species is discussed. Analysis of the group formation within the respective populations showed a varying influence of plant genotype on the classification, depending on the species, and permits conclusions regarding the respective rigidity or plasticity towards environmental changes.Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) shows adaptation of grass pollen compositionpublishedVersio

Combining Chemical Information From Grass Pollen in Multimodal Characterization

The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters.Peer Reviewe

Combining Chemical Information From Grass Pollen in Multimodal Characterization

The analysis of pollen chemical composition is important to many fields, including agriculture, plant physiology, ecology, allergology, and climate studies. Here, the potential of a combination of different spectroscopic and spectrometric methods regarding the characterization of small biochemical differences between pollen samples was evaluated using multivariate statistical approaches. Pollen samples, collected from three populations of the grass Poa alpina, were analyzed using Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, surface enhanced Raman scattering (SERS), and matrix assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS). The variation in the sample set can be described in a hierarchical framework comprising three populations of the same grass species and four different growth conditions of the parent plants for each of the populations. Therefore, the data set can work here as a model system to evaluate the classification and characterization ability of the different spectroscopic and spectrometric methods. ANOVA Simultaneous Component Analysis (ASCA) was applied to achieve a separation of different sources of variance in the complex sample set. Since the chosen methods and sample preparations probe different parts and/or molecular constituents of the pollen grains, complementary information about the chemical composition of the pollen can be obtained. By using consensus principal component analysis (CPCA), data from the different methods are linked together. This enables an investigation of the underlying global information, since complementary chemical data are combined. The molecular information from four spectroscopies was combined with phenotypical information gathered from the parent plants, thereby helping to potentially link pollen chemistry to other biotic and abiotic parameters