3D imaging of a rice pollen grain using transmission X-ray microscopy

Wang, Shengxiang; Wang, Dajiang; Wu, Qiao; Gao, Kun; Wang, Zhili; Zhang, Wu

doi:10.1107/s1600577515009716

Cited by 9 publications

(9 citation statements)

References 26 publications

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“…These bands are associated with carbohydrates, such as starch (Schulte et al, 2008;Pigorsch, 2009;Bagcioglu et al, 2017). Pollen are storing their nutrients in lipid bodies as well as in starch bodies, which are occupying most of the space in pollen grains (Wang et al, 2015). Our results confirm that plants growing under different nutrient conditions vary in their quality and/ or amount of such storage bodies inside the pollen.…”

Section: Cpca For the Classification Of Pollen Samples According To Dsupporting

confidence: 73%

Combining Chemical Information From Grass Pollen in Multimodal Characterization

et al. 2020

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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.

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Section: Cpca For the Classification Of Pollen Samples According To Dsupporting

confidence: 73%

Combining Chemical Information From Grass Pollen in Multimodal Characterization

et al. 2020

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“…Therefore, possible variations from the difference of dilution rate are likely to be ignorable as far as data analysis is on the relative abundance basis. Given into that considerable spatial heterogeneity in organelle arrangement occurs in mature rice pollen at polarity formation 10,27 , it is not surprising that slight differences in the insertion location of the target pollen grain might have caused some variations in MS spectra (see p-values in Table S1).…”

Section: Discussionmentioning

confidence: 99%

On-site single pollen metabolomics reveals varietal differences in phosphatidylinositol synthesis under heat stress conditions in rice

Wada

Hatakeyama

Nakashima

et al. 2020

Sci Rep

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Although a loss of healthy pollen grains induced by metabolic heat responses has been indicated to be a major cause of heat-induced spikelet sterility under global climate change, to date detailed information at pollen level has been lacking due to the technical limitations. In this study, we used picolitre pressure-probe-electrospray-ionization mass spectrometry (picoPPESI-MS) to directly determine the metabolites in heat-treated single mature pollen grains in two cultivars, heat-tolerant cultivar, N22 and heat-sensitive cultivar, Koshihikari. Heat-induced spikelet fertility in N22 and Koshihikari was 90.0% and 46.8%, respectively. While no treatment difference in in vitro pollen viability was observed in each cultivar, contrasting varietal differences in phosphatidylinositol (PI)(34:3) have been detected in mature pollen, together with other 106 metabolites. Greater PI content was detected in N22 pollen regardless of the treatment, but not for Koshihikari pollen. In contrast, there was little detection for phosphoinositide in the single mature pollen grains in both cultivars. Our findings indicate that picoPPESI-MS analysis can efficiently identify the metabolites in intact single pollen. Since PI is a precursor of phosphoinositide that induces multiple signaling for pollen germination and tube growth, the active synthesis of PI(34:3) prior to germination may be closely associated with sustaining spikelet fertility even at high temperatures.

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“…The interior of a pollen grain contains the germ cells, starch, and liquid bodies as well as typical cell compartments, such as the endoplasmic reticulum, vacuoles, and mitochondria. 24,25 The species identification is limited if pollen grains have similar size and shape. For example, the Poaceae family contains several thousand plant species, 26,27 where species identification is challenging due to their similar size and shape.…”

Section: Motivation and Structure Of The Thesismentioning

confidence: 99%

Multivariate Raman mapping for phenotypic characterization in plant tissue sections

Liedtke

Diehn

Heiner

et al. 2021

Spectrochimica Acta Part A: Molecular and Biomolecular Spectros

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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 this thesis is the optimized utilization of data from plant tissues, which include matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF MS), Raman spectroscopy, and FTIR 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 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 samples comprise grass pollens from large greenhouse experiments and from environmental samples, as well as tissue sections of the species Sorghum bicolor and Cucumis sativus from dedicated physiological and ecological studies. In this way, several results of this work contribute directly to these projects. 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. This was indicated by the discrimination of pollen from the grass species Poa alpina regarding different populations and environmental conditions. As another application of a multimodal analysis for classification, the combination of FTIR microspectra from individual pollen grains with their Raman mapping data and with MALDI-MS data of pollen extract is discussed. Moreover, as shown for Raman mapping data of tissue sections of Sorghum bicolor, the data of many Raman maps can be combined with other phenotypical data for an extensive insight into tissue biochemical composition. Moreover, some important problems of the individual methods with regard to non-relevant variances in the respective data sets are addressed. In the case of Raman microspectra, a high non-Raman based variance in the data set can be caused by a strong fluorescence background, as is discussed here for the spectra of individual pollen grains. On the other hand, FTIR microspectra of individual pollen grains display scattering artifacts, unless they are obtained from samples that are embedded in paraffin, which, in turn, leads to paraffin signals in the pollen spectra. In MALDI-MS imaging data, the extracts of pollen mixtures can overlap and lead to superposition of unknown origin and extent. Optimized data pre-treatment strategies are discussed for each of these problems. They include advanced baseline correction methods by asymmetric least squares, decomposition by non-negative matrix factorizati...

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3D imaging of a rice pollen grain using transmission X-ray microscopy

Cited by 9 publications

References 26 publications

Combining Chemical Information From Grass Pollen in Multimodal Characterization

Combining Chemical Information From Grass Pollen in Multimodal Characterization

On-site single pollen metabolomics reveals varietal differences in phosphatidylinositol synthesis under heat stress conditions in rice

Multivariate Raman mapping for phenotypic characterization in plant tissue sections

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