Combining Chemical Information From Grass Pollen in Multimodal Characterization.

Sabrina Diehn,Janina Kneipp,Siri Fjellheim,Murat Bağcıoğlu,Achim Kohler,Mikael Ohlson,Valeria Tafintseva,Boris Zimmermann,Steffen Weidner,Stephan Seifert

doi:10.3389/fpls.2019.01788

Abstract

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.

Highlights

The analysis of pollen samples is a crucial task that is necessary in several fields, including agriculture, plant physiology, ecology, allergology, and climate studies
The well-defined sample set (Figure 1) was measured by the four different methods Fourier-transform infrared (FTIR), Raman, and surface enhanced Raman scattering (SERS) spectroscopy and MALDI-TOF mass spectrometry, after different pre-processing of the samples according to the requirement of each spectroscopy, leading to the probing of complementary constituents of the pollen
The bands at 483, 1082, and 1322 cm-1 are assigned to carbohydrates (Schulte et al, 2008; Pigorsch, 2009) that can occur at high local concentrations in the pollen grains as starch deposits

Summary

INTRODUCTION

The analysis of pollen samples is a crucial task that is necessary in several fields, including agriculture, plant physiology, ecology, allergology, and climate studies. We apply CPCA to the pollen data of the four complementary methods FTIR spectroscopy, Raman microspectroscopy, SERS, and MALDI-TOF MS and compare the results to those of PCA of each of the single data blocks. The parent plants originate from three different populations, within which four different growth conditions were applied to individuals of identical genetic constitution (Figure 1) The design of this experiment generates two initially separate questions. 1,000 spectra with an accumulation time of 1 s per spectrum were collected This procedure yielded SERS data sets containing 144,000 individual spectra in total (2,000 spectra per pollen sample). Consensus principal component analysis (CPCA) was used to combine the five different kinds of data (FTIR, Raman, SERS, and MALDI-TOF MS spectra, as well as a block with the additional data obtained from the parent plants), each one pre-processed as described before. To calculate the variation in the data induced by the different design factors, such as population, nutrients, temperature, as well as their interaction, we used an approach underlying ANOVA-

Method

RESULTS AND DISCUSSION

CONCLUSION

DATA AVAILABILITY STATEMENT