A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

Abstract

Background: Analysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques.

Methodology: Pollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques.

Results: The vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data.

Fig 1. Measurement of Abies cephalonica pollen.

(a) FTIR spectra obtained from different sampling techniques (from top downwards): transmission IR of KBr pellet, ATR-IR of intact and ground grains, IR microspectroscopy of multigrain (mg) and single grain (sg). (b) Raman spectra obtained from two different regions of a single grain: corpus and saccus regions. The marked signals are associated with the vibrational bands of (P) proteins, (L) lipids, (C) carbohydrates and (S) sporopollenins. (c) SEM image of pollen grains in various orientations: equatorial view (up left), distal polar view (up right) and proximal polar view (down left). (d) SEM image of pollen grain in equatorial view, with saccus (up, two hemispherical substructures) and corpus regions (down, large hemispherical substructure).

Fig 2. Spectra of representative samples of pollen measured as: (a) ATR of ground pollen, (b) Raman of corpus region, and (c) Raman of saccus region.

The spectral set consists of EMSC normalized average spectra; for better viewing the spectra are offset.

Fig 3. FTIR spectra of biochemicals.

carbohydrates (amylose and cellulose), protein (gluten), lipid (tristearin) and phenylpropanoids (p-coumaric, ferulic and sinapic acids); for better viewing the spectra are offset.

Fig 4. Raman spectra of biochemicals.

carbohydrates (amylose and cellulose), protein (gluten), lipid (tristearin), phenylpropanoids (p-coumaric, ferulic and sinapic acids) and carotenoid (β-carotene); for better viewing the spectra are offset.

Fig 5. Score plots of individual blocks and global scores of consensus principal component analysis (CPCA).

ATR-FTIR of (a) (ATI) intact pollen, (b) (ATG) ground pollen; (c) (KBR) Transmission FTIR of KBr pellets; Transmission IR microspectroscopy of (d) (MGR) multigrain and (e) (SGR) single grain; Raman spectroscopy of (f) (RMS) saccus region and (g) (RMC) corpus region of single pollen grain; (h) Global scores. Samples are labelled in accordance to pollen genus: Abies (red), Picea (green), Pinus (blue), Podocarpus (magenta), and Cedrus (cyan). The percent variances for the PCs are given in supplementary part.

Fig 6. CPCA correlation loading plots for the first three principal components.

(a) Correlation between pollen genera (black), FTIR of KBr pellets (blue), ATR of ground pollen (red) and ATR of intact pollen (green). (b) Correlation between pollen genera (black), Raman of corpus region (red) and Raman of saccus region (green). For the sake of clarity, only the selected variables are presented. The percent variances for the first five PCs are 56.0, 12.2, 9.4, 7.5 and 5.1.