Multivariate Analysis of MALDI Imaging Mass Spectrometry Data of Mixtures of Single Pollen Grains

  • Franziska Lauer
  • Sabrina Diehn
  • Stephan Seifert
  • Janina Kneipp
  • Volker Sauerland
  • Cesar Barahona
  • Steffen WeidnerEmail author
Research Article


Mixtures 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 Abstract


MALDI imaging MS Pollen grains Multivariate statistics Hierarchical cluster analysis Principal component analysis 


Recent techniques for the determination of pollen are predominantly based on the microscopic evaluation of pollen grains collected in so-called pollen traps, such as the Burkard spore sampler [1]. Usually, pollen and other airborne particles, such as fungus spores, are collected on an adhesive, transparent polyester tape mounted on a drum with a fixed circumference for 7 days. However, the microscopic evaluation requires skilled employees who investigate only a few randomly selected regions of the tape. Moreover, the sampling is strongly influenced by local environmental effects, e.g., by single bushes and trees in the close vicinity of the trap. Since the number of pollen traps is limited (e.g., only 51 traps in Germany), a global underestimation of pollen species must be considered. Combined with the microscopic determination, these data represent the basis for establishing calendars used for pollen forecasts.

Thus, alternative techniques for a reliable and fast identification of pollen grains based on spectroscopic and spectrometric methods are desirable. The use of Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and surface-enhanced Raman scattering (SERS) for the characterization of pollen samples was reported recently by several groups [2, 3, 4, 5, 6, 7]. A classification of pollen species could be obtained in combination with multivariate statistics such as hierarchical cluster analysis (HCA), partial least squares regression (PLSR), and principal component analysis (PCA) [4, 8, 9]. Autofluorescence represents another technique that has been successfully applied for the classification of different pollen species [10]. Specifically in combination with morphological properties (e.g., size), the identification of various species based on the blue/red autofluorescence ratio was described [11].

In the last years, our group established MALDI-TOF MS as a new technique for the identification and classification of pollen grains [12, 13, 14]. Different from MALDI-TOF MS approaches that investigated purified pollen compounds, including lipids, proteins, glycans, saccharides, and sporopollenin for structure elucidation [15, 16, 17, 18, 19, 20, 21, 22, 23], MALDI-based pollen species identification does not require extraction, chemical modification, or chromatographic separation of particular molecular species and can therefore be regarded as a simple and fast technique [24]. Furthermore, the applicability of this technique for the characterization of different species in mixtures consisting of larger amounts of pollen grains was shown [13]. In combination with PCA, we identified specific mass regions and peak patterns to determine pollen genus. Moreover, MALDI-TOF MS enables also the differentiation of various pollen species within the same genus [14]. Initially, our MALDI-TOF MS approach required separate fixation and extraction steps followed by adding the matrix. This still time-consuming preparation was then modified to simplify the pollen preparation for MALDI-TOF analysis by fixing pollen grains on the target with an adhesive carbon tape and combining the extraction agents with the matrix to further reduce the preparation steps [24]. That the conductive tape is also applicable in imaging MS of plant pieces was shown by Kuwayama et al. [25].

Here, we present an advanced approach based on this simplified sample preparation procedure [24] combined with imaging mass spectrometry. Various numbers of pollen grains (from < 10 to 1) were deposited on a carbon tape and covered by matrix. To determine the sensitivity of the mass spectrometer, mixtures of three different pollen grains with different numbers of grains of each species were analyzed by MALDI imaging MS. Since peak suppression in MALDI-TOF mass spectra of mixtures should be considered, our approach compared two different spatial resolutions [26, 27, 28, 29]. As we will demonstrate here, it is possible to combine MALDI imaging of single pollen grains with chemometric tools in order to improve classification and identification based on a database of MALDI-TOF pollen mass spectra. This database meanwhile contains MALDI mass spectra of several hundred species of different orders (8) and genera (53). Thereby, we utilize the potential of multivariate tools to exploit the full molecular range [30, 31] for imaging of pollen grain mixtures.



Three pollen samples (Corylus avellana, Alnus cordata, and Pinus sylvestris) of two plant orders (Coniferales and Fagales) were collected in the Botanic Garden of Berlin. The pollen grains were stored at – 20 °C until usage. The size of ten pollen grains of each species was determined using a microscope camera (Olympus UC 90). Photomicrographs with a magnification of 20 and 100 are shown in the suppl. part in Figure S1a–c. Their statistical evaluation is shown in Figure S2.

Sample Preparation

In the first experiment, a different amount of pollen grains (< 10 to 1) was deposited separately on an MTP 384 standard target that was previously covered with a double-faced adhesive carbon tape (P77817, Science Services GmbH, Munich, Germany). In the second experiment, mixtures of pollen grains (Corylus, Alnus, and Pinus—each with 10, 5, 3, and 1 pollen grains) were prepared on the tape. For each experiment, 0.5 μL α-cyano-4-hydroxycinnamic acid (HCCA) served as matrix (10 mg mL−1 in acetonitrile:water (1:1, v:v) containing 1.25% trifluoroacetic acid) and was spotted onto the pollen grains. After solvent evaporation, the target was inserted into the mass spectrometer.

Data Acquisition

For all measurements, an Autoflex speed MALDI-TOF mass spectrometer (Bruker Daltonik GmbH, Bremen, Germany) equipped with a Smartbeam-II laser (355 nm) was used [32]. In the first experiment, spectra were recorded in positive linear mode by accumulating 5000 laser pulses in a mass range of m/z 2000 to 12,000.

To check the possible influence of pollen grain sizes on a mass shift of the peaks, an additional test was performed using a MALDI instrument (rapifleX®, Bruker Daltonik) equipped with a probe stage of alterable height. A peptide standard mixture (Bruker Daltonik) was measured. The variation by 25 μm resulted in a mass shift of 0.2 Da for somatostatin (m/z 3147.47) (shown in the suppl. part in Figure S3).

For the imaging experiments, the manufacturer’s FlexImaging™ software was used for acquisition. One thousand laser pulses were accumulated for each spot. Two different pixel sizes (similar to a spatial resolution) of 50 and 100 μm were applied. For better visualization, all images were additionally evaluated using multivariate methods.

Data Analysis

Spectral pretreatment and multivariate analysis were performed using Matlab (Version R2015a, Mathworks, Inc., Natick, MA, USA). Therefore, we compiled a sequence of standard functions in Matlab and its Statistics and Machine Learning toolbox, specifically interp1 (for interpolation), baseline (baseline correction), pdist and linkage (for HCA), and princomp (for PCA), respectively. Raw spectra were interpolated in the mass region between m/z 2000 and 12,000 with a step size of 2 followed by baseline correction and vector normalization. Data sets were analyzed by HCA using Euclidean distances and Ward’s algorithm. The number of clusters was manually chosen based on a heterogeneity value (distance linkage) between 1.3 and 1.5. As another approach to assess spectral differences, PCA was applied to gain more information from the mixture images.

Results and Discussion

MALDI-TOF MS of Single Pollen Grains

In the first experiment, the sensitivity of MALDI-TOF MS for the detection of single pollen grain was tested. A decreasing number of pollen grains (from < 10 to 1) were subjected to MALDI-TOF analysis. The corresponding interpolated and background processed average mass spectra of several repetitive measurements are shown in Figure 1. Sometimes, a shift in the annotated peak masses of ± 2 m/z can be observed. This is mainly due to the interpolation of the raw spectra in the mass region between m/z 2000 and 12,000 with a step size of 2. An influence of the size of the pollen grains can be excluded. This was confirmed by an additional experiment performed on a MALDI instrument with variable source design (see the “Experimental” section and suppl. part Figure S3).
Figure 1

(ac) MALDI-TOF MS spectra (interpolated and baseline corrected) of selected pollen samples with an increasing number of grains (n = number of accumulated spectra used for averaging)

As shown in Figure 1, for each plant species (Corylus avellana, Alnus cordata, and Pinus sylvestris), mass spectra with individual peak patterns in different mass ranges were obtained. The spectra of Corylus avellana (Figure 1a) show intense peaks at m/z 2944 and 4106 and a specific peak pattern between m/z 5200 and 6894. The spectra of Alnus cordata (Figure 1b) reveal four intense peaks at m/z of 3444, 5210, 5602, and 6884. Compared to the spectra of these species, the spectra of Pinus sylvestris (Figure 1c) show less intense peaks and most peaks can be found in the lower mass region (m/z 3947–4658) of the spectra. These results are in good agreement with our previously published data [12, 14, 24]. In one of these publications, we tried to elucidate the structure of such molecular classifiers by MALDI-TOF MS/MS fragmentation analysis. Typical patterns were obtained that could be attributed to sugar units (e.g., hexose, fucose, pentose, and sialic acid). However, the fragmentation of precursor ions with masses larger than m/z 3000 (which represent the majority in the pollen spectra) was not feasible [12]. In a detailed study, Fraser et al. were able to identify carotenoids by MALDI-TOF MS in a mass range from m/z 530 to 548 [33]. Higher mass sporopollenin was found in a m/z region of 4400–4800 also using MALDI-TOF MS [34]. A more detailed analysis of other molecular classifiers (e.g., glycans, lipids, peptides, and proteins) would require additional biochemical analyses (e.g., using tryptic digestion) and the use of LC-MS/MS techniques.

For the Corylus avellana (Figure 1a) and Alnus cordata (Figure 1b) samples, even one single pollen grain shows the same characteristic peak patterns, whereas in accordance with Pinus sylvestris (Figure 1c), spectra show less intense and less characteristic signals. With increasing amount of pollen grains, the spectra display an increase of peak intensities accompanied by a decrease of the background noise. The noisy signals especially in the low mass range might be the result of matrix and tape interference. Moreover, additional small peaks are visible in the higher mass range from m/z 9000 to 12,000 using higher amounts of pollen grains (> 10 pollen grains).

Thus, the pollen of Corylus avellana (Figure 1a) and Alnus cordata (Figure 1b) show species-specific peak patterns at single pollen level. Nevertheless, the peaks of the averaged spectra of all three species were applied as a reference for the following imaging experiments.

MALDI-TOF Imaging MS of Pollen Grain Mixtures (Resolution 100 μm)

In the following part, a set of pollen grain mixtures of the three species each with 10, 5, 3, and 1 pollen grains was investigated by MALDI imaging MS applying a spatial resolution of 100 μm. Thereby, the spatial resolution was set by the spot-to-spot distance. A direct visualization of single pollen grains in the pictures was not feasible since grain sizes were too small and the glue used on the conductive tape reflected too much. Another reason was the coverage of the grains by a droplet of matrix. This step can also lead to a dislocation of the pollen grain within the droplet.

In Figure 2a, an image showing the selected imaging region of a mixture of pollen samples (each with 10 grains) is displayed. Spectra were recorded at positions symbolized by the dots. The obtained data set was pretreated as described above, and HCA was applied to sort the dataset into groups of similar spectra. The averaged spectra from each cluster are displayed in Figure 2b. Figure 2c shows the image that is created using the class assignment that is the result of the HCA. In addition, the individual spectra and their respective number in each cluster (gray lines) are depicted (Figure 2b).
Figure 2

(a) Image of the sample spot of a mixture of pollen grains of three species (ten of each), spot distance 100 μm. (b) Averaged spectra of the four biggest clusters determined by HCA (n = number of spectra averaged for one cluster). (c) HCA image with different colors for each cluster

Considering the annotated peaks of the spectra used to generate cluster 1 (Figure 2b, violet), this cluster contains information from all three pollen species. In contrast, more differences can be seen between the averaged spectrum in cluster 2 and cluster 3. The averaged spectrum in cluster 2 shows a more species-specific peak pattern for Alnus, while the spectra in cluster 3 are more similar to pollen spectra of Corylus. By comparing these results with the HCA image (Figure 2c), an enrichment of Alnus pollen grains can be assumed in the center of the image (red), whereas Corylus pollen grains (blue) are more located towards the right-hand side. Cluster 4, which mostly consists of spectra with low signal-to-noise ratios (Figure 2b), represents a transition region between pollen extract and background (Figure 2c, dark gray).

In the following experiments, the amount of pollen grains was decreased. The results of the evaluation of a mixture composed of five grains of each species are shown in the suppl. part in Figure S4. The spectra of the clusters 1, 2, and 4 (Figure S4b) show species-specific peak patterns (blue, violet, and red), whereas cluster 3 mostly contains noisy signals. Different specific peaks can be identified comparing the spectra of cluster 1 and 4, whereas cluster 2 contains a mixture of spectral information of the two other clusters. The averaged spectrum of cluster 1 is similar to the Corylus reference spectra (see Figure 1a). In the averaged spectrum of cluster 2, the peak at m/z 4106 can be attributed to Corylus species (see Figure 1a). Simultaneously, the peak at m/z 4660 can be attributed to Pinus (see Figure 1c), and peaks at m/z 3446 and m/z 6888 match well with peaks of the Alnus reference spectra (see Figure 1b). Finally, the average spectrum of cluster 4 coincides with the reference Alnus cordata spectra.

From the corresponding image (Figure S4c), we conclude that the pollen grains are mainly distributed in the bottom part of the sample. Here, the Corylus pollen extract is located on the left-hand side of the image (blue), while a very localized distribution (400 × 200 μm) of Alnus pollen (red) could be detected next to the Corylus pollen extract. Further, an area on the right-hand side is classified as the above described mixture of pollen extracts (cluster 2).

The data of an experiment conducted with a further decreased pollen grain number (3 of each) is shown in Figure S5. Figure S5a contained 115 dots, which represent one mass spectrum each. Due to the recorded molecular information of image spectra, the individual spectra were assigned to four clusters (Figure S5b and S5c) using HCA. Compared to the previous imaging experiment (Figure S4), fewer differences are present in the averaged spectra (Figure S5b). Cluster 1 contains mostly background signals, whereas in the spectra of cluster 2, many different peaks are present that can be particularly found with higher intensities in cluster 3 too. Finally, in the averaged spectrum of cluster 4, a peak pattern in the mass range m/z 4500–5000 is visible. As indicated by the reference spectra in Figure 1, the annotated peaks of cluster 2 mark a transition region between the pollen grains and the background, while cluster 3 can be assigned to Corylus avellana spectra (compare Figure 1a), and we assume that in the blue region (400 × 300 μm) of the Figure S5c image (cluster 3), Corylus pollen grains are present.

Another experiment conducted with only one pollen grain of each species is depicted in Figure S6. The averaged HCA spectra of the four clusters (Figure S6b) show high similarities, which made a differentiation of different pollen species more complicated. In most cases, the annotated peaks do not match with the peaks of the reference spectra (Figure 1).

So far, we have shown that MALDI imaging of pollen grain mixtures is possible, and spectra with reliable peak information can be obtained. Recorded peak patterns can be differentiated by HCA and visually assigned to reference spectra. Furthermore, we proved that HCA of MALDI imaging MS experiments is a suitable tool for a higher amount of pollen (> 5 pollen grains) but failed in the analyses of single pollen. Here, a crucial point is the separation of measurement data of pollen grains, pollen extract, and background clusters.

As recently reviewed, the spatial resolution in imaging MS is important in order to determine the quality of molecular pictures [35]. In order to collect as much information from the extracts around the pollen grains as possible and to better distinguish between pollen grains of different species adjacent to one another, the lateral resolution was reduced to 50 μm. Furthermore, for analysis of the data, PCA was applied in addition to classification by HCA.

MALDI-TOF Imaging MS of Pollen Grain Mixtures (Resolution 50 μm)

The image shown in Figure 3a was obtained at the higher spatial resolution of 50 μm (see scale bars) applied to a new sample spot containing a mixture of pollen samples (each with 5 grains). Due to the higher resolution and the low sample amount, several spectra on the conductive tape were measured that only contained background signals.
Figure 3

(a) Image of the sample spot of a mixture of pollen grains of three species (five of each), spot distance 50 μm. (b) Averaged spectra of the clusters determined by HCA (n = number of spectra averaged for one cluster). (c) HCA image with different colors for each cluster. (d) PCA loadings of the first three PC. (e) Positive and negative images of PCA scores

As shown in Figure 3b, c, the 631 spectra were obtained of that mixture, formed in total of nine clusters in an HCA. All clusters, which contained noisy spectra (535 of 631), were pooled (cluster 1–5, Figure 3b). The second group in Figure 3b summarizes information from two individual clusters (cluster 6 + 7) that showed spectral similarities. Here, a mixture of peak patterns of different species as well as noisy signals could be observed. Furthermore, two clusters with peaks characteristic of Corylus (cluster 8) and Alnus (cluster 9) can be distinguished by HCA (Figure 3b).

Figure 3c shows the corresponding HCA image, indicating the assignment of each spectrum to one of the clusters. Here, the positions of the background spectra are depicted in gray (clusters 1–5). The positions of Corylus pollen spectra were colored in blue (cluster 8), whereas the spectra assigned to Alnus pollen grains are shown in red (cluster 9). The violet regions were obtained from spectra of clusters 6 and 7, where distinct species-specific information were difficult to assign. These regions are supposed to contain diluted extracts of several pollen species.

The suitability of combining MALDI imaging MS data with a combination of HCA and PCA to obtain 2D and 3D information of tissue material was initially reported by Deininger et al. and Weaver et al [36, 37]. Therefore, in addition to our previously shown HCA (Figure 3a–c), a PCA was performed on the same data set. A similar data presentation of PCA score plots was shown by Eijkel et al. and Amstalden van Hove et al. [38, 39]. With respect to our data, the loadings of the first three principal components (PC) are shown in Figure 3d, and in Figure 3e, the appropriate positive and negative images of PCA scores are displayed. Additionally, within the loading plots (Figure 3d), the percentages of the total variance represented by each component are given.

In the loading plots (Figure 3d) of PC 1 (negative loading values) and PC 2 (positive loading values up to m/z 5000 and to some extent also the negative loading values in the spectral region up to m/z 2500), peak pattern can be found, which consist mainly of background signals. In contrast, the positive and negative loading signals of the PC 3 show species-specific signals. Here, positive loading values, such as peaks at m/z 3446, 5212, and 6886, can be assigned to Alnus, whereas the negative loading values at m/z 2942, 4100, and 6108 are specific for Corylus spectra. Thus, the PC 3 positive and negative score images in Figure 3e show two different regions, where either Alnus or Corylus pollen grains occurred.

A comparison of this PCA image (Figure 3e, PC 3) to the HCA image (Figure 3c) shows similar results. A separation of Corylus and Alnus pollen spectra by MALDI-TOF imaging could be achieved by both HCA and PCA. However, the identification of Pinus pollen grains in this mixture was neither possible using HCA nor PCA.

A second attempt using the higher-resolution settings (50-μm spot distance) was made with another sample spot containing a mixture of pollen samples (each with 3 grains, see Figure 4a). In total, 11 clusters were selected based on the HCA to differentiate areas of interest from the background spectra (Figure 4b, c). In the first plot of Figure 4b, 829 background spectra were pooled (clusters 1, 2, 5, 6, 8, 10, and 11). In the second plot of Figure 4b, spectra showing specific Corylus peaks at m/z 2946, 4106, and 6116 were combined (clusters 3 and 4), while the third plot displays spectra with peak characteristic of Alnus pollen at m/z of 3444, 5210, and 6886 (clusters 7 and 9). Figure 4c shows the reconstructed HCA image. Surprisingly, four regions indicating the occurrence of Corylus pollen (blue) are visible although only three pollen grains were used. This additional signal might result either by a separation of the extracts of one pollen grain or by accidentally deposition of one additional pollen grain during sample preparation. Since the optical proof of the number of deposited pollen grains was made using a reflected-light microscopy, strong reflections of the tape glue can affect their correct determination.
Figure 4

(a) Image of the sample spot of a mixture of pollen grains of three species (three of each), spot distance 50 μm. (b) Averaged spectra of the clusters determined by HCA (n = number of spectra averaged for one cluster). (c) HCA image with different colors for each cluster. (d) PCA loadings of the first three PC. (e) Positive and negative images of PCA scores

A PCA applied to the same data set is depicted in Figure 4d, e and gave similar results. The loading vector of PC 1 (Figure 4d, first plot) describes the main variance in the data set, with the negative contributions representing background signals and the positive loading signals combine several different peak patterns. Thus, more interesting are the differences within these patterns, which showed up in PC 2 and PC 3. The positive region of the PC 2 loading (Figure 4d, middle) is similar to the positive region of PC 1 with just one exception. The peak pattern around m/z 4734 produced negative loading signals now. Moreover, the loading of PC 3 (Figure 4d, bottom) revealed the previously described difference between specific peaks for Alnus (at m/z 3444, 5210, and 6884) and Corylus (at m/z 2946, 4106). The corresponding score values, shown in the PC 3 positive and negative score images of Figure 4e, indicate the lateral position of the Alnus and Corylus pollen grains. These areas can be found at the same place of the sample as the corresponding assignments in the HCA image (Figure 4c).

Finally, the reliability of this higher-resolution approach was tested with a mixture of pollen samples (each with 1 grain, shown in Figure 5). Here, 622 spectra were acquired and sorted by HCA into five clusters (Figure 5b, c). The 602 spectra of clusters 1, 2, and 3 were determined as background spectra (Figure 5b), while the other 20 spectra of clusters 4 and 5 showed peak patterns, which can be assigned to the pollen reference spectra (Figure 1). In the average spectrum of cluster 4 (Figure 5b), peaks specific of Corylus (m/z 2944, 4104, and 6114) can be found, whereas cluster 5 shows spectra with peaks specific of Alnus (m/z 3448, 6888). In the corresponding HCA image shown in Figure 5c, the Corylus pollen grain (blue) is directly located next to the Alnus pollen grain (red). Moreover, a second Alnus pollen grain was indicated in the upper edge of the sample. Our data show that the extraction of analyte molecules of one single pollen grain is in the range of 100–150 μm, even the grain size is much lower.
Figure 5

(a) Image of the sample spot of a mixture of pollen grains of three species (one of each), spot distance 50 μm. (b) Averaged spectra of the clusters determined by HCA (n = number of spectra averaged for one cluster). (c) HCA image with different colors for each cluster. (d) PCA loadings of the first three PC. (e) Positive and negative images of PCA scores

The PCA of this data set is displayed in Figure 5d (loadings of the first three principal components (PC)) and Figure 5e (corresponding images of the PCA scores). Here again, the loading of PC 3 (Figure 5d) distinguished peaks specific for Corylus spectra (as positive loading signals) and peaks specific for Alnus spectra (negative loading signals). The position of both pollen grain species on the target, given by the score images of PC 3+ and − (Figure 5e), corresponds well to the HCA image (Figure 5c).

Identification of Pollen Grains in Mixtures Using Independent Reference Samples

In this part, an alternative method to the visible identification of classified spectra is presented. Therefore, 270 independent reference spectra of the same pollen species as prior were recorded with varying sample amounts on different days. In the first step, a PCA (Figure 6a, b) was performed using these data. The loading plots (Figure 6a) and the score plots of PC 1 and PC 2 (Figure 6b) indicate a separation of the three plant species. Here, Corylus-typical information is represented by those regions of the loadings (Figure 6a) marked in blue and by negative score values in PC 1 and PC 2 (Figure 6b). Alnus specific spectral features are obtained in those regions of the loading vectors marked in red and by positive score values of PC 1 and negative values of PC 2. Characteristic Pinus pollen peaks cannot be found in PC 1 but were present as positive score values in the PC 2 (Figure 6b).
Figure 6

PCA of independent set of reference samples depicted as loadings (a) and scores plot of the first and second PC (b). Projection (ce) of the image spectra into the given PCA subspace (c) contains the dataset of Figure 3 (631 spectra, mixture of five pollen grains of each species) and (d) shows the corresponding mixture of three pollen grains of Figure 4 (870 spectra) and (e) the 622 spectra of Figure 5 (mixture of 1 pollen grain each)

Based on this differentiation and similar to Jansen et al. (POCHEMON), the 631, 870, or 622 spectra, recorded in the previously shown imaging runs (Figures 3, 4, and 5), were projected into the reference PCA (Figure 6c–e, top, violet dots) [40]. Subsequently, constant threshold values (scattered lines) were set. The blue region (Corylus) was limited by score values up to − 0.2 of PC 1, the red region (Alnus) started at score values above 0.1 of PC 1, and the yellow region (Pinus) was determined by score values higher than 0.3 for PC 2. Afterwards, this threshold information was transferred into the acquired images (Figure 6c–e, bottom). The automatic assignment of the PCA projections shown in Figure 6c–e completely supports the results obtained using HCA and PCA (Figures 3, 4, and 5). Individual pollen information can be observed at identical places in the images. Additionally, in the data set of the pollen grain mixture (three of each), spectra belonging to Pinus could be identified (Figure 6d, yellow pixels).


The sensitivity of MALDI-TOF mass spectrometry is sufficient for the analysis of single pollen of various species. Moreover, MALDI imaging of pollen mixtures has been shown to be a suitable approach to detect and identify individual pollen grains in mixtures. We could also demonstrate that better coverage of the selected imaging region, which could be achieved by higher spatial resolution (smaller pixel size), was necessary for a higher sensitivity. Since the image spectra contained complex information, multivariate evaluation was essential for a successful separation and identification. Using hierarchical cluster analysis (HCA), spectra can be divided into clusters based on overall spectral differences. The visual comparison of the average cluster spectra with separately measured pollen spectra provided the first details on the different spectral features. The localization of the pollen grains was achieved by combining the cluster information with the respective spatial position of each spectrum. Performing PCA supported and confirmed the assignments. Further, the classification of recorded imaging spectra with respect to reference spectra in a variance-weighted PCA space additionally enabled an independent identification of the pollen grains in the mixtures.



The authors thank Thomas Dürbye of the Botanic Garden and Botanical Museum Berlin-Dahlem for their support in sample collection.

Funding Information

Janina Kneipp received funding from the European Research Council (ERC) (grant no. 259432).

Supplementary material

13361_2018_2036_MOESM1_ESM.docx (3.4 mb)
ESM 1 (DOCX 3447 kb)


  1. 1.
    Hirst, J.M.: An automatic volumetric spore trap. Ann Appl Biol. 39, 257 (1952)CrossRefGoogle Scholar
  2. 2.
    Dell'Anna, R., Lazzeri, P., Frisanco, M., Monti, F., Campeggi, F.M., Gottardini, E., Bersani, M.: Pollen discrimination and classification by Fourier transform infrared (FT-IR) microspectroscopy and machine learning. Anal. Bioanal. Chem. 394, 1443–1452 (2009)CrossRefGoogle Scholar
  3. 3.
    Joseph, V., Schulte, F., Rooch, H., Feldmann, I., Dorfel, I., Osterle, W., Panne, U., Kneipp, J.: Surface-enhanced Raman scattering with silver nanostructures generated in situ in a sporopollenin biopolymer matrix. Chem. Commun. 47, 3236–3238 (2011)CrossRefGoogle Scholar
  4. 4.
    Schulte, F., Lingott, J., Panne, U., Kneipp, J.: Chemical characterization and classification of pollen. Anal. Chem. 80, 9551–9556 (2008)CrossRefGoogle Scholar
  5. 5.
    Schulte, F., Mader, J., Kroh, L.W., Panne, U., Kneipp, J.: Characterization of pollen carotenoids with in situ and high-performance thin-layer chromatography supported resonant Raman spectroscopy. Anal. Chem. 81, 8426–8433 (2009)CrossRefGoogle Scholar
  6. 6.
    Schulte, F., Panne, U., Kneipp, J.: Molecular changes during pollen germination can be monitored by Raman microspectroscopy. J. Biophotonics. 3, 542–547 (2010)CrossRefGoogle Scholar
  7. 7.
    Zimmermann, B.: Characterization of pollen by vibrational spectroscopy. Appl. Spectrosc. 64, 1364–1373 (2010)CrossRefGoogle Scholar
  8. 8.
    Seifert, S., Merk, V., Kneipp, J.: Identification of aqueous pollen extracts using surface enhanced Raman scattering (SERS) and pattern recognition methods. J. Biophotonics. 9, 181–189 (2016)CrossRefGoogle Scholar
  9. 9.
    Zimmerman, B., Tafintseva, V., Bagcioglu, M., Hoegh Berdahl, M., Kohler, A.: Analysis of allergenic pollen by FTIR microspectroscopy. Anal. Chem. 88, 803–811 (2016)CrossRefGoogle Scholar
  10. 10.
    Driessen, M.N.B.M., Willemse, M.T.M., Vanluijn, J.A.G.: Grass-pollen grain determination by light-microscopy and UV-microscopy. Grana. 28, 115–122 (1989)CrossRefGoogle Scholar
  11. 11.
    Mitsumoto, K., Yabusaki, K., Aoyagi, H.: Classification of pollen species using autofluorescence image analysis. J. Biosci. Bioeng. 107, 90–94 (2009)CrossRefGoogle Scholar
  12. 12.
    Krause, B., Seifert, S., Panne, U., Kneipp, J., Weidner, S.M.: Matrix-assisted laser desorption/ionization mass spectrometric investigation of pollen and their classification by multivariate statistics. Rapid. Commun. Mass. Sp. 26, 1032–1038 (2012)CrossRefGoogle Scholar
  13. 13.
    Weidner, S., Schultze, R.D., Enthaler, B.: Matrix-assisted laser desorption/ionization imaging mass spectrometry of pollen grains and their mixtures. Rapid. Commun. Mass. Sp. 27, 896–903 (2013)CrossRefGoogle Scholar
  14. 14.
    Seifert, S., Weidner, S.M., Panne, U., Kneipp, J.: Taxonomic relationships of pollens from matrix-assisted laser desorption/ionization time-of-flight mass spectrometry data using multivariate statistics. Rapid. Commun. Mass. Sp. 29, 1145–1154 (2015)CrossRefGoogle Scholar
  15. 15.
    Adhami, F., Leitzenberger, I., Wagner, S., Scheiner, O., Breiteneder, H.: Recombinant hevein and hevein-like domains from Hevea latex, avocado and banana bind cross-reactive IgE from latex-allergic patients. Allergy. 57, 82–83 (2002)Google Scholar
  16. 16.
    Chow, L.P., Chiu, L.L., Khoo, K.H., Peng, H.J., Yang, S.Y., Huang, S.W., Su, S.N.: Purification and structural analysis of the novel glycoprotein allergen Cyn d 24, a pathogenesis-related protein PR-1, from Bermuda grass pollen. FEBS J. 272, 6218–6227 (2005)CrossRefGoogle Scholar
  17. 17.
    de Kivit, S., Kraneveld, A.D., Garssen, J., Willemsen, L.E.M.: Glycan recognition at the interface of the intestinal immune system: target for immune modulation via dietary components. Eur. J. Pharmacol. 668, S124–S132 (2011)CrossRefGoogle Scholar
  18. 18.
    Iraneta, S.G., Acosta, D.M., Duran, R., Apicella, C., Orlando, U.D., Seoane, M.A., Alonso, A., Duschak, V.G.: MALDI-TOF MS analysis of labile Lolium perenne major allergens in mixes. Clin. Exp. Allergy. 38, 1391–1399 (2008)CrossRefGoogle Scholar
  19. 19.
    Lauer, I., Alessandri, S., Pokoj, S., Reuter, A., Conti, A., Vieths, S., Scheurer, S.: Expression and characterization of three important panallergens from hazelnut. Mol. Nutr. Food Res. 52, S262–S271 (2008)PubMedPubMedCentralGoogle Scholar
  20. 20.
    Moore, S.E.M., Hemsley, A.R., French, A.N., Dudley, E., Newton, R.P.: New insights from MALDI-ToF MS, NMR, and GC-MS: mass spectrometry techniques applied to palynology. Protoplasma. 228, 151–157 (2006)CrossRefGoogle Scholar
  21. 21.
    Raftery, M.J., Saldanha, R.G., Geczy, C.L., Kumar, R.K.: Mass spectrometric analysis of electrophoretically separated allergens and proteases in grass pollen diffusates. Respir. Res. 4, (2003)Google Scholar
  22. 22.
    Westphal, S., Kolarich, D., Foetisch, K., Lauer, I., Altmann, F., Conti, A., Crespo, J.F., Rodriguez, J., Enrique, E., Vieths, S., Scheurer, S.: Molecular characterization and allergenic activity of Lyc e 2 (beta-fructofuranosidase), a glycosylated allergen of tomato. Eur. J. Biochem. 270, 1327–1337 (2003)CrossRefGoogle Scholar
  23. 23.
    Zeleny, R., Altmann, F., Praznik, W.: Structural characterization of the N-linked oligosaccharides from tomato fruit. Phytochemistry. 51, 199–210 (1999)CrossRefGoogle Scholar
  24. 24.
    Lauer, F., Seifert, S., Kneipp, J., Weidner, S.M.: Simplifying the preparation of pollen grains for MALDI-TOF MS classification. Int. J. Mol. Sci. 18, (2017)CrossRefGoogle Scholar
  25. 25.
    Kuwayama, K., Yamamuro, T., Tsujikawa, K., Miyaguchi, H., Kanamori, T., Iwata, Y.T., Inoue, H.: Utilization of matrix-assisted laser desorption/ionization imaging mass spectrometry to search for cannabis in herb mixtures. Anal. Bioanal. Chem. 406, 4789–4794 (2014)CrossRefGoogle Scholar
  26. 26.
    Shinohara, Y., Furukawa, J.-i., Niikura, K., Miura, N., Nishimura, S.-I.: Direct N-glycan profiling in the presence of tryptic peptides on MALDI-TOF by controlled ion enhancement and suppression upon glycan-selective derivatization. Anal. Chem. 76, 6989–6997 (2004)CrossRefGoogle Scholar
  27. 27.
    Wang, M.Z., Fitzgerald, M.C.: A solid sample preparation method that reduces signal suppression effects in the MALDI analysis of peptides. Anal. Chem. 73, 625–631 (2001)CrossRefGoogle Scholar
  28. 28.
    Kumaran, S., Abdelhamid, H.N., Wu, H.-F.: Quantification analysis of protein and mycelium contents upon inhibition of melanin for Aspergillus niger: a study of matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS). RSC Adv. 7, 30289–30294 (2017)CrossRefGoogle Scholar
  29. 29.
    Marsico, A.L., Duncan, B., Landis, R.F., Tonga, G.Y., Rotello, V.M., Vachet, R.W.: Enhanced laser desorption/ionization mass spectrometric detection of biomolecules using gold nanoparticles, matrix, and the coffee ring effect. Anal. Chem. 89, 3009–3014 (2017)CrossRefGoogle Scholar
  30. 30.
    McCombie, G., Staab, D., Stoeckli, M., Knochenmuss, R.: Spatial and spectral correlations in MALDI mass spectrometry images by clustering and multivariate analysis. Anal. Chem. 77, 6118–6124 (2005)CrossRefGoogle Scholar
  31. 31.
    Klerk, L.A., Broersen, A., Fletcher, I.W., van Liere, R., Heeren, R.M.A.: Extended data analysis strategies for high resolution imaging MS: new methods to deal with extremely large image hyperspectral datasets. Int. J. Mass Spectrom. 260, 222–236 (2007)CrossRefGoogle Scholar
  32. 32.
    Holle, A., Haase, A., Kayser, M., Höhndorf, J.: Optimizing UV laser focus profiles for improved MALDI performance. J. Mass Spectrom. 41, 705–716 (2006)CrossRefGoogle Scholar
  33. 33.
    Fraser, P.D., Enfissi, E., Goodfellow, M., Eguchi, T., Bramley, P.M.: Metabolite profiling of plant carotenoids using the matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Plant J. 49, 552–564 (2007)CrossRefGoogle Scholar
  34. 34.
    Moore, S., Hemsley, A., French, A., Dudley, E., Newton, R.: New insights from MALDI-ToF MS, NMR, and GC-MS: mass spectrometry techniques applied to palynology. Protoplasma. 228, 151 (2006)CrossRefGoogle Scholar
  35. 35.
    Heeren, R.M.: Getting the picture: the coming of age of imaging MS. Int. J. Mass Spectrom. 377, 672–680 (2015)CrossRefGoogle Scholar
  36. 36.
    Deininger, S.O., Ebert, M.P., Futterer, A., Gerhard, M., Rocken, C.: MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers. J. Proteome Res. 7, 5230–5236 (2008)CrossRefGoogle Scholar
  37. 37.
    Weaver, E.M., Hummon, A.B., Keithley, R.B.: Chemometric analysis of MALDI mass spectrometric images of three-dimensional cell culture systems. Anal. Methods. 7, 7208–7219 (2015)CrossRefGoogle Scholar
  38. 38.
    Eijkel, G.B., Kaletas, B.K., van der Wiel, I.M., Kros, J.M., Luider, T.M., Heeren, R.M.A.: Correlating MALDI and SIMS imaging mass spectrometric datasets of biological tissue surfaces. Surf. Interface Anal. 41, 675–685 (2009)CrossRefGoogle Scholar
  39. 39.
    Amstalden van Hove, E.R., Smith, D.F., Heeren, R.M.: A concise review of mass spectrometry imaging. J. Chromatogr. A. 1217, 3946–3954 (2010)CrossRefGoogle Scholar
  40. 40.
    Jansen, J.J., Blanchet, L., Buydens, L.M.C., Bertrand, S., Wolfender, J.-L.: Projected Orthogonalized CHemical Encounter MONitoring (POCHEMON) for microbial interactions in co-culture. Metabolomics. 11, 908–919 (2014)CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2018

Authors and Affiliations

  1. 1.Bundesanstalt für Materialforschung und-prüfung (BAM)BerlinGermany
  2. 2.Humboldt-Universität zu BerlinBerlinGermany
  3. 3.Bruker Daltonik GmbHBremenGermany

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