Localization of oligosaccharides in barley during germination.

<p>A) Cryo-sections of non-germinated (0d) and three day germinated (G3d) barley. B) Average mass spectrum of all MS acquired from germinated barley with [M+Na]⁺ (red) and [M+K]⁺ (blue) ions of oligosaccharides. *DP: degree of polymerization. C) Intensity heat maps of oligosaccharides with three, six, and nine hexoses in sodium [M+Na]⁺ and potassium [M+H]⁺ adducts in ungerminated barley (0d) and after three days of germination (G3d). MS intensities were normalized to the TIC of each mass spectrum; the highest relative intensity of all MS was set to 100%. The distributions of these compounds at all time points of the germination process are provided as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150208#pone.0150208.s005" target="_blank">S5 Fig</a>.</p

Spatio-Temporal Metabolite Profiling of the Barley Germination Process by MALDI MS Imaging

<div><p>MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals’ germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest.</p></div

Localization and signal intensities of hydroxycinnamic acid derivatives and hordatines in germinating barley.

<p>Top: Localization of p-coumaroylagmatine (CA) as representative for hydroxycinnamic acid amides and hordatine B as representative for hordatines that co-localized to hordatine B when occurring in the same modification state. Intensity maps depict the non-glycosylated (<i>m/z</i> 581), glycosylated (<i>m/z</i> 743), and disaccharide-modified form (<i>m/z</i> 905) at three time points during germination (0d: non-germinated barley, G3d: three days germinated, G5d: five days germination) in longitudinal and transversal section plane. Hordatines were not detected in cross sections in non-germinated barley. The last panel shows an overlay of the three modification forms. Ion intensities were normalized to the TIC, the highest relative intensity was set to 100%. Middle panel: Average mass spectra from annotated embryo measurement regions (right) in non-germinated (green), three days (blue) and five days (red) germinated barley. Bottom: Mass spectrum with indicated peaks of hydroxycinnamic acid amides as hordatine precursors (<i>m/z</i> 250–350) and of hordatine A, B, C, and D (D not detected, grey font), hydroxylated hordatines (-OH), and hexose-modified derivates (Hex / 2 Hex) at <i>m/z</i> 550–1000. CA: coumaroylagmatine, FA: feruloylagmatine, CA-OH / FA-OH: hydroxylated CA and FA.</p

The barley germination process: Seedling development and sampling time points.

<p>A) Time scheme of mini malting of the <i>Optic</i> barley at 16°C for the collection of samples. Arrows indicate sampling time points with their sample name. 0d: raw barley, S: steeping, G: germination day, K: kilned malt (K not used for MSI). W: water, A: air rest, K1: kilning at 45°C (7h), K2: kilning at 65°C (17 h). B) Growth of the barley seeds during malting. Barley (0d, <i>T</i> = 1), steeped barley (S1d, <i>T</i> = 2), three of the five time points during germination (G1d, G3d, G5d (<i>T</i> = 4,6,8)) and final kilned malt (K1d) are shown as representatives. Main seed organs and compartments are indicated at the raw barley seed.</p

Detected and identified compounds from MS imaging of barley seeds.

<p>Detected and identified compounds from MS imaging of barley seeds.</p

Unsupervised spatial segmentation of eight independent MALDI MSI analyses covering the first days of barley germination using WHIDE.

<p>A: Image scans with outlines of labeled seed compartments. B: Cluster analysis of all mass spectra of the whole grain areas. C: Cluster analysis of all mass spectra of the embryo areas as annotated in A. D: Cluster analysis of all mass spectra of the endosperm as annotated in A. E: Effect of the cluster granularity (7, 21, 56 clusters) on mapping results, exemplarily shown for four days germinated barley (G4d) from analysis B. The visualized cluster, granularity was set to 7 in A, B, and C to assign clear cluster profiles (right panels). 93 <i>m/z</i> values were selected for spatial segmentation in all analyses. Their contribution to the distinct clusters is indicated by the bar size in the right panels; for <i>m/z</i> identification see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150208#pone.0150208.t001" target="_blank">Table 1</a>. For an interactive exploration of the results in WHIDE see <a href="https://ani.cebitec.uni-bielefeld.de/barleymsi" target="_blank">https://ani.cebitec.uni-bielefeld.de/barleymsi</a>.</p

presentation_1.pdf

<p>Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e., biomovies. They show the behavior of cells over time under controlled conditions. One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information. The extraction of the cell line by human observers is time-consuming and error-prone. Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high-resolution images with high noise. Our task is to characterize subpopulations in biomovies. In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even subpopulations, we propose to represent the cells by two new abstractions: the particle and the patch. We use a three-step framework: preprocessing, particle tracking, and construction of the patch lineage. First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames. Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space. Particle analysis includes the following: particle tracking, trajectory linking, filtering, and color information, respectively. Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time. Typical tracking problems may occur (e.g., appearance or disappearance of cells, spurious artifacts). They are effectively processed using trajectory linking and filtering. Third, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e., proximity and fluorescence intensity) and feature common ancestry. This step is based on patch finding, patching trajectory propagation, patch splitting, and patch merging. The main idea is to group together the trajectories of particles in order to gain spatial coherence. The final result of CYCASP is the complete graph of the patch lineage. Finally, the graph encodes the temporal and spatial coherence of the development of cellular colonies. We present results showing a computation time of less than 5 min for biomovies and simulated films. The method, presented here, allowed for the separation of colonies into subpopulations and allowed us to interpret the growth of colonies in a timely manner.</p

Illustration of color reference plate and camera setup.

<p>On the left the original color reference plate, used to obtain color constancy within the experiment, is displayed. Colors are referring to the RGB triplets (top-down): (0,0,255), (255,0,0), (255,0,255), (0,255,0), (0,255,255), (255,255,0). On the right a schematic layout of the underwater camera setup is presented, consisting of a water filled cylinder containing the sample, the color reference plate and the camera in the waterproof housing fixed on a tripod.</p

A screenshot of the BIIGLE system in a web-browser.

<p>The images of the calcareous algae were examined and labeled using the BIIGLE system. Images can also be zoomed to examine more details. The experts were allowed to select single point label or customizable frame labels. The single point labels are represented as filled colored circles, the customizable frame labels are represented as white outlined rectangles with a filled colored circle in the upper left corner of the individual rectangle. The colors of the filled circles indicate the class of the individual label. Red is representing “live” calcareous algae, yellow “stressed” calcareous algae, green “dead” calcareous algae and Pink “bare substratum”.</p

The complete (semi-)automated detection process.

<p>Different transects with several thousand images are stored in the BIIGLE online platform (top left). These images can be accessed by experts via the WWW (bottom left). For this experiment, a subset of one transect (marked green on the upper left) was shown to five experts to create a manually labelled training set for a group of pre-defined taxa. Those manual labels were at first used to optimize an image pre-processing for illumination correction (top middle). Afterwards, high dimensional feature vectors were extracted at the label positions to gain a training and test set for SVM optimization (bottom middle). The trained SVMs were then applied pixel-wise to the full field of view, to obtain a confidence value for each pixel and taxon (top right). These confidence values were then post-processed into a classification map, where each pixel is assigned to one taxon which allows taxon counts per image. These taxon counts can then be plotted along the length of the transect (bottom right).</p