Mass spectrometry imaging (MSI) is a mass spectrometry based molecular ion imaging technique. It provides the means for ascertaining the spatial distribution of a large variety of analytes directly on tissue sample surfaces without any labeling or staining steps. These advantages make it an attractive molecular histology tool in medical, pharmaceutical and biological research. Likewise, MSI has started gaining popularity in plant sciences; yet, information regarding sample preparation methods specific for plant tissues is still limited. Sample preparation is a crucial step that is directly associated with the quality and authenticity of the imaging results, it therefore demands in-depth studies based on the characteristics of plant samples. In this review, a sample preparation pipeline is discussed in detail and illustrated through selected practical examples. In particular, special concerns regarding sample preparation for plant imaging are critically evaluated. Finally, the applications of various MSI techniques in plants are reviewed according to different classes of plant metabolites.

The ability of finding complex associations in large omics datasets, assessing their significance, and prioritizing them according to their strength can be of great help in the data exploration phase. Mutual information-based measures of association are particularly promising, in particular after the recent introduction of the TIC_e and MIC_e estimators, which combine computational efficiency with superior bias/variance properties. An open-source software implementation of these two measures providing a complete procedure to test their significance would be extremely useful. Here, we present MICtools, a comprehensive and effective pipeline that combines TIC_e and MIC_e into a multistep procedure that allows the identification of relationships of various degrees of complexity. MICtools calculates their strength assessing statistical significance using a permutation-based strategy. The performances of the proposed approach are assessed by an extensive investigation in synthetic datasets and an example of a potential application on a metagenomic dataset is also illustrated. We show that MICtools, combining TIC_e and MIC_e, is able to highlight associations that would not be captured by conventional strategies.

Downy mildew (Plasmopara viticola) is one of the most destructive diseases of the cultivated species Vitis vinifera. The use of resistant varieties, originally derived from backcrosses of North American Vitis spp., is a promising solution to reduce disease damage in the vineyards. To shed light on the type and the timing of pathogen-triggered resistance, this work aimed at discovering biomarkers for the defense response in the resistant variety Bianca, using leaf discs after infection with a suspension of P. viticola. We investigated primary and secondary metabolism at 12, 24, 48 and 96 hours post inoculation (hpi). We used methods of identification and quantification for lipids (LC-MS/MS), phenols (LC-MS/MS), primary compounds (GC-MS), and semi-quantification for volatile compounds (GC-MS). We were able to identify and quantify or semi-quantify 176 metabolites among which 53 were modulated in response to pathogen infection. The earliest changes occurred in primary metabolism at 24-48 hpi and involved lipid compounds, specifically unsaturated fatty acid and ceramide; amino acids, in particular proline; and some acids and sugars. At 48 hpi, we also found changes in volatile compounds and accumulation of benzaldehyde, a promoter of salicylic acid-mediated defense. Secondary metabolism was strongly induced only at later stages. The classes of compounds that increased at 96 hpi included phenylpropanoids, flavonols, stilbenes and stilbenoids. Among stilbenoids we found an accumulation of ampelopsin H+vaticanol C, pallidol, ampelopsin D +quadrangularin A, Z-miyabenol C and α-viniferin in inoculated samples. Some of these compounds are known as phytoalexins, while others are novel biomarkers for the defense response in Bianca. This work highlighted some important aspects of the host response to P. viticola in a commercial variety under controlled conditions, providing biomarkers for a better understanding of the mechanism of plant defense and a potential application in field studies of resistant varieties.