The pressing global issue of food insecurity due to population growth, diminishing land and variable climate can only be addressed in agriculture by improving both maximum crop yield potential and resilience. Genetic modification is one potential solution, but has yet to achieve worldwide acceptance, particularly for crops such as wheat. Trehalose-6-phosphate (T6P), a central sugar signal in plants, regulates sucrose use and allocation, underpinning crop growth and development. Here we show that application of a chemical intervention strategy directly modulates T6P levels in planta. Plant-permeable analogues of T6P were designed and constructed based on a 'signalling-precursor' concept for permeability, ready uptake and sunlight-triggered release of T6P in planta. We show that chemical intervention in a potent sugar signal increases grain yield, whereas application to vegetative tissue improves recovery and resurrection from drought. This technology offers a means to combine increases in yield with crop stress resilience. Given the generality of the T6P pathway in plants and other small-molecule signals in biology, these studies suggest that suitable synthetic exogenous small-molecule signal precursors can be used to directly enhance plant performance and perhaps other organism function.

The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively.

The amount of data produced by spectral imaging techniques, such as mass spectrometry imaging, is rapidly increasing as technology and instrumentation advances. This, combined with an increasingly multimodal approach to analytical science, presents a significant challenge in the handling of large data from multiple sources. Here, we present software that can be used through the entire analysis workflow, from raw data through preprocessing (including a wide range of methods for smoothing, baseline correction, normalization, and image generation) to multivariate analysis (for example, memory efficient principal component analysis (PCA), non-negative matrix factorization (NMF), maximum autocorrelation factor (MAF), and probabilistic latent semantic analysis (PLSA)), for data sets acquired from single experiments to large multi-instrument, multimodality, and multicenter studies. SpectralAnalysis was also developed with extensibility in mind to stimulate development, comparisons, and evaluation of data analysis algorithms.

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A memory efficient algorithm for the computation of principal component analysis (PCA) of large mass spectrometry imaging data sets is presented. Mass spectrometry imaging (MSI) enables two- and three-dimensional overviews of hundreds of unlabeled molecular species in complex samples such as intact tissue. PCA, in combination with data binning or other reduction algorithms, has been widely used in the unsupervised processing of MSI data and as a dimentionality reduction method prior to clustering and spatial segmentation. Standard implementations of PCA require the data to be stored in random access memory. This imposes an upper limit on the amount of data that can be processed, necessitating a compromise between the number of pixels and the number of peaks to include. With increasing interest in multivariate analysis of large 3D multislice data sets and ongoing improvements in instrumentation, the ability to retain all pixels and many more peaks is increasingly important. We present a new method which has no limitation on the number of pixels and allows an increased number of peaks to be retained. The new technique was validated against the MATLAB (The MathWorks Inc., Natick, Massachusetts) implementation of PCA (princomp) and then used to reduce, without discarding peaks or pixels, multiple serial sections acquired from a single mouse brain which was too large to be analyzed with princomp. Then, k-means clustering was performed on the reduced data set. We further demonstrate with simulated data of 83 slices, comprising 20,535 pixels per slice and equaling 44 GB of data, that the new method can be used in combination with existing tools to process an entire organ. MATLAB code implementing the memory efficient PCA algorithm is provided.

Matrix assisted laser desorption ionisation mass spectrometry (MALDI-MS) at atmospheric pressure (AP) is, with a few notable exceptions, overshadowed by its vacuum based forms and AP transmission mode (TM) MALDI-MS lacks the uptake its potential benefits might suggest. The reasons for this are not fully understood and it is clear further development is required to realise the flexibility and power of this ionisation method and geometry. Here we report the build of a new AP-TM-MALDI-MSI ion source with plasma ionisation enhancement. This novel ion source is used to analyse a selection of increasingly complex systems from molecular standards to murine brain tissue sections. Significant enhancement of detected ion intensity is observed in both positive and negative ion mode in all systems, with up to 2000 fold increases observed for a range of tissue endogenous species. The substantial improvements conferred by the plasma enhancement are then employed to demonstrate the acquisition of proof of concept tissue images, with high quality spectra obtained down to 10 × 10 µm pixel size.

Atmospheric pressure ionization methods confer a number of advantages over more traditional vacuum based techniques, in particular ease of hyphenation to a range of mass spectrometers. For atmospheric pressure matrix assisted desorption/ionization (AP-MALDI), several ion sources, operating in a range of geometries have been reported. Most of these platforms have, to date, generally demonstrated relatively low ion yields and/or poor ion transmission compared to vacuum sources. To improve the detection of certain ions, we have developed a second-generation transmission mode (TM) AP-MALDI imaging platform with in-line plasma postionization using the commercially available SICRIT device, replacing the previously used low temperature plasma probe from our developmental AP-TM-MALDI stage. Both plasma devices produce a significant ionization enhancement for a range of compounds, but the overall higher enhancement obtained by the SICRIT device in addition to the ease of installation and the minimal need for optimization presents this commercially available tool as an attractive method for simple postionization in AP-MALDI MSI.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) techniques are continually being assessed with a view to improving the quality of information obtained from a given sample. A single tissue section will typically only be analyzed once by MALDI MSI and is then either used for histological staining or discarded. In this study, we explore the idea of repeat analysis of a single tissue section by MALDI MSI as a route toward improving sensitivity, structural characterization, and diversity of detected analyte classes. Repeat analysis of a single tissue section from a fresh frozen mouse brain is investigated with both α-cyano-4-hydroxycinnamic acid (CHCA) and para-nitroaniline (PNA). Repeat analysis is then applied to the acquisition of MALDI MSI and MALDI tandem mass spectrometry imaging employing collision induced dissociation (MS/MS imaging employing CID) from a formalin-fixed mouse brain section. Finally, both lipid and protein data are acquired from the same tissue section via repeat analysis utilizing CHCA, sinapinic acid (SA), and a tissue wash step. PNA was found to outperform CHCA as a matrix for repeat analysis; multiple lipids were identified using MS/MS imaging; both lipid and protein images were successfully acquired from a single tissue section. Figure Repeat analysis by MALDI MS imaging of a single tissue section is investigated with multiple matrices and tissue washes to provide increased molecular information from a single tissue section.