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Imaging mass spectrometry (IMS) has proven to be a useful tool when investigating the spatial distributions of metabolites and proteins in a biological system. One of the biggest advantages of IMS is the ability to maintain the 3D chemical composition of a sample and analyze it in a label-free manner. However, acquiring the spatial information leads to an increase in data size. Due to the increased availability of commercial mass spectrometers capable of IMS, there has been an exciting development of different statistical tools that can help decipher the spatial relevance of an analyte in a biological sample. To address this need, software packages like SCiLS and the open source R package Cardinal have been designed to perform unbiased spectral grouping based on the similarity of spectra in an IMS data set. In this note, we evaluate SCiLS and Cardinal compatibility with MALDI-TOF IMS data sets of the Gram-negative pathogen Pseudomonas aeruginosa PA14. Both software were able to perform unsupervised segmentation with similar performance. There were a few notable differences which are discussed related to the identification of statistically significant features which required optimization of preprocessing steps, region of interest, and manual analysis.

Covering: 2009-2019 Over the last decade, methods in imaging mass spectrometry (IMS) have progressively improved and diversified toward a variety of applications in natural products research. Because IMS allows for the spatial mapping of the production and distribution of biologically active molecules in situ, it facilitates phenotype and organelle driven discovery efforts. As practitioners of IMS for natural products discovery, we find one of the most important aspects of these experiments is the sample preparation and compatibility with different ionization sources that are available to a given researcher. As such, we have focused this mini review to cover types of ionization sources that have been used in natural products discovery applications and provided concrete examples of use for natural products discovery while discussing the advantages and limitations of each method. We aim for this article to serve as a resource to guide the broader natural product community interested in IMS toward the application/method that would best serve their natural product discovery needs given the sample and analyte(s) of interest. This mini review has been limited to applications using natural products and thus is not exhaustive of all possible ionization methods which have only been applied to image other types of samples such as mammalian tissues. Additionally, we briefly review how IMS has been coupled with other imaging platforms, such as microscopy, to enhance information outputs as well as offer our future perspectives on the incorporation of IMS in natural products discovery.

Mutations can occur throughout the virus genome and may be beneficial, neutral or deleterious. We are interested in mutations that yield a C next to a G, producing CpG sites. CpG sites are rare in eukaryotic and viral genomes. For the eukaryotes, it is thought that CpG sites are rare because they are prone to mutation when methylated. In viruses, we know less about why CpG sites are rare. A previous study in HIV suggested that CpG-creating transition mutations are more costly than similar non-CpG-creating mutations. To determine if this is the case in other viruses, we analyzed the allele frequencies of CpG-creating and non-CpG-creating mutations across various strains, subtypes, and genes of viruses using existing data obtained from Genbank, HIV Databases, and Virus Pathogen Resource. Our results suggest that CpG sites are indeed costly for most viruses. By understanding the cost of CpG sites, we can obtain further insights into the evolution and adaptation of viruses., Evolutionary Ecology, 34, ISSN:0269-7653, ISSN:1573-8477

Metabolites give us a window into the chemistry of microbes and are split into two subclasses: primary and secondary. Primary metabolites are required for life whereas secondary metabolites have historically been classified as those appearing after exponential growth and are not necessarily needed for survival. Many microbial species are estimated to produce hundreds of metabolites and can be affected by differing nutrients. Using various analytical techniques, metabolites can be directly detected in order to elucidate their biological significance. Currently, a single experiment can produce anywhere from megabytes to terabytes of data. This big data has motivated scientists to develop informatics tools to help target specific metabolites or sets of metabolites. Broadly, it is imperative to identify clear biological questions before embarking on a study of metabolites (metabolomics). For instance, studying the effect of a transposon insertion on phenazine biosynthesis in Pseudomonas is a very different from asking what molecules are present in a specific banana-derived strain of Pseudomonas. This review is meant to serve as a primer for a ‘choose your own adventure’ approach for microbiologists with limited mass spectrometry expertise, with a strong focus on liquid chromatography mass spectrometry based workflows developed or optimized within the past five years.

Multispecies microbiome systems are known to be closely linked to human, animal, and plant life processes. The growing field of metabolomics presents the opportunity to detect changes in overall metabolomic profiles of microbial species interactions. These metabolomic changes provide insight into function of metabolites as they correlate to different species presence and the observed phenotypic changes, but detection of subtle changes is often difficult in samples with complex backgrounds. Natural environments such as soil and food contain many molecules that convolute mass spectrometry-based analyses, and identification of microbial metabolites amongst environmental metabolites is an informatics problem we begin to address here. Our microbes are grown on solid or liquid cheese curd media. This medium, which is necessary for microbial growth, contains high amounts of salts, lipids, and casein breakdown products which make statistical analyses using LC-MS/MS data difficult due to the high background from the media. We have developed a simple algorithm to carry out background subtraction from microbes grown on solid or liquid cheese curd media to aid in our ability to conduct statistical analyses so that we may prioritize metabolites for further structure elucidation. Graphical Abstract .