Normalization in MALDI-TOF imaging datasets of proteins: practical considerations

Normalization is critically important for the proper interpretation of matrix-assisted laser desorption/ionization (MALDI) imaging datasets. The effects of the commonly used normalization techniques based on total ion count (TIC) or vector norm normalization are significant, and they are frequently beneficial. In certain cases, however, these normalization algorithms may produce misleading results and possibly lead to wrong conclusions, e.g. regarding to potential biomarker distributions. This is typical for tissues in which signals of prominent abundance are present in confined areas, such as insulin in the pancreas or β-amyloid peptides in the brain. In this work, we investigated whether normalization can be improved if dominant signals are excluded from the calculation. Because manual interaction with the data (e.g., defining the abundant signals) is not desired for routine analysis, we investigated two alternatives: normalization on the spectra noise level or on the median of signal intensities in the spectrum. Normalization on the median and the noise level was found to be significantly more robust against artifact generation compared to normalization on the TIC. Therefore, we propose to include these normalization methods in the standard “toolbox” of MALDI imaging for reliable results under conditions of automation

Impact of a Transposon Insertion in phzF2 on the Specialized Metabolite Production and Interkingdom Interactions of Pseudomonas aeruginosa

In microbiology, gene disruption and subsequent experiments often center on phenotypic changes caused by one class of specialized metabolites (quorum sensors, virulence factors, or natural products), disregarding global downstream metabolic effects. With the recent development of mass spectrometry-based methods and technologies for microbial metabolomics investigations, it is now possible to visualize global production of diverse classes of microbial specialized metabolites simultaneously. Using imaging mass spectrometry (IMS) applied to the analysis of microbiology experiments, we can observe the effects of mutations, knockouts, insertions, and complementation on the interactive metabolome. In this study, a combination of IMS and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to visualize the impact on specialized metabolite production of a transposon insertion into a Pseudomonas aeruginosa phenazine biosynthetic gene, phzF2. The disruption of phenazine biosynthesis led to broad changes in specialized metabolite production, including loss of pyoverdine production. This shift in specialized metabolite production significantly alters the metabolic outcome of an interaction with Aspergillus fumigatus by influencing triacetylfusarinine production

Mapping enzyme catalysis with metabolic biosensing.

Enzymes are represented across a vast space of protein sequences and structural forms and have activities that far exceed the best chemical catalysts; however, engineering them to have novel or enhanced activity is limited by technologies for sensing product formation. Here, we describe a general and scalable approach for characterizing enzyme activity that uses the metabolism of the host cell as a biosensor by which to infer product formation. Since different products consume different molecules in their synthesis, they perturb host metabolism in unique ways that can be measured by mass spectrometry. This provides a general way by which to sense product formation, to discover unexpected products and map the effects of mutagenesis