IONICS Mass Spectrometry (Canada)

Tyramide signal amplification, used in immunohistochemistry to image low-abundance biomolecules, has seen adaptation beyond conventional microscopy and is utilized in flow cytometry and electron microscopy. Here, we applied tyramide signal amplification to image biomolecules via elemental mass spectrometry imaging (MSI). Elemental MSI techniques such as laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) are used to image biomolecules via metal-conjugated probes, and resolution is typically hampered by a low detection sensitivity. We report the synthesis of a bespoke ruthenium complex (TyrRu) for tyramide amplification and describe its application as a multimodal analyte to image and quantify glial fibrillary acidic protein in mouse brain by complementary immunofluorescence and elemental MSI. Pairing TyrRu and avidin–biotin-HRP amplification produced a signal-to-noise ratio 2× higher than that obtained with a commercial metal-chelating polymer, allowing LA-ICP-MS image resolution of 1 μm. Data obtained using immunofluorescence and LA-ICP-MS showed excellent biomolecule agreement (r = 0.86). TyrRu was also applied alongside primary-conjugated antibodies in a multiplexed analysis, and the enhanced detection sensitivity facilitated the generation of an image with submicrometer resolution after super-resolution reconstruction.

High-throughput analysis has become a critical component in chemical biology and analytical chemistry due to the large libraries of compounds that are screened every day for drug development. Mass spectrometry (MS)-based proteomics is the methodology of choice for large-scale identification and quantification of protein modifications, both chemically deposited and biological post-translational modifications (PTMs). With the advent of antibody drug conjugates (ADCs) and other novel protein-based conjugates, the demand for such an analysis has skyrocketed. Here, we present a new protocol that achieves quantitative data for modified peptides in approximately 30 s of MS acquisition time. This platform includes a direct injection MS approach coupled with new software named iFishMass to extract targeted signals from hundreds of runs. iFishMass automatically generates plots and statistics. This platform will enable a faster analysis of synthetic modifications installed on monoclonal antibodies to create ADCs, and it is potentially scalable to biological PTMs. Sample preparation can be parallelized for 384 samples by using multichannel pipettes and 96-well plates, paving the way to an inexpensive but effective platform for high-throughput screening of conjugation sites on proteins.

In untargeted metabolomics, reference MS/MS libraries are essential for structural annotation, yet currently explain only 6.9% of the more than 1.7 billion MS/MS spectra in public repositories. We hypothesized that many unannotated features arise from simple, biologically plausible transformations of endogenous and exposure-derived compounds. To test this, we created a reference resource by synthesizing over 100,000 compounds using multiplexed reactions that mimic such biochemical transformations. 91% of the compounds synthesized are absent from existing structural databases. Through improvements in the construction of the computational infrastructure that enables pan repository-scale MS/MS comparisons, searching this biologically inspired MS/MS library increased the overall reference-based match rate by 17.4%, yielding over 60 million new matches and raising the global pan-repository MS/MS annotation rate to 8.1%. By facilitating structural hypotheses for previously uncharacterized MS/MS data, this framework expands the accessible detectable biochemical landscape across human, animal, plant, and microbial systems, revealing previously undescribed metabolites such as ibuprofen-carnitine and 5-ASA-phenylpropionic acid conjugates arising from drug-host and host-microbiome co-metabolism.

Acidic crude oils are becoming more prominent in global markets due to technological advances that enable the exploitation of unconventional reserves. Understanding their chemical nature is essential for refining strategies, especially as acidity poses processing challenges. Unsupervised pattern recognition offers a way to uncover meaningful relationships between molecular composition and macroscopic acidity, reducing the manual interpretation effort. This study proposes an unsupervised learning framework to reveal such patterns efficiently. Crude oil samples from the Sergipe-Alagoas Basin (northeast Brazil) were analyzed using negative electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI(−)-FT-ICR MS), focusing on polar compounds. Bulk acidity was assessed through total acid number (TAN), naphthenic acidity index (NAI), and percentage of naphthenic acids (%NA). K-means clustering applied to these parameters identified four acidity-level groups. Clusters I and II contained low-TAN oils (<1 mg KOH g–1), differing in %NA, while Clusters III and IV grouped high-TAN oils (>1 mg KOH g–1), also separated by %NA. Multidimensional scaling (MDS) was applied to the autoscaled abundance of globally shared molecular formulas from the O1–O4 oxygen classes. While individual classes had limited discriminatory power, combined O1–O3 formulas enabled clear differentiation among acidity levels. This framework demonstrates that the data-driven analysis of high-resolution mass spectrometry data can be automated to enhance the molecular understanding of acidic oils, providing valuable insights for refining decisions and linking molecular fingerprints to macroscopic acidity behavior.

Phytic acid (PA), the most abundant inositol phosphate in plants and a material currently classified as agricultural waste, is a naturally occurring Brønsted acid with unique properties. This study presents an innovative and eco-friendly strategy for N-allylation of carbazoles using PA as a nontoxic, highly reusable, and anticorrosive Brønsted acid catalyst in aqueous media, achieving up to 94% isolated yield and exceptional selectivity for N-allylcarbazoles. In this transformation, PA not only serves as a Brønsted acid catalyst but also promotes substrate dispersion in aqueous systems through micelle formation. Moreover, the system exhibits excellent anticorrosive performance for carbon steel compared to conventional Brønsted acids. Notably, the PA-aqueous catalytic system demonstrates remarkable recyclability and could undergo at least 16 cycles without any activity or selectivity loss in current catalysis. This multifunctional PA aqueous system has great potential for advancing diverse Brønsted acid-catalyzed reactions toward greener and lower-cost industrial processes.

The evolution of agriculture has given rise to the agro-food system that extends beyond the farm. The food we eat consists of micro and macro molecules, these molecules contribute to the flavour and nutritional profile. Besides this, there are also contaminants such as heavy metals, pesticides or additional ingredients in the food. Therefore, understanding the molecular profile is important for food quality, safety and development of new products. Analytical techniques such as chromatography coupled to mass spectrometry provides identification and quantification of wide variety of molecules. However, it involves homogenizing the sample like a smoothie. This process lacks spatial distribution, knowing the localization of molecules of interest is important to understand physiological processes of plants, spoilage of food and modifying the food according to the consumer demand. This thesis utilized mass spectrometry imaging (MSI) techniques to study taste compounds in mushrooms, ingredient classification in burgers and temporal dynamics of molecules and bacteria in an organic vineyard.