Experimental Open Air Burning of Vegetation Enhances Organic Matter Chemical Heterogeneity Compared to Laboratory Burns.

Wildfires produce solid residuals that have unique chemical and physical properties compared to unburned materials, which influence their cycling and fate in the natural environment. Visual burn severity assessment is used to evaluate post-fire alterations to the landscape in field-based studies, yet muffle furnace methods are commonly used in laboratory studies to assess molecular scale alterations along a temperature continuum. Here, we examined solid and leachable organic matter characteristics from chars visually characterized as low burn severity that were created either on an open air burn table or from low-temperature muffle furnace burns. We assessed how the different combustion conditions influence solid and dissolved organic matter chemistries and explored the potential influence of these results on the environmental fate and reactivity. Notably, muffle furnace chars produced less leachable carbon and nitrogen than open air chars across land cover types. Organic matter produced from muffle furnace burns was more homogeneous than open air chars. This work highlights chemical heterogeneities that exist within a single burn severity category, potentially influencing our conceptual understanding of pyrogenic organic matter cycling in the natural environment, including transport and processing in watersheds. Therefore, we suggest that open air burn studies are needed to further advance our understanding of pyrogenic organic matter's environmental reactivity and fate.

Relating Molecular Properties to the Persistence of Marine Dissolved Organic Matter with Liquid Chromatography-Ultrahigh-Resolution Mass Spectrometry.

Marine dissolved organic matter (DOM) contains a complex mixture of small molecules that eludes rapid biological degradation. Spatial and temporal variations in the abundance of DOM reflect the existence of fractions that are removed from the ocean over different time scales, ranging from seconds to millennia. However, it remains unknown whether the intrinsic chemical properties of these organic components relate to their persistence. Here, we elucidate and compare the molecular compositions of distinct DOM fractions with different lability along a water column in the North Atlantic Gyre. Our analysis utilized ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry at 21 T coupled to liquid chromatography and a novel data pipeline developed in CoreMS that generates molecular formula assignments and metrics of isomeric complexity. Clustering analysis binned 14 857 distinct molecular components into groups that correspond to the depth distribution of semilabile, semirefractory, and refractory fractions of DOM. The more labile fractions were concentrated near the ocean surface and contained more aliphatic, hydrophobic, and reduced molecules than the refractory fraction, which occurred uniformly throughout the water column. These findings suggest that processes that selectively remove hydrophobic compounds, such as aggregation and particle sorption, contribute to variable removal rates of marine DOM.

Ectomycorrhizal fungi enhance pine growth by stimulating iron-dependent mechanisms with trade-offs in symbiotic performance.

Iron (Fe) is crucial for metabolic functions of living organisms. Plants access occluded Fe through interactions with rhizosphere microorganisms and symbionts. Yet, the interplay between Fe addition and plant-mycorrhizal interactions, especially the molecular mechanisms underlying mycorrhiza-assisted Fe processing in plants, remains largely unexplored. We conducted mesocosms in Pinus plants inoculated with different ectomycorrhizal fungi (EMF) Suillus species under conditions with and without Fe coatings. Meta-transcriptomic, biogeochemical, and X-ray fluorescence imaging analyses were applied to investigate early-stage mycorrhizal roots. While Fe addition promoted Pinus growth, it concurrently reduced mycorrhiza formation rate, symbiosis-related metabolites in plant roots, and aboveground plant carbon and macronutrient content. This suggested potential trade-offs between Fe-enhanced plant growth and symbiotic performance. However, the extent of this trade-off may depend on interactions between host plants and EMF species. Interestingly, dual EMF species were more effective at facilitating plant Fe uptake by inducing diverse Fe-related functions than single-EMF species. This subsequently triggered various Fe-dependent physiological and biochemical processes in Pinus roots, significantly contributing to Pinus growth. However, this resulted in a greater carbon allocation to roots, relatively reducing the aboveground plant carbon content. Our study offers critical insights into how EMF communities rebalance benefits of Fe-induced effects on symbiotic partners.

Natural Abundance Isotope Ratio Measurements of Organic Molecules Using 21 T FTICR MS.

Subtle variations in stable isotope ratios at natural abundance are challenging to measure but can yield critical insights into biological, physical, and geochemical processes. Well-established methods, particularly multicollector, gas-source, or plasma isotope ratio mass spectrometry, are the gold standard for stable isotope measurement, but inherent limitations in these approaches make them ill-suited to determining site-specific and multiply substituted isotopic abundances of all but a few compounds or to characterizing larger intact molecules. Fourier transform mass spectrometry, namely, Orbitrap mass spectrometry, has recently demonstrated the ability to measure natural abundance isotope ratios with chemically informative accuracy and precision. Here, we report the first use of Fourier transform ion cyclotron resonance mass spectrometry for the accurate (<1‰) and precise (<1‰ standard error) simultaneous determination of δ13C and δ15N in caffeine isotopologues and provide a discussion of the critical instrumental parameters necessary to make such measurements. We further report the ability to make these measurements with online liquid chromatography, expanding the ability of this technique to explore mixtures in the future.

Experimental Assessment of Mammalian Lipidome Complexity Using Multimodal 21 T FTICR Mass Spectrometry Imaging.

Herein, we assess the complementarity and complexity of data that can be detected within mammalian lipidome mass spectrometry imaging (MSI) via matrix-assisted laser desorption ionization (MALDI) and nanospray desorption electrospray ionization (nano-DESI). We do so by employing 21 T Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) with absorption mode FT processing in both cases, allowing unmatched mass resolving power per unit time (≥613k at m/z 760, 1.536 s transients). While our results demonstrated that molecular coverage and dynamic range capabilities were greater in MALDI analysis, nano-DESI provided superior mass error, and all annotations for both modes had sub-ppm error. Taken together, these experiments highlight the coverage of 1676 lipids and serve as a functional guide for expected lipidome complexity within nano-DESI-MSI and MALDI-MSI. To further assess the lipidome complexity, mass splits (i.e., the difference in mass between neighboring peaks) within single pixels were collated across all pixels from each respective MSI experiment. The spatial localization of these mass splits was powerful in informing whether the observed mass splits were biological or artificial (e.g., matrix related). Mass splits down to 2.4 mDa were observed (i.e., sodium adduct ambiguity) in each experiment, and both modalities highlighted comparable degrees of lipidome complexity. Further, we highlight the persistence of certain mass splits (e.g., 8.9 mDa; double bond ambiguity) independent of ionization biases. We also evaluate the need for ultrahigh mass resolving power for mass splits ≤4.6 mDa (potassium adduct ambiguity) at m/z > 1000, which may only be resolved by advanced FTICR-MS instrumentation.

Atomistic simulations for investigation of substrate effects on lipid in-source fragmentation in secondary ion mass spectrometry.

In beam-based ionization methods, the substrate plays an important role on the desorption mechanism of molecules from surfaces. Both the specific orientation that a molecule adopts at a surface and the strength of the molecule-surface interaction can greatly influence desorption processes, which in turn will affect the ion yield and the degree of in-source fragmentation of a molecule. In the beam-based method of secondary ion mass spectrometry (SIMS), in-source fragmentation can be significant and molecule specific due to the hard ionization method of using a primary ion beam for molecule desorption. To investigate the role of the substrate on orientation and in-source fragmentation, we have used atomistic simulations-molecular dynamics in combination with density functional theory calculations-to explore the desorption of a sphingolipid (palmitoylsphingomyelin) from a model surface (gold). We then compare SIMS data from this model system to our modeling findings. Using this approach, we found that the combined adsorption and binding energy of certain bonds associated with the headgroup fragments (C3H8N+, C5H12N+, C5H14NO+, and C5H15PNO4 +) was a good predictor for fragment intensities (as indicated by relative ion yields). This is the first example where atomistic simulations have been applied in beam-based ionization of lipids, and it presents a new approach to study biointerfacial lipid ordering effects on SIMS imaging.

193 nm Ultraviolet Photodissociation for the Characterization of Singly Charged Proteoforms Generated by MALDI.

MALDI imaging allows for the near-cellular profiling of proteoforms directly from microbial, plant, and mammalian samples. Despite detecting hundreds of proteoforms, identification of unknowns with only intact mass information remains a distinct challenge, even with high mass resolving power and mass accuracy. To this end, many supplementary methods have been used to create experimental databases for accurate mass matching, including bulk or spatially resolved bottom-up and/or top-down proteomics. Herein, we describe the application of 193 nm ultraviolet photodissociation (UVPD) for fragmentation of quadrupole isolated singly charged ubiquitin (m/z 8565) by MALDI-UVPD on a UHMR HF Orbitrap. This platform permitted the high-resolution accurate mass measurement of not just terminal fragments but also large internal fragments. The outlined workflow demonstrates the feasibility of top-down analyses of isolated MALDI protein ions and the potential toward more comprehensive characterization of proteoforms in MALDI imaging applications.

Molecular Characterization of Organophosphorus Compounds in Wildfire Smoke Using 21-T Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry.

We present a detailed molecular characterization of organophosphorus compounds in ambient organic aerosol influenced by wildfire smoke. Biomass burning organic aerosol (BBOA) is an important source of phosphorus (P) to surface waters, where even a small imbalance in the P flux can lead to substantial effects on water quality, such as eutrophication, algal blooms, and oxygen depletion. We aimed to exploit the ultrahigh resolving power, mass accuracy, and sensitivity of Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) to explore the molecular composition of an ambient BBOA sample collected downwind of Pacific Northwest wildfires. The 21-T FT-ICR MS yielded 10 533 distinct formulae, which included molecular species comprising C, H, O, and P with or without N, i.e., organophosphorus compounds that have long been quantified in wildfire smoke but have not yet been characterized at the molecular level. The lack of detailed molecular characterization of organophosphorus compounds in BBOA is primarily due to their inherently low concentrations in aerosols and poor ionization efficiency in complex mixtures. We demonstrate that the exceptional sensitivity of the 21-T FT-ICR MS allows qualitative analysis of a previously uncharacterized fraction of BBOA without its selective concentration from the organic matrix, exemplifying the need for ultrahigh-resolution tools for a more detailed and accurate molecular depiction of such complex mixtures.

Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection.

Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles in the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS)-based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near-cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled a matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q Exactive HF Orbitrap MS upgraded with ultrahigh mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass (HR/AM) measurements of proteoforms up to 16.5 kDa directly from tissues using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a considerable increase in duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. The use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps toward high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.

Imaging and Direct Sampling Capabilities of Nanospray Desorption Electrospray Ionization with Absorption-Mode 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometry.

Nanospray desorption electrospray ionization mass spectrometry, a powerful ambient sampling and imaging technique, is herein coupled as an isolated source with 21 Tesla (21T) Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS). Absorption-mode data, enabled by an external data acquisition system, is applied for improved mass resolution, accuracy, and dynamic range without compromising spectral acquisition rates. Isotopic fine structure (IFS) information is obtained from the ambient sampling of living Bacillus and Fusarium species, allowing for high confidence in molecular annotations with a resolution >830 k (at m/z 825). Tandem mass spectrometry experiments for biological samples are shown to retain the IFS in addition to gained fragmentation information, providing a further degree of annotation confidence from ambient analyses. Rat brain was imaged by nanospray desorption electrospray ionization (nano-DESI) 21T FTICR MS in ∼5 h using 768 ms transients, producing over 800 molecular annotations using the METASPACE platform and low-parts-per-billion mass accuracy at a spatial resolution of ∼25 × 180 µm. Finally, nano-DESI 21T FTICR MS imaging is demonstrated to reveal images corresponding to the IFS, as well as hundreds of additional molecular features (including demonstrated differences as low as 8.96 mDa) that are otherwise undetected by a more conventional imaging methodology.

Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems.

The application of Machine Learning to cheminformatics is a large and active field of research, but there exist few papers which discuss whether ensembles of different Machine Learning methods can improve upon the performance of their component methodologies. Here we investigated a variety of methods, including kernel-based, tree, linear, neural networks, and both greedy and linear ensemble methods. These were all tested against a standardised methodology for regression with data relevant to the pharmaceutical development process. This investigation focused on QSPR problems within drug-like chemical space. We aimed to investigate which methods perform best, and how the 'wisdom of crowds' principle can be applied to ensemble predictors. It was found that no single method performs best for all problems, but that a dynamic, well-structured ensemble predictor would perform very well across the board, usually providing an improvement in performance over the best single method. Its use of weighting factors allows the greedy ensemble to acquire a bigger contribution from the better performing models, and this helps the greedy ensemble generally to outperform the simpler linear ensemble. Choice of data preprocessing methodology was found to be crucial to performance of each method too.