Metabolic imaging using nanostructure-initiator mass spectrometry (NIMS)

Charting Metabolism Heterogeneity by Nanostructure Imaging Mass Spectrometry: From Biological Systems to Subcellular Functions

The study of metabolism heterogeneity is essential to understand the role of metabolites in supporting and regulating biological functions. To this end, several mass spectrometry imaging (MSI) approaches have been proposed for the detection of small molecule metabolites. However, high noise from the ionization matrix and low metabolome coverage hinder their applicability for untargeted metabolomics studies across space. In this context, nanostructure imaging (/initiator) mass spectrometry (NIMS) and NIMS with fluorinated gold nanoparticles (f-AuNPs) are attractive strategies for comprehensive MSI of metabolites in biological systems, which can provide heterogeneous metabolome coverage, ultrahigh sensitivity, and high lateral resolution. In particular, NIMS with f-AuNPs permits the simultaneous detection of polar metabolites and lipids in a single and cohesive analytical session, thus allowing the systems-level interpretation of metabolic changes. In this Perspective article, we discuss the use of NIMS and f-AuNPs in the exploration of metabolism heterogeneity and provide a critical outlook on future applications of this technology for revealing the metabolic architecture that supports biological functions in health and disease, from whole organisms to tissues, single cells, and subcellular compartments.

Morphology-Driven Control of Metabolite Selectivity Using Nanostructure-Initiator Mass Spectrometry

Nanostructure-initiator mass spectrometry (NIMS) is a laser desorption/ionization analysis technique based on the vaporization of a nanostructure-trapped liquid "initiator" phase. Here we report an intriguing relationship between NIMS surface morphology and analyte selectivity. Scanning electron microscopy and spectroscopic ellipsometry were used to characterize the surface morphologies of a series of NIMS substrates generated by anodic electrochemical etching. Mass spectrometry imaging was applied to compare NIMS sensitivity of these various surfaces toward the analysis of diverse analytes. The porosity of NIMS surfaces was found to increase linearly with etching time where the pore size ranged from 4 to 12 nm with corresponding porosities estimated to be 7-70%. Surface morphology was found to significantly and selectively alter NIMS sensitivity. The small molecule (<2k Da) sensitivity was found to increase with increased porosity, whereas low porosity had the highest sensitivity for the largest molecules examined. Estimation of molecular sizes showed that this transition occurs when the pore size is <3× the maximum of molecular dimensions. While the origins of selectivity are unclear, increased signal from small molecules with increased surface area is consistent with a surface area restructuring-driven desorption/ionization process where signal intensity increases with porosity. In contrast, large molecules show highest signal for the low-porosity and small-pore-size surfaces. We attribute this to strong interactions between the initiator-coated pore structures and large molecules that hinder desorption/ionization by trapping large molecules. This finding may enable us to design NIMS surfaces with increased specificity to molecules of interest.

Engineering microbial consortia for controllable outputs

Much research has been invested into engineering microorganisms to perform desired biotransformations; nonetheless, these efforts frequently fall short of expected results due to the unforeseen effects of biofeedback regulation and functional incompatibility. In nature, metabolic function is compartmentalized into diverse organisms assembled into robust consortia, in which the division of labor is thought to lead to increased community efficiency and productivity. Here we consider whether and how consortia can be designed to perform bioprocesses of interest beyond the metabolic flexibility limitations of a single organism. Advances in post-genomic analysis of microbial consortia and application of high-resolution global measurements now offer the promise of systems-level understanding of how microbial consortia adapt to changes in environmental variables and inputs of carbon and energy. We argue that, when combined with appropriate modeling frameworks, systems-level knowledge can markedly improve our ability to predict the fate and functioning of consortia. Here we articulate our collective perspective on the current and future state of microbial community engineering and control while placing specific emphasis on ecological principles that promote control over community function and emergent properties.