Key Concepts Surrounding Studies of Stable Isotope-Resolved Metabolomics

Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.

Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data

In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions to relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu), and provides a JSON format for easy data exchange and software interoperability. By generalized pre-annotation, khipu makes it feasible to connect metabolomics data with common data science tools, and supports flexible experimental designs.

Using measures of metabolic flux to align screening and clinical development: Avoiding pitfalls to enable translational studies

Screening campaigns, especially those aimed at modulating enzyme activity, often rely on measuring substrate→product conversions. Unfortunately, the presence of endogenous substrates and/or products can limit one's ability to measure conversions. As well, coupled detection systems, often used to facilitate optical readouts, are subject to interference. Stable isotope labeled substrates can overcome background contamination and yield a direct readout of enzyme activity. Not only can isotope kinetic assays enable early screening, but they can also be used to follow hit progression in translational (pre)clinical studies. Herein, we consider a case study surrounding lipid biology to exemplify how metabolic flux analyses can connect stages of drug development, caveats are highlighted to ensure reliable data interpretations. For example, when measuring enzyme activity in early biochemical screening it may be enough to quantify the formation of a labeled product. In contrast, cell-based and in vivo studies must account for variable exposure to a labeled substrate (or precursor) which occurs via tracer dilution and/or isotopic exchange. Strategies are discussed to correct for these complications. We believe that measures of metabolic flux can help connect structure-activity relationships with pharmacodynamic mechanisms of action and determine whether mechanistically differentiated biophysical interactions lead to physiologically relevant outcomes. Adoption of this logic may allow research programs to (i) build a critical bridge between primary screening and (pre)clinical development, (ii) elucidate biology in parallel with screening and (iii) suggest a strategy aimed at in vivo biomarker development.

Mapping Lipogenic Flux: A Gold LDI-MS Approach for Imaging Neutral Lipid Kinetics

Spatial characterization of triglyceride metabolism is an area of significant interest which can be enabled by mass spectrometry imaging via recent advances in neutral lipid laser desorption analytical approaches. Here, we extend recent advancements in gold-assisted neutral lipid imaging and demonstrate the potential to map lipid flux in rodents. We address here critical issues surrounding the analytical configuration and interpretation of the data for a group of select triglycerides. Specifically, we examined how the signal intensity and spatial resolution would impact the apparent isotope ratio in a given analyte (which is an important consideration when performing MS based kinetics studies of this kind) with attention given to molecular ions and not fragments. We evaluated the analytics by contrasting lipid flux in well characterized mouse models, including fed vs fed states and different dietary perturbations. In total, the experimental paradigm described here should enable studies of hepatic lipogenesis; presumably, this logic can be enhanced via the inclusion of ion mobility and/or fragmentation. Although this study was carried out in robust models of liver lipogenesis, we expect that the model system could be expanded to a variety of tissues where zonated (or heterogeneous) lipid synthesis may occur, including solid tumor metabolism.