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Ben Place, a Research Chemist within the Organic Chemical Metrology Group at the National Institute of Standards and Technology (NIST), will be giving a talk on “The NIST Per- and Polyfluoroalkyl Substances Interlaboratory Study – Exercise 1”. In addition to his work at NIST, Ben is one of the founding chairs of BP4NTA and the current leader of the BP4NTA Study Planning Tool working group.

This meeting will occur on Tuesday, February 18th, from 12 pm – 1 pm EST.

Abstract: As HRMS-based non-targeted analysis (NTA) techniques advance and become more routine, there is a significant need by the research community to understand the interlaboratory comparability of NTA methods and results. As part of a US Department of Defense-funded effort, researchers at NIST designed an interlaboratory study focused on the identification of per- and polyfluoroalkyl substances (PFAS) in three prepared extract samples. Thirty-four laboratories with experience performing NTA were shipped the samples with a standard reporting form; 27 of the participating laboratories submitted data by the deadline. The initial interpretation of the results show that laboratories reported a wide range of PFAS identities across all three samples and a range of identification confidence (using the Charbonnet et al. PFAS confidence of identification scale). The presentation will cover the design of the study goals and samples, the administration and process of the interlaboratory study, and the results of the study, along with some lessons learned and potential future studies.

Click here to view the recording: BP4NTA Monthly Meeting-20250218_180754-Meeting Recording.mp4

The topics/speakers of the next three meetings will be:

March 18: BP4NTA working group updates

April 15: Jaanus Liigand, Quantem Analytics

May 20: Carsten Baessmann, Bruker Mass Spectrometry

Dr. Alexander Aksenov is an Assistant Professor at the University of Connecticut. His lab focuses on exploring the metabolome of living systems, focusing on the “dark matter” – the unknown unknowns of the system. For more information, please explore the lab’s website: https://aksenovgroup.chemistry.uconn.edu/

This meeting will occur on Tuesday, January 21st, from 12 pm – 1 pm EST.

Title: Illuminating the Dark Matter of Metabolomics Through Molecular Community Networking

Introduction: Molecular networking connects structurally similar metabolites by leveraging MS/MS fragmentation pattern similarities. This approach has enabled a slew of discoveries over the past decade. However, conventional methods rely on arbitrary global spectral similarity thresholds, despite optimal connectivity being molecule class-specific. We present molecular community networking (MCN), an advanced approach that utilizes unpruned full connectivity metabolite networks parsed using network science tools to identify naturally present “molecular communities.” This strategy preserves intra-community connectivity information and optimizes connectivity patterns for each metabolite class, enabling the rescue of lost relationships and the capture of otherwise “hidden” portions of the metabolome.

Methods: Full connectivity metabolite networks were constructed using LC-MS/MS or EI GC- MS data. The Louvain clustering algorithm was employed to identify naturally occurring “molecular communities” within the unpruned networks. Each community was treated as a separate network and pruned using the maximum weight spanning tree algorithm to preserve connectivity while retaining only the most meaningful information. The resulting MCNs represent partitions of the original networks into continua of molecular space, where connections within each molecular family cluster represent the most similar pairs of metabolites across the entire detected metabolome.

Preliminary Data: We validated MCNs using reference spectra and experimental data, demonstrating their ability to assemble molecular space into continua reflecting structural relationships. MCNs rescue lost connectivity between related molecules fractured by conventional networking, for example, link sodiated ion variants to corresponding protonated precursors. We showcase MCN’s utility in discovering new bile acid structures with dipeptide conjugation produced by human microbial cultures, revealing the metabolic capacity of the human microbiota. These molecules were previously undetectable with conventional networks. MCNs exhibit high modularity, suggesting a natural tendency for molecules to group into communities resembling “small-world” structures found in online social networks. This approach empowers molecular discovery in areas such as natural products research, including the reanalysis of existing data to explore previously unconnected molecules.

Click here if you’d like to access the recording: BP4NTA Monthly Meeting-20250121_183034-Meeting Recording.mp4

Mass Spectrum libraries: RECETOX Exposome HR-[EI+]-MS library (a collection of mostly anthropogenic compounds)
RECETOX Metabolome HR-[EI+]-MS library (a collection of mostly endogenous compounds from MetaSci Human Metabolite Library)