New paper out in Briefings in Bioinformatics
📰SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation academic.oup.com/bib/article/...
SEES Lab
I created a Metabolomics starter pack. A list of researchers from the wonderful world of #metabolomics. If you would like to be added (or removed) just let me know. go.bsky.app/J3VPYKm
If you’ve been following #metabolomics literature, you’ve probably seen a lot of debate on in-source fragmentation. We’ve put together a manuscript to clarify what it is, how to deal with it, and what it means for discovery in #metabolomics and #exposomics.
doi.org/10.26434/che...
1/ In MALDI-MSI, matrix deposition is everything. It impacts sensitivity, spatial resolution, and reproducibility. We asked: can we improve matrix application using a dry, solvent-free, controlled method?
David Broadhurst
Yasin El Abiead
ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings url: academic.oup.com/bib/article/...
Abstract. Machine learning offers a promising path to annotating the large number of unidentified MS/MS spectra in metabolomics, addressing the limited cov
2/ We adapted Low-Temperature Thermal Evaporation (LTE)—originally used in nanotechnology and solar cell applications—for matrix deposition in #MALDI-MSI. The result: a reproducible, vacuum-based method that offers precise control over matrix thickness and produces ultra-pure coatings.
5/ LTE purifies the matrix during deposition — great for improving signal-to-noise, even when starting with lower-purity matrix
6/ The matrix stays stable at −80°C for at least 2 weeks. No loss in ionization efficiency or image quality after storage — big win for throughput & experimental planning
We’re excited to share our latest work in @jasms.bsky.social:
“Improving MALDI Mass Spectrometry Imaging Performance: Low-Temperature Thermal Evaporation for Controlled Matrix Deposition and Improved Image Quality”
🧵👇 pubs.acs.org/doi/10.1021/...
3/ We validated LTE using two matrices:
✅ DHB
✅ DAN
Calibrated thickness vs. deposition time = ✔️ reproducibility.
4/ ESEM images showed beautiful, uniform sub-micron matrix crystals across the tissue. Small crystals = better ionization = sharper images.
In/post-source fragments (ISFs) arise during electrospray ionization or ion transfer in mass spectrometry when molecular bonds break, generating ions that can complicate data interpretation. Although ISFs have been recognized for decades, their contribution to untargeted metabolomics - particularly in the context of the so-called “dark matter” (unannotated MS or MS/MS spectra) and the “dark metabolome” (unannotated molecules) - remains unsettled. This ongoing debate reflects a central tension: while some caution against overinterpreting unidentified signals lacking biological evidence, others argue that dismissing them too quickly risks overlooking genuine molecular discoveries. These discussions also raise a deeper question: what exactly should be considered part of the metabolome? As metabolomics advances toward large-scale data mining and high-throughput computational analysis, resolving these conceptual and methodological ambiguities has become essential. In this perspective, we propose a refined definition of the “dark metabolome” and present a systematic overview of ISFs and related ion forms, including adducts and multimers. We examine their impact on metabolite annotation, experimental design, statistical analysis, computational workflows, and repository-scale data mining. Finally, we provide practical recommendations - including a set of dos and don’ts for researchers and reviewers - and discuss the broader implications of ISFs for how the field explores unknown molecular space. By embracing a more nuanced understanding of ISFs, metabolomics can achieve greater rigor, reduce misinterpretation, and unlock new opportunities for discovery.
