Every medical revolution has been driven by a technological breakthrough: chemical synthesis paved the way for modern medicines, genetics for the first gene therapies. The next frontier will be determ...

The Wikimedia Movement contributes every day toward a better internet, one that is free – filled with reliable information that belongs to everyone and benefits everyone. This is the ideal that the…

The Biodiversity Heritage Library (BHL) has over 63 million pages of content, which presents a major challenge: how do we discover content in these millions of pages? To date, discovery has relied on ...

imageimageThis study developed a systematic workflow leveraging transcriptomics and metabolomics to identify input signals for 41 transcription factors in E. coli. A systematic workflow was developed...

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.

A transformer model is used to construct the DreaMS Atlas—a molecular network of 201 million MS/MS spectra.

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This link will take you to a page that’s not on LinkedIn

This link will take you to a page that’s not on LinkedIn

Inria is opening three positions in the context of the ANR-SNF MetaboLinkAI project, which aspires to improve the analysis and interpretation of metabolomics…