The initial post on lipid annotation in CXSMILES was flawed but has since been revised. The new approach adheres to SMILES standards, with a distinction between chemically valid structures and unrepresented features stored after closing pipes. The revision efficiently communicates constraints and unlocalised positions using invented tokens, ensuring clarity in representation.
One of the recurring themes of our work is how to make results clear to the rest of the world. This is the I in the FAIR principles: interoperable. Generally speaking, this challenge boils down to using a common language or an ontology to describe an object or a relation. In metabolomics, however, we regularly have to deal with another level of information: confidence,…
This is the second part of our mini-series on how we analyse untargeted metabolomics data. After outlining the needs and the core processing logic in Part I, we now turn to how all the parts come together with orchestration, user experience, zero-knowledge processing, FAIR reporting, benchmarking, and automation. Spoiler: it's actually a lot of fun! 10. One architecture to serve them all? One of…
As a lab primarily interested in metabolomics applications, our publications usually focus on the biological results and their relevance. Much more rarely do we describe the infrastructure that turns raw LC-MS/MS data into detected features, annotations, and ultimately the files that accompany a publication, whether tables, mzTab exports, or other deliverables. We sometimes allude to this infrastructure in talks, but there is rarely enough…
ASMS is just around the corner, and we are all eager to meet both old and new colleagues, network, and seek inspiration from thousands of posters. For the galaxy of companies that orbit mass spectrometers (MS), it’s the prime stage to launch new products and concepts. As a user and consumer, I am also curious. Some of these will be pleasantly surprising and inspiring,…
A recent commentary by Giera et al. (link) sparked a discussion about in-source fragmentation (ISF). The authors claimed that “ISF could account for over 70% of the peaks observed in typical LC–MS/MS metabolomic datasets”. This claim, echoed by other outlets, even went as far as suggesting that “over 70% of the dark metabolome could be an experimental artefact” (link). Many metabolomics practitioners reacted with…
NOTE: Results and software are available on ChemRxiv One of the grand challenges in metabolomics using mass spectrometry (MS) is the limited and somewhat redundant information provided by collision-induced dissociation (CID). CID is incredibly versatile and quick but tends to fragment weaker bonds, such as C-O bonds. This bias allows us to easily observe fragments indicative of specific functional groups—such as ethanolamine, phosphatidic groups,…
This year we celebrate the 10th anniversary of one of our QTOFs. It is not among the oldest mass spectrometers that we have in the lab, but it is a special case in many senses. The anniversary evoked both retrospective and prospective reflections, which I wanted to share to hear about the experience of others. The first ten years The birthday boy is an…
Imagine running a study with 100 samples by untargeted LC-MS (polar or lipids, you pick!). Data acquisition by DIA/DDA in complete in 1-2 days, and comes the step of data analysis. The minimum expectation for the software is to find all peaks across samples (rare and frequent, high and low, symmetric and tailing peaks), take care of shifts in m/z and RT, group isotopologues…
Background For >10 years, we analysed all kinds of samples by flow injection analysis - HRMS. I will not discuss the pros and cons of including a column (LC-MS) or omitting it (FIA-MS). In brief, FIA-MS is great to profile endogenous metabolites from cellular systems, less for the more exotic samples that require in-depth MS2. Believe it or not, we used FIA to profile>1.4…