Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixture’s constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks

Automatic metabolite annotation in complex LC-MS(n ≥ 2) data using MAGMa

Poster presented at the Analytical Tools for Cutting-edge Metabolomics meeting in London, 30 April 201

Collaborative resonant writing and musical improvisation to explore the concept of resonance

Resonance is often used to characterize relationships, but it is acomplex concept that explains quite different physical,physiological and psychological processes. With the aim of gainingdeeper insight into the concept of resonance, a group of ten musictherapy researchers, all colleagues, embarked on a joint journey ofexploration. This included an aim of letting the internal learningprocess be disseminated in a way that could give others insight, notonly from the findings, but also from the process. Findings includea dual understanding of resonance as (1) a visible and orderedphenomenon consisting of physical vibrations and acousticsounding that offers a clear logic, and (2) a metaphoricalconceptualization used to describe and understand complexpsychological processes of human relationships. The process ofcollaborative writing led to the discovery or development of a ninestepprocedure including different collaborative resonant writingprocedures and musical improvisation, as well as of a series ofmetaphors to explain therapeutic interaction, resonant learning andways of resonant exploration

Magnetic Resonance Imaging of the Brain in Diabetes

Diabetic patients are at increased risk for stroke, but little is known about the presence of other brain lesions. We studied the association of magnetic resonance imaging–detected brain lesions to diabetes in 1,252 individuals aged 65–75 years who were randomly selected from eight European population registries or defined working populations. All scans were centrally read for brain abnormalities, including infarcts, white matter lesions, and atrophy. We used a three-point scale to rate periventricular white matter lesions, and the volume of subcortical lesions was calculated according to their number and size. Subjective grading of cortical atrophy by lobe and summation of the lobar grades resulted in a total cortical atrophy score. The mean of three linear measurements of the ventricular diameter relative to the intracranial cavity defined the severity of subcortical atrophy. After adjustment for possible confounders, diabetes was associated with cortical brain atrophy but not with any focal brain lesions or subcortical atrophy. There was a strong interaction of diabetes and hypertension, such that the association between diabetes and cortical atrophy existed only in hypertensive but not in normotensive participants. Cognitive and pathological data are needed to determine the clinical significance of these findings as well as to understand the mechanisms underlying these associations

Spec2Vec: improved mass spectral similarity scoring through learning of structural relationships

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds

“Keep your distance for me”: A field experiment on empathy prompts to promote distancing during the COVID-19 pandemic

The outbreak of COVID-19 has turned out to be a major challenge to societies all over the globe. Curbing the pandemic requires rapid and extensive behavioural change to limit social interaction, including physical distancing. In this study, we tested the notion that inducing empathy for people vulnerable to the virus may result in actual distancing behaviour beyond the mere motivation to do so. In a large field experiment with a sequential case–control design, we found that (a) empathy prompts may increase distancing as assessed by camera recordings and (b) effectiveness of prompts depends on the dynamics of the pandemic and associated public health policies. In sum, the present study demonstrates the potential of empathy-generating interventions to promote pro-social behaviour and emphasizes the necessity of field experiments to assess the role of context before advising policy makers to implement measures derived from behavioural science. Please refer to Supplementary Material to find this article's Community and Social Impact Statement