Biology subjects

Strittmatter, N.

Publications and source records attributed to Strittmatter, N..

4 recordsLinked to original sources

bioRxiv2024

Large-Scale Evaluation of Spatial Metabolomics Protocols and Technologies

Spatial metabolomics using imaging mass spectrometry (MS) enables untargeted and label-free metabolite mapping in biological samples. Despite the range of available imaging MS protocols and technologies, our understanding of metabolite detection under specific conditions is limited due to sparse empirical data and predictive theories. Consequently, challenges persist in designing new experiments, and accurately annotating and interpreting data. In this study, we systematically measured the detectability of 172 biologically-relevant metabolites across common imaging MS protocols using custom reference samples. We evaluated 24 MALDI-imaging MS protocols for untargeted metabolomics, and demonstrated the applicability of our findings to complex biological samples through comparison with animal tissue data. We showcased the potential for extending our results to further analytes by predicting metabolite detectability based on molecular properties. Additionally, our interlaboratory comparison of 10 imaging MS technologies, including MALDI, DESI, and IR-MALDESI, showed extensive metabolite coverage and comparable results, underscoring the broad applicability of our findings within the imaging MS community. We share our results and data through a new interactive web application integrated with METASPACE. This resource offers an extensive catalogue of detectable metabolite ions, facilitating protocol selection, supporting data annotation, and benefiting future untargeted spatial metabolomics studies.

systems biology↗

bioRxiv2020

Microbiome-derived metabolites reproduce the mitochondrial dysfunction and decreased insulin sensitivity observed in type 2 diabetes

Diabetes is a global health problem that was estimated to be the 7th leading cause of death worldwide in 2016. Type 2 diabetes mellitus (T2DM) is classically associated with genetic and environmental factors, however recent studies have demonstrated that the gut microbiome, which is altered in T2DM patients, is also likely to play a significant role in disease development. Despite this, the identity of microbiome-derived metabolites that influence T2DM onset and/or progression remain elusive. Here we demonstrate that a serum biomarker for T2DM, previously of unknown structure and origin, is actually two microbiome-derived metabolites, 3-methyl-4-(trimethylammonio)butanoate (3M-4-TMAB) and 4-(trimethylammonio)pentanoate (4-TMAP). These metabolites are produced by the Lachnospiraceae family of bacteria, which are highly prevalent in the gut microbiome of T2DM patients and are associated with high dietary fat intake. Treatment of human liver cells with 3M-4-TMAB and 4-TMAP results in a distinct change in the acylcarnitine profile in these cells and significantly reduced their insulin sensitivity; both indicators of T2DM. These results provide evidence of a mechanistic link between gut microbiome derived metabolites and T2DM.

microbiology↗

bioRxiv2020

Mapping the influence of the gut microbiota on small molecules in the brain through mass spectrometry imaging

BackgroundThe gut microbiota is known to influence virtually all facets of human health. Recent work has highlighted a potential role for the gut microbiota in neurological health through the microbiome-gut-brain axis. Microbes can influence the brain both directly and indirectly; through neurotransmitter production, induction of host immunomodulators, or through the release or induction of other microbial or host molecules. MethodsHere we used mass spectrometry imaging (MSI), a label-free imaging tool, to map the molecular changes that occur in the murine gut and brain in germ-free, antibiotic-treated and control mice. ResultsWe determined the spatial distribution and relative quantification of neurotransmitters and their precursors across brain and gut sections in response to the microbiome. Using untargeted MSI of small molecules, we detected a significant change in the levels of four identified metabolites in the brains of germ-free animals compared to controls; vitamin B5, 3-hydroxy-3-methylglutaric acid, 3-methyl-4-(trimethylammonio)butanoate and 4-(trimethylammonio)pentanoate. However, antibiotic treatment induced no significant changes in these metabolites in the brain after one week of treatment. ConclusionsThis work exemplifies the utility of MSI as a tool in determining the spatial distribution and quantification of bacterial and host metabolites in the gut and brain whilst also offering the potential for discovery of novel mediators of microbiome-gut-brain axis communication.

microbiology↗

bioRxiv2017

Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development.

A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.\n\nSignificanceNetwork analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers frequently manifest widely varying metastatic tendencies. Thus, the methodology introduced here more robustly defines the risk of metastases based on tumor biochemical heterogeneity.\n\nAuthor contributionsP.I., Z.T., R.C.G.: designed the study, developed the workflow, analyzed the data, interpreted the results, wrote the paper; N.S. collected the MS, performed the H...E staining, wrote the paper; L.D.: interpreted the results, wrote the paper; A.M.: collected the MS; A.S.: histological assessment; L.P.: collected the tissue specimens and clinical metadata; A.D.: collected the MS; H.K.: performed the H...E staining; R.M.: collected the tissue specimens and clinical metadata. R.G.: histological assessment; J.K.N: designed the study, edited the paper.

cancer biology↗