Chemical mapping of the colorectal cancer microenvironment via MALDI imaging mass spectrometry (MALDI-MSI) reveals novel cancer-associated field effects.

Matrix-assisted laser desorption ionisation imaging mass spectrometry (MALDI-MSI) is a rapidly advancing technique for intact tissue analysis that allows simultaneous localisation and quantification of biomolecules in different histological regions of interest. This approach can potentially offer novel insights into tumour microenvironmental (TME) biochemistry. In this study we employed MALDI-MSI to evaluate fresh frozen sections of colorectal cancer (CRC) tissue and adjacent healthy mucosa obtained from 12 consenting patients undergoing surgery for confirmed CRC. Specifically, we sought to address three objectives: (1) To identify biochemical differences between different morphological regions within the CRC TME; (2) To characterise the biochemical differences between cancerous and healthy colorectal tissue using MALDI-MSI; (3) To determine whether MALDI-MSI profiling of tumour-adjacent tissue can identify novel metabolic 'field effects' associated with cancer. Our results demonstrate that CRC tissue harbours characteristic phospholipid signatures compared with healthy tissue and additionally, different tissue regions within the CRC TME reveal distinct biochemical profiles. Furthermore we observed biochemical differences between tumour-adjacent and tumour-remote healthy mucosa. We have referred to this 'field effect', exhibited by the tumour locale, as cancer-adjacent metaboplasia (CAM) and this finding builds on the established concept of field cancerisation.

Subtle metabolic and liver gene transcriptional changes underlie diet-induced fatty liver susceptibility in insulin-resistant mice.

AIMS/HYPOTHESIS: Complex changes in gene expression are associated with insulin resistance and non-alcoholic fatty liver disease (NAFLD) promoted by feeding a high-fat diet (HFD). We used functional genomic technologies to document molecular mechanisms associated with diet-induced NAFLD. MATERIALS AND METHODS: Male 129S6 mice were fed a diet containing 40% fat (high-fat diet, HFD) for 15 weeks. Glucose tolerance, in vivo insulin secretion, plasma lipid profile and adiposity were determined. Plasma metabonomics and liver transcriptomics were used to identify changes in gene expression associated with HFD-induced NAFLD. RESULTS: In HFD-fed mice, NAFLD and impaired glucose and lipid homeostasis were associated with increased hepatic transcription of genes involved in fatty acid uptake, intracellular transport, modification and elongation, whilst genes involved in beta-oxidation and lipoprotein secretion were, paradoxically, also upregulated. NAFLD developed despite strong and sustained downregulation of transcription of the gene encoding stearoyl-coenzyme A desaturase 1 (Scd1) and uncoordinated regulation of transcription of Scd1 and the gene encoding sterol regulatory element binding factor 1c (Srebf1c) transcription. Inflammatory mechanisms appeared to be stimulated by HFD. CONCLUSIONS/INTERPRETATION: Our results provide an accurate representation of subtle changes in metabolic and gene expression regulation underlying disease-promoting and compensatory mechanisms, collectively contributing to diet-induced insulin resistance and NAFLD. They suggest that proposed models of NAFLD pathogenesis can be enriched with novel diet-reactive genes and disease mechanisms.

Probing latent biomarker signatures and in vivo pathway activity in experimental disease states via statistical total correlation spectroscopy (STOCSY) of biofluids: application to HgCl2 toxicity.

A new multivariate statistical approach, based on the novel combination of projection on latent structure analysis with an inbuilt orthogonal filter (OPLS-DA) coupled with a spectroscopic correlation method statistical total correlation spectroscopy (STOCSY), was used to characterize the in vivo metabolic pathway perturbations of a model renal cortical toxin HgCl2, in the rat, using urine as an indicator of metabolic homeostasis disruption. This method provided an unbiased, sensitive approach to biomarker extraction and identification, and showed potential for generating potential novel pathway connectivities.