The genomic landscape of colorectal cancer (CRC) is shaped by inactivating mutations in tumour suppressors such as APC , and oncogenic mutations such as mutant KRAS . Here we used genetically engineered mouse models, and multimodal mass spectrometry-based metabolomics to study the impact of common genetic drivers of CRC on the metabolic landscape of the intestine. We show that untargeted metabolic profiling can be applied to stratify intestinal tissues according to underlying genetic alterations, and use mass spectrometry imaging to identify tumour, stromal and normal adjacent tissues. By identifying ions that drive variation between normal and transformed tissues, we found dysregulation of the methionine cycle to be a hallmark of APC-deficient CRC. Loss of Apc in the mouse intestine was found to be sufficient to drive expression of one of its enzymes, adenosylhomocysteinase (AHCY), which was also found to be transcriptionally upregulated in human CRC. Targeting of AHCY function impaired growth of APC-deficient organoids in vitro, and prevented the characteristic hyperproliferative/crypt progenitor phenotype driven by acute deletion of Apc in vivo, even in the context of mutant Kras . Finally, pharmacological inhibition of AHCY reduced intestinal tumour burden in Apc ^Min/+ mice indicating its potential as a metabolic drug target in CRC.

Brillouin imaging (BI) has become a valuable tool for micromechanical material characterisation, thanks to extensive progress in instrumentation in the last few decades. This powerful technique is contactless and label-free, thus making it especially suitable for biomedical applications. Nonetheless, to fully harness the non-contact and non-destructive nature of BI, transformational changes in instrumentation are still needed to extend the technology’s utility into the domain of in vivo and in situ operation, which we foresee to be particularly crucial for wide spread usage of BI, e.g. in medical diagnostics and pathology screening. This work addresses this challenge by presenting the first demonstration of a fibre-optic Brillouin probe, capable of mapping the micromechanical properties of a tissue-mimicking phantom. This is achieved through combination of miniaturised optical design, advanced hollow-core fibre fabrication and high-resolution 3D printing. Our prototype probe is compact, background-free and possesses the highest collection efficiency to date, thus providing the foundation of a fibre-based Brillouin device for remote, in situ measurements in challenging and otherwise difficult-to-reach environments in biomedical, material science and industrial applications.

Mass spectrometry imaging (MSI) has been a key driver of groundbreaking discoveries in a number of fields since its inception more than 50 years ago. Recently, MSI development trends have shifted towards ambient MSI (AMSI) as the removal of sample-preparation steps and the possibility of analysing biological specimens in their natural state have drawn the attention of multiple groups across the world. Nevertheless, the lack of spatial resolution has been cited as one of the main limitations of AMSI. While significant research effort has presented hardware solutions for improving the resolution, software solutions are often overlooked, although they can usually be applied in a cost-effective manner after image acquisition. In this vein, we present two computational methods that we have developed to directly enhance the image resolution post-acquisition. Robust and quantitative resolution improvement is demonstrated for 12 cases of openly accessible datasets across laboratories around the globe. Using the same universally applicable Fourier imaging model, we discuss the possibility of true super-resolution by software for future studies.

To establish infections in human hosts, Pseudomonas aeruginosa must overcome innate immune-generated oxidative stress, such as the hypochlorous acid (HOCl) produced by neutrophils. We set out to find specific biomarkers of oxidative stress through the development of a protocol for the metabolic profiling of P. aeruginosa cultures grown in the presence of different oxidants using a novel ionization technique for mass spectrometry, laser desorption rapid evaporative ionization mass spectrometry (LD-REIMS). We demonstrated the ability of LD-REIMS to classify samples as untreated or treated with a specific oxidant with 100% accuracy and identified a panel of 54 metabolites with significantly altered concentrations after exposure to one or more of the oxidants. Key metabolic changes were conserved in P. aeruginosa clinical strains isolated from patients with cystic fibrosis lung infections. These data demonstrated that HOCl stress impacted the Pseudomonas quinolone signal (PQS) quorum sensing system. Ten 2-alkyl-4-quinolones (AHQs) associated with the PQS system were significantly lower in concentration in HOCl-stressed P. aeruginosa cultures, including 2-heptyl-3-hydroxy-4(1H)-quinolone (PQS), the most active signal molecule of the PQS system. The PQS system regulates the production of virulence factors, including pyocyanin and elastase, and their levels were markedly affected by HOCl stress. No pyocyanin was detectable and elastase concentrations were reduced by more than 75% in cultures grown with sub-lethal concentrations of HOCl, suggesting that this neutrophil-derived oxidant may disrupt the ability of P. aeruginosa to establish infections through interference with production of PQS-associated virulence factors. IMPORTANCE: This work demonstrates that a high-throughput ambient ionization mass spectrometry method can be used successfully to study a bacterial stress response. Its application to the opportunistic pathogen Pseudomonas aeruginosa led to the identification of specific oxidative stress biomarkers, and demonstrated that hypochlorous acid, an oxidant specifically produced by human neutrophils during infection, affects quorum sensing and reduces production of the virulence factors pyocyanin and elastase. No pyocyanin was detectable and elastase levels were reduced by more than 75% in bacteria grown in the presence of hypochlorous acid. This approach has the potential to be widely applicable to the characterization of the stress responses of bacteria.

Optical microscopy has long been the gold standard to analyse tissue samples for the diagnostics of various diseases, such as cancer. The current diagnostic workflow is time-consuming and labour-intensive, and manual annotation by a qualified pathologist is needed. With the ever-increasing number of tissue blocks and the complexity of molecular diagnostics, new approaches have been developed as complimentary or alternative solutions for the current workflow, such as digital pathology and mass spectrometry imaging (MSI). This study compares the performance of a digital pathology workflow using deep learning for tissue recognition and an MSI approach utilising shallow learning to annotate formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue microarrays (TMAs). Results show that both deep learning algorithms based on conventional optical images and MSI-based shallow learning can provide automated diagnostics with F1-scores higher than 90%, with the latter intrinsically built on biochemical information that can be used for further analysis.