Implications of Peak Selection in the Interpretation of Unsupervised Mass Spectrometry Imaging Data Analyses.

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t-distributed stochastic neighbor embedding and k-means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

The haemochromatosis gene Hfe and Kupffer cells control LDL cholesterol homeostasis and impact on atherosclerosis development.

AIMS: Imbalances of iron metabolism have been linked to the development of atherosclerosis. However, subjects with hereditary haemochromatosis have a lower prevalence of cardiovascular disease. The aim of our study was to understand the underlying mechanisms by combining data from genome-wide association study analyses in humans, CRISPR/Cas9 genome editing, and loss-of-function studies in mice. METHODS AND RESULTS: Our analysis of the Global Lipids Genetics Consortium (GLGC) dataset revealed that single nucleotide polymorphisms (SNPs) in the haemochromatosis gene HFE associate with reduced low-density lipoprotein cholesterol (LDL-C) in human plasma. The LDL-C lowering effect could be phenocopied in dyslipidaemic ApoE-/- mice lacking Hfe, which translated into reduced atherosclerosis burden. Mechanistically, we identified HFE as a negative regulator of LDL receptor expression in hepatocytes. Moreover, we uncovered liver-resident Kupffer cells (KCs) as central players in cholesterol homeostasis as they were found to acquire and transfer LDL-derived cholesterol to hepatocytes in an Abca1-dependent fashion, which is controlled by iron availability. CONCLUSION: Our results disentangle novel regulatory interactions between iron metabolism, KC biology and cholesterol homeostasis which are promising targets for treating dyslipidaemia but also provide a mechanistic explanation for reduced cardiovascular morbidity in subjects with haemochromatosis.

Metabolic profiling stratifies colorectal cancer and reveals adenosylhomocysteinase as a therapeutic target.

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 ApcMin/+ mice indicating its potential as a metabolic drug target in CRC.