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Now possible: metabolome and microbiome cartography of an organ

A team of University of California scientists have developed an open-source tool for 3-D imaging of microbiome and metabolome data directly from organs. Their results, published in Cell Host & Microbe, help scientists understand the effect that some compounds, like microbial metabolites and drugs, have on a diseased organ. The methodology has mapped significant omics data, such as the metabolome, and information from DNA studies, to mention just a few of the applications conceived by Pieter Dorrestein’s team.

“Our understanding of the spatial variation of the chemical and microbial make-up of a human organ remains limited. This is in part due to the size and variability of human organs, and the sheer amount of data we get from metabolomics and genomics studies”, claims the principal investigator from the University of California (San Diego). To overcome this challenge, the North American scientists have developed a tool that lets them visualize the metabolomic, microbiome and other data, in a 3D organ reconstruction built off radiological images. This open-source workflow has enabled the direct visualization of the microbial and chemical makeup of a cystic fibrosis patient’s lung.

First, researchers generated 3D models of the patient’s lungs from images captured through computed tomography. The software used to carry out the molecular cartography is based on a previous program, an extension of Google Chrome called ili, that was modified to map omics data in 3D. This toolbox, initially developed at the European Molecular Biology Laboratory with the collaboration of Dorrestein and Knight’s laboratories, helps “put molecular data into spatial context by visualizing it overlaid onto a photo of sampling sites, a geographical map or 3D surface”.

 

 

The open-source extension has also been used for studying microbes and metabolites on the human skin, molecules on wings of a bee and natural products of cyanobacteria, among other applications. Once the three-dimensional model is built, any researcher can set the information for the organ being studied from metagenomic, transcriptomic, 16S and 18S RNA sequencing, metabolomic, proteomics or ITS sequencing data. “As proof of principle, we used this methodology to map and correlate the 16S rRNA gene sequencing and metabolomics data onto the left-side lung of a patient with CF”, says one of the authors from Pieter Dorrestein’s in their article.

The scientists took a sample from the left lung of the patient, with the purpose of performing a 16S RNA sequencing study, as a bacterial marker, and a mass spectrometry (MS/MS) analysis to identify the most relevant metabolites. Working this way, they created an atlas of microbes and metabolites through the lung. Using RNA 16S sequencing, researchers revealed the bacteria that had colonized the patient’s lungs. According to their results, Pseudomonas was the predominant pathogen (95.8%–99.9%). On another front, the scientists used mass spectrometry to identify the metabolites found in the lung of a cystic fibrosis patient. “The known annotations include drugs administered to the patient and phthalates from the environment, as well small molecules (such as peptides, amino acids and amino acid metabolites, sugars, fatty acids, lipids, and bile acids) from the host and the microbes (virulence factors)”, say the authors.

The spatial maps generated also revealed two more interesting findings: not all medications administered had uniformly penetrated the lung, and the metabolism of the antibiotics varied depending on their location. The open-source tool analyzed the degree of penetration by the antibiotics administered intravenously to the patient. “Different drugs may differentially penetrate the lung, limiting exposure to effective dosage and promoting the development of antibiotic resistance”, say the specialists, who also suggest that the bacteria can persist in regions of the lung where the antibiotic penetration is sub-therapeutic, making for a clinical risk from the standpoint of microbial resistance to these medications, limiting the possibilities of treatment success.