Direct Mass Spectrometry-Based Approaches in Metabolomics

Rapid fingerprinting of extractable and non-extractable polyphenols from tropical fruit peels using direct analysis in real time coupled to orbitrap mass spectrometry

A simple and rapid direct analysis in real-time coupled to high-resolution mass spectrometry (DART-HRMS) methodology was developed to generate the extractable and non-extractable polyphenols (NEPs) fingerprint for four different passion fruits, G. mangostana, and A. squamosa peels as case-study to investigate the influence of alkaline hydrolysis and enzymatic-assisted extraction (EAE) on the recovery of NEPs. The extraction residue obtained after these treatments was also analyzed by DART-HRMS. Data compiled from DART-HRMS mass spectra were processed with principal component analysis to discriminate among the different treatments. EAE with Depol enzyme enabled to obtain NEPs with the highest signal intensity in DART-HRMS analysis from all peels except for P. edulis and A. squamosa peels. In these two cases, NEPs were better extracted by EAE with Promod enzyme and alkaline hydrolysis. Results showed that the applied treatments were efficient to extract NEPs since their signal intensities in the extraction residues were very low compared with their extracts.

An accessible, scalable ecosystem for enabling and sharing diverse mass spectrometry imaging analyses

Mass spectrometry imaging (MSI) is used in an increasing number of biological applications. Typical MSI datasets contain unique, high-resolution mass spectra from tens of thousands of spatial locations, resulting in raw data sizes of tens of gigabytes per sample. In this paper, we review technical progress that is enabling new biological applications and that is driving an increase in the complexity and size of MSI data. Handling such data often requires specialized computational infrastructure, software, and expertise. OpenMSI, our recently described platform, makes it easy to explore and share MSI datasets via the web - even when larger than 50 GB. Here we describe the integration of OpenMSI with IPython notebooks for transparent, sharable, and replicable MSI research. An advantage of this approach is that users do not have to share raw data along with analyses; instead, data is retrieved via OpenMSI's web API. The IPython notebook interface provides a low-barrier entry point for data manipulation that is accessible for scientists without extensive computational training. Via these notebooks, analyses can be easily shared without requiring any data movement. We provide example notebooks for several common MSI analysis types including data normalization, plotting, clustering, and classification, and image registration.