We present omniSpect, an open source web- and Matlab-based software tool for both desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) that performs computationally intensive functions on a remote server. These functions include converting data from a variety of file formats into a common format easily manipulated in MATLAB, transforming time-series mass spectra into mass spectrometry images based on a probe spatial raster path, and multivariate analysis. OmniSpect provides an extensible suite of tools to meet the computational requirements needed for visualizing open and proprietary format MSI data.
Mass spectrometry imaging (MSI) performed under ambient conditions is a convenient and information-rich method that allows for the comprehensive mapping of chemical species throughout biological tissues with typical spatial resolution in the 40-200 μm range. Ambient MSI methods such as desorption electrospray ionization (DESI) eliminate necessary sample preparation but suffer from lower spatial resolution than laser-based and vacuum techniques. In order to take advantage of the benefits of ambient imaging and to compensate for the somewhat limited spatial resolution, a secondary orthogonal separation nested in the imaging scheme was implemented for more selective discernment of tissue features in the spectral domain. Differential mobility spectrometry (DMS), an ion mobility-based separation that selectively transmits ions based on their high-to-low electric field mobility differences, can significantly reduce background chemical interferences, allowing for increased peak capacity. In this work, DESI DM-MSI experiments on biological tissue samples such as sea algae and mouse brain tissue sections were conducted using fixed DMS compensation voltages that selectively transferred one or a class of targeted compounds. By reducing chemical noise, the signal-to-noise ratio was improved 10-fold and the image contrast was doubled, effectively increasing image quality.
In this report, we present a robotic sample introduction/ionization system for mass spectrometry (MS) for spot analysis and imaging of non-planar surfaces. The system operates by probing the sample surface with an acupuncture needle, followed by direct plasma chemical ionization time-of-flight MS.
High-grade serous carcinoma (HGSC) is the most common and deadliest form of ovarian cancer. Yet it is largely asymptomatic in its initial stages. Studying the origin and early progression of this disease is thus critical in identifying markers for early detection and screening purposes. Tissue-based mass spectrometry imaging (MSI) can be employed as an unbiased way of examining localized metabolic changes between healthy and cancerous tissue directly, at the onset of disease. In this study, we describe MSI results from Dicer-Pten double-knockout (DKO) mice, a mouse model faithfully reproducing the clinical nature of human HGSC. By using non-negative matrix factorization (NMF) for the unsupervised analysis of desorption electrospray ionization (DESI) datasets, tissue regions are segregated based on spectral components in an unbiased manner, with alterations related to HGSC highlighted. Results obtained by combining NMF with DESI-MSI revealed several metabolic species elevated in the tumor tissue and/or surrounding blood-filled cyst including ceramides, sphingomyelins, bilirubin, cholesterol sulfate, and various lysophospholipids. Multiple metabolites identified within the imaging study were also detected at altered levels within serum in a previous metabolomic study of the same mouse model. As an example workflow, features identified in this study were used to build an oPLS-DA model capable of discriminating between DKO mice with early-stage tumors and controls with up to 88% accuracy.
Mass spectrometry imaging (MSI) is valuable for biomedical applications because it links molecular and morphological information. However, MSI datasets can be very large, and analyzing them to identify important biological patterns is a challenging computational problem. Many types of unsupervised analysis have been applied to MSI data, and in particular, clustering has recently gained attention for this application. In this paper, we present an exploratory study of the performance of different analysis pipelines using k-means and fuzzy k-means clustering. The results indicate the effects of different pre-processing and parameter selections on identifying biologically relevant patterns in MSI data.
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