A major update to the mass spectrometry imaging (MSI) software MSiReader is presented, offering a multitude of newly added features critical to MSI analyses. MSiReader is a free, open-source, and vendor-neutral software written in the MATLAB platform and is capable of analyzing most common MSI data formats. A standalone version of the software, which does not require a MATLAB license, is also distributed. The newly incorporated data analysis features expand the utility of MSiReader beyond simple visualization of molecular distributions. The MSiQuantification tool allows researchers to calculate absolute concentrations from quantification MSI experiments exclusively through MSiReader software, significantly reducing data analysis time. An image overlay feature allows the incorporation of complementary imaging modalities to be displayed with the MSI data. A polarity filter has also been incorporated into the data loading step, allowing the facile analysis of polarity switching experiments without the need for data parsing prior to loading the data file into MSiReader. A quality assurance feature to generate a mass measurement accuracy (MMA) heatmap for an analyte of interest has also been added to allow for the investigation of MMA across the imaging experiment. Most importantly, as new features have been added performance has not degraded, in fact it has been dramatically improved. These new tools and the improvements to the performance in MSiReader v1.0 enable the MSI community to evaluate their data in greater depth and in less time. Graphical Abstract ᅟ.
Mass spectrometry imaging (MSI) is a rapidly evolving field for monitoring the spatial distribution and abundance of analytes in biological tissue sections. It allows for direct and simultaneous analysis of hundreds of different compounds in a label-free manner. In order to obtain a comprehensive metabolite and lipid data, a polarity switching MSI method using infrared matrix assisted laser desorption electrospray ionization (IR-MALDESI) was developed and optimized where the electrospray polarity was alternated from one voxel to the next. Healthy and cancerous ovarian hen tissue sections were analyzed using this method. Distribution and relative abundance of different metabolites and lipids within each tissue section were discerned, and differences between the two were revealed. Additionally, the utility of using mass spectrometry concepts such as spectral accuracy and sulfur counting for confident identification of analytes in an untargeted method are discussed.
Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.
High spatial resolution in mass spectrometry imaging (MSI) is crucial to understanding the biology dictated by molecular distributions in complex tissue systems. Here, we present MSI using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) at 50 μm resolution. An adjustable iris, beam expander, and aspherical focusing lens were used to reduce tissue ablation diameters for MSI at high resolution. The laser beam caustic was modeled using laser ablation paper to calculate relevant laser beam characteristics. The minimum laser spot diameter on the tissue was determined using tissue staining and microscopy. Finally, the newly constructed optical system was used to image hen ovarian tissue with and without oversampling, detailing tissue features at 50 μm resolution.
Laser systems are widely used in mass spectrometry as sample probes and ionization sources. Mid-infrared lasers are particularly suitable for analysis of high water content samples such as animal and plant tissues, using water as a resonantly excited sacrificial matrix. Commercially available mid-IR lasers have historically been bulky and expensive due to cooling requirements. This work presents a novel air-cooled miniature mid-IR laser with adjustable burst-mode output and details an evaluation of its performance for mass spectrometry imaging. The miniature laser was found capable of generating sufficient energy for complete ablation of animal tissue in the context of an IR-MALDESI experiment with exogenously added ice matrix, yielding several hundred confident metabolite identifications. Graphical abstract The use of a novel miniature 2.94 μm burst-mode laser in IR-MALDESI allows for rapid and sensitive mass spectrometry imaging of a whole mouse.
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