@article{eisenberg_muddiman_2024, title={Improved detection in untargeted lipidomics through silver-doped infrared matrix-assisted laser desorption electrospray ionization}, volume={38}, ISSN={["1097-0231"]}, DOI={10.1002/rcm.9832}, abstractNote={Silver doping of electrospray is known to increase the abundance of olefinic compounds detected by mass spectrometry. While demonstrated in targeted experiments, this has yet to be investigated in an untargeted study. Utilizing infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI-MSI), an untargeted lipidomics experiment on mouse liver was performed to evaluate the advantages of silver-doped electrospray.}, number={15}, journal={RAPID COMMUNICATIONS IN MASS SPECTROMETRY}, author={Eisenberg, Seth M. and Muddiman, David C.}, year={2024}, month={Aug} }
 @article{eisenberg_knizner_muddiman_2023, title={Development of an object-based image analysis tool for mass spectrometry imaging ion classification}, volume={5}, ISSN={["1618-2650"]}, DOI={10.1007/s00216-023-04764-x}, abstractNote={Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.}, journal={ANALYTICAL AND BIOANALYTICAL CHEMISTRY}, author={Eisenberg, Seth M. and Knizner, Kevan T. and Muddiman, David C.}, year={2023}, month={May} }
 @article{eisenberg_knizner_muddiman_2023, title={Metabolite Annotation Confidence Score (MACS): A Novel MSI Identification Scoring Tool}, volume={8}, ISSN={["1879-1123"]}, DOI={10.1021/jasms.3c00178}, abstractNote={Mass spectrometry imaging (MSI) is an analytical technique capable of measuring and visualizing the spatial distribution of thousands of ions across a sample. Measured ions can be putatively identified and annotated by comparing their mass-to-charge ratio (m/z) to a database of known compounds. For high-resolution, accurate mass (HRAM) imaging data sets, this is commonly performed by the annotation platform METASPACE. Annotations are reported with a metabolite-signal-match (MSM) score as a measure of the annotation's confidence level. However, the MSM scores reported by METASPACE often do not reflect a reasonable confidence level of an annotation and are not assigned consistently. The metabolite annotation confidence score (MACS) is an alternative scoring system based on fundamental mass spectrometry imaging metrics (mass measurement accuracy, spectral accuracy, and spatial distribution) to generate values that reflect the confidence of a specific annotation in HRAM-MSI data sets. Herein, the MACS system is characterized and compared to MSM scores from ions annotated by METASPACE.}, journal={JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY}, author={Eisenberg, Seth M. and Knizner, Kevan T. and Muddiman, David C.}, year={2023}, month={Aug} }
 @article{knizner_eisenberg_muddiman_2024, title={Prototyping an ionization source for non-engineers}, volume={59}, ISSN={["1096-9888"]}, DOI={10.1002/jms.4995}, abstractNote={AbstractNovel mass spectrometry (MS) based analytical platforms have enabled scientists to detect and quantify molecules within biological and environmental samples more accurately. Novel MS instrumentation starts as a prototype and, after years of development, can become a commercial product to be used by the larger MS community. Without the initial prototype, many MS‐based instruments today would not be produced. Additionally, biotechnology companies are the main drivers for research, development, and production of novel instruments, but the tools for prototyping instrumentation have never been more accessible. Here, we present a tutorial on prototyping instrumentation through the case study of developing the Next Generation IR‐MALDESI source to show that an engineering degree is not required to design and construct a prototype instrument with modern hardware and software. We discuss the prototyping process, the necessary skills required for efficient prototyping, and information about common hardware and software used within initial prototypes.}, number={1}, journal={JOURNAL OF MASS SPECTROMETRY}, author={Knizner, Kevan T. and Eisenberg, Seth M. and Muddiman, David C.}, year={2024}, month={Jan} }