An adaptive teosinte mexicana introgression modulates phosphatidylcholine levels and is associated with maize flowering time.

Native Americans domesticated maize (Zea mays ssp. mays) from lowland teosinte parviglumis (Zea mays ssp. parviglumis) in the warm Mexican southwest and brought it to the highlands of Mexico and South America where it was exposed to lower temperatures that imposed strong selection on flowering time. Phospholipids are important metabolites in plant responses to low-temperature and phosphorus availability and have been suggested to influence flowering time. Here, we combined linkage mapping with genome scans to identify High PhosphatidylCholine 1 (HPC1), a gene that encodes a phospholipase A1 enzyme, as a major driver of phospholipid variation in highland maize. Common garden experiments demonstrated strong genotype-by-environment interactions associated with variation at HPC1, with the highland HPC1 allele leading to higher fitness in highlands, possibly by hastening flowering. The highland maize HPC1 variant resulted in impaired function of the encoded protein due to a polymorphism in a highly conserved sequence. A meta-analysis across HPC1 orthologs indicated a strong association between the identity of the amino acid at this position and optimal growth in prokaryotes. Mutagenesis of HPC1 via genome editing validated its role in regulating phospholipid metabolism. Finally, we showed that the highland HPC1 allele entered cultivated maize by introgression from the wild highland teosinte Zea mays ssp. mexicana and has been maintained in maize breeding lines from the Northern United States, Canada, and Europe. Thus, HPC1 introgressed from teosinte mexicana underlies a large metabolic QTL that modulates phosphatidylcholine levels and has an adaptive effect at least in part via induction of early flowering time.

Evaluating the optimal tissue thickness for mass spectrometry imaging using infrared matrix-assisted laser desorption electrospray ionization.

RATIONALE: Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) utilizes a 2970 nm mid-IR laser to desorb samples with depth resolutions (Z) on the order of micrometers. Conventionally, 5-20 µm thick tissue sections are used to characterize different applications of the IR-MALDESI source, but an optimal thickness has not been systematically investigated. METHODS: Mouse liver was sectioned to various thicknesses and analyzed using IR-MALDESI mass spectrometry imaging (MSI). Height profiles of tissue sections of various cryosectioned thicknesses were acquired to affirm tissue thickness. Tissue sections of each thickness were measured using a Keyence microscope. Paraffin wax was cryosectioned, mounted on microscope slides, and measured using a chromatic confocal sensor system to determine the cryostat sectioning accuracy. RESULTS: Analyzing sectioned tissues at higher thickness (>10 µm) leads to lower ion abundance, a decrease in signal over long analysis times, and more frequent instrument cleaning. Additionally, increasing tissue thickness above the optimum (7 µm) does not result in a significant increase in lipid annotations. CONCLUSIONS: This work defines an optimal sample thickness for IR-MALDESI-MSI and demonstrates the utility of optimizing tissue thickness for MSI platforms of comparable Z resolution.

Development of an object-based image analysis tool for mass spectrometry imaging ion classification.

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.

Prototyping an ionization source for non-engineers.

Novel 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.

Metabolite Annotation Confidence Score (MACS): A Novel MSI Identification Scoring Tool.

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.

Automatic z-Axis Correction for IR-MALDESI Mass Spectrometry Imaging of Uneven Surfaces.

Two-dimensional mass spectrometry imaging (2D MSI) experiments mainly involve samples with a flat surface and constant thickness, but some samples are challenging to section due to the texture and topography. Herein, we present an MSI method that automatically corrects for discernible height differences across surfaces during imaging experiments. A chromatic confocal sensor was incorporated into the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) system to measure the sample surface height at the location of each analytical scan. The height profile is subsequently used for adjusting the z-axis position of the sample during MSI data acquisition. We evaluated this method using a tilted mouse liver section and an unsectioned Prilosec tablet due to their exterior quasi-homogeneity and height differences of approximately ∼250 µm. MSI with automatic z-axis correction showed consistent ablated spot sizes and shapes, revealing the measured ion spatial distribution across a mouse liver section and a Prilosec tablet. Conversely, irregular spots and reduced signals with large variability were observed when no z-axis correction was applied.

Maximized Spatial Information and Minimized Acquisition Time of Top-Hat IR-MALDESI-MSI of Zebrafish Using Nested Regions of Interest (nROIs).

Increasing the spatial resolution of a mass spectrometry imaging (MSI) method results in a more defined heatmap of the spatial distribution of molecules across a sample, but it is also associated with the disadvantage of increased acquisition time. Decreasing the area of the region of interest to achieve shorter durations results in the loss of potentially valuable information in larger specimens. This work presents a novel MSI method to reduce the time of MSI data acquisition with variable step size imaging: nested regions of interest (nROIs). Using nROIs, a small ROI may be imaged at a higher spatial resolution while nested inside a lower-spatial-resolution peripheral ROI. This conserves the maximal spatial and chemical information generated from target regions while also decreasing the necessary acquisition time. In this work, the nROI method was characterized on mouse liver and applied to top-hat MSI of zebrafish using a novel optical train, which resulted in a significant improvement in both acquisition time and spatial detail of the zebrafish. The nROI method can be employed with any step size pairing and adapted to any method in which the acquisition time of larger high-resolution ROIs poses a practical challenge.

Normalization techniques for high-throughput screening by infrared matrix-assisted laser desorption electrospray ionization mass spectrometry.

Mass spectrometry (MS) is an effective analytical tool for high-throughput screening (HTS) in the drug discovery field. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MS is a high-throughput platform that has achieved analysis times of sub-seconds-per-sample. Due to the high-throughput analysis speed, methods are needed to increase the analyte signal while decreasing the variability in IR-MALDESI-MS analyses to improve data quality and reduce false-positive hits. The Z-factor is used as a statistic of assay quality that can be improved by reducing the variation of target ion abundances or increasing signal. Herein we report optimal solvent compositions for increasing measured analyte abundances with direct analysis by IR-MALDESI-MS. We also evaluate normalization strategies, such as adding a normalization standard that is similar or dissimilar in structure to the model target drug, to reduce the variability of measured analyte abundances with direct analyses by IR-MALDESI-MS in both positive and negative ionization modes.

On the importance of color in mass spectrometry imaging.

Mass spectrometry imaging (MSI) data visualization relies on heatmaps to show the spatial distribution and measured abundances of molecules within a sample. Nonuniform color gradients such as jet are still commonly used to visualize MSI data, increasing the probability of data misinterpretation and false conclusions. Also, the use of nonuniform color gradients and the combination of hues used in common colormaps make it challenging for people with color vision deficiencies (CVDs) to visualize and accurately interpret data. Here we present best practices for choosing a colormap to accurately display MSI data, improve readability, and accommodate all CVDs. We also provide other resources on the misuse of color in the scientific field and resources on scientifically derived colormaps presented herein.

High-Throughput Deconvolution of Intact Protein Mass Spectra for the Screening of Covalent Inhibitors.

Deconvolution from intact protein mass-to-charge spectra to mass spectra is essential to generate interpretable data for mass spectrometry (MS) platforms coupled to ionization sources that produce multiply charged species. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) can be used to analyze intact proteins in multiwell microtiter plates with speed matching small molecule analyses (at least 1 Hz). However, the lack of compatible deconvolution software has limited its use in high-throughput screening applications. Most existing automated deconvolution software packages work best for data generated from LC-MS, and to the best of our knowledge, there is no software capable of performing fast plate-based mass spectral deconvolution. Herein we present the use of a new workflow in ProSight Native for the deconvolution of protein spectra from entire well plates that can be completed within 3 s. First, we successfully demonstrated the potential increased throughput benefits produced by the combined IR-MALDESI-MS - ProSight Native platform using protein standards. We then conducted a screen for Bruton's tyrosine kinase (BTK) covalent binders against a well-annotated compound collection consisting of 2232 compounds and applied ProSight Native to deconvolute the protein spectra. Seventeen hits including five known BTK covalent inhibitors in the compound set were identified. By alleviating the data processing bottleneck using ProSight Native, it may be feasible to analyze and report covalent screening results for >200,000 samples in a single day.

Optimized C-Trap Timing of an Orbitrap 240 Mass Spectrometer for High-Throughput Screening and Native MS by IR-MALDESI.

Infrared matrix-assisted laser desorption ionization (IR-MALDESI) is a hybrid mass spectrometry ionization source that combines the benefits of electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) making it a great analytical tool for high-throughput screening (HTS) analyses. IR-MALDESI is coupled to an Orbitrap Exploris 240 mass spectrometer that utilizes a bent quadrupole (C-trap) to inject accumulated ions into the high-field Orbitrap mass analyzer. Here, we present a study on the optimized C-trap timing for HTS analyses by IR-MALDESI mass spectrometry. The timing between initial ion generation and the C-trap opening time was optimized to reduce unnecessary ambient ion accumulation in the mass spectrometer. The time in which the C-trap was held open, the ion accumulation time, was further optimized to maximize the accumulation of analyte ions generated using IR-MALDESI. The resulting C-trap opening scheme benefits small-molecule HTS analyses by IR-MALDESI by maximizing target ion abundances, minimizing ambient ion abundances, and minimizing the total analysis time per sample. The proposed C-trap timing scheme for HTS does not translate to large molecules; a NIST monoclonal antibody standard reference material was analyzed to demonstrate that larger analytes require longer ion accumulation times and that IR-MALDESI can measure intact antibodies in their native state.

Next-Generation Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Source for Mass Spectrometry Imaging and High-Throughput Screening.

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a hybrid, ambient ionization source that combines the advantages of electrospray ionization and matrix-assisted laser desorption/ionization, making it a versatile tool for both high-throughput screening (HTS) and mass spectrometry imaging (MSI) studies. To expand the capabilities of the IR-MALDESI source, an entirely new architecture was designed to overcome the key limitations of the previous source. This next-generation (NextGen) IR-MALDESI source features a vertically mounted IR-laser, a planar translation stage with computerized sample height control, an aluminum enclosure, and a novel mass spectrometer interface plate. The NextGen IR-MALDESI source has improved user-friendliness, improved overall versatility, and can be coupled to numerous Orbitrap mass spectrometers to accommodate more research laboratories. In this work, we highlight the benefits of the NextGen IR-MALDESI source as an improved platform for MSI and direct analysis. We also optimize the NextGen MALDESI source component geometries to increase target ion abundances over a wide m/z range. Finally, documentation is provided for each NextGen IR-MALDESI part so that it can be replicated and incorporated into any lab space.