macroMS: Image-Guided Analysis of Random Objects by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Mass spectrometry imaging is well-suited to characterizing sample surfaces for their chemical content in a spatially resolved manner. However, when the surface contains small objects with significant empty spaces between them, more efficient approaches to sample acquisition are possible. Image-guided mass spectrometry (MS) enables high-throughput analysis of a diverse range of sample types, such as microbial colonies, liquid microdroplets, and others, by recognizing and analyzing selected location targets in an image. Here, we describe an imaging protocol and macroMS, an online software suite that can be used to enhance MS measurements of macroscopic samples that are imaged by a camera or a flatbed scanner. The web-based tool enables users to find and filter targets from the optical images, correct optical distortion issues for improved spatial location of selected targets, input the custom geometry files into an MS device to acquire spectra at the selected locations, and finally, perform limited data analysis and use visualization tools to aid locating samples containing compounds of interest. Using the macroMS suite, an enzyme mutant library of Saccharomyces cerevisiae and nL droplet arrays of Escherichia coli and Pseudomonas fluorescens have been assayed at a rate of ∼2 s/sample.

Analysis of Peptide Stereochemistry in Single Cells by Capillary Electrophoresis-Trapped Ion Mobility Spectrometry Mass Spectrometry

Single cell analysis strives to probe molecular heterogeneity in morphologically similar cell populations through quantitative or qualitative measurements of genetic, proteomic, or metabolic products. Here, we applied mass analysis of single neurons to investigate cell-cell signaling peptides. The multiplicity of endogenous cell-cell signaling peptides is a common source of chemical diversity among cell populations. Certain peptides can undergo post-translational isomerization of select residues, which has important physiological consequences. The limited number of single cell analysis techniques that are sensitive to peptide stereochemistry make it challenging to study isomerization at the individual cell level. We performed capillary electrophoresis (CE) with mass spectrometry (MS) detection to characterize the peptide content of single cells. Using complementary trapped ion mobility spectrometry (TIMS) separations, we measured the stereochemical configurations of three neuropeptide gene products derived from the pleurin precursor in individual neurons (N = 3) isolated from the central nervous system of Aplysia californica. An analysis of the resultant mobility profiles indicated >98% of the detectable pleurin-derived peptides exist as the nonisomerized, all-l forms in individual neuron cell bodies. However, we observed 44% of the Plrn2 peptide from the pleurin precursor was present as the isomerized, d-residue-containing form in the nerve tissue. These findings demonstrate an unusual distribution of isomerized peptides in A. californica and establish CE-TIMS MS as a powerful analytical tool for investigating peptide stereochemistry at the single cell level.

3D Particle Free Printing of Biocompatible Conductive Hydrogel Platforms for Neuron Growth and Electrophysiological Recording

Electrically conductive 3D periodic microscaffolds are fabricated using a particle-free direct ink writing approach for use as neuronal growth and electrophysiological recording platforms. A poly (2-hydroxyethyl methacrylate) (pHEMA)/pyrrole ink, followed by chemical in situ polymerization of pyrrole, enables hydrogel printing through nozzles as small as 1 μm. These conductive hydrogels can pattern complex 2D and 3D structures and have good biocompatibility with test cell cultures (~94.5% viability after 7 days). Hydrogel arrays promote extensive neurite outgrowth of cultured Aplysia californica pedal ganglion neurons. This platform allows extracellular electrophysiological recording of steady-state and stimulated electrical neuronal activities. In summation, this 3D conductive ink printing process enables preparation of biocompatible and micron-sized structures to create customized in vitro electrophysiological recording platforms.

Biopolymer Patterning-Directed Secretion in Mucoid and Nonmucoid Strains of Pseudomonas aeruginosa Revealed by Multimodal Chemical Imaging

Quinolone, pyocyanin, and rhamnolipid production were studied in Pseudomonas aeruginosa by spatially patterning mucin, a glycoprotein important to infection of lung epithelia. Mass spectrometric imaging and confocal Raman microscopy are combined to probe P. aeruginosa biofilms from mucoid and nonmucoid strains grown on lithographically defined patterns. Quinolone signatures from biofilms on patterned vs unpatterned and mucin vs mercaptoundecanoic acid (MUA) surfaces were compared. Microbial attachment is accompanied by secretion of 2-alkyl-4-quinolones as well as rhamnolipids from the mucoid and nonmucoid strains. Pyocyanin was also detected both in the biofilm and in the supernatant in the mucoid strain only. Significant differences in the spatiotemporal distributions of secreted factors are observed between strains and among different surface patterning conditions. The mucoid strain is sensitive to composition and patterning while the nonmucoid strain is not, and in promoting community development in the mucoid strain, nonpatterned surfaces are better than patterned, and mucin is better than MUA. Also, the mucoid strain secretes the virulence factor pyocyanin in a way that correlates with distress. A change in the relative abundance for two rhamnolipids is observed in the mucoid strain during exposure to mucin, whereas minimal variation is observed in the nonmucoid strain. Differences between mucoid and nonmucoid strains are consistent with their strain-specific phenology, in which the mucoid strain develops highly protected and withdrawn biofilms that achieve Pseudomonas quinolone signal production under limited conditions.

Enhanced Understanding of Molecular Interactions and Function Underlying Pain Processes Through Networks of Transcript Isoforms, Genes, and Gene Families

Introduction

Molecular networks based on the abundance of mRNA at the gene level and pathway networks that relate families or groups of paralog genes have supported the understanding of interactions between molecules. However, multiple molecular mechanisms underlying health and behavior, such as pain signal processing, are modulated by the abundances of the transcript isoforms that originate from alternative splicing, in addition to gene abundances. Alternative splice variants of growth factors, ion channels, and G-protein-coupled receptors can code for proteoforms that can have different effects on pain and nociception. Therefore, networks inferred using abundance from more agglomerative molecular units (eg, gene family, or gene) have limitations in capturing interactions at a more granular level (eg, gene, or transcript isoform, respectively) do not account for changes in the abundance at the transcript isoform level.

Objective

The objective of this study was to evaluate the relative benefits of network inference using abundance patterns at various aggregate levels.

Methods

Sparse networks were inferred using Gaussian Markov random fields and a novel aggregation criterion was used to aggregate network edges. The relative advantages of network aggregation were evaluated on two molecular systems that have different dimensions and connectivity, circadian rhythm and Toll-like receptor pathways, using RNA-sequencing data from mice representing two pain level groups, opioid-induced hyperalgesia and control, and two central nervous system regions, the nucleus accumbens and the trigeminal ganglia.

Results

The inferred networks were benchmarked against the Kyoto Encyclopedia of Genes and Genomes reference pathways using multiple criteria. Networks inferred using more granular information performed better than networks inferred using more aggregate information. The advantage of granular inference varied with the pathway and data set used.

Discussion

The differences in inferred network structure between data sets highlight the differences in OIH effect between central nervous system regions. Our findings suggest that inference of networks using alternative splicing variants can offer complementary insights into the relationship between genes and gene paralog groups.

Effects of maternal immune activation in porcine transcript isoforms of neuropeptide and receptor genes

The prolonged effects of maternal immune activation in response stressors during gestation on the offspring's molecular pathways after birth are beginning to be understood. An association between maternal immune activation and neurodevelopmental and behavior disorders such as autism and schizophrenia spectrum disorders has been detected in long-term gene dysregulation. The incidence of alternative splicing among neuropeptides and neuropeptide receptor genes, critical cell-cell signaling molecules, associated with behavior may compromise the replicability of reported maternal immune activation effects at the gene level. This study aims to advance the understanding of the effect of maternal immune activation on transcript isoforms of the neuropeptide system (including neuropeptide, receptor and connecting pathway genes) underlying behavior disorders later in life. Recognizing the wide range of bioactive peptides and functional receptors stemming from alternative splicing, we studied the effects of maternal immune activation at the transcript isoform level on the hippocampus and amygdala of three-week-old pigs exposed to maternal immune activation due to viral infection during gestation. In the hippocampus and amygdala, 29 and 9 transcript isoforms, respectively, had maternal immune activation effects (P-value < 0.01). We demonstrated that the study of the effect of maternal immune activation on neuropeptide systems at the isoform level is necessary to expose opposite effects among transcript isoforms from the same gene. Genes were maternal immune activation effects have also been associated with neurodevelopmental and behavior disorders. The characterization of maternal immune activation effects at the transcript isoform level advances the understanding of neurodevelopmental disorders and identifies precise therapeutic targets.

Accelerating Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry Imaging Using a Subspace Approach

We present a subspace method that accelerates data acquisition using Fourier transform-ion cyclotron resonance (FT-ICR) mass spectrometry imaging (MSI). For MSI of biological tissue samples, there is a finite number of heterogeneous tissue types with distinct chemical profiles that introduce redundancy in the high-dimensional measurements. Our subspace model exploits the redundancy in data measured from whole-slice tissue samples by decomposing the transient signals into linear combinations of a set of basis transients with the desired spectral resolution. This decomposition allowed us to design a strategy that acquires a subset of long transients for basis determination and short transients for the remaining pixels, drastically reducing the acquisition time. The computational reconstruction strategy can maintain high-mass-resolution and spatial-resolution MSI while providing a 10-fold improvement in throughput. We validated the capability of the subspace model using a rat sagittal brain slice imaging data set. Comprehensive evaluation of the quality of the mass spectral and ion images demonstrated that the reconstructed data produced by the reported method required only 15% of the typical acquisition time and exhibited both qualitative and quantitative consistency when compared to the original data. Our method enables either higher sample throughput or higher-resolution images at similar acquisition lengths, providing greater flexibility in obtaining FT-ICR MSI measurements.

Removing Formaldehyde-Induced Peptidyl Crosslinks Enables Mass Spectrometry Imaging of Peptide Hormone Distributions from Formalin-Fixed Paraffin-Embedded Tissues

Linking molecular and chemical changes to human disease states depends on the availability of appropriate clinical samples, mostly preserved as formalin-fixed paraffin-embedded (FFPE) specimens stored in tissue banks. Mass spectrometry imaging (MSI) enables the visualization of the spatiotemporal distribution of molecules in biological samples. However, MSI is not effective for imaging FFPE tissues because of the chemical modifications of analytes, including complex crosslinking between nucleophilic moieties. Here we used an MS-compatible inorganic nucleophile, hydroxylamine hydrochloride, to chemically reverse inter- and intra-crosslinks from endogenous molecules. The analyte restoration appears specific for formaldehyde-reactive amino acids. This approach enabled the MSI-assisted localization of pancreatic peptides expressed in the alpha, beta, and gamma cells. Pancreatic islet-like distributions of islet hormones were observed in human FFPE tissues preserved for more than five years, demonstrating that samples from biobanks can effectively be investigated with MSI.

Advancing d-amino acid-containing peptide discovery in the metazoan

The discovery of enzyme-derived d-amino acid-containing peptides (DAACPs) that have physiological importance in the metazoan challenges previous assumptions about the homochirality of animal proteins while simultaneously revealing new analytical challenges in the structural and functional characterization of peptides. Most known DAACPs have been identified though laborious activity-guided purification studies or by homology to previously identified DAACPs. Peptide characterization experiments are increasingly dominated by high throughput mass spectrometry-based peptidomics, with stereochemistry rarely considered due to the technical challenges of identifying l/d isomerization. This review discusses the prevalence of enzyme-derived DAACPs among animals and the physiological consequences of peptide isomerization. Also highlighted are the analytical methods that have been applied for structural characterization/discovery of DAACPs, including results of several recent studies using non-targeted discovery methods for revealing novel DAACPs, strongly suggesting that more DAACPs remain to be uncovered.

Biphasic Liquid Microjunction Extraction for Profiling Neuronal RNA Modifications by Liquid Chromatography-Tandem Mass Spectrometry

RNA modifications are emerging as critical players in the spatiotemporal regulation of gene expression. Although liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables the simultaneous quantification of numerous enzymatically modified RNAs in a biological sample, conventional RNA extraction and enzymatic digestion protocols that are employed prior to analysis have precluded the application of this technique for small-volume samples. In this study, a biphasic liquid microjunction (LMJ) extraction system using coaxial capillaries that direct and aspirate extraction solvents onto a ∼350 μm diameter sample spot was developed and applied for the extraction of RNA from individual cell clusters in the central nervous system of the marine mollusk Aplysia californica. To maximize RNA recoveries, optimized extraction solvents consisting of 10% methanol and chloroform were evaluated under dynamic and static extraction conditions. An MS-compatible RNA digestion buffer was developed to minimize the number of sample-transfer steps and facilitate the direct enzymatic digestion of extracted RNA within the sample collection tube. Compared to RNA extraction using a conventional phenol-chloroform method, the LMJ-based method provided a 3-fold greater coverage of the neuronal epitranscriptome for similar amounts of tissues and also produced mRNA of sufficient purity for reverse transcription polymerase chain reaction amplification. Using this approach, the expression of RNA-modifying enzymes in a given neuronal cell cluster can be characterized and simultaneously correlated with the LC-MS/MS analysis of RNA modifications within the same subset of neurons.

Single-Cell Classification Using Mass Spectrometry through Interpretable Machine Learning

The brain consists of organized ensembles of cells that exhibit distinct morphologies, cellular connectivity, and dynamic biochemistries that control the executive functions of an organism. However, the relationships between chemical heterogeneity, cell function, and phenotype are not always understood. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry have enabled the high-throughput, multiplexed chemical analysis of single cells, capable of resolving hundreds of molecules in each mass spectrum. We developed a machine learning workflow to classify single cells according to their mass spectra based on cell groups of interest (GOI), e.g., neurons vs astrocytes. Three data sets from various cell groups were acquired on three different mass spectrometer platforms representing thousands of individual cell spectra that were collected and used to validate the single cell classification workflow. The trained models achieved >80% classification accuracy and were subjected to the recently developed instance-based model interpretation framework, SHapley Additive exPlanations (SHAP), which locally assigns feature importance for each single-cell spectrum. SHAP values were used for both local and global interpretations of our data sets, preserving the chemical heterogeneity uncovered by the single-cell analysis while offering the ability to perform supervised analysis. The top contributing mass features to each of the GOI were ranked and selected using mean absolute SHAP values, highlighting the features that are specific to the defined GOI. Our approach provides insight into discriminating the chemical profiles of the single cells through interpretable machine learning, facilitating downstream analysis and validation.

Quantitative Imprint Mass Spectrometry Imaging of Endogenous Ceramides in Rat Brain Tissue with Kinetic Calibration

Quantitative mass spectrometry imaging (MSI) is an effective technique for determining the spatial distribution of molecules in a variety of sample types; however, the quality of the ion signals is related to the chemical and morphological properties of the tissue and the targeted analyte(s). Issues may arise with the incorporation of standards into the tissue at repeatable, well-defined concentrations, as well as with the extraction and incorporation of endogenous analytes versus standards from tissue into the matrix. To address these concerns, we combine imprint MSI (iMSI) with kinetic calibration and use it to quantify lipids in rat brain tissue samples. Briefly, tissues were imprinted on slides coated with a dopamine-modified TiO₂ monolith pretreated with analyte standards, resulting in the adsorption of endogenous analytes onto the coating and desorption of standards into the tissue. The incorporation of standards into the tissue enabled quantification of the measured analytes using kinetic calibration. Moreover, matrix effects were reduced, and the intensities of analyte standard signals became more uniform. The symmetry of the adsorption of endogenous ceramides and the desorption of ceramide standards suggest that the content of adsorbed endogenous ceramide can be determined by measuring the content of desorbed ceramide standard. Using kinetic calibration, endogenous ceramide concentrations were calculated for a range of dry and wet tissue imprinting conditions and compared to quantitative MSI using a standard spiking approach. We validated the relative quantitative values from iMSI using liquid chromatography tandem mass spectrometry (LC-MS/MS) and found that the ratios from iMSI as compared to LC-MS/MS were in the range of 70-200% over the concentration range of endogenous ceramides; the correlation coefficient between iMSI and LC-MS/MS was over 0.9 (Pearson's r), while the relative recoveries via traditional standard spiking were between 200% and 5000% depending on the brain region and sample preparation conditions.

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs) as key components of crucial nutrients, soaps, industrial chemicals, and fuels. However, our ability to control the composition of microbially synthesized FFAs is still limited, particularly, for producing medium-chain fatty acids (MCFAs). This is mainly due to the lack of high-throughput approaches for FFA analysis to engineer enzymes with desirable product specificity. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. In particular, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and a higher m/z peak ratio at 730 and 758 was used as an indication for improved MCFA production. This colony-based method can be performed at a rate of ~2 s per sample, representing a substantial improvement over gas chromatography-MS (typically >30 min per sample) as the gold standard method for FFA detection. To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified nine missense mutations that resulted in improved MCFA production relative to the wild-type strain. Colony-based MALDI-ToF MS screening provides an effective approach for engineering microbial fatty acid compositions in a high-throughput manner.

Characterization of the prohormone complement in Amphiprion and related fish species integrating genome and transcriptome assemblies

The Amphiprion (anemonefish or clownfish) family of teleost fish, which is not a common model species, exhibits multiple unique characteristics, including social control of body size and protandrous sex change. The social changes in sex and body size are modulated by neuropeptide signaling pathways. These neuropeptides are formed from complex processing from larger prohormone proteins; understanding the neuropeptide complement requires information on complete prohormones sequences. Genome and transcriptome information within and across 22 teleost fish species, including 11 Amphiprion species, were assembled and integrated to achieve the first comprehensive survey of their prohormone genes. This information enabled the identification of 175 prohormone isoforms from 159 prohormone proteins across all species. This included identification of 9 CART prepropeptide genes and the loss of insulin-like 5B and tachykinin precursor 1B genes in Pomacentridae species. Transcriptome assemblies generally detected most prohormone genes but provided fewer prohormone genes than genome assemblies due to the lack of expression of prohormone genes or specific isoforms and tissue sampled. Comparisons between duplicate genes indicated that subfunctionalization, degradation, and neofunctionalization may be occurring between all copies. Characterization of the prohormone complement lays the foundation for future peptidomic investigation of the molecular basis of social physiology and behavior in the teleost fish.

Quantitative Characterization of the Neuropeptide Level Changes in Dorsal Horn and Dorsal Root Ganglia Regions of the Murine Itch Models

Chronic itch can be extremely devastating and, in many cases, difficult to treat. One challenge in treating itch disorders is the limited understanding of the multitude of chemical players involved in the communication of itch sensation from the peripheral to the central nervous system. Neuropeptides are intercellular signaling molecules that are known to be involved in the transmission of itch signals from primary afferent neurons, which detect itch in the skin, to higher-order circuits in the spinal cord and brain. To investigate the role of neuropeptides in transmitting itch signals, we generated two mouse models of chronic itch-Acetone-Ether-Water (AEW, dry skin) and calcipotriol (MC903, atopic dermatitis). For peptide identification and quantitation, we analyzed the peptide content of dorsal root ganglia (DRG) and dorsal horn (DH) tissues from chronically itchy mice using liquid chromatography coupled to tandem mass spectrometry. De novo-assisted database searching facilitated the identification and quantitation of 335 peptides for DH MC903, 318 for DH AEW, 266 for DRG MC903, and 271 for DRG AEW. Of these quantifiable peptides, we detected 30 that were differentially regulated in the tested models, after accounting for multiple testing correction (q ≤ 0.1). These include several peptide candidates derived from neuropeptide precursors, such as proSAAS, protachykinin-1, proenkephalin, and calcitonin gene-related peptide, some of them previously linked to itch. The peptides identified in this study may help elucidate our understanding about these debilitating disorders. Data are available via ProteomeXchange with identifier PXD015949.