Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry.

Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

A novel series of metazoan L/D peptide isomerases.

The function of endogenous cell-cell signaling peptides rely on their interactions with cognate receptors, which in turn are influenced by the peptides' structures, necessitating a comprehensive understanding of the suite of post-translational modifications (PTMs) of the peptide. Herein, we report the initial characterization of putative peptide isomerase enzymes extracted from R. norvegicus, A. californica, and B. taurus tissues. These enzymes are both tissue and substrate-specific across all three organisms. Notably, the lungs of the mammalian species, and the central nervous system of the mollusk displayed the highest isomerase activity among the examined tissues. In-vitro enzymatic conversion was observed for several endogenous peptides, such as the tetrapeptide GFFD in A. californica, and mammalian neuropeptide FF in R. norvegicus and B. taurus. To understand their mode of action, we explored the effects of several inhibitors on these enzymes, which suggests common active site residues. While further characterization of these enzymes is required, the investigations emphasize a widespread and overlooked enzyme activity related to the creation of bioactive peptides.

Image-guided MALDI mass spectrometry for high-throughput single-organelle characterization.

Peptidergic dense-core vesicles are involved in packaging and releasing neuropeptides and peptide hormones-critical processes underlying brain, endocrine and exocrine function. Yet, the heterogeneity within these organelles, even for morphologically defined vesicle types, is not well characterized because of their small volumes. We present image-guided, high-throughput mass spectrometry-based protocols to chemically profile large populations of both dense-core vesicles and lucent vesicles for their lipid and peptide contents, allowing observation of the chemical heterogeneity within and between these two vesicle populations. The proteolytic processing products of four prohormones are observed within the dense-core vesicles, and the mass spectral features corresponding to the specific peptide products suggest three distinct dense-core vesicle populations. Notable differences in the lipid mass range are observed between the dense-core and lucent vesicles. These single-organelle mass spectrometry approaches are adaptable to characterize a range of subcellular structures.

High-resolution 1H-MRSI at 9.4 T by integrating relaxation enhancement and subspace imaging.

Achieving high-resolution and high signal-to-noise ratio (SNR) in vivo metabolic imaging via fast magnetic resonance spectroscopic imaging (MRSI) has been a longstanding challenge. This study combines the methods of relaxation enhancement (RE) and subspace imaging for the first time, enabling high-resolution and high-SNR in vivo MRSI of rodent brains at 9.4 T. Specifically, an RE-based chemical shift imaging sequence, which combines a frequency-selective pulse to excite only the metabolite frequencies with minimum perturbation of the water spins and a pair of adiabatic pulses to spatially localize the slice of interest, is designed and evaluated in vivo. This strategy effectively shortens the apparent T1 of metabolites, thereby increasing the SNR during relatively short repetition time ((TR) compared with acquisitions with only spatially selective wideband excitations, and does not require water suppression. The SNR was further enhanced via a state-of-the-art subspace reconstruction method. A novel subspace learning strategy tailored for 9.4 T and RE acquisitions is developed. In vivo, high-resolution (e.g., voxel size of 0.6 × 0.6 × 1.5 mm3) MRSI of both healthy mouse brains and a glioma-bearing mouse brain in 12.5 min has been demonstrated.

Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry.

Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.

Profiling 26,000 Aplysia californica neurons by single cell mass spectrometry reveals neuronal populations with distinct neuropeptide profiles.

Neuropeptides are a chemically diverse class of cell-to-cell signaling molecules that are widely expressed throughout the central nervous system, often in a cell-specific manner. While cell-to-cell differences in neuropeptides is expected, it is often unclear how exactly neuropeptide expression varies among neurons. Here we created a microscopy-guided, high-throughput single cell matrix-assisted laser desorption/ionization mass spectrometry approach to investigate the neuropeptide heterogeneity of individual neurons in the central nervous system of the neurobiological model Aplysia californica, the California sea hare. In all, we analyzed more than 26,000 neurons from 18 animals and assigned 866 peptides from 66 prohormones by mass matching against an in silico peptide library generated from known Aplysia prohormones retrieved from the UniProt database. Louvain-Jaccard (LJ) clustering of mass spectra from individual neurons revealed 40 unique neuronal populations, or LJ clusters, each with a distinct neuropeptide profile. Prohormones and their related peptides were generally found in single cells from ganglia consistent with the prohormones' previously known ganglion localizations. Several LJ clusters also revealed the cellular colocalization of behaviorally related prohormones, such as an LJ cluster exhibiting achatin and neuropeptide Y, which are involved in feeding, and another cluster characterized by urotensin II, small cardiac peptide, sensorin A, and FRFa, which have shown activity in the feeding network or are present in the feeding musculature. This mass spectrometry-based approach enables the robust categorization of large cell populations based on single cell neuropeptide content and is readily adaptable to the study of a range of animals and tissue types.

Chemical Decrosslinking-Based Peptide Characterization of Formaldehyde-Fixed Rat Pancreas Using Fluorescence-Guided Single-Cell Mass Spectrometry.

Approaches for the characterization of proteins/peptides in single cells of formaldehyde-fixed (FF) tissues via mass spectrometry (MS) are still under development. The lack of a general method for selectively eliminating formaldehyde-induced crosslinking is a major challenge. A workflow is shown for the high-throughput peptide profiling of single cells isolated from FF tissues, here the rodent pancreas, which possesses multiple peptide hormones from the islets of Langerhans. The heat treatment is enhanced by a collagen-selective multistep thermal process assisting efficient isolation of islets from the FF pancreas and, subsequently, their dissociation into single islet cells. Hydroxylamine-based chemical decrosslinking helped restore intact peptide signals from individual isolated cells. Subsequently, an acetone/glycerol-assisted cell dispersion was optimized for spatially resolved cell deposition onto glass slides, while a glycerol solution maintained the hydrated state of the cells. This sample preparation procedure allowed peptide profiling in FF single cells by fluorescence-guided matrix-assisted laser desorption ionization MS. Here, 2594 single islet cells were analyzed and 28 peptides were detected, including insulin C-peptides and glucagon. T-distributed stochastic neighbor embedding (t-SNE) data visualization demonstrated that cells cluster based on cell-specific pancreatic peptide hormones. This workflow expands the sample availability for single-cell MS characterization to a wide range of formaldehyde-treated tissue specimens stored in biobanks.

Single-Cell and Subcellular Analysis Using Ultrahigh Resolution 21 T MALDI FTICR Mass Spectrometry.

The mammalian brain contains ∼20,000 distinct lipid species that contribute to its structural organization and function. The lipid profiles of cells change in response to a variety of cellular signals and environmental conditions that result in modulation of cell function through alteration of phenotype. The limited sample material combined with the vast chemical diversity of lipids makes comprehensive lipid profiling of individual cells challenging. Here, we leverage the resolving power of a 21 T Fourier-transform ion cyclotron resonance (FTICR) mass spectrometer for chemical characterization of individual hippocampal cells at ultrahigh mass resolution. The accuracy of the acquired data allowed differentiation of freshly isolated and cultured hippocampal cell populations, as well as finding differences in lipids between the soma and neuronal processes of the same cell. Differences in lipids include TG 42:2 observed solely in the cell bodies and SM 34:1;O2 found only in the cellular processes. The work represents the first mammalian single cells analyzed at ultrahigh resolution and is an advance in the performance of mass spectrometry (MS) for single-cell research.

Enhancing the Throughput of FT Mass Spectrometry Imaging Using Joint Compressed Sensing and Subspace Modeling.

Mass spectrometry imaging (MSI) allows for untargeted mapping of the chemical composition of tissues with attomole detection limits. MSI using Fourier transform (FT)-based mass spectrometers, such as FT-ion cyclotron resonance (FT-ICR), grants the ability to examine the chemical space with unmatched mass resolution and mass accuracy. However, direct imaging of large tissue samples using FT-ICR is slow. In this work, we present an approach that combines the subspace modeling of ICR temporal signals with compressed sensing to accelerate high-resolution FT-ICR MSI. A joint subspace and spatial sparsity constrained model computationally reconstructs high-resolution MSI data from the sparsely sampled transients with reduced duration, allowing a significant reduction in imaging time. Simulation studies and experimental implementation of the proposed method in investigation of brain tissues demonstrate a 10-fold enhancement in throughput of FT-ICR MSI, without the need for instrumental or hardware modifications.

Single-Neuron RNA Modification Analysis by Mass Spectrometry: Characterizing RNA Modification Patterns and Dynamics with Single-Cell Resolution.

The entire collection of post-transcriptional modifications to RNA, known as the epitranscriptome, has been increasingly recognized as a critical regulatory layer in the cellular translation machinery. However, contemporary methods for the analysis of RNA modifications are limited to the detection of highly abundant modifications in bulk tissue samples, potentially obscuring unique epitranscriptomes of individual cells with population averages. We developed an approach, single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS), that enables the detection and quantification of numerous post-transcriptionally modified nucleosides in single cells. When compared to a conventional RNA extraction approach that does not allow detection of RNA modifications in single cells, SNRMA-MS leverages an optimized sample preparation approach to detect up to 16 RNA modifications in individual neurons from the central nervous system of Aplysia californica. SNRMA-MS revealed that the RNA modification profiles of identified A. californica neurons with different physiological functions were mostly cell specific. However, functionally homologous neurons tended to demonstrate similar modification patterns. Stable isotope labeling with CD3-Met showed significant differences in RNA methylation rates that were dependent on the identity of the modification and the cell, with metacerebral cells (MCCs) displaying the fastest incorporation of CD3 groups into endogenous RNAs. Quantitative SNRMA-MS showed higher intracellular concentrations for 2'-O-methyladenosine and 2'-O-methylcytidine in homologous R2/LPl1 cell pairs than in MCCs. Overall, SNRMA-MS is the first analytical approach capable of simultaneously quantifying numerous RNA modifications in single neurons and revealing cell-specific modification profiles.

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.

PACAP and Other Neuropeptide Targets Link Chronic Migraine and Opioid-induced Hyperalgesia in Mouse Models.

Chronic use of opioids can produce opioid-induced hyperalgesia (OIH), and when used to treat migraine, these drugs can result in increased pain and headache chronicity. We hypothesized that overlapping mechanisms between OIH and chronic migraine occur through neuropeptide dysregulation. Using label-free, non-biased liquid chromatography-mass spectrometry to identify and measure changes in more than 1500 neuropeptides under these two conditions, we observed only 16 neuropeptides that were altered between the two conditions. The known pro-migraine molecule, calcitonin-gene related peptide, was among seven peptides associated with chronic migraine, with several pain-processing neuropeptides among the nine other peptides affected in OIH. Further, composite peptide complements Pituitary adenylate cyclase-activating polypeptide (PACAP), Vasoactive intestinal peptide (VIP) and Secretogranin (SCG) showed significant changes in both chronic migraine and OIH. In a follow-up pharmacological study, we confirmed the role of PACAP in models of these two disorders, validating the effectiveness of our peptidomic approach, and identifying PACAP as a mechanistic link between chronic migraine and OIH. Data are available via ProteomeXchange with identifier PXD013362.

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.

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.

Three-dimensional mesostructures as high-temperature growth templates, electronic cellular scaffolds, and self-propelled microrobots.

Recent work demonstrates that processes of stress release in prestrained elastomeric substrates can guide the assembly of sophisticated 3D micro/nanostructures in advanced materials. Reported application examples include soft electronic components, tunable electromagnetic and optical devices, vibrational metrology platforms, and other unusual technologies, each enabled by uniquely engineered 3D architectures. A significant disadvantage of these systems is that the elastomeric substrates, while essential to the assembly process, can impose significant engineering constraints in terms of operating temperatures and levels of dimensional stability; they also prevent the realization of 3D structures in freestanding forms. Here, we introduce concepts in interfacial photopolymerization, nonlinear mechanics, and physical transfer that bypass these limitations. The results enable 3D mesostructures in fully or partially freestanding forms, with additional capabilities in integration onto nearly any class of substrate, from planar, hard inorganic materials to textured, soft biological tissues, all via mechanisms quantitatively described by theoretical modeling. Illustrations of these ideas include their use in 3D structures as frameworks for templated growth of organized lamellae from AgCl-KCl eutectics and of atomic layers of WSe2 from vapor-phase precursors, as open-architecture electronic scaffolds for formation of dorsal root ganglion (DRG) neural networks, and as catalyst supports for propulsive systems in 3D microswimmers with geometrically controlled dynamics. Taken together, these methodologies establish a set of enabling options in 3D micro/nanomanufacturing that lie outside of the scope of existing alternatives.

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.

Multidimensional Top-Down Proteomics of Brain-Region-Specific Mouse Brain Proteoforms Responsive to Cocaine and Estradiol.

Cocaine addiction afflicts nearly 1 million adults in the United States, and to date, there are no known treatments approved for this psychiatric condition. Women are particularly vulnerable to developing a cocaine use disorder and suffer from more serious cardiac consequences than men when using cocaine. Estrogen is one biological factor contributing to the increased risk for females to develop problematic cocaine use. Animal studies have demonstrated that estrogen (17ß-estradiol or E2) enhances the rewarding properties of cocaine. Although E2 affects the dopamine system, the molecular and cellular mechanisms of E2-enhanced cocaine reward have not been characterized. In this study, quantitative top-down proteomics was used to measure intact proteins in specific regions of the female mouse brain after mice were trained for cocaine-conditioned place preference, a behavioral test of cocaine reward. Several proteoform changes occurred in the ventral tegmental area after combined cocaine and E2 treatments, with the most numerous proteoform alterations on myelin basic protein, indicating possible changes in white matter structure. There were also changes in histone H4, protein phosphatase inhibitors, cholecystokinin, and calmodulin proteoforms. These observations provide insight into estrogen signaling in the brain and may guide new approaches to treating women with cocaine use disorder.

Lipid Analysis of 30 000 Individual Rodent Cerebellar Cells Using High-Resolution Mass Spectrometry.

Single-cell measurements aid our understanding of chemically heterogeneous systems such as the brain. Lipids are one of the least studied chemical classes, and their cell-to-cell heterogeneity remains largely unexplored. We adapted microscopy-guided single-cell profiling using matrix-assisted laser desorption/ionization ion cyclotron resonance mass spectrometry to profile the lipid composition of over 30 000 individual rat cerebellar cells. We detected 520 lipid features, many of which were found in subsets of cells; Louvain clustering identified 101 distinct groups that can be correlated to neuronal and astrocytic classifications and lipid classes. Overall, the two most common lipids found were [PC(32:0)+H]+ and [PC(34:1)+H]+, which were present within 98.9 and 89.5% of cells, respectively; lipid signals present in <1% of cells were also detected, including [PC(34:1)+K]+ and [PG(40:2(OH))+Na]+. These results illustrate the vast lipid heterogeneity found within rodent cerebellar cells and hint at the distinct functional consequences of this heterogeneity.

Lipid Heterogeneity between Astrocytes and Neurons Revealed by Single-Cell MALDI-MS Combined with Immunocytochemical Classification.

Transcriptomics characterizes cells based on their potential molecular repertoire whereas direct mass spectrometry (MS) provides information on the actual compounds present within cells. Single-cell matrix-assisted laser desorption/ionization (MALDI) MS can measure the chemical contents of individual cells but spectra are difficult to correlate to conventional cell types, limiting the metabolic information obtained. We present a protocol that combines MALDI-MS with immunocytochemistry to assay over a thousand individual rat brain cells. The approach entwines the wealth of knowledge obtained by immunocytochemical profiling with mass spectral information on the predominant lipids within each cell. While many lipid species showed a high degree of similarity between neurons and astrocytes, seventeen significantly differed between the two cell types, including four phosphatidylethanolamines elevated in astrocytes and nine phosphatidylcholines in neurons.

Neuropeptidomics of the Rat Habenular Nuclei.

Conserved across vertebrates, the habenular nuclei are a pair of small symmetrical structures in the epithalamus. The nuclei functionally link the forebrain and midbrain by receiving input from and projecting to several brain regions. Each habenular nucleus comprises two major asymmetrical subnuclei, the medial and lateral habenula. These subnuclei are associated with different physiological processes and disorders, such as depression, nicotine addiction, and encoding aversive stimuli or omitting expected rewarding stimuli. Elucidating the functions of the habenular nuclei at the molecular level requires knowledge of their neuropeptide complement. In this work, three mass spectrometry (MS) techniques-liquid chromatography (LC) coupled to Orbitrap tandem MS (MS/MS), LC coupled to Fourier transform (FT)-ion cyclotron resonance (ICR) MS/MS, and matrix-assisted laser desorption/ionization (MALDI) FT-ICR MS-were used to uncover the neuropeptide profiles of the rodent medial and lateral habenula. With the assistance of tissue stabilization and bioinformatics, a total of 262 and 177 neuropeptides produced from 27 and 20 prohormones were detected and identified from the medial and lateral habenula regions, respectively. Among these neuropeptides, 136 were exclusively found in the medial habenula, and 51 were exclusively expressed in the lateral habenula. Additionally, novel sites of sulfation, a rare post-translational modification, on the secretogranin I prohormone are identified. The results demonstrate that these two small brain nuclei have a rich and differentiated peptide repertoire, with this information enabling a range of follow-up studies.