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.

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.

NanoLC-timsTOF-Assisted Analysis of Glycated Albumin in Diabetes-Affected Plasma and Tears.

Glycation is a spontaneous and nonenzymatic glycosylation. Glycated albumin (GA), which serves as an important biomarker in plasma in the diagnosis and characterization of diabetes, can be passively filtered from the plasma to tears. Tears are important targets for research in clinical diagnostics due to the ability to collect this biofluid noninvasively and repeatably. Therefore, the analysis of GA in tear film provides information for monitoring diabetes progression independent of blood pathologies. Due to the limited volume (1-5 µL) of natural tear film, we developed a small volume assay using a nano liquid chromatography-trapped ion mobility spectrometry-time-of-flight MS (nanoLC-timsTOF) platform for the analysis of glycated albumin in human plasma and tear films affected by diabetes. The peptides containing lysine 525, which is the main glycation site in GA, were relatively quantified and represented as the GA level. The results of the measurements showed that GA levels were significantly higher in diabetes-affected plasma and tears compared to controls with a p-value < 0.01. A strong correlation of glycated albumin levels was observed for the plasma and tear film in diabetes samples (Pearson coefficient 0.92 with a p-value 0.0012). Moreover, the number of GA glycation sites was significantly higher in diabetes-affected plasma and tear comparatively to controls. Among all the glycation sites in plasma albumin, the GA level quantified by lysine 136/137 had a strong correlation with more commonly used lysine 525, suggesting that lysine 136 /137 is an alternative diabetes biomarker in plasma. Overall, our findings demonstrate GA in tears as a biomarker for monitoring diabetes progression, highlighting new possibilities for quick and noninvasive diabetes detection and monitoring.

Relations between Glucose and d-Amino Acids in the Modulation of Biochemical and Functional Properties of Rodent Islets of Langerhans.

The cell-to-cell signaling role of d-amino acids (d-AAs) in the mammalian endocrine system, particularly in the islets of Langerhans, has drawn growing interest for their potential involvement in modulating glucose metabolism. Previous studies found colocalization of serine racemase [produces d-serine (d-Ser)] and d-alanine (d-Ala) within insulin-secreting beta cells and d-aspartate (d-Asp) within glucagon-secreting alpha cells. Expressed in the islets, functional N-methyl-d-aspartate receptors are involved in the modulation of glucose-stimulated insulin secretion and have binding sites for several d-AAs. However, knowledge of the regulation of d-AA levels in the islets during glucose stimulation as well as the response of islets to different levels of extracellular d-AAs is limited. In this study, we determined the intracellular and extracellular levels of d-Ser, d-Ala, and d-Asp in cultures of isolated rodent islets exposed to different levels of extracellular glucose. We found that the intracellular levels of the enantiomers demonstrated large variability and, in general, were not affected by extracellular glucose levels. However, significantly lower levels of extracellular d-Ser and d-Ala were observed in the islet media supplemented with 20 mM concentration of glucose compared to the control condition utilizing 3 mM glucose. Glucose-induced oscillations of intracellular free calcium concentration ([Ca2+]i), a proxy for insulin secretion, were modulated by the exogenous application of d-Ser and d-Ala but not by their l-stereoisomers. Our results provide new insights into the roles of d-AAs in the biochemistry and function of pancreatic islets.

Biodistribution and racemization of gut-absorbed L/D-alanine in germ-free mice.

Microbiome-derived metabolites are important for the microbiome-gut-brain axis and the discovery of new disease treatments. D-Alanine (D-Ala) is found in many animals as a potential co-agonist of the N-methyl-D-aspartate receptors (NMDAR), receptors widely used in the nervous and endocrine systems. The gut microbiome, diet and putative endogenous synthesis are the potential sources of D-Ala in animals, although there is no direct evidence to show the distribution and racemization of gut-absorbed L-/D-Ala with regards to host-microbe interactions in mammals. In this work, we utilized germ-free mice to control the interference from microbiota and isotopically labeled L-/D-Ala to track their biodistribution and racemization in vivo. Results showed time-dependent biodistribution of gut-absorbed D-Ala, particularly accumulation of gut-absorbed D-Ala in pancreatic tissues, brain, and pituitary. No endogenous synthesis of D-Ala via racemization was observed in germ-free mice. The sources of D-Ala in mice were revealed as microbiota and diet, but not endogenous racemization. This work indicates the importance of further investigating the in vivo biological functions of gut-microbiome derived D-Ala, particularly on NMDAR-related activities, for D-Ala as a potential signaling molecules in the microbiome-gut-brain axis.

Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry.

Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical 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 via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing 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 developments of multiscale technologies for biochemical characterization of the brain.

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.

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.

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.

d-Amino Acids and Classical Neurotransmitters in Healthy and Type 2 Diabetes-Affected Human Pancreatic Islets of Langerhans.

The pancreatic islets of Langerhans are clusters of cells that function as endocrine units synthesizing and releasing insulin and a range of additional peptide hormones. The structural and chemical characteristics of islets change during type 2 diabetes development. Although a range of metabolites including neurotransmitters has been reported in rodent islets, the involvement of these cell-to-cell signaling molecules within human pancreatic islets in the pathophysiology of type 2 diabetes is not well known, despite studies suggesting that these molecules impact intra- and inter-islet signaling pathways. We characterize the enigmatic cell-to-cell signaling molecules, d-serine (d-Ser) and d-aspartate (d-Asp), along with multiple classical neurotransmitters and related molecules, in healthy versus type 2 diabetes-affected human islets using capillary electrophoresis separations. Significantly reduced d-Ser percentage and gamma-aminobutyric acid (GABA) levels were found in type 2 diabetes-affected islets compared to healthy islets. In addition, the negative correlations of many of the signaling molecules, such as d-Ser percentage (r = -0.35), d-Asp (r = -0.32), serotonin (r = -0.42), and GABA (r = -0.39) levels, with hemoglobin A1c (HbA1c) levels and thus with the progression of type 2 diabetes further demonstrate the disruption in intra- or inter-islet signaling pathways and suggest that these cell-to-cell signaling molecules may be potential therapeutic targets.

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.

Characterizing RNA Modifications in Single Neurons Using Mass Spectrometry.

Post-transcriptional modifications (PTMs) of RNA represent an understudied mechanism involved in the regulation of translation in the central nervous system (CNS). Recent evidence has linked specific neuronal RNA modifications to learning and memory paradigms. Unfortunately, conventional methods for the detection of these epitranscriptomic features are only capable of characterizing highly abundant RNA modifications in bulk tissues, precluding the assessment of unique PTM profiles that may exist for individual neurons within the activated behavioral circuits. In this protocol, an approach is described-single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS)-to simultaneously detect and quantify numerous modified ribonucleosides in single neurons. The approach is validated using individual neurons of the marine mollusk, Aplysia californica, beginning with surgical isolation and enzymatic treatment of major CNS ganglia to expose neuron cell bodies, followed by manual single-neuron isolation using sharp needles and a micropipette. Next, mechanical and thermal treatment of the sample in a small volume of buffer is done to liberate RNA from an individual cell for subsequent RNA digestion. Modified nucleosides are then identified and quantified using an optimized liquid chromatography-mass spectrometry method. SNRMA-MS is employed to establish RNA modification patterns for single, identified neurons from A. californica that have known morphologies and functions. Examples of qualitative and quantitative SNRMA-MS are presented that highlight the heterogeneous distribution of RNA modifications across individual neurons in neuronal networks.

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.

Droplet Microfluidics with MALDI-MS Detection: The Effects of Oil Phases in GABA Analysis.

Microfluidic and mass spectrometry (MS) methods are widely used to sample and probe the chemical composition of biological systems to elucidate chemical correlates of their healthy and disease states. Though matrix-assisted laser desorption/ionization-mass spectrometry (MALDI)-MS has been hyphenated to droplet microfluidics for offline analyses, the effects of parameters related to droplet generation, such as the type of oil phase used, have been understudied. To characterize these effects, five different oil phases were tested in droplet microfluidics for producing samples for MALDI-MS analysis. Picoliter to nanoliter aqueous droplets containing 0.1 to 100 mM γ-aminobutyric acid (GABA) and inorganic salts were generated inside a polydimethylsiloxane microfluidic chip and deposited onto a conductive glass slide. Optical microscopy, Raman spectroscopy, and MALDI-mass spectrometry imaging (MSI) of the droplet samples and surrounding areas revealed patterns of solvent and oil evaporation and analyte deposition. Optical microscopy detected the presence of salt crystals in 50-100 µm diameter dried droplets, and Raman and MSI were used to correlate GABA signals to the visible droplet footprints. MALDI-MS analyses revealed that droplets prepared in the presence of octanol oil led to the poorest detectability of GABA, whereas the oil phases containing FC-40 provided the best detectability; GABA signal was localized to the footprint of 65 pL droplets with a limit of detection of 23 amol. The effect of the surfactant perfluorooctanol on analyte detection was also investigated.

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.

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.

Spatiotemporal biodistribution of α-tocopherol is impacted by the source of 13C-labeled α-tocopherol in mice following a single oral dose.

Natural (RRR-) α-tocopherol (αT) is more bioactive than synthetic (all racemic, all rac-) αT, but not enough is known about the tissue kinetics of the 2 αT sources. We examined the time-course bioaccumulation of natural versus synthetic αT in tissues of young, marginally vitamin E-deficient mice using 13C-RRR-αT or 13C-all rac-αT tracers. In experiment 1, 3-week old male wild-type mice were fed a vitamin E-deficient diet for 0, 1, 2, or 3 weeks (n = 5/time point). Tissue αT levels were analyzed by HPLC-PDA. Feeding a vitamin E-deficient diet for up to 3 weeks decreased total αT concentrations in all analyzed tissues except the brain, which maintained its αT level. In experiment 2, a 2-week αT-depletion period was followed by administration of a single oral dose of 0.5 mg of 13C-RRR-αT or 13C-all rac-αT. At 12 hr, 1, 2, and 4 days post-dose, serum and multiple tissues were collected (n = 3/time point). αT was quantified by HPLC-PDA, and 13C-αT enrichment was determined by LC-MS. Both sources of 13C-αT reached maximum serum levels at 12 hr post-dose. 13C-RRR-αT levels were significantly higher than 13C-all rac-αT in serum at 1 d post-dose, and in heart, lungs, and kidney at 2d post-dose. In brain, 13C-RRR-αT concentrations were significantly higher than 13C-all rac-αT at 2 and 4 d post-dose. At 4 d post-dose, 13C-αT levels were similar between the 2 sources in examined tissues except for brain and adipose tissue where 13C-RRR-αT was higher. In conclusion, αT bioaccumulation over time varied substantially depending on αT source and tissue type.

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.

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.

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.