Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Subcellular omics: a new frontier pushing the limits of resolution, complexity and throughput

We argue that the study of single-cell subcellular organelle omics is needed to understand and regulate cell function. This requires and is being enabled by new technology development.

Mass Spectrometry-Based High-Throughput Quantification of Bioproducts in Liquid Culture

To meet the ever-increasing need for high-throughput screening in metabolic engineering, information-rich, fast screening methods are needed. Mass spectrometry (MS) provides an efficient and general approach for metabolite screening and offers the capability of characterizing a broad range of analytes in a label-free manner, but often requires a range of sample clean-up and extraction steps. Liquid extraction surface analysis (LESA) coupled MS is an image-guided MS surface analysis approach that directly samples and introduces metabolites from a surface to MS. Here, we combined the advantages of LESA-MS and an acoustic liquid handler with stable isotope-labeled internal standards. This approach provides absolute quantitation of target chemicals from liquid culture-dried droplets and enables high-throughput quantitative screening for microbial metabolites. In this study, LESA-MS was successfully applied to quantify several different metabolites (itaconic acid, triacetic acid lactone, and palmitic acid) from different yeast strains in different mediums, demonstrating its versatility, accuracy, and efficiency across a range of microbial engineering applications.

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.

Effect of Micro-Patterned Mucin on Quinolone and Rhamnolipid Profiles of Mucoid Pseudomonas aeruginosa under Antibiotic Stress

Pseudomonas aeruginosa (P. aeruginosa) is commonly implicated in hospital-acquired infections where its capacity to form biofilms on a variety of surfaces and the resulting enhanced antibiotic resistance seriously limit treatment choices. Because surface attachment sensitizes P. aeruginosa to quorum sensing (QS) and induces virulence through both chemical and mechanical cues, we investigate the effect of surface properties through spatially patterned mucin, combined with sub-inhibitory concentrations of tobramycin on QS and virulence factors in both mucoid and non-mucoid P. aeruginosa strains using multi-modal chemical imaging combining confocal Raman microscopy and matrix-assisted laser desorption/ionization-mass spectrometry. Samples comprise surface-adherent static biofilms at a solid-water interface, supernatant liquid, and pellicle biofilms at an air-water interface at various time points. Although the presence of a sub-inhibitory concentration of tobramycin in the supernatant retards growth and development of static biofilms independent of strain and surface mucin patterning, we observe clear differences in the behavior of mucoid and non-mucoid strains. Quinolone signals in a non-mucoid strain are induced earlier and are influenced by mucin surface patterning to a degree not exhibited in the mucoid strain. Additionally, phenazine virulence factors, such as pyocyanin, are observed in the pellicle biofilms of both mucoid and non-mucoid strains but are not detected in the static biofilms from either strain, highlighting the differences in stress response between pellicle and static biofilms. Differences between mucoid and non-mucoid strains are consistent with their strain-specific phenology, in which the mucoid strain develops highly protected biofilms.

Single cell metabolism: current and future trends

Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.

Attomole-Level Multiplexed Detection of Neurochemicals in Picoliter Droplets by On-Chip Nanoelectrospray Ionization Coupled to Mass Spectrometry

While droplet microfluidics is becoming an effective tool for biomedical research, sensitive detection of droplet content is still challenging, especially for multiplexed analytes compartmentalized within ultrasmall droplets down to picoliter volumes. To enable such measurements, we demonstrate a silicon-based integrated microfluidic platform for multiplexed analysis of neurochemicals in picoliter droplets via nanoelectrospray ionization (nESI)-mass spectrometry (MS). An integrated silicon microfluidic chip comprising downscaled 7 μm-radius channels, a compact T-junction for droplet generation, and an integrated nESI emitter tip is used for segmentation of analytes into picoliter compartments and their efficient delivery for subsequent MS detection. The developed system demonstrates effective detection of multiple neurochemicals encapsulated within oil-isolated plugs down to low picoliter volumes. Quantitative measurements for each neurochemical demonstrate limits of detection at the attomole level. Such results are promising for applications involving label-free and small-volume detection for monitoring a range of brain chemicals.

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 of d-alanine production by the microbial isolates of rat gut microbiota

d-alanine (d-Ala) and several other d-amino acids (d-AAs) act as hormones and neuromodulators in nervous and endocrine systems. Unlike the endogenously synthesized d-serine in animals, d-Ala may be from exogenous sources, e.g., diet and intestinal microorganisms. However, it is unclear if the capability to produce d-Ala and other d-AAs varies among different microbial strains in the gut. We isolated individual microorganisms of rat gut microbiota and profiled their d-AA production in vitro, focusing on d-Ala. Serial dilutions of intestinal contents from adult male rats were plated on agar to obtain clonal cultures. Using MALDI-TOF MS for rapid strain typing, we identified 38 unique isolates, grouped into 11 species based on 16S rRNA gene sequences. We then used two-tier screening to profile bacterial d-AA production, combining a d-amino acid oxidase-based enzymatic assay for rapid assessment of non-acidic d-AA amount and chiral LC-MS/MS to quantify individual d-AAs, revealing 19 out of the 38 isolated strains as d-AA producers. LC-MS/MS analysis of the eight top d-AA producers showed high levels of d-Ala in all strains tested, with substantial inter- and intra-species variations. Though results from the enzymatic assay and LC-MS/MS analysis aligned well, LC-MS/MS further revealed the existence of d-glutamate and d-aspartate, which are poor substrates for this enzymatic assay. We observed large inter- and intra-species variation of d-AA production profiles from rat gut microbiome species, demonstrating the importance of chemical profiling of gut microbiota in addition to sequencing, furthering the idea that microbial metabolites modulate host physiology.

Mass Spectrometry Approaches Empowering Neuropeptide Discovery and Therapeutics

The discovery of insulin in the early 1900s ushered in the era of research related to peptides acting as hormones and neuromodulators, among other regulatory roles. These essential gene products are found in all organisms, from the most primitive to the most evolved, and carry important biologic information that coordinates complex physiology and behavior; their misregulation has been implicated in a variety of diseases. The evolutionary origins of at least 30 neuropeptide signaling systems have been traced to the common ancestor of protostomes and deuterostomes. With the use of relevant animal models and modern technologies, we can gain mechanistic insight into orthologous and paralogous endogenous peptides and translate that knowledge into medically relevant insights and new treatments. Groundbreaking advances in medicine and basic science influence how signaling peptides are defined today. The precise mechanistic pathways for over 100 endogenous peptides in mammals are now known and have laid the foundation for multiple drug development pipelines. Peptide biologics have become valuable drugs due to their unique specificity and biologic activity, lack of toxic metabolites, and minimal undesirable interactions. This review outlines modern technologies that enable neuropeptide discovery and characterization, and highlights lessons from nature made possible by neuropeptide research in relevant animal models that is being adopted by the pharmaceutical industry. We conclude with a brief overview of approaches/strategies for effective development of peptides as drugs. SIGNIFICANCE STATEMENT: Neuropeptides, an important class of cell-cell signaling molecules, are involved in maintaining a range of physiological functions. Since the discovery of insulin's activity, over 100 bioactive peptides and peptide analogs have been used as therapeutics. Because these are complex molecules not easily predicted from a genome and their activity can change with subtle chemical modifications, mass spectrometry (MS) has significantly empowered peptide discovery and characterization. This review highlights contributions of MS-based research towards the development of therapeutic peptides.

Metabolomics-based mass spectrometry methods to analyze the chemical content of 3D organoid models

Metabolomics, the study of metabolites present in biological samples, can provide a global view of sample state as well as insights into biological changes caused by disease or environmental interactions. Mass spectrometry (MS) is commonly used for metabolomics analysis given its high-throughput capabilities, high sensitivity, and capacity to identify multiple compounds in complex samples simultaneously. MS can be coupled to separation methods that can handle small volumes, making it well suited for analyzing the metabolome of organoids, miniaturized three-dimensional aggregates of stem cells that model in vivo organs. Organoids are being used in research efforts to study human disease and development, and in the design of personalized drug treatments. For organoid models to be useful, they need to recapitulate morphological and chemical aspects, such as the metabolome, of the parent tissue. This review highlights the separation- and imaging-based MS-based metabolomics methods that have been used to analyze the chemical contents of organoids. Future perspectives on how MS techniques can be optimized to determine the accuracy of organoid models and expand the field of organoid research are also discussed.

Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation

Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.

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.

Comparative Analysis of Neuropeptides in Homologous Interneurons and Prohormone Annotation in Nudipleuran Sea Slugs

Despite substantial research on neuronal circuits in nudipleuran gastropods, few peptides have been implicated in nudipleuran behavior. In this study, we expanded the understanding of peptides in this clade, using three species with well-studied nervous systems, Hermissenda crassicornis, Melibe leonina, and Pleurobranchaea californica. For each species, we performed sequence homology analysis of de novo transcriptome predictions to identify homologs to 34 of 36 prohormones previously characterized in the gastropods Aplysia californica and Lymnaea stagnalis. We then used single-cell mass spectrometry to characterize peptide profiles in homologous feeding interneurons: the multifunctional ventral white cell (VWC) in P. californica and the small cardioactive peptide B large buccal (SLB) cells in H. crassicornis and M. leonina. The neurons produced overlapping, but not identical, peptide profiles. The H. crassicornis SLB cells expressed peptides from homologs to the FMRFamide (FMRFa), small cardioactive peptide (SCP), LFRFamide (LFRFa), and feeding circuit activating peptides prohormones. The M. leonina SLB cells expressed peptides from homologs to the FMRFa, SCP, LFRFa, and MIP-related peptides prohormones. The VWC, previously shown to express peptides from the FMRFa and QNFLa (a homolog of A. californica pedal peptide 4) prohormones, was shown to also contain SCP peptides. Thus, each neuron expressed peptides from the FMRFa and SCP families, the H. crassicornis and M. leonina SLB cells expressed peptides from the LFRFa family, and each neuron contained peptides from a prohormone not found in the others. These data suggest each neuron performs complex co-transmission, which potentially facilitates a multifunctional role in feeding. Additionally, the unique feeding characteristics of each species may relate, in part, to differences in the peptide profiles of these neurons. These data add chemical insight to enhance our understanding of the neuronal basis of behavior in nudipleurans and other gastropods.

Physiopathological Relevance of D-Serine in the Mammalian Cochlea

NMDA receptors (NMDARs) populate the complex between inner hair cell (IHC) and spiral ganglion neurons (SGNs) in the developing and mature cochlea. However, in the mature cochlea, activation of NMDARs is thought to mainly occur under pathological conditions such as excitotoxicity. Ototoxic drugs such as aspirin enable cochlear arachidonic-acid-sensitive NMDAR responses, and induced chronic tinnitus was blocked by local application of NMDAR antagonists into the cochlear fluids. We largely ignore if other modulators are also engaged. In the brain, D-serine is the primary physiological co-agonist of synaptic NMDARs. Whether D-serine plays a role in the cochlea had remained unexplored. We now reveal the presence of D-serine and its metabolic enzymes prior to, and at hearing onset, in the sensory and non-neuronal cells of the cochlea of several vertebrate species. In vivo intracochlear perfusion of D-serine in guinea pigs reduces sound-evoked activity of auditory nerve fibers without affecting the receptor potentials, suggesting that D-serine acts specifically on the postsynaptic auditory neurons without altering the functional state of IHC or of the stria vascularis. Indeed, we demonstrate in vitro that agonist-induced activation of NMDARs produces robust calcium responses in rat SGN somata only in the presence of D-serine, but not of glycine. Surprisingly, genetic deletion in mice of serine racemase (SR), the enzyme that catalyzes D-serine, does not affect hearing function, but offers protection against noise-induced permanent hearing loss as measured 3 months after exposure. However, the mechanisms of activation of NMDA receptors in newborn rats may be different from those in adult guinea pigs. Taken together, these results demonstrate for the first time that the neuro-messenger D-serine has a pivotal role in the cochlea by promoting the activation of silent cochlear NMDAR in pathological situations. Thus, D-serine and its signaling pathway may represent a new druggable target for treating sensorineural hearing disorders (i.e., hearing loss, tinnitus).

Identification of Lipid Heterogeneity and Diversity in the Developing Human Brain

The lipidome is currently understudied but fundamental to life. Within the brain, little is known about cell-type lipid heterogeneity, and even less is known about cell-to-cell lipid diversity because it is difficult to study the lipids within individual cells. Here, we used single-cell mass spectrometry-based protocols to profile the lipidomes of 154 910 single cells across ten individuals consisting of five developmental ages and five brain regions, resulting in a unique lipid atlas available via a web browser of the developing human brain. From these data, we identify differentially expressed lipids across brain structures, cortical areas, and developmental ages. We inferred lipid profiles of several major cell types from this data set and additionally detected putative cell-type specific lipids. This data set will enable further interrogation of the developing human brain lipidome.

Droplet-assisted electrospray phase separation using an integrated silicon microfluidic platform

We report a silicon microfluidic platform that enables monolithic integration of transparent micron-scale microfluidic channels, an on-chip segmentation of analyte flows into picoliter-volume droplets, and a nano-electrospray ionization emitter that enables spatial and temporal separation of oil and aqueous phases during electro-spray for subsequent mass spectrometry analysis.

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.

Alternative Splicing Mechanisms Underlying Opioid-Induced Hyperalgesia

Prolonged use of opioids can cause opioid-induced hyperalgesia (OIH). The impact of alternative splicing on OIH remains partially characterized. A study of the absolute and relative modes of action of alternative splicing further the understanding of the molecular mechanisms underlying OIH. Differential absolute and relative isoform profiles were detected in the trigeminal ganglia and nucleus accumbens of mice presenting OIH behaviors elicited by chronic morphine administration relative to control mice. Genes that participate in glutamatergic synapse (e.g., Grip1, Grin1, Wnk3), myelin protein processes (e.g., Mbp, Mpz), and axon guidance presented absolute and relative splicing associated with OIH. Splicing of genes in the gonadotropin-releasing hormone receptor pathway was detected in the nucleus accumbens while splicing in the vascular endothelial growth factor, endogenous cannabinoid signaling, circadian clock system, and metabotropic glutamate receptor pathways was detected in the trigeminal ganglia. A notable finding was the prevalence of alternatively spliced transcription factors and regulators (e.g., Ciart, Ablim2, Pbx1, Arntl2) in the trigeminal ganglia. Insights into the nociceptive and antinociceptive modulatory action of Hnrnpk were gained. The results from our study highlight the impact of alternative splicing and transcriptional regulators on OIH and expose the need for isoform-level research to advance the understanding of morphine-associated hyperalgesia.

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.

Characterization of Neuronal RNA Modifications during Non-associative Learning in Aplysia Reveals Key Roles for tRNAs in Behavioral Sensitization

Subtle changes in the landscape of post-transcriptional modifications have emerged as putative regulators of central nervous system plasticity and activity-induced protein synthesis. However, simultaneous characterization of multiple RNA modifications and their covariation during learning and memory paradigms has been impeded by the complexity of animal models and lack of untargeted approaches for identifying pathway-relevant RNA modifications in small-volume samples. Here, we used mass spectrometry to profile spatiotemporal changes in dozens of neuronal RNA modifications in Aplysia californica during behavioral sensitization of a simple defensive reflex. Unique RNA modification patterns were observed in the major ganglia of trained and naı̇ve animals, with two tRNA modifications, namely, 5-methoxycarbonylmethyl-2-thiouridine (mcm⁵s²U) and 1-methyladenosine (m¹A), at significantly higher levels in trained subjects. We report that tRNAs, and their modifications, correlate with increased polyglutamine synthesis and excitability in neurons, characterizing the first link between noncoding RNA modifications and non-associative learning.