Stacy D. Sherrod

Silver nanoparticles as selective ionization probes for analysis of olefins by mass spectrometry

Laser desorption/ionization (LDI) using silver nanoparticles (AgNPs) is shown to selectively ionize olefinic compounds, e.g., cholesterol, 1-palmitoyl-2-oleoyl- sn-glycero-3-phosphocholine (POPC), and carotenoids. Selective AgNP LDI can be carried out from complex mixtures without the addition of an organic matrix, sample cleanup, or prefractionation. Results presented in this report are the first to demonstrate the selective ionization of specific compounds from a complex mixture using metal nanoparticles.

Engineering Challenges for Instrumenting and Controlling Integrated Organ-on-Chip Systems

The sophistication and success of recently reported microfabricated organs-on-chips and human organ constructs have made it possible to design scaled and interconnected organ systems that may significantly augment the current drug development pipeline and lead to advances in systems biology. Physiologically realistic live microHuman (μHu) and milliHuman (mHu) systems operating for weeks to months present exciting and important engineering challenges such as determining the appropriate size for each organ to ensure appropriate relative organ functional activity, achieving appropriate cell density, providing the requisite universal perfusion media, sensing the breadth of physiological responses, and maintaining stable control of the entire system, while maintaining fluid scaling that consists of ~5 mL for the mHu and ~5 μL for the μHu. We believe that successful mHu and μHu systems for drug development and systems biology will require low-volume microdevices that support chemical signaling, microfabricated pumps, valves and microformulators, automated optical microscopy, electrochemical sensors for rapid metabolic assessment, ion mobility-mass spectrometry for real-time molecular analysis, advanced bioinformatics, and machine learning algorithms for automated model inference and integrated electronic control. Toward this goal, we are building functional prototype components and are working toward top-down system integration.

Neurovascular unit on a chip: implications for translational applications

The blood-brain barrier (BBB) dynamically controls exchange between the brain and the body, but this interaction cannot be studied directly in the intact human brain or sufficiently represented by animal models. Most existing in vitro BBB models do not include neurons and glia with other BBB elements and do not adequately predict drug efficacy and toxicity. Under the National Institutes of Health Microtissue Initiative, we are developing a three-dimensional, multicompartment, organotypic microphysiological system representative of a neurovascular unit of the brain. The neurovascular unit system will serve as a model to study interactions between the central nervous system neurons and the cerebral spinal fluid (CSF) compartment, all coupled to a realistic blood-surrogate supply and venous return system that also incorporates circulating immune cells and the choroid plexus. Hence all three critical brain barriers will be recapitulated: blood-brain, brain-CSF, and blood-CSF. Primary and stem cell-derived human cells will interact with a variety of agents to produce critical chemical communications across the BBB and between brain regions. Cytomegalovirus, a common herpesvirus, will be used as an initial model of infections regulated by the BBB. This novel technological platform, which combines innovative microfluidics, cell culture, analytical instruments, bioinformatics, control theory, neuroscience, and drug discovery, will replicate chemical communication, molecular trafficking, and inflammation in the brain. The platform will enable targeted and clinically relevant nutritional and pharmacologic interventions for or prevention of such chronic diseases as obesity and acute injury such as stroke, and will uncover potential adverse effects of drugs. If successful, this project will produce clinically useful technologies and reveal new insights into how the brain receives, modifies, and is affected by drugs, other neurotropic agents, and diseases.

Label-free quantitation of protein modifications by pseudo selected reaction monitoring with internal reference peptides.

Liquid chromatography tandem mass spectrometry (LC–MS/MS) based methods provide powerful tools for the quantitative analysis of modified proteins. We have developed a label-free approach using internal reference peptides (IRP) from the target protein for signal normalization without the need for isotope labeling. Ion-trap mass spectrometry and pseudo-selected reaction monitoring (pSRM) were used to acquire full MS/MS and MS3 spectra from target peptides. Skyline, a widely used software for SRM experiments, was used for chromatographic ion extraction. Phosphopeptides spiked into a BSA background yielded concentration response curves with high correlation coefficients (typically >0.9) and low coefficients of variation (≤15%) over a 200-fold concentration range. Stable isotope dilution (SID) and IRP methods were compared for quantitation of six site-specific phosphorylations in the epidermal growth factor receptor (EGFR) in epidermal growth factor-stimulated A431 cells with or without the addition of EGFR inhibitors cetuximab and gefitinib. Equivalent responses were observed with both IRP and SID methods, although analyses using the IRP method typically had higher median CVs (22–31%) than SID (10–20%). Analyses using both methods were consistent with immunoblot using site-selective antibodies. The ease of implementation and the suitability for targeted quantitative comparisons make this method suitable for broad application in protein biochemistry.

Untargeted Metabolomics Strategies—Challenges and Emerging Directions

AbstractMetabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes and, as such, the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS-based untargeted metabolomics studies—specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described. Graphical Abstractᅟ

Metabolic consequences of inflammatory disruption of the blood-brain barrier in an organ-on-chip model of the human neurovascular unit

BackgroundUnderstanding blood-brain barrier responses to inflammatory stimulation (such as lipopolysaccharide mimicking a systemic infection or a cytokine cocktail that could be the result of local or systemic inflammation) is essential to understanding the effect of inflammatory stimulation on the brain. It is through the filter of the blood-brain barrier that the brain responds to outside influences, and the blood-brain barrier is a critical point of failure in neuroinflammation. It is important to note that this interaction is not a static response, but one that evolves over time. While current models have provided invaluable information regarding the interaction between cytokine stimulation, the blood-brain barrier, and the brain, these approaches—whether in vivo or in vitro—have often been only snapshots of this complex web of interactions.MethodsWe utilize new advances in microfluidics, organs-on-chips, and metabolomics to examine the complex relationship of inflammation and its effects on blood-brain barrier function ex vivo and the metabolic consequences of these responses and repair mechanisms. In this study, we pair a novel dual-chamber, organ-on-chip microfluidic device, the NeuroVascular Unit, with small-volume cytokine detection and mass spectrometry analysis to investigate how the blood-brain barrier responds to two different but overlapping drivers of neuroinflammation, lipopolysaccharide and a cytokine cocktail of IL-1β, TNF-α, and MCP1,2.ResultsIn this study, we show that (1) during initial exposure to lipopolysaccharide, the blood-brain barrier is compromised as expected, with increased diffusion and reduced presence of tight junctions, but that over time, the barrier is capable of at least partial recovery; (2) a cytokine cocktail also contributes to a loss of barrier function; (3) from this time-dependent cytokine activation, metabolic signature profiles can be obtained for both the brain and vascular sides of the blood-brain barrier model; and (4) collectively, we can use metabolite analysis to identify critical pathways in inflammatory response.ConclusionsTaken together, these findings present new data that allow us to study the initial effects of inflammatory stimulation on blood-brain barrier disruption, cytokine activation, and metabolic pathway changes that drive the response and recovery of the barrier during continued inflammatory exposure.

Phenotypic Mapping of Metabolic Profiles Using Self-Organizing Maps of High-Dimensional Mass Spectrometry Data

A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.

Metabolic consequences of interleukin-6 challenge in developing neurons and astroglia

BackgroundMaternal immune activation and subsequent interleukin-6 (IL-6) induction disrupt normal brain development and predispose the offspring to developing autism and schizophrenia. While several proteins have been identified as having some link to these developmental disorders, their prevalence is still small and their causative role, if any, is not well understood. However, understanding the metabolic consequences of environmental predisposing factors could shed light on disorders such as autism and schizophrenia.MethodsTo gain a better understanding of the metabolic consequences of IL-6 exposure on developing central nervous system (CNS) cells, we separately exposed developing neuron and astroglia cultures to IL-6 for 2 hours while collecting effluent from our gravity-fed microfluidic chambers. By coupling microfluidic technologies to ultra-performance liquid chromatography-ion mobility-mass spectrometry (UPLC-IM-MS), we were able to characterize the metabolic response of these CNS cells to a narrow window of IL-6 exposure.ResultsOur results revealed that 1) the use of this technology, due to its superb media volume:cell volume ratio, is ideally suited for analysis of cell-type-specific exometabolome signatures; 2) developing neurons have low secretory activity at baseline, while astroglia show strong metabolic activity; 3) both neurons and astroglia respond to IL-6 exposure in a cell type-specific fashion; 4) the astroglial response to IL-6 stimulation is predominantly characterized by increased levels of metabolites, while neurons mostly depress their metabolic activity; and 5) disturbances in glycerophospholipid metabolism and tryptophan/kynurenine metabolite secretion are two putative mechanisms by which IL-6 affects the developing nervous system.ConclusionsOur findings are potentially critical for understanding the mechanism by which IL-6 disrupts brain function, and they provide information about the molecular cascade that links maternal immune activation to developmental brain disorders.

Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard's msConvert.

We report the implementation of high-quality signal processing algorithms into ProteoWizard, an efficient, open-source software package designed for analyzing proteomics tandem mass spectrometry data. Specifically, a new wavelet-based peak-picker (CantWaiT) and a precursor charge determination algorithm (Turbocharger) have been implemented. These additions into ProteoWizard provide universal tools that are independent of vendor platform for tandem mass spectrometry analyses and have particular utility for intralaboratory studies requiring the advantages of different platforms convergent on a particular workflow or for interlaboratory investigations spanning multiple platforms. We compared results from these tools to those obtained using vendor and commercial software, finding that in all cases our algorithms resulted in a comparable number of identified peptides for simple and complex samples measured on Waters, Agilent, and AB SCIEX quadrupole time-of-flight and Thermo Q-Exactive mass spectrometers. The mass accuracy of matched precursor ions also compared favorably with vendor and commercial tools. Additionally, typical analysis runtimes (∼1–100 ms per MS/MS spectrum) were short enough to enable the practical use of these high-quality signal processing tools for large clinical and research data sets.

Nanoparticles for Selective Laser Desorption/Ionization in Mass Spectrometry

The selective desorption/ionization of analytes using nanomaterials is investigated using metallic nanoparticles. By replacing the sodium citrate capping of gold nanoparticles with self-assembled monolayers, we are able to both enhance analyte ionization and selectively capture analytes. Capping gold nanoparticles with a monolayer of 4-mercaptobenzoic acid enhances analyte ionization while greatly decreasing chemical noise resulting from alkali adducted species. Selective capture and sequential desorption/ionization of the peptide bradykinin (1–7) from a two peptide mixture is achieved using β-cyclodextrin capped gold nanoparticles. Finally, by switching from gold to silver nanoparticles, we are able to ionize both folic acid and amphotericin B. These results demonstrate that through careful control of nanoparticle surface chemistry and composition one can achieve selective analyte ionization for MS applications.