Mass spectrometry and informatics: distribution of molecules in the PubChem database and general requirements for mass accuracy in surface analysis

Spatially Resolved Molecular Analysis of Host Response to Medical Device Implantation Using the 3D OrbiSIMS Highlights a Critical Role for Lipids

A key goal for implanted medical devices is that they do not elicit a detrimental immune response. Macrophages play critical roles in the modulation of the host immune response and are the cells responsible for persistent inflammatory reactions to implanted biomaterials. Two novel immune-instructive polymers that stimulate pro- or anti-inflammatory responses from macrophages in vitro are investigated. These also modulate in vivo foreign body responses (FBR) when implanted subcutaneously in mice. Immunofluorescent staining of tissue abutting the polymer reveals responses consistent with pro- or anti-inflammatory responses previously described for these polymers. Three Dimensional OrbiTrap Secondary Ion Mass Spectrometry (3D OrbiSIMS) analysis to spatially characterize the metabolites in the tissue surrounding the implant, providing molecular histology insight into the metabolite response in the host is applied. For the pro-inflammatory polymer, monoacylglycerols (MG) and diacylglycerols (DG) are observed at increased intensity, while for the anti-inflammatory coating, the number of phospholipid species detected decreased, and pyridine and pyrimidine levels are elevated. Small molecule signatures from single-cell studies of M2 macrophages in vitro correlate with the in vivo observations, suggesting potential for prediction. Metabolite characterization by the 3D OrbiSIMS is shown to provide insight into the mechanism of bio-instructive materials as medical devices and to inform on the FBR to biomaterials.

Isoforskolin modulates AQP4-SPP1-PIK3C3 related pathway for chronic obstructive pulmonary disease via cAMP signaling

BACKGROUND

Cyclic adenosine monophosphate (cAMP) levels are directly activated by adenylate cyclase (AC) and play an anti-inflammatory role in chronic obstructive pulmonary disease (COPD). Previously, we have shown that isoforskolin (ISOF) can effectively activate AC1 and AC2 in vitro, improve pulmonary ventilation and reduce the inflammatory response in COPD model rats, supporting that ISOF may be a potential drug for the prevention and treatment of COPD, but the mechanism has not been explored in detail.

METHODS

The potential pharmacological mechanisms of ISOF against COPD were analyzed by network pharmacology and multi-omics based on pharmacodynamic study. To use specific agonists, inhibitors and/or SiRNA for gene regulation function studies, combined qPCR, WB were applied to detect changes in mRNA and protein expression of important targets PIK3C3, AKT, mTOR, SPP1 and AQP4 which related to ISOF effect on COPD. And the key inflammatory factors detected by ELISA.

RESULTS

Bioinformatics suggested that the anti-COPD pharmacological mechanism of ISOF was related to PI3K-AKT signaling pathway, and suggested target protein like PIK3C3, AQP4, SPP1, AKT, mTOR. Using the AQP4 inhibitor,or inhibiting SPP1 expression by siRNA-SPP1 could block the PIK3C3-AKT-mTOR pathway and ameliorate chronic inflammation. ISOF showed cAMP-promoting effect then suppressed AQP4 expression, together with decreased level of IL-1β, IL-6, and IL-8.

CONCLUSIONS

These findings demonstrate ISOF controlled the cAMP-regulated PIK3C3-AKT-mTOR pathway, thereby alleviating inflammatory development in COPD. The cAMP/AQP4/PIK3C3 axis also modulate Th17/Treg differentiation, revealed potential therapeutic targets for this disease.

Label-free Chemical Characterization of Polarized Immune Cells in vitro and Host Response to Implanted Bio-instructive Polymers in vivo Using 3D OrbiSIMS

The Three-dimensional OrbiTrap Secondary Ion Mass Spectrometry (3D OrbiSIMS) is a secondary ion mass spectrometry instrument, a combination of a Time of Flight (ToF) instrument with an Orbitrap analyzer. The 3D OrbiSIMS technique is a powerful tool for metabolic profiling in biological samples. This can be achieved at subcellular spatial resolution, high sensitivity, and high mass-resolving power coupled with MS/MS analysis. Characterizing the metabolic signature of macrophage subsets within tissue sections offers great potential to understand the response of the human immune system to implanted biomaterials. Here, we describe a protocol for direct analysis of individual cells after in vitro differentiation of naïve monocytes into M1 and M2 phenotypes using cytokines. As a first step in vivo, we investigate explanted silicon catheter sections as a medical device in a rodent model of foreign body response. Protocols are presented to allow the host response to different immune instructive materials to be compared. The first demonstration of this capability illustrates the great potential of direct cell and tissue section analysis for in situ metabolite profiling to probe functional phenotypes using molecular signatures. Details of the in vitro cell approach, materials, sample preparation, and explant handling are presented, in addition to the data acquisition approaches and the data analysis pipelines required to achieve useful interpretation of these complex spectra. This method is useful for in situ characterization of both in vitro single cells and ex vivo tissue sections. This will aid the understanding of the immune response to medical implants by informing the design of immune-instructive biomaterials with positive interactions. It can also be used to investigate a broad range of other clinically relevant therapeutics and immune dysregulations. Graphical overview.

Unraveling the Molecular Mechanism of Xuebijing Injection in the Treatment of Chronic Obstructive Pulmonary Disease by Combining Network Pharmacology and Affymetrix Array

Xuebijing injection (XBJ), one of the classical prescriptions for treating inflammation-related diseases, has been used to chronic obstructive pulmonary disease (COPD) in clinical practice. However, its molecular mechanism is still unclear. Network pharmacology combined with Affymetrix arrays and molecular docking techniques were applied to explore the molecular mechanism of XBJ for COPD. Predictive analysis of 728 active compounds in XBJ and 6 sets of Affymetrix arrays expression data resulted in 106 potential therapeutic targets. Next, based on the active compound-co-target network topology analysis, most of these targets were found to be modulated by quercetin, myricetin, and ellagic acid. Furthermore, protein–protein interaction (PPI) analysis revealed that the key targets may be EGFR, STAT3, AKT1, CCND1, MMP9, AR, ESR1, and PTGS2. Then, by constructing a component-target-pathway network, we found that XBJ was a multi-pathway, multi-target, multi-compound synergistic therapy for COPD, and four key targets were involved in the FoxO signaling pathway. Luteolin and salvianolic acid b had the optimal binding ability to several key proteins. Therefore, we hypothesize that quercetin, myricetin, ellagic acid, luteolin, and salvianolic acid b mainly contribute to the therapeutic effect of XBJ on COPD by modulating the FoxO signaling pathway by regulating EGFR, STAT3, AKT1, and CCND1. XBJ exerts anti-inflammatory and antioxidative stress effects through the PI3K/Akt/FoxO axis combined with MMP9, AR, ESR1, and PTGS2 to regulate other signaling pathways.

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets

Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types.

The WEIZMASS spectral library for high-confidence metabolite identification

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

Unambiguous metabolite annotation is a critical, yet problematic step, in mass spectrometry based metabolomics. Here, Shahaf et al. present WEIZMASS, a platform consisting of a diverse spectral library of more than 3500 plant metabolites and software to aid their identification in biological samples.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Non-targeted screening approaches for contaminants and adulterants in food using liquid chromatography hyphenated to high resolution mass spectrometry

The majority of analytical methods for food safety monitor the presence of a specific compound or defined set of compounds. Non-targeted screening methods are complementary to these approaches by detecting and identifying unexpected compounds present in food matrices that may be harmful to public health. However, the development and implementation of generalized non-targeted screening workflows are particularly challenging, especially for food matrices due to inherent sample complexity and diversity and a large analyte concentration range. One approach that can be implemented is liquid chromatography coupled to high-resolution mass spectrometry, which serves to reduce this complexity and is capable of generating molecular formulae for compounds of interest. Current capabilities, strategies, and challenges will be reviewed for sample preparation, mass spectrometry, chromatography, and data processing workflows. Considerations to increase the accuracy and speed of identifying unknown molecular species will also be addressed, including suggestions for achieving sufficient data quality for non-targeted screening applications.

Secondary ion mass spectrometry imaging of Dictyostelium discoideum aggregation streams

High resolution imaging mass spectrometry could become a valuable tool for cell and developmental biology, but both, high spatial and mass spectral resolution are needed to enable this. In this report, we employed Bi₃ bombardment time-of-flight (Bi₃ ToF-SIMS) and C₆₀ bombardment Fourier transform ion cyclotron resonance secondary ion mass spectrometry (C₆₀ FTICR-SIMS) to image Dictyostelium discoideum aggregation streams. Nearly 300 lipid species were identified from the aggregation streams. High resolution mass spectrometry imaging (FTICR-SIMS) enabled the generation of multiple molecular ion maps at the nominal mass level and provided good coverage for fatty acyls, prenol lipids, and sterol lipids. The comparison of Bi₃ ToF-SIMS and C₆₀ FTICR-SIMS suggested that while the first provides fast, high spatial resolution molecular ion images, the chemical complexity of biological samples warrants the use of high resolution analyzers for accurate ion identification.

Constructing a mass measurement error surface to improve automatic annotations in liquid chromatography/mass spectrometry based metabolomics

RATIONALE

Estimation of mass measurement accuracy is an elementary step in the application of mass spectroscopy (MS) data towards metabolite annotations and has been addressed several times in the past. However, the reproducibility of mass measurements over a diverse set of analytes and in variable operating conditions, which are common in high-throughput metabolomics studies, has, to the best of our knowledge, not been addressed so far.

METHODS

A method to automatically extract mass measurement errors from a large data set of measurements made on a quadrupole time-of-flight (QTOF) MS instrument has been developed. The size of the data processed in this study has enabled us to use a statistical data driven approach to build a model which reliably predicts the confidence interval of the absolute mass measurement error based on individual ion peak conditions in a fast, high-throughput manner.

RESULTS

We show that our model predictions are reproducible in external datasets generated in similar, but not identical conditions, and have demonstrated the advantage of our approach over the common practice of fixed mass measurement error limits.

CONCLUSIONS

Outlined is an approach which can promote a more rational use of MS technology by automatically evaluating the absolute mass measurement error based on the individual peak conditions. The immediate application of our method is integration in high-throughput peak annotation pipelines for database searches.

Identification and separation of protein, contaminant and substrate peaks using gentle-secondary ion mass spectrometry and the g-ogram

RATIONALE

Secondary ion mass spectrometry (SIMS) is an important technique for the characterization of proteins at surfaces. However, interpretation of the mass spectra is complicated owing to confusion with peaks from contaminants and the substrate which is further compounded by complex fragmentation mechanisms. We test a new development of the G-SIMS method called the g-ogram to separate out spectral components without a priori information about which peaks to include in the analysis and which peaks relate to each component.

METHODS

The effectiveness of the g-ogram method is investigated using a model system of lysozyme adsorbed onto a silicon wafer and indium tin oxide substrates. In the method, two SIMS spectra are acquired using Bi(+) and Mn(+) primary ions which create lower and higher fragmentation in the spectra, respectively. The g-ogram separates out components using a separation parameter that is related to the fragmentation energy.

RESULTS

The g-ogram separates the spectrum of lysozyme adsorbed onto a silicon wafer into three components: (i) the substrate and PDMS contamination; (ii) a second, but unexpected, contaminant; and (iii) peaks from the protein amino acids. Similar results are achieved for the indium tin oxide substrate. In addition, evidence of fragments from plural amino acids with two candidate peaks at 140.12 Da and 185.08 Da is observed.

CONCLUSIONS

The g-ogram method effectively separates out mass peaks relating to the substrate, contamination and protein without any a priori information or subjective decisions about which peaks to include in the analysis (so called 'peak picking'). This is a great help to analysts. We find two possible peaks from plural amino acids but no evidence of pluralities is found for peaks above 240 Da that are generated from when using Bi or Mn primary ions.

Laser desorption VUV postionization MS imaging of a cocultured biofilm

Laser desorption postionization mass spectrometry (LDPI-MS) imaging is demonstrated with a 10.5 eV photon energy source for analysis and imaging of small endogenous molecules within intact biofilms. Biofilm consortia comprised of a synthetic Escherichia coli K12 coculture engineered for syntrophic metabolite exchange are grown on membranes and then used to test LDPI-MS analysis and imaging. Both E. coli strains displayed many similar peaks in LDPI-MS up to m/z 650, although some observed differences in peak intensities were consistent with the appearance of byproducts preferentially expressed by one strain. The relatively low mass resolution and accuracy of this specific LDPI-MS instrument prevented definitive assignment of species to peaks, but strategies are discussed to overcome this shortcoming. The results are also discussed in terms of desorption and ionization issues related to the use of 10.5 eV single-photon ionization, with control experiments providing additional mechanistic information. Finally, 10.5 eV LDPI-MS was able to collect ion images from intact, electrically insulating biofilms at ~100 μm spatial resolution. Spatial resolution of ~20 μm was possible, although a relatively long acquisition time resulted from the 10 Hz repetition rate of the single-photon ionization source.

Postacquisition mass resolution improvement in time-of-flight secondary ion mass spectrometry

Good mass resolution can be difficult to achieve in time-of-flight secondary ion mass spectrometry (TOF-SIMS) when the analysis area is large or when the surface being analyzed is rough. In most cases, a significant improvement in mass resolution can be achieved by postacquisition processing of raw data. Methods are presented in which spectra are extracted from smaller regions within the original analysis area, recalibrated, and selectively summed to produce spectra with higher mass resolution than the original. No hardware modifications or specialized instrument tuning are required. The methods can be extended to convert the original raw file into a new raw file containing high mass resolution data. To our knowledge, this is the first report of conversion of a low mass resolution raw file into a high mass resolution raw file using only the data contained within the low mass resolution raw file. These methods are applicable to any material but are expected to be particularly useful in analysis of difficult samples such as fibers, powders, and freeze-dried biological specimens.