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Plain language summary: Biomarkers may offer unforeseen insights into clinical diagnosis, as well as the likely course and outcome of a condition. In this paper, the focus is on the use of biological molecules found in body fluids or tissues for diagnosis and prediction of outcome in ovarian cancer patients. In cancer care, biomarkers are being used to develop personalised treatment plans for patients based on the unique characteristics of their tumour. This tailoring of care can be used to pursue specific targets identified by biomarkers, or treat the patient according to specific tumour characteristics. Surgery is one of the core treatments for ovarian cancer, whether it is offered in primary surgery or following chemotherapy in delayed surgery. Biomarkers already exist to guide the treatment of tumours with chemotherapy, but very little research has determined the value of biomarkers in tailoring surgical care for ovarian cancer. Such research is required to identify new biomarkers and assess their effectiveness in a clinical setting as well as to help identify specific tumour types to guide surgery. Biomarkers could help to determine the success of removing the disease surgically, or help to identify tumour deposits that persist after chemotherapy. All of these aspects would improve current practice. This Scientific Impact Paper highlights research that may pave the way towards bespoke surgery according to the biological characteristics of a tumour and aid gynaecological oncologists to provide surgical treatment according to individual need, rather than a blanket approach for all. [ABSTRACT FROM AUTHOR]

Background: While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes., Methods: A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas., Results: Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites., Conclusions: The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach., (© 2024. Waters Technologies Corporation and The Author(s).)

Ion mobility-mass spectrometry (IM-MS) has become increasingly popular with the rapid expansion of available techniques and instrumentation. To enable accuracy, standardization, and repeatability of IM-MS measurements, the community requires reliable and well-defined reference materials for calibration and tuning of the equipment. To address this need, synthetic dendrimers of high chemical and structural purity were tested on three ion mobility platforms as potential calibrants. First, synthesized dendrimers were characterized by drift tube ion mobility (DTIMS), using an Agilent 6560 IM-qTOF-MS to assess their drift tube collision cross section ( DT CCS) values. Then, assessment of obtained CCS values on trapped ion mobility (TIMS) and traveling wave ion mobility (TWIMS) ion mobility platforms were compared to those found by DTIMS. Across all three systems, dendrimers were found to have high potential for m / z and ion mobility calibration in the CCS range of 160-1700 Å 2 . To further validate their use as calibrants, drift tube calculated CCS values for dendrimers were utilized to calibrate calculations of CCS for known standards including Agilent Tuning mix, the CCS Major mix from Waters, and SPLASH LIPIDOMIX. Additionally, structures of sodiated dendrimers were computated along with theoretical CCS values which showed good agreement with the experimental CCS values. On the basis of the results presented, we recommend the use of dendrimers as alternatives and/or complementary compounds to commonly used calibrants for ion mobility platforms.

Rapid evaporative ionization mass spectrometry (REIMS) is a direct tissue metabolic profiling technique used to accurately classify tissues using pre-built mass spectral databases. The reproducibility of the analytical equipment, methodology and tissue classification algorithms has yet to be evaluated over multiple sites, which is an essential step for developing this technique for future clinical applications. In this study, we harmonized REIMS methodology using single-source reference material across four sites with identical equipment: Imperial College London (UK); Waters Research Centre (Hungary); Maastricht University (The Netherlands); and Queen's University (Canada). We observed that method harmonization resulted in reduced spectral variability across sites. Each site then analyzed four different types of locally-sourced food-grade animal tissue. Tissue recognition models were created at each site using multivariate statistical analysis based on the different metabolic profiles observed in the m/z range of 600-1000, and these models were tested against data obtained at the other sites. Cross-validation by site resulted in 100% correct classification of two reference tissues and 69-100% correct classification for food-grade meat samples. While we were able to successfully minimize between-site variability in REIMS signals, differences in animal tissue from local sources led to significant variability in the accuracy of an individual site's model. Our results inform future multi-site REIMS studies applied to clinical samples and emphasize the importance of carefully-annotated samples that encompass sufficient population diversity.

Biomarkers may offer unforeseen insights into clinical diagnosis, as well as the likely course and outcome of a condition. In this paper, the focus is on the use of biological molecules found in body fluids or tissues for diagnosis and prediction of outcome in ovarian cancer patients. In cancer care, biomarkers are being used to develop personalised treatment plans for patients based on the unique characteristics of their tumour. This tailoring of care can be used to pursue specific targets identified by biomarkers, or treat the patient according to specific tumour characteristics. Surgery is one of the core treatments for ovarian cancer, whether it is offered in primary surgery or following chemotherapy in delayed surgery. Biomarkers already exist to guide the treatment of tumours with chemotherapy, but very little research has determined the value of biomarkers in tailoring surgical care for ovarian cancer. Such research is required to identify new biomarkers and assess their effectiveness in a clinical setting as well as to help identify specific tumour types to guide surgery. Biomarkers could help to determine the success of removing the disease surgically, or help to identify tumour deposits that persist after chemotherapy. All of these aspects would improve current practice. This Scientific Impact Paper highlights research that may pave the way towards bespoke surgery according to the biological characteristics of a tumour and aid gynaecological oncologists to provide surgical treatment according to individual need, rather than a blanket approach for all., (© 2022 John Wiley & Sons Ltd.)

Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1 H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1 H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels ( r 2 = 0.423, p < 2.2 × 10 -16 ). This correlation was significantly reduced ( r 2 = 0.163, p < 2.2 × 10 -16 ) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1 H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.

Summary: Untargeted liquid chromatography-mass spectrometry (LC-MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real-time data quality assessment., Availability and Implementation: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO 2 ) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO 2 laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy.

An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important to understand the information provided by one technique in the context of the other to achieve a more holistic overview of such complex samples. One way to achieve this is to use annotations from one modality to investigate additional modalities. For microscopy-based techniques, these annotations could be manually generated using digital pathology software or automatically generated by machine learning (including deep learning) methods. Here, we present a generic method for using annotations from one microscopy modality to extract information from complementary modalities. We also present a fast, general, multimodal registration workflow [evaluated on multiple mass spectrometry imaging (MSI) modalities, matrix-assisted laser desorption/ionization, desorption electrospray ionization, and rapid evaporative ionization mass spectrometry] for automatic alignment of complex data sets, demonstrating an order of magnitude speed-up compared to previously published work. To demonstrate the power of the annotation transfer and multimodal registration workflows, we combine MSI, histological staining (such as hematoxylin and eosin), and deep learning (automatic annotation of histology images) to investigate a pancreatic cancer mouse model. Neoplastic pancreatic tissue regions, which were histologically indistinguishable from one another, were observed to be metabolically different. We demonstrate the use of the proposed methods to better understand tumor heterogeneity and the tumor microenvironment by transferring machine learning results freely between the two modalities.

Mass spectrometry imaging can produce large amounts of complex spectral and spatial data. Such data sets are often analyzed with unsupervised machine learning approaches, which aim at reducing their complexity and facilitating their interpretation. However, choices made during data processing can impact the overall interpretation of these analyses. This work investigates the impact of the choices made at the peak selection step, which often occurs early in the data processing pipeline. The discussion is done in terms of visualization and interpretation of the results of two commonly used unsupervised approaches: t -distributed stochastic neighbor embedding and k -means clustering, which differ in nature and complexity. Criteria considered for peak selection include those based on hypotheses (exemplified herein in the analysis of metabolic alterations in genetically engineered mouse models of human colorectal cancer), particular molecular classes, and ion intensity. The results suggest that the choices made at the peak selection step have a significant impact in the visual interpretation of the results of either dimensionality reduction or clustering techniques and consequently in any downstream analysis that relies on these. Of particular significance, the results of this work show that while using the most abundant ions can result in interesting structure-related segmentation patterns that correlate well with histological features, using a smaller number of ions specifically selected based on prior knowledge about the biochemistry of the tissues under investigation can result in an easier-to-interpret, potentially more valuable, hypothesis-confirming result. Findings presented will help researchers understand and better utilize unsupervised machine learning approaches to mine high-dimensionality data.

The incidence of esophageal adenocarcinoma is rising, survival remains poor, and new tools to improve early diagnosis and precise treatment are needed. Cancer phospholipidomes quantified with mass spectrometry imaging (MSI) can support objective diagnosis in minutes using a routine frozen tissue section. However, whether MSI can objectively identify primary esophageal adenocarcinoma is currently unknown and represents a significant challenge, as this microenvironment is complex with phenotypically similar tissue-types. Here, we used desorption electrospray ionization-MSI (DESI-MSI) and bespoke chemometrics to assess the phospholipidomes of esophageal adenocarcinoma and relevant control tissues. Multivariate models derived from phospholipid profiles of 117 patients were highly discriminant for esophageal adenocarcinoma both in discovery (AUC = 0.97) and validation cohorts (AUC = 1). Among many other changes, esophageal adenocarcinoma samples were markedly enriched for polyunsaturated phosphatidylglycerols with longer acyl chains, with stepwise enrichment in premalignant tissues. Expression of fatty acid and glycerophospholipid synthesis genes was significantly upregulated, and characteristics of fatty acid acyls matched glycerophospholipid acyls. Mechanistically, silencing the carbon switch ACLY in esophageal adenocarcinoma cells shortened glycerophospholipid chains, linking de novo lipogenesis to the phospholipidome. Thus, DESI-MSI can objectively identify invasive esophageal adenocarcinoma from a number of premalignant tissues and unveils mechanisms of phospholipidomic reprogramming. SIGNIFICANCE: These results call for accelerated diagnosis studies using DESI-MSI in the upper gastrointestinal endoscopy suite, as well as functional studies to determine how polyunsaturated phosphatidylglycerols contribute to esophageal carcinogenesis., (©2020 American Association for Cancer Research.)

By leveraging advances in DNA synthesis and molecular cloning techniques, synthetic biology increasingly makes use of large construct libraries to explore large design spaces. For biosynthetic pathway engineering, the ability to screen these libraries for a variety of metabolites of interest is essential. If the metabolite of interest or the metabolic phenotype is not easily measurable, screening soon becomes a major bottleneck involving time-consuming culturing, sample preparation, and extraction. To address this, we demonstrate the use of automated laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS)-a form of ambient laser desorption ionization mass spectrometry-to perform rapid mass spectrometry analysis direct from agar plate yeast colonies without sample preparation or extraction. We use LA-REIMS to assess production levels of violacein and betulinic acid directly from yeast colonies at a rate of 6 colonies per minute. We then demonstrate the throughput enabled by LA-REIMS by screening over 450 yeast colonies within <4 h, while simultaneously generating recoverable glycerol stocks of each colony in real time. This showcases LA-REIMS as a prescreening tool to complement downstream quantification methods such as liquid chromatography-mass spectroscopy (LCMS). By prescreening several hundred colonies with LA-REIMS, we successfully isolate and verify a strain with a 2.5-fold improvement in betulinic acid production. Finally, we show that LA-REIMS can detect 20 out of a panel of 27 diverse biological molecules, demonstrating the broad applicability of LA-REIMS to metabolite detection. The rapid and automated nature of LA-REIMS makes this a valuable new technology to complement existing screening technologies currently employed in academic and industrial workflows.

Mass spectrometry is a powerful tool in the investigation of the human fecal metabolome. However, current approaches require time-consuming sample preparation, chromatographic separations, and consequently long analytical run times. Rapid evaporative ionization mass spectrometry (REIMS) is a method of ambient ionization mass spectrometry and has been utilized in the metabolic profiling of a diverse range of biological materials, including human tissue, cell culture lines, and microorganisms. Here, we describe the use of an automated, high-throughput REIMS robotic platform for direct analysis of human feces. Through the analysis of fecal samples from five healthy male participants, REIMS analytical parameters were optimized and used to assess the chemical information obtainable using REIMS. Within the fecal samples analyzed, bile acids, including primary, secondary, and conjugate species, were identified, and phospholipids of possible bacterial origin were detected. In addition, the effect of storage conditions and consecutive freeze/thaw cycles was determined. Within the REIMS mass spectra, the lower molecular weight metabolites, such as fatty acids, were shown to be significantly affected by storage conditions for prolonged periods at temperatures above -80 °C and consecutive freeze/thaw cycles. However, the complex lipid region was shown to be unaffected by these conditions. A further cohort of 50 fecal samples, collected from patients undergoing bariatric surgery, were analyzed using the optimized REIMS parameters and the complex lipid region mass spectra used for multivariate modeling. This analysis showed a predicted separation between pre- and post-surgery specimens, suggesting that REIMS analysis can detect biological differences, such as microbiome-level differences, which have traditionally been reliant upon methods utilizing extensive sample preparations and chromatographic separations and/or DNA sequencing.

Preoperative radiotherapy (RT) plays an important role in the management of locally advanced rectal cancer (RC). Tumor regression after RT shows marked variability, and robust molecular methods are needed to help predict likely response. The aim of this study was to review the current published literature and use Gene Ontology (GO) analysis to define key molecular biomarkers governing radiation response in RC. A systematic review of electronic bibliographic databases (Medline, Embase) was performed for original articles published between 2000 and 2015. Biomarkers were then classified according to biological function and incorporated into a hierarchical GO tree. Both significant and nonsignificant results were included in the analysis. Significance was binarized on the basis of univariate and multivariate statistics. Significance scores were calculated for each biological domain (or node), and a direct acyclic graph was generated for intuitive mapping of biological pathways and markers involved in RC radiation response. Seventy-two individual biomarkers across 74 studies were identified. On highest-order classification, molecular biomarkers falling within the domains of response to stress, cellular metabolism, and pathways inhibiting apoptosis were found to be the most influential in predicting radiosensitivity. Homogenizing biomarker data from original articles using controlled GO terminology demonstrated that cellular mechanisms of response to RT in RC-in particular the metabolic response to RT-may hold promise in developing radiotherapeutic biomarkers to help predict, and in the future modulate, radiation response., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The accurate and timely identification of the causative organism of infection is important in ensuring the optimum treatment regimen is prescribed for a patient. Rapid evaporative ionisation mass spectrometry (REIMS), using electrical diathermy for the thermal disruption of a sample, has been shown to provide fast and accurate identification of microorganisms directly from culture. However, this method requires contact to be made between the REIMS probe and microbial biomass; resulting in the necessity to clean or replace the probes between analyses. Here, optimisation and utilisation of ambient laser desorption ionisation (ALDI) for improved speciation accuracy and analytical throughput is shown. Optimisation was completed on 15 isolates of Escherichia coli, showing 5 W in pulsatile mode produced the highest signal-to-noise ratio. These parameters were used in the analysis of 150 clinical isolates from ten microbial species, resulting in a speciation accuracy of 99.4% - higher than all previously reported REIMS modalities. Comparison of spectral data showed high levels of similarity between previously published electrical diathermy REIMS data. ALDI does not require contact to be made with the sample during analysis, meaning analytical throughput can be substantially improved, and further, increases the range of sample types which can be analysed in potential direct-from-sample pathogen detection.

The interaction between microbial communities and their environment, such as the human gastrointestinal tract, has been an area of microbiology rapidly advanced, by developments in sequencing technology. However, these techniques are largely limited to the detection of the taxonomic composition of a microbial community and/or its genetic functional capacity. Here, we discuss a range of mass spectrometry-based approaches which researchers can employ to explore the host-microbiome interactions at the metabolic level. Traditional approaches to mass spectrometry are detailed, alongside new developments in the field, namely ambient ionisation mass spectrometry and imaging mass spectrometry, which we believe will prove to be important to future work in this field. We further discuss considerations for experimental workflows, data analysis options and propose a methodology for the establishment of causal relationships between functional host-microbiome interactions with regards to health and disease in the human gastrointestinal tract., (Copyright © 2018. Published by Elsevier Inc.)

Rapid evaporative ionisation mass spectrometry (REIMS) is a novel technique for the real-time analysis of biological material. It works by conducting an electrical current through a sample, causing it to rapidly heat and evaporate, with the analyte containing vapour channelled to a mass spectrometer. It was used to characterise the metabolome of 45 Pseudomonas aeruginosa (P. aeruginosa) isolates from cystic fibrosis (CF) patients and compared to 80 non-CF P. aeruginosa. Phospholipids gave the highest signal intensity; 17 rhamnolipids and 18 quorum sensing molecules were detected, demonstrating that REIMS has potential for the study of virulence-related metabolites. P. aeruginosa isolates obtained from respiratory samples showed a higher diversity, which was attributed to the chronic nature of most respiratory infections. The analytical sensitivity of REIMS allowed the detection of a metabolome that could be used to classify individual P. aeruginosa isolates after repeated culturing with 81% accuracy, and an average 83% concordance with multilocus sequence typing. This study underpins the capacities of REIMS as a tool with clinical applications, such as metabolic phenotyping of the important CF pathogen P. aeruginosa, and highlights the potential of metabolic fingerprinting for fine scale characterisation at a sub-species level.

Background: Survival from ovarian cancer (OC) is improved with surgery, but surgery can be complex and tumour identification, especially for borderline ovarian tumours (BOT), is challenging. The Rapid Evaporative Ionisation Mass Spectrometric (REIMS) technique reports tissue histology in real-time by analysing aerosolised tissue during electrosurgical dissection., Methods: Aerosol produced during diathermy of tissues was sampled with the REIMS interface. Histological diagnosis and mass spectra featuring complex lipid species populated a reference database on which principal component, linear discriminant and leave-one-patient-out cross-validation analyses were performed., Results: A total of 198 patients provided 335 tissue samples, yielding 3384 spectra. Cross-validated OC classification vs separate normal tissues was high (97·4% sensitivity, 100% specificity). BOT were readily distinguishable from OC (sensitivity 90.5%, specificity 89.7%). Validation with fresh tissue lead to excellent OC detection (100% accuracy). Histological agreement between iKnife and histopathologist was very good (kappa 0.84, P < 0.001, z = 3.3). Five predominantly phosphatidic acid (PA(36:2)) and phosphatidyl-ethanolamine (PE(34:2)) lipid species were identified as being significantly more abundant in OC compared to normal tissue or BOT (P < 0.001, q < 0.001)., Conclusions: The REIMS iKnife distinguishes gynaecological tissues by analysing mass-spectrometry-derived lipidomes from tissue diathermy aerosols. Rapid intra-operative gynaecological tissue diagnosis may improve surgical care when histology is unknown, leading to personalised operations tailored to the individual.

Members of the genus Candida, such as C. albicans and C. parapsilosis, are important human pathogens. Other members of this genus, previously believed to carry minimal disease risk, are increasingly recognised as important human pathogens, particularly because of variations in susceptibilities to widely used anti-fungal agents. Thus, rapid and accurate identification of clinical Candida isolates is fundamental in ensuring timely and effective treatments are delivered. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) has previously been shown to provide a high-throughput platform for the rapid and accurate identification of bacterial and fungal isolates. In comparison to commercially available matrix assisted laser desorption ionisation time of flight mass spectrometry (MALDI-ToF), REIMS based methods require no preparative steps nor time-consuming cell extractions. Here, we report on the ability of REIMS-based analysis to rapidly and accurately identify 153 clinical Candida isolates to species level. Both handheld bipolar REIMS and high-throughput REIMS platforms showed high levels of species classification accuracy, with 96% and 100% of isolates classified correctly to species level respectively. In addition, significantly different (FDR corrected P value < 0.05) lipids within the 600 to 1000 m/z mass range were identified, which could act as species-specific biomarkers in complex microbial communities., Competing Interests: This work was partly funded and supported by the Waters Corporation (through funding of Waters Corporation staff salaries, provision of equipment and consumables, and research funding to Imperial College London), and through funding from the UK Biotechnology and Biological Sciences Research Council via grant BB/L020858/1. ZT provides paid consultancy to the Waters Corporation. Neither funder had input into initial study design nor on decision of when and where to publish this manuscript. This paper does not promote any commercially available product of the Waters Corporation.

Efforts to apply nanotechnology in cancer have focused almost exclusively on the delivery of cytotoxic drugs to improve therapeutic index. There has been little consideration of molecularly targeted agents, in particular kinase inhibitors, which can also present considerable therapeutic index limitations. We describe the development of Accurin polymeric nanoparticles that encapsulate the clinical candidate AZD2811, an Aurora B kinase inhibitor, using an ion pairing approach. Accurins increase biodistribution to tumor sites and provide extended release of encapsulated drug payloads. AZD2811 nanoparticles containing pharmaceutically acceptable organic acids as ion pairing agents displayed continuous drug release for more than 1 week in vitro and a corresponding extended pharmacodynamic reduction of tumor phosphorylated histone H3 levels in vivo for up to 96 hours after a single administration. A specific AZD2811 nanoparticle formulation profile showed accumulation and retention in tumors with minimal impact on bone marrow pathology, and resulted in lower toxicity and increased efficacy in multiple tumor models at half the dose intensity of AZD1152, a water-soluble prodrug of AZD2811. These studies demonstrate that AZD2811 can be formulated in nanoparticles using ion pairing agents to give improved efficacy and tolerability in preclinical models with less frequent dosing. Accurins specifically, and nanotechnology in general, can increase the therapeutic index of molecularly targeted agents, including kinase inhibitors targeting cell cycle and oncogenic signal transduction pathways, which have to date proved toxic in humans., (Copyright © 2016, American Association for the Advancement of Science.)

Biotinidase activity assay is included in most newborn screening protocols, and the positive results are confirmed by quantitative enzyme activity measurements. In our study, we describe a new quantitative analytical method for the determination of biotinidase activity using the blood sample deposited onto filter paper as the assay medium, by predepositing N-biotinyl-p-aminobenzoic acid onto the standard sample collection paper. The analysis of the assay mixture requires a simple extraction step from a dried blood spot followed by the quantification of product by LC-MS. The method provides a simple and reliable enzyme assay method that enables the rapid diagnosis of biotinidase deficiency (BD). Out of the measured 36 samples, 13 were healthy with lower enzyme activities, 16 were patients with partial BD, and 7 were patients with profound BD with residual activity below 10%. Expression of enzyme activity in percentage of mean activity of negative controls allows comparison of the different techniques. The obtained results are in good agreement with activity data determined from both dried blood spots and serum samples, giving an informative diagnostic value.

All higher plants produce polyphenols, for defence against above-ground herbivory. These polyphenols also influence the soil micro- and macro-fauna that break down plant leaf litter. Polyphenols therefore indirectly affect the fluxes of soil nutrients and, ultimately, carbon turnover and ecosystem functioning in soils. It is unknown how earthworms, the major component of animal biomass in many soils, cope with high-polyphenol diets. Here, we show that earthworms possess a class of unique surface-active metabolites in their gut, which we term 'drilodefensins'. These compounds counteract the inhibitory effects of polyphenols on earthworm gut enzymes, and high-polyphenol diets increase drilodefensin concentrations in both laboratory and field populations. This shows that drilodefensins protect earthworms from the harmful effects of ingested polyphenols. We have identified the key mechanism for adaptation to a dietary challenge in an animal group that has a major role in organic matter recycling in soils worldwide.

Activation of G protein-coupled receptors (GPCRs) can induce vasoconstriction via calcium signal-mediated and Rho-dependent pathways. Earlier reports have shown that diacylglycerol produced during calcium signal generation can be converted to an endocannabinoid, 2-arachidonoylglycerol (2-AG). Our aim was to provide evidence that GPCR signaling-induced 2-AG production and activation of vascular type1 cannabinoid receptors (CB1R) is capable of reducing agonist-induced vasoconstriction and hypertension. Rat and mouse aortic rings were examined by myography. Vascular expression of CB1R was demonstrated with immunohistochemistry. Rat aortic vascular smooth muscle cells (VSMCs) were cultured for calcium measurements and 2-AG-determination. Inhibition or genetic loss of CB1Rs enhanced vasoconstriction induced by angiotensin II (AngII) or phenylephrine (Phe), but not by prostaglandin(PG)F2α. AngII-induced vasoconstriction was augmented by inhibition of diacylglycerol lipase (tetrahydrolipstatin) and was attenuated by inhibition of monoacylglycerol lipase (JZL184) suggesting a functionally relevant role for endogenously produced 2-AG. In Gαq/11-deficient mice vasoconstriction was absent to AngII or Phe, which activate Gq/11-coupled receptors, but was maintained in response to PGF2α. In VSMCs, AngII-stimulated 2-AG-formation was inhibited by tetrahydrolipstatin and potentiated by JZL184. CB1R inhibition increased the sustained phase of AngII-induced calcium signal. Pharmacological or genetic loss of CB1R function augmented AngII-induced blood pressure rise in mice. These data demonstrate that vasoconstrictor effect of GPCR agonists is attenuated via Gq/11-mediated vascular endocannabinoid formation. Agonist-induced endocannabinoid-mediated CB1R activation is a significant physiological modulator of vascular tone. Thus, the selective modulation of GPCR signaling-induced endocannabinoid release has a therapeutic potential in case of increased vascular tone and hypertension., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Here we present a proof of concept cross-platform normalization approach to convert raw mass spectra acquired by distinct desorption ionization methods and/or instrumental setups to cross-platform normalized analyte profiles. The initial step of the workflow is database driven peak annotation followed by summarization of peak intensities of different ions from the same molecule. The resulting compound-intensity spectra are adjusted to a method-independent intensity scale by using predetermined, compound-specific normalization factors. The method is based on the assumption that distinct MS-based platforms capture a similar set of chemical species in a biological sample, though these species may exhibit platform-specific molecular ion intensity distribution patterns. The method was validated on two sample sets of (1) porcine tissue analyzed by laser desorption ionization (LDI), desorption electrospray ionization (DESI), and rapid evaporative ionization mass spectrometric (REIMS) in combination with Fourier transformation-based mass spectrometry; and (2) healthy/cancerous colorectal tissue analyzed by DESI and REIMS with the latter being combined with time-of-flight mass spectrometry. We demonstrate the capacity of our method to reduce MS-platform specific variation resulting in (1) high inter-platform concordance coefficients of analyte intensities; (2) clear principal component based clustering of analyte profiles according to histological tissue types, irrespective of the used desorption ionization technique or mass spectrometer; and (3) accurate "blind" classification of histologic tissue types using cross-platform normalized analyte profiles.

Cassette dosing of compounds for preclinical drug plasma pharmacokinetic analysis has been shown to be a powerful strategy within the pharmaceutical industry for increasing throughput while decreasing the number of animals used. Presented here for the first time is data on the application of a cassette dosing strategy for label-free tissue distribution studies. The aim of the study was to image the spatial distribution of eight nonproprietary drugs (haloperidol, bufuralol, midazolam, clozapine, terfenadine, erlotinib, olanzapine, and moxifloxacin) in multiple tissues after oral and intravenous cassette dosing (four compounds per dose route). An array of mass spectrometry imaging technologies, including matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI), liquid extraction surface analysis tandem mass spectrometry (LESA-MS/MS), and desorption electrospray ionization mass spectrometry (DESI-MS) was used. Tissue analysis following intravenous and oral administration of discretely and cassette-dosed compounds demonstrated similar relative abundances across a range of tissues indicating that a cassette dosing approach was applicable. MALDI MSI was unsuccessful in detecting all of the target compounds; therefore, DESI MSI, a complementary mass spectrometry imaging technique, was used to detect additional target compounds. In addition, by adapting technology used for tissue profiling (LESA-MS/MS) low spatial resolution mass spectrometry imaging (∼1 mm) was possible for all targets across all tissues. This study exemplifies the power of multiplatform MSI analysis within a pharmaceutical research and development (R&D) environment. Furthermore, we have illustrated that the cassette dosing approach can be readily applied to provide combined, label-free pharmacokinetic and drug distribution data at an early stage of the drug discovery/development process while minimizing animal usage.

Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near-real-time characterization of human tissue in vivo by analysis of the aerosol ("smoke") released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.

Background: Accumulation of advanced glycation end products (AGEs) in tissues is a major risk factor for diabetes-associated complications. Skin autofluorescence (SAF) values measured by a specific noninvasive approach (AGE Reader; DiagnOptics Technologies B.V., Gröningen, The Netherlands) reflect the overall AGE exposure in skin., Subjects and Methods: In 16 adolescents with type 1 diabetes (age range, 11-18 years) we tested the association between SAF measured with an AGE Reader and the presence of glucuronic acid, 3-indoxyl sulfate, 3-hydroxybutyrate, phenol sulfate, and pentosidine in skin tissue determined with desorption electrospray ionization mass spectrometry (DESI-MS). These compounds are implicated in long-term diabetes complications., Results: SAF values significantly correlated with levels of compounds measured by DESI-MS (r>0.9 and P<0.001 for each)., Conclusions: The strong correlation between adolescents' SAF values measured with the AGE Reader and some glycation products measured with DESI-MS indicates that SAF values may be used as surrogate markers of skin exposure to glycemic end products in type 1 diabetes.

A novel, single stage high resolution mass spectrometry-based method is presented for the population level screening of inborn errors of metabolism. The approach proposed here extends traditional electrospray tandem mass spectrometry screening by introducing nanospray ionization and high resolution mass spectrometry, allowing the selective detection of more than 400 individual metabolic constituents of blood including acylcarnitines, amino acids, organic acids, fatty acids, carbohydrates, bile acids, and complex lipids. Dried blood spots were extracted using a methanolic solution of isotope labeled internal standards, and filtered extracts were electrosprayed using a fully automated chip-based nanospray ion source in both positive and negative ion mode. Ions were analyzed using an Orbitrap Fourier transformation mass spectrometer at nominal mass resolution of 100,000. Individual metabolic constituents were quantified using standard isotope dilution methods. Concentration threshold (cutoff) level-based analysis allows the identification of newborns with metabolic diseases, similarly to the traditional electrospray tandem mass spectrometry (ESI-MS/MS) method; however, the detection of additional known biomarkers (e.g., organic acids) results in improved sensitivity and selectivity. The broad range of detected analytes allowed the untargeted multivariate statistical analysis of spectra and identification of additional diseased states, therapeutic artifacts, and damaged samples, besides the metabolic disease panel.

An analysis method for aqueous samples by the direct combination of C18/SCX mixed mode thin-film microextraction (TFME) and desorption electrospray ionization mass spectrometry (DESI-MS) was developed. Both techniques make analytical workflow simpler and faster, hence the combination of the two techniques enables considerably shorter analysis time compared to the traditional liquid chromatography mass spectrometry (LC-MS) approach. The method was characterized using carbamazepine and triclosan as typical examples for pharmaceuticals and personal care product (PPCP) components which draw increasing attention as wastewater-derived environmental contaminants. Both model compounds were successfully detected in real wastewater samples and their concentrations determined using external calibration with isotope labeled standards. Effects of temperature, agitation, sample volume, and exposure time were investigated in the case of spiked aqueous samples. Results were compared to those of parallel HPLC-MS determinations and good agreement was found through a three orders of magnitude wide concentration range. Serious matrix effects were observed in treated wastewater, but lower limits of detection were still found to be in the low ng L(-1) range. Using an Orbitrap mass spectrometer, the technique was found to be ideal for screening purposes and led to the detection of various different PPCP components in wastewater treatment plant effluents, including beta-blockers, nonsteroidal anti-inflammatory drugs, and UV filters.

The ABCG2 multidrug transporter protein has been identified as a key player in cancer drug resistance and xenobiotic elimination, as its actively transported substrates include anticancer drugs, intermediates of heme metabolism, xenobiotics, and also drug conjugates. Several transported substrates at higher concentrations, and some anticancer agents even at low concentrations directly inhibit the ABCG2 transporter, thus it is difficult to provide estimation for pharmacologically important ABCG2-dependent interactions. In addition, as documented here, in mutant variants of the transporter, inhibitors of the wild-type ABCG2 may become actively transported substrates. In this paper we describe a rapid in vitro assay to identify transport modulation by measuring the cell surface interaction of a conformation sensitive monoclonal antibody (5D3) with ABCG2 in intact cells. As documented, in conjunction with membrane ATPase, transport and cytotoxicity measurements, this assay provides a reliable estimate of concentration-dependent modulation of ABCG2 by newly emerging pharmacophores. A high-throughput, 96-well plate assay platform is also provided., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Aim: Our aim was to investigate the levels of hepcidin at parturition and 3 days after delivery and to relate hepcidin levels to parameters of iron homeostasis., Materials and Methods: We measured hepcidin levels with mass spectrometry in serum samples of 38 term pregnant women taken just prior to and 3 days after vaginal delivery (n = 23) or cesarean section (CS) (n = 15). Hepcidin levels were related to iron homeostasis parameters and interleukin (IL)-6 levels. Parameters measured before and after delivery were compared with the Wilcoxon test., Results: Serum iron levels (median, interquartile range) decreased (14.3, 9.6-21.1 vs. 8.9, 6.8-11.5 µmol/L, P < 0.01), while hepcidin levels increased (2.73, 2.2-3.45 vs. 10.62, 6.70-15.89 µg/L, P < 0.01) by the third day after parturition compared to those measured before delivery. IL-6 levels were comparable before and after delivery. No direct association between serum hepcidin and iron homeostasis parameters or IL-6 levels was found., Conclusions: Factors triggering hepcidin synthesis dominate 3 days after delivery. Studies are needed to assess the contribution of hepcidin to iron homeostasis during the periparturition period., (© 2011 The Authors. Journal of Obstetrics and Gynaecology Research © 2011 Japan Society of Obstetrics and Gynecology.)

The feasibility of electrospray (ES) ionization of aerosols generated by electrosurgical disintegration methods was investigated. Although electrosurgery itself was demonstrated to produce gaseous ions, post-ionization methods were implemented to enhance the ion yield, especially in those cases when the ion current produced by the applied electrosurgical method is not sufficient for MS analysis. Post-ionization was implemented by mounting an ES emitter onto a Venturi pump, which is used for ion transfer. The effect of various parameters including geometry, high voltage setting, flow parameters, and solvent composition was investigated in detail. Experimental setups were optimized accordingly. ES post-ionization was found to yield spectra similar to those obtained by the REIMS technique, featuring predominantly lipid-type species. Signal enhancement was 20- to 50-fold compared with electrosurgical disintegration in positive mode, while no improvement was observed in negative mode. ES post-ionization was also demonstrated to allow the detection of non-lipid type species in the electrosurgical aerosol, including drug molecules. Since the tissue specificity of the MS data was preserved in the ES post-ionization setup, feasibility of tissue identification was demonstrated using different electrosurgical methods.

Direct combination of cavitron ultrasonic surgical aspirator (CUSA) and sonic spray ionization mass spectrometry is presented. A commercially available ultrasonic surgical device was coupled to a Venturi easy ambient sonic-spray ionization (V-EASI) source by directly introducing liquified tissue debris into the Venturi air jet pump. The Venturi air jet pump was found to efficiently nebulize the suspended tissue material for gas phase ion production. The ionization mechanism involving solely pneumatic spraying was associated with that of sonic spray ionization. Positive and negative ionization spectra were obtained from brain and liver samples reflecting the primary application areas of the surgical device. Mass spectra were found to feature predominantly complex lipid-type constituents of tissues in both ion polarity modes. Multiply charged peptide anions were also detected. The influence of instrumental settings was characterized in detail. Venturi pump geometry and flow parameters were found to be critically important in ionization efficiency. Standard solutions of phospholipids and peptides were analyzed in order to test the dynamic range, sensitivity, and suppression effects. The spectra of the intact tissue specimens were found to be highly specific to the histological tissue type. The principal component analysis (PCA) and linear discriminant analysis (LDA) based data analysis method was developed for real-time tissue identification in a surgical environment. The method has been successfully tested on post-mortem and ex vivo human samples including astrocytomas, meningeomas, metastatic brain tumors, and healthy brain tissue., (© 2011 American Chemical Society)

Laser desorption ionization-mass spectrometric (LDI-MS) analysis of vital biological tissues and native, ex vivo tissue specimens is described. It was found that LDI-MS analysis yields tissue specific data using lasers both in the ultraviolet and far-infrared wavelength regimes, while visible and near IR lasers did not produce informative MS data. LDI mass spectra feature predominantly phospholipid-type molecular ions both in positive and negative ion modes, similar to other desorption ionization methods. Spectra were practically identical to rapid evaporative ionization MS (REIMS) spectra of corresponding tissues, indicating a similar ion formation mechanism. LDI-MS analysis of intact tissues was characterized in detail. The effect of laser fluence on the spectral characteristics (intensity and pattern) was investigated in the case of both continuous wave and pulsed lasers at various wavelengths. Since lasers are utilized in various fields of surgery, a surgical laser system was combined with a mass spectrometer in order to develop an intraoperative tissue identification device. A surgical CO(2) laser was found to yield sufficiently high ion current during normal use. The principal component analysis-based real-time data analysis method was developed for the quasi real-time identification of mass spectra. Performance of the system was demonstrated in the case of various malignant tumors of the gastrointestinal tract.

In vivo skin analysis by Desorption Electrospray Ionization was characterized on healthy human volunteers by directing pneumatically assisted electrospray directly onto their fingertips. In order to eliminate the risk of electric shock, a high ohmic resistor was built into the system. Positive ion DESI-MS analysis yields low intensity spectra, while negative ion spectra feature a number of various biogenic carboxylic acids. Compounds of external origin and excreted molecules were found to have different analysis kinetics, with the exception of highly hydrophobic species. The difference was demonstrated in the case of nicotine and cotinine. Pharmacokinetic studies were performed using a rat animal model. The kinetics of the anesthetic ketamine was followed by DESI, and results were in agreement with off-line HPLC-MS blood analysis. Using a similar approach for N,N'-dimethylthiourea (DMTU), a novel method was developed for the real-time quantification of oxidative stress. DMTU was administered to the animals, and the ratio of the molecule and its oxidized form was monitored from the skin surface. The ratio was found to be highly sensitive to experimentally induced diabetes mellitus type I and angiotensin-induced chronic oxidative stress. It was concluded that the method has a number of potential applications in the fields of forensics, pharmacology and clinical chemistry.