Assessment of risks, implications, and opportunities of waterborne neurotoxic pesticides.

Pesticides are a well-known family of chemicals that have contaminated water systems globally. Four common subfamilies of pesticides include organochlorines, organophosphates, pyrethroids, and carbamate insecticides which have been shown to adversely affect the human nervous system. Studies have shown a link between pesticide exposure and decreased viability, proliferation, migration, and differentiation of murine neural stem cells. Besides human exposure directly through water systems, additional factors such as pesticide bioaccumulation, biomagnification and potential synergism due to co-exposure to other environmental contaminants must be considered. A possible avenue to investigate the molecular mechanisms and biomolecules impacted by the various classes of pesticides includes the field of -omics. Discovery of the precise molecular mechanisms behind pesticide-mediated neurodegenerative disorders may facilitate development of targeted therapeutics. Likewise, discovery of pesticide biodegradation pathways may enable novel approaches for water system bioremediation using genetically engineered microorganisms. In this mini-review, we discuss recently established harmful impacts of various categories of pesticides on the nervous system and the application of -omics field for discovery, validation, and mitigation of pesticide neurotoxicity.

Rational Design and Development of SARS-CoV-2 Serological Diagnostics by Immunoprecipitation-Targeted Proteomics.

Current design of serological tests utilizes conservative immunoassay approaches and is focused on fast and convenient assay development, throughput, straightforward measurements, and affordability. Limitations of common serological assays include semiquantitative measurements, cross-reactivity, lack of reference standards, and no differentiation between human immunoglobulin subclasses. In this study, we suggested that a combination of immunoaffinity enrichments with targeted proteomics would enable rational design and development of serological assays of infectious diseases, such as COVID-19. Immunoprecipitation-targeted proteomic assays allowed for sensitive and specific measurements of NCAP_SARS2 protein with a limit of detection of 313 pg/mL in serum and enabled differential quantification of anti-SARS-CoV-2 antibody isotypes (IgG, IgA, IgM, IgD, and IgE) and individual subclasses (IgG1-4 and IgA1-2) in plasma and saliva. Simultaneous evaluation of the numerous antigen-antibody subclass combinations revealed a receptor-binding domain (RBD)-IgG1 as a combination with the highest diagnostic performance. Further validation revealed that anti-RBD IgG1, IgG3, IgM, and IgA1 levels were significantly elevated in convalescent plasma, while IgG2, IgG4, and IgA2 were not informative. Anti-RBD IgG1 levels in convalescent (2138 ng/mL) vs negative (95 ng/mL) plasma revealed 385 ng/mL as a cutoff to detect COVID-19 convalescent plasma. Immunoprecipitation-targeted proteomic assays will facilitate improvement and standardization of the existing serological tests, enable rational design of novel tests, and offer tools for the comprehensive investigation of immunoglobulin subclass cooperation in immune response.

In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality.

Integration between a hand-held mass spectrometry desorption probe based on picosecond infrared laser technology (PIRL-MS) and an optical surgical tracking system demonstrates in situ tissue pathology from point-sampled mass spectrometry data. Spatially encoded pathology classifications are displayed at the site of laser sampling as color-coded pixels in an augmented reality video feed of the surgical field of view. This is enabled by two-way communication between surgical navigation and mass spectrometry data analysis platforms through a custom-built interface. Performance of the system was evaluated using murine models of human cancers sampled in situ in the presence of body fluids with a technical pixel error of 1.0 ± 0.2 mm, suggesting a 84% or 92% (excluding one outlier) cancer type classification rate across different molecular models that distinguish cell-lines of each class of breast, brain, head and neck murine models. Further, through end-point immunohistochemical staining for DNA damage, cell death and neuronal viability, spatially encoded PIRL-MS sampling is shown to produce classifiable mass spectral data from living murine brain tissue, with levels of neuronal damage that are comparable to those induced by a surgical scalpel. This highlights the potential of spatially encoded PIRL-MS analysis for in vivo use during neurosurgical applications of cancer type determination or point-sampling in vivo tissue during tumor bed examination to assess cancer removal. The interface developed herein for the analysis and the display of spatially encoded PIRL-MS data can be adapted to other hand-held mass spectrometry analysis probes currently available.

Rapid determination of medulloblastoma subgroup affiliation with mass spectrometry using a handheld picosecond infrared laser desorption probe.

Medulloblastoma (MB), the most prevalent malignant childhood brain tumour, consists of at least 4 distinct subgroups each of which possesses a unique survival rate and response to treatment. To rapidly determine MB subgroup affiliation in a manner that would be actionable during surgery, we subjected murine xenograft tumours of two MB subgroups (SHH and Group 3) to Mass Spectrometry (MS) profiling using a handheld Picosecond InfraRed Laser (PIRL) desorption probe and interface developed by our group. This platform provides real time MS profiles of tissue based on laser desorbed lipids and small molecules with only 5-10 seconds of sampling. PIRL-MS analysis of ex vivo MB tumours offered a 98% success rate in subgroup determination, observed over 194 PIRL-MS datasets collected from 19 independent tumours (∼10 repetitions each) utilizing 6 different established MB cell lines. Robustness was verified by a 5%-leave-out-and-remodel test. PIRL ablated tissue material was collected on a filter paper and subjected to high resolution LC-MS to provide ion identity assignments for the m/z values that contribute most to the statistical discrimination between SHH and Group 3 MB. Based on this analysis, rapid classification of MB with PIRL-MS utilizes a variety of fatty acid chains, glycerophosphates, glycerophosphoglycerols and glycerophosphocholines rapidly extracted from the tumours. In this work, we provide evidence that 5-10 seconds of sampling from ex vivo MB tissue with PIRL-MS can allow robust tumour subgroup classification, and have identified several biomarker ions responsible for the statistical discrimination of MB Group 3 and the SHH subgroup. The existing PIRL-MS platform used herein offers capabilities for future in vivo use.

Correction: Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration.

Correction for 'Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration' by Michael Woolman et al., Analyst, 2017, 142, 3250-3260.

Rapid determination of the tumour stroma ratio in squamous cell carcinomas with desorption electrospray ionization mass spectrometry (DESI-MS): a proof-of-concept demonstration.

Squamous cell carcinomas constitute a major class of head & neck cancers, where the tumour stroma ratio (TSR) carries prognostic information. Patients affected by stroma-rich tumours exhibit a poor prognosis and a higher chance of relapse. As such, there is a need for a technology platform that allows rapid determination of the tumour stroma ratio. In this work, we provide a proof-of-principle demonstration that Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) can be used to determine tumour stroma ratios. Slices from three independent mouse xenograft tumours from the human FaDu cell line were subjected to DESI-MS imaging, staining and detailed analysis using digital pathology methods. Using multivariate statistical methods we compared the MS profiles with those of isolated stromal cells. We found that m/z 773.53 [PG(18:1)(18:1) - H]-, m/z 835.53 [PI(34:1) - H]- and m/z 863.56 [PI(18:1)(18:0) - H]- are biomarker ions that can distinguish FaDu cancer from cancer associated fibroblast (CAF) cells. A comparison with DESI-MS analysis of controlled mixtures of the CAF and FaDu cells showed that the abundance of the biomarker ions above can be used to determine, with an error margin of close to 5% compared with quantitative pathology estimates, TSR values. This proof-of-principle demonstration is encouraging and must be further validated using human samples and a larger sample base. At maturity, DESI-MS thus may become a stand-alone molecular pathology tool providing an alternative rapid cancer assessment without the need for time-consuming staining and microscopy methods, potentially further conserving human resources.

An Assessment of the Utility of Tissue Smears in Rapid Cancer Profiling with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS).

Mass spectrometry imaging with desorption electrospray ionization mass spectrometry (DESI-MS) is used to characterize cancer from ex vivo slices of tissues. The process is time-consuming. The use of tissue smears for DESI-MS analysis has been proposed as it eliminates the time required to snap-freeze and section the tissue. To assess the utility of tissue smears for rapid cancer characterization, principal component analysis (PCA) was performed to evaluate the concordance between DESI-MS profiles of breast cancer from tissue slices and smears prepared on various surfaces. PCA suggested no statistical discrimination between DESI-MS profiles of tissue sections and tissue smears prepared on glass, polytetrafluoroethylene (PTFE), and porous PTFE. However, the abundances of cancer biomarker ions varied between sections and smears, with DESI-MS analysis of tissue sections yielding higher ion abundances of cancer biomarkers compared with smears. Coefficient of variance (CV) analysis suggests DESI-MS profiles from tissue smears are as reproducible as the ones from tissue sections. The limit of detection with smear samples from single pixel analysis is comparable to tissue sections that average the signal from a tissue area of 0.01 mm2. The smears prepared on the PTFE surface possessed a higher degree of homogeneity compared with the smears prepared on the glass surface. This allowed single MS scans (~1 s) from random positions across the surface of the smear to be used in rapid cancer typing with good reproducibility, providing pathologic information for cancer typing at speeds suitable for clinical utility. Graphical Abstract ᅟ.