Demonstrating the Use of Non-targeted Analysis for Identification of Unknown Chemicals in Rapid Response Scenarios.

Several thousand intentional and unintentional chemical releases occur annually in the U.S., with the contents of almost 30% being of unknown composition. When targeted methods are unable to identify the chemicals present, alternative approaches, including non-targeted analysis (NTA) methods, can be used to identify unknown analytes. With new and efficient data processing workflows, it is becoming possible to achieve confident chemical identifications via NTA in a timescale useful for rapid response (typically 24-72 h after sample receipt). To demonstrate the potential usefulness of NTA in rapid response situations, we have designed three mock scenarios that mimic real-world events, including a chemical warfare agent attack, the contamination of a home with illicit drugs, and an accidental industrial spill. Using a novel, focused NTA method that utilizes both existing and new data processing/analysis methods, we have identified the most important chemicals of interest in each of these designed mock scenarios in a rapid manner, correctly assigning structures to more than half of the 17 total features investigated. We have also identified four metrics (speed, confidence, hazard information, and transferability) that successful rapid response analytical methods should address and have discussed our performance for each metric. The results reveal the usefulness of NTA in rapid response scenarios, especially when unknown stressors need timely and confident identification.

Multi-internal standard calibration applied to inductively coupled plasma optical emission spectrometry.

Several species are simultaneously used for internal standardization to improve accuracy in inductively coupled plasma optical emission spectrometry (ICP-OES). In multi-internal standard calibration (MISC), signal ratios between the analyte and each of several internal standard species are used for calibration. A single calibration solution is required, and the MISC graph is built with intensity ratios calculated with analytical signals recorded for the sample (IA,sam) on the y-axis, while those recorded for the calibration standard (IA,std) are plot on the x-axis (i.e. IA,sam/IIS(i)vs. IA,std/IIS(i), where IIS(i) represents the signal intensity for a given internal standard species). Nine analytes (As, Cd, Cr, Cu, Fe, Mg, Mn, Pb and Zn), and two sets of internal standard species (i.e. Bi, Ge, In, Rh, Sc, Te, Tl and Y in solution, or eighteen emission lines from plasma naturally occurring Ar) were evaluated in this proof-of-concept study. The MISC method's efficiency was evaluated by analyte addition and recovery experiments and by analyzing two certified reference materials. Figures of merit for MISC (limit of detection, repeatability and trueness) were comparable to those obtained for the traditional external standard calibration (EC) and internal standard (IS) methods. Different from IS, MISC requires no time-consuming study to identify an ideal internal standard species, and signal biases are minimized by an averaged, more encompassing internal standardization effect.

The mechanism of cell death induced by silver nanoparticles is distinct from silver cations.

BACKGROUND: Precisely how silver nanoparticles (AgNPs) kill mammalian cells still is not fully understood. It is not clear if AgNP-induced damage differs from silver cation (Ag+), nor is it known how AgNP damage is transmitted from cell membranes, including endosomes, to other organelles. Cells can differ in relative sensitivity to AgNPs or Ag+, which adds another layer of complexity to identifying specific mechanisms of action. Therefore, we determined if there were specific effects of AgNPs that differed from Ag+ in cells with high or low sensitivity to either toxicant. METHODS: Cells were exposed to intact AgNPs, Ag+, or defined mixtures of AgNPs with Ag+, and viability was assessed. The level of dissolved Ag+ in AgNP suspensions was determined using inductively coupled plasma mass spectrometry. Changes in reactive oxygen species following AgNP or Ag+ exposure were quantified, and treatment with catalase, an enzyme that catalyzes the decomposition of H2O2 to water and oxygen, was used to determine selectively the contribution of H2O2 to AgNP and Ag+ induced cell death. Lipid peroxides, formation of 4-hydroxynonenol protein adducts, protein thiol oxidation, protein aggregation, and activation of the integrated stress response after AgNP or Ag+ exposure were quantified. Lastly, cell membrane integrity and indications of apoptosis or necrosis in AgNP and Ag+ treated cells were examined by flow cytometry. RESULTS: We identified AgNPs with negligible Ag+ contamination. We found that SUM159 cells, which are a triple-negative breast cancer cell line, were more sensitive to AgNP exposure less sensitive to Ag+ compared to iMECs, an immortalized, breast epithelial cell line. This indicates that high sensitivity to AgNPs was not predictive of similar sensitivity to Ag+. Exposure to AgNPs increased protein thiol oxidation, misfolded proteins, and activation of the integrated stress response in AgNP sensitive SUM159 cells but not in iMEC cells. In contrast, Ag+ cause similar damage in Ag+ sensitive iMEC cells but not in SUM159 cells. Both Ag+ and AgNP exposure increased H2O2 levels; however, treatment with catalase rescued cells from Ag+ cytotoxicity but not from AgNPs. Instead, our data support a mechanism by which damage from AgNP exposure propagates through cells by generation of lipid peroxides, subsequent lipid peroxide mediated oxidation of proteins, and via generation of 4-hydroxynonenal (4-HNE) protein adducts. CONCLUSIONS: There are distinct differences in the responses of cells to AgNPs and Ag+. Specifically, AgNPs drive cell death through lipid peroxidation leading to proteotoxicity and necrotic cell death, whereas Ag+ increases H2O2, which drives oxidative stress and apoptotic cell death. This work identifies a previously unknown mechanism by which AgNPs kill mammalian cells that is not dependent upon the contribution of Ag+ released in extracellular media. Understanding precisely which factors drive the toxicity of AgNPs is essential for biomedical applications such as cancer therapy, and of importance to identifying consequences of unintended exposures.

Low Doses of Silver Nanoparticles Selectively Induce Lipid Peroxidation and Proteotoxic Stress in Mesenchymal Subtypes of Triple-Negative Breast Cancer.

Molecular profiling of tumors shows that triple-negative breast cancer (TNBC) can be stratified into mesenchymal (claudin-low breast cancer; CLBC) and epithelial subtypes (basal-like breast cancer; BLBC). Subtypes differ in underlying genetics and in response to therapeutics. Several reports indicate that therapeutic strategies that induce lipid peroxidation or proteotoxicity may be particularly effective for various cancers with a mesenchymal phenotype such as CLBC, for which no specific treatment regimens exist and outcomes are poor. We hypothesized that silver nanoparticles (AgNPs) can induce proteotoxic stress and cause lipid peroxidation to a greater extent in CLBC than in BLBC. We found that AgNPs were lethal to CLBCs at doses that had little effect on BLBCs and were non-toxic to normal breast epithelial cells. Analysis of mRNA profiles indicated that sensitivity to AgNPs correlated with expression of multiple CLBC-associated genes. There was no correlation between sensitivity to AgNPs and sensitivity to silver cations, uptake of AgNPs, or proliferation rate, indicating that there are other molecular factors driving sensitivity to AgNPs. Mechanistically, we found that the differences in sensitivity of CLBC and BLBC cells to AgNPs were driven by peroxidation of lipids, protein oxidation and aggregation, and subsequent proteotoxic stress and apoptotic signaling, which were induced in AgNP-treated CLBC cells, but not in BLBC cells. This study shows AgNPs are a specific treatment for CLBC and indicates that stratification of TNBC subtypes may lead to improved outcomes for other therapeutics with similar mechanisms of action.

Evaluation of different approaches to applying the standard additions calibration method.

The extrapolation approach, traditionally used with standard additions (SA), is compared with the alternative strategies of interpolation, reversed-axis, and normalization. The interpolation approach is based on employing twice the analytical signal recorded for the sample (ysam) to determine an unknown analyte concentration. In the reversed-axis strategy, x- and y-axes are swapped when building the SA calibration plot to facilitate uncertainty estimation. A new strategy, based on signal normalization using ysam, is also described and compared to the other approaches. Results from 3 instrumental methods, 396 sample replicates, 16 analytes, and 2 certified reference materials are included in this study. For most applications, all four SA approaches provide statistically similar trueness and precision. However, extrapolation and reversed-axis provide more consistent values (within narrower ranges) than the other strategies when employing inductively coupled plasma optical emission spectrometry (ICP OES). On the other hand, normalization provides better trueness for the less robust method of microwave-induced plasma OES (MIP OES), as it is capable of minimizing systematic errors associated with different points of the calibration curve. Normalization is particularly useful for quickly processing data, without the need for inspecting each individual calibration plot to identify outlying points. Reversed-axis and normalization are the most adequate approaches for SA applications involving MIP OES and ICP-based methods. In addition to providing similar accuracies to the traditional extrapolation approach, these strategies present the advantage of a simple uncertainty estimation, which can be easily calculated using commonly available software such as Microsoft Excel and R.

Binding of Targeted Semiconducting Photothermal Polymer Nanoparticles for Intraperitoneal Detection and Treatment of Colorectal Cancer.

Nanoparticles offer many promising advantages for improving current surgical regimens through their ability to detect and treat disseminated colorectal cancer (CRC). Hybrid Donor-Acceptor Polymer Particles (HDAPPs) have recently been shown to fluorescently detect and thermally ablate tumors in a murine model. Here, HDAPPS were functionalized with hyaluronic acid (HA) to improve their binding specificity to CT26 mouse CRC cells using HA to target the cancer stem cell marker CD44. In this work, we compared the binding of HA functionalized HDAPPs (HA-HDAPPs) in in vitro, ex vivo, and in vivo environments. The HA-HDAPPs bound to CT26 cells 2-fold more in vitro and 2.3-fold higher than un-functionalized HDAPPs ex vivo. Compared to intraoperative abdominal perfusion, intraperitoneal injection prior to laser stimulation for nanoparticle heat generation provides a superior modality of HA-HDAPPs delivery for CRC tumor selectivity. Photothermal treatment of disseminated CRC showed that only HA-HDAPPs delivered via intraperitoneal injection had a reduction in the tumor burden, and these nanoparticles also remained in the abdomen following resolution of the tumor. The results of this work confirm that HA-HDAPPs selectively bind to disseminated CRC, with ex vivo tumors having bound HA-HDAPPs capable of photothermal ablation. HA-HDAPPs demonstrated superior binding to tumor regions compared to HDAPPs. Overall, this study displays the theranostic potential of HDAPPs, emphasizing their capacity to detect and photothermally treat disseminated CRC tumors.

Automated matrix-matching calibration using standard dilution analysis with two internal standards and a simple three-port mixing chamber.

Simple data processing and unattended calibration are achieved in automated standard dilution analysis (aSDA) using two internal standards and an inline lab-made mixing chamber furnished from a common plastic syringe. Only two calibration solutions are required per sample, which minimizes reagent consumption and waste generation. Solution 1 contains 50% sample and 50% of a standard containing the analytes and internal standard 1 (IS1). Solution 2 has 50% sample and 50% of a blank containing internal standard 2 (IS2). The concentration of analyte in the sample is calculated from (i) the slope and intercept of an analyte vs. IS1 plot, (ii) the concentration of analyte in the standard added to Solution 1, and (iii) the intercept of a second plot with IS1vs. IS2. The aSDA method was used to determine Cd, Co, Cr, Cu, Pb and Zn in tap and creek water, beer, cola soft drink, mouthwash, cough syrup and cachaça by ICP OES. Addition/recovery experiments involving these same samples and other challenging matrices (i.e. 40% v/v HNO3, and 1% m/v Na, Ca or C) were performed to evaluate the method's accuracy. The results were compared with values obtained with external standard calibration (EC), internal standardization (IS) and standard additions (SA). Considering all samples and analytes evaluated, aSDA provided the best accuracy, with an average absolute error (ε‾=|analytepercentrecovery-100|) of 4% (EC, IS and SA had 13%, 9% and 7% errors, respectively). The aSDA strategy is a simple and inexpensive alternative to traditional methods. It has great potential for broad implementation with existing ICP OES instrumentation, as it requires little modification to systems already in place in routine laboratories.

Effects of platinum-based anticancer drugs on the trace element profile of liver and kidney tissue from mice.

BACKGROUND: Platinum-based anticancer drugs are relatively successful chemotherapeutic agents, which can cause significant elemental changes in key organs and are known for undesirable side effects, such as nephrotoxicity (damage to the kidneys). OBJECTIVES AND METHODS: Inductively coupled plasma mass spectrometry (ICP-MS) and traditional statistical tools such as two-sample Student's t-test and Pearson's correlation analysis are used to evaluate the effects of different investigational Pt-based anticancer drugs on the elemental constitution of kidneys and liver of mice. Principal component analysis is used to uncover relationships in element concentration and potential correlations between those and clinical effects. Random forest importance is used to identify elements most associated with the drug's maximum tolerated doses (MTDs). RESULTS: Strong negative correlations between Pt and both Cu (-0.814) and Zn (-0.784) in kidneys were observed for one of the Pt-acridine anticancer agents evaluated (Drug C). Strong positive correlations were observed between Cu in both kidneys (0.834) and liver (0.756) with Zn in liver for the same compound. Cisplatin administration correlates to higher concentrations of Ca, Cu, Rb and Zn in liver. Calcium and Mo in kidneys and Pt and Zn in liver are the features most associated with MTDs. CONCLUSIONS: The results indicate that the Pt-based agents investigated are major modulators of ion homeostasis in excretory organs, which most likely contributes to their systemic toxicity and limits their efficacy. A better understanding of subtle patterns and correlations among elements in key organs may provide deeper insights into the mechanisms of action and ultimately contribute for better, safer drugs. To achieve this goal, researchers involved in cancer drug development may leverage the high sensitivity and high sample throughput of ICP-MS, and the capabilities of modern statistical tools to extract relevant information from a large dataset.

Identifying and assessing matrix effect severity in inductively coupled plasma optical emission spectrometry using non-analyte signals and unsupervised learning.

An unsupervised data-driven methodology is used to quantify matrix effects caused by carbon and easily ionizable elements (EIEs) in inductively coupled plasma optical emission spectrometry (ICP OES). Background signals from nine plasma naturally-occurring species of Ar, H and O are used with principal component analysis (PCA) and affinity propagation (AP) clustering to evaluate the effects of complex matrices on ionic emission lines of Cd, Co, Cr and Pb. Matrix effect severity is then quantified based on Euclidean distance in principal component space from an average calibration curve point. The method has been applied to spiked solutions of Mediterranean Sea and Dead Sea water samples, and a significant correlation (- 0.997) was found between Euclidean distance and analyte recoveries. For sea water analysis, accurate results are found using external standard calibration (EC) when Euclidean distance < 1 for a given sample, and/or when that sample point groups with the calibration curve after affinity propagation clustering. Thus, by applying the PCA-AP strategy, one needs to perform no addition/recovery experiment to evaluate EC applicability. In addition, it can be carried out on the fly, as the background species used to monitor plasma changes are simultaneously recorded with the analytical signals, and a specific algorithm can be added to the instrument control software to flag instances in which EC may be ineffective. This is a proof-of-concept study, and additional work is required to evaluate the method's applicability to a larger number of analytes and sample matrices. However, the PCA-AP method described here for ICP OES can be used to quantify matrix effects, allowing for informed decisions regarding calibration. It requires no additional sample preparation and can be easily implemented in routine analyses of such complex-matrix samples as sea water.