Adiabatically tapered hyperbolic metamaterials for dispersion control of high-k waves.

Hyperbolic metamaterials (HMMs) have shown great promise in the optical and quantum communities due to their extremely large, broadband photonic density of states. This feature is a direct consequence of supporting photonic modes with unbounded k-vectors. While these materials support such high-k waves, they are intrinsically confined inside the HMM and cannot propagate into the far-field, rendering them impractical for many applications. Here, we demonstrate how the magnitude of k-vectors can be engineered as the propagating radiation passes through media of differing dispersion relations (including type II HMMs and dielectrics) in the in-plane direction. The total outcoupling efficiency of waves in the in-plane direction is shown to be on average 2 orders of magnitude better than standard out-of-plane outcoupling methods. In addition, the outcoupling can be further enhanced using a proposed tapered HMM waveguide that is fabricated using a shadowed glancing angle deposition technique; thereby proving the feasibility of the proposed device. Applications for this technique include converting high-k waves to low-k waves that can be out-coupled into free-space and creating extremely high-k waves that are quickly quenched. Most importantly, this method of in-plane outcoupling acts as a bridge through which waves can cross between the regimes of low-k waves in classical dielectric materials and the high-k waves in HMMs with strongly reduced reflective losses.

All-dielectric subwavelength metasurface focusing lens.

We have proposed, designed, manufactured and tested low loss dielectric micro-lenses for infrared (IR) radiation based on a dielectric metamaterial layer. This metamaterial layer was created by patterning a dielectric surface and etching to sub-micron depths. For a proof-of-concept lens demonstration, we have chosen a fine patterned array of nano-pillars with variable diameters. Gradient index (GRIN) properties were achieved by engineering the nano-pattern characteristics across the lens, so that the effective optical density of the dielectric metamaterial layer peaks around the lens center, and gradually drops at the lens periphery. A set of lens designs with reduced reflection and tailorable phase gradients have been developed and tested, demonstrating focal distances of a few hundred microns, beam area contraction ratio up to three, and insertion losses as low as 11%.

Establishment and assessment of a new human embryonic stem cell-based biomarker assay for developmental toxicity screening.

A metabolic biomarker-based in vitro assay utilizing human embryonic stem (hES) cells was developed to identify the concentration of test compounds that perturbs cellular metabolism in a manner indicative of teratogenicity. This assay is designed to aid the early discovery-phase detection of potential human developmental toxicants. In this study, metabolomic data from hES cell culture media were used to assess potential biomarkers for development of a rapid in vitro teratogenicity assay. hES cells were treated with pharmaceuticals of known human teratogenicity at a concentration equivalent to their published human peak therapeutic plasma concentration. Two metabolite biomarkers (ornithine and cystine) were identified as indicators of developmental toxicity. A targeted exposure-based biomarker assay using these metabolites, along with a cytotoxicity endpoint, was then developed using a 9-point dose-response curve. The predictivity of the new assay was evaluated using a separate set of test compounds. To illustrate how the assay could be applied to compounds of unknown potential for developmental toxicity, an additional 10 compounds were evaluated that do not have data on human exposure during pregnancy, but have shown positive results in animal developmental toxicity studies. The new assay identified the potential developmental toxicants in the test set with 77% accuracy (57% sensitivity, 100% specificity). The assay had a high concordance (≥75%) with existing in vivo models, demonstrating that the new assay can predict the developmental toxicity potential of new compounds as part of discovery phase testing and provide a signal as to the likely outcome of required in vivo tests.

Metabolic biomarkers of prenatal alcohol exposure in human embryonic stem cell-derived neural lineages.

BACKGROUND: Fetal alcohol spectrum disorders (FASD) are a leading cause of neurodevelopmental disability. The mechanisms underlying FASD are incompletely understood, and biomarkers to identify those at risk are lacking. Here, we perform metabolomic analysis of embryoid bodies and neural lineages derived from human embryonic stem (hES) cells to identify the neural secretome produced in response to ethanol (EtOH) exposure. METHODS: WA01 and WA09 hES cells were differentiated into embryoid bodies, neural progenitors, or neurons. Cells along this progression were cultured for 4 days with 0, 0.1, or 0.3% EtOH. Supernatants were subjected to C18 chromatography followed by ESI-QTOF-MS. Features were annotated using public databases, and the identities of 4 putative biomarkers were confirmed with purified standards and comparative MS/MS. RESULTS: EtOH treatment induced statistically significant changes to metabolite abundance in human embryoid bodies (180 features), neural progenitors (76 features), and neurons (42 features). There were no shared significant features between different cell types. Fifteen features showed a dose-response to EtOH. Four chemical identities were confirmed: L-thyroxine, 5'-methylthioadenosine, and the tryptophan metabolites, L-kynurenine and indoleacetaldehyde. One feature with a putative annotation of succinyladenosine was significantly increased in both EtOH treatments. Additional features were selective to EtOH treatment but were not annotated in public databases. CONCLUSIONS: EtOH exposure induces statistically significant changes to the metabolome profile of human embryoid bodies, neural progenitors, and neurons. Several of these metabolites are normally present in human serum, suggesting their usefulness as potential serum FASD biomarkers. These findings suggest the biochemical pathways that are affected by EtOH in the developing nervous system and delineate mechanisms of alcohol injury during human development.

Predicting human developmental toxicity of pharmaceuticals using human embryonic stem cells and metabolomics.

Teratogens, substances that may cause fetal abnormalities during development, are responsible for a significant number of birth defects. Animal models used to predict teratogenicity often do not faithfully correlate to human response. Here, we seek to develop a more predictive developmental toxicity model based on an in vitro method that utilizes both human embryonic stem (hES) cells and metabolomics to discover biomarkers of developmental toxicity. We developed a method where hES cells were dosed with several drugs of known teratogenicity then LC-MS analysis was performed to measure changes in abundance levels of small molecules in response to drug dosing. Statistical analysis was employed to select for specific mass features that can provide a prediction of the developmental toxicity of a substance. These molecules can serve as biomarkers of developmental toxicity, leading to better prediction of teratogenicity. In particular, our work shows a correlation between teratogenicity and changes of greater than 10% in the ratio of arginine to asymmetric dimethylarginine levels. In addition, this study resulted in the establishment of a predictive model based on the most informative mass features. This model was subsequently tested for its predictive accuracy in two blinded studies using eight drugs of known teratogenicity, where it correctly predicted the teratogenicity for seven of the eight drugs. Thus, our initial data shows that this platform is a robust alternative to animal and other in vitro models for the prediction of the developmental toxicity of chemicals that may also provide invaluable information about the underlying biochemical pathways.

Amino Acid Dysregulation Metabotypes: Potential Biomarkers for Diagnosis and Individualized Treatment for Subtypes of Autism Spectrum Disorder.

BACKGROUND: Autism spectrum disorder (ASD) is behaviorally and biologically heterogeneous and likely represents a series of conditions arising from different underlying genetic, metabolic, and environmental factors. There are currently no reliable diagnostic biomarkers for ASD. Based on evidence that dysregulation of branched-chain amino acids (BCAAs) may contribute to the behavioral characteristics of ASD, we tested whether dysregulation of amino acids (AAs) was a pervasive phenomenon in individuals with ASD. This is the first article to report results from the Children's Autism Metabolome Project (CAMP), a large-scale effort to define autism biomarkers based on metabolomic analyses of blood samples from young children. METHODS: Dysregulation of AA metabolism was identified by comparing plasma metabolites from 516 children with ASD with those from 164 age-matched typically developing children recruited into the CAMP. ASD subjects were stratified into subpopulations based on shared metabolic phenotypes associated with BCAA dysregulation. RESULTS: We identified groups of AAs with positive correlations that were, as a group, negatively correlated with BCAA levels in ASD. Imbalances between these two groups of AAs identified three ASD-associated amino acid dysregulation metabotypes. The combination of glutamine, glycine, and ornithine amino acid dysregulation metabotypes identified a dysregulation in AA/BCAA metabolism that is present in 16.7% of the CAMP subjects with ASD and is detectable with a specificity of 96.3% and a positive predictive value of 93.5% within the ASD subject cohort. CONCLUSIONS: Identification and utilization of metabotypes of ASD can lead to actionable metabolic tests that support early diagnosis and stratification for targeted therapeutic interventions.

Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children.

BACKGROUND: The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. OBJECTIVES: To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. METHODS: Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. RESULTS: A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. CONCLUSIONS: This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.