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Lung cancers are documented to have remarkable intratumoral genetic heterogeneity. However, little is known about the heterogeneity of biophysical properties, such as cell motility, and its relationship to early disease pathogenesis and micrometastatic dissemination. In this study, we identified and selected a subpopulation of highly migratory premalignant airway epithelial cells that were observed to migrate through microscale constrictions at up to 100-fold the rate of the unselected immortalized epithelial cell lines. This enhanced migratory capacity was found to be Rac1-dependent and heritable, as evidenced by maintenance of the phenotype through multiple cell divisions continuing more than 8 weeks after selection. The morphology of this lung epithelial subpopulation was characterized by increased cell protrusion intensity. In a murine model of micrometastatic seeding and pulmonary colonization, the motility-selected premalignant cells exhibit both enhanced survival in short-term assays and enhanced outgrowth of premalignant lesions in longer-term assays, thus overcoming important aspects of “metastatic inefficiency.” Overall, our findings indicate that among immortalized premalignant airway epithelial cell lines, subpopulations with heritable motility-related biophysical properties exist, and these may explain micrometastatic seeding occurring early in the pathogenesis of lung cancer. Understanding, targeting, and preventing these critical biophysical traits and their underlying molecular mechanisms may provide a new approach to prevent metastatic behavior. Cancer Prev Res; 10(9); 514–24. ©2017 AACR.See related editorial by Hynds and Janes, p. 491

Breast cancer cells secrete abundant nanometer-sized vesicles. Small extracellular vesicle (or sEV) cargos are known to have similar biomolecular signatures to their secreting parental breast cance...

Extracellular vesicles (EVs) are a unique, heterogeneous class of biological nanoparticles secreted by most cells. As potential a class of novel diagnostics and therapeutics, the physio‐chemical characterization as well as the biomolecular composition of EVs are widely investigated. However, there is emerging evidence suggesting that biomechanical analysis of lipid‐bilayer membrane‐bound single EVs may provide key insights into their biological structure, biomarker functions, and potential therapeutic functions. In this review, we focus on the unique biomechanical properties of single EVs such as elasticity, stiffness, and deformability. We compare common indentation models used in atomic force microscopy (AFM)‐based biomechanical analysis of EVs, as well as the benefits and drawbacks of each model encompassing the heterogeneous EV sub‐populations—mainly the small EVs (or exosomes). Next, we discuss high‐throughput approaches to determine the biomechanical landscape of EVs that may overcome some of the challenges associated with the accurate determination of particle sizes and particle‐by‐particle indentations. Finally, we highlight exciting new opportunities for EV biomechanical fingerprinting emanating from machine learning capabilities. In particular, we propose multi‐parametric AFM structure‐mechanical analysis to further advance label‐free, orthogonal biophysical understanding of EVs beyond biomolecular or particle size analysis, with significant implications for research and clinical use.

Extracellular vesicles (EVs) have raised high expectations as a novel class of diagnostics and therapeutics. However, variabilities in EV isolation methods and the unresolved structural complexity of these biological-nanoparticles (sub-100 nm) necessitate rigorous biophysical characterization of single EVs. Here, using atomic force microscopy (AFM) in conjunction with direct stochastic optical reconstruction microscopy (dSTORM), micro-fluidic resistive pore sizing (MRPS), and multi-angle light scattering (MALS) techniques, we compared the size, structure and unique surface properties of breast cancer cell-derived small EVs (sEV) obtained using four different isolation methods. AFM and dSTORM particle size distributions showed coherent unimodal and bimodal particle size populations isolated via centrifugation and immune-affinity methods respectively. More importantly, AFM imaging revealed striking differences in sEV nanoscale morphology, surface nano-roughness, and relative abundance of non-vesicles among different isolation methods. Precipitation-based isolation method exhibited the highest particle counts, yet nanoscale imaging revealed the additional presence of aggregates and polymeric residues. Together, our findings demonstrate the significance of orthogonal label-free surface characteristics of single sEVs, not discernable via conventional particle sizing and counts alone. Quantifying key nanoscale structural characteristics of sEVs, collectively termed ‘EV-nano-metrics’ enhances the understanding of the complexity and heterogeneity of sEV isolates, with broad implications for EV-analyte based research and clinical use.

Fabrication of a two-dimensional covalent network of honeycomb nanosheets comprising small 1,3,5-triamino benzene and benzene-1,3,5-tricarboxaldehyde aromatic building blocks was conducted on Au(111) in a pH-controlled aqueous solution. In situ scanning tunneling microscopy revealed a large defect-free and homogeneous honeycomb π-conjugated nanosheet at the Au(111)/liquid interface. An electrochemical potential dependence indicated that the nanosheets were the result of thermodynamic self-assembly based not only on the reaction equilibrium but also on the adsorption (partition) equilibrium, which was controlled by the building block surface coverage as a function of electrode potential.

Inductively coupled plasma-mass spectrometry (ICP-MS) is a powerful analytical technique that can quantify elements of interest at parts-per-trillion concentrations. In this laboratory class, students performed ICP-MS analysis to quantify mercury concentration of standard reference material (SRM) 1947 (Lake Michigan fish tissue) and canned tuna from a local supermarket. These two samples were digested in two different matrices (HNO3/ H2O2 or HNO3/HCl/H2O2) and then analyzed using no-gas mode or helium mode with two different kinetic energy discrimination voltages (2V or 4V). The inclusion of HCl in the matrix produced more accurate results and stabilized mercury over the 8-day period after the digestion. Based on their analysis, the students were asked to draw their own conclusions about what they perceived to be the most accurate representation of the true mercury concentration of the tuna samples. This laboratory class provides students with a wide range of scientific concepts to explore such as method verification with SRM, kinetic energy discrimination, matrix effect, and trace metal stability over time.

Neuromorphic Nanowire Networks (NWNs) are a novel class of information processing hardware devices, combining the advantage of memristive cross-point junctions and neural-like complex network topology. In addition to their low operating power, NWNs are also easy to fabricate with bottom-up self-assembly. Here, we implement the MNIST handwritten digit classification task on simulated NWNs to demonstrate their ability to perform complex learning tasks. Using a CNN-inspired kernel method and a reservoir computing framework, our simulation results attain an accuracy of nearly 98%. Moreover, this is achieved using only a fraction of the total MNIST training data and without requiring hardware accelerators. We also investigate the information theoretic metrics of mutual information (MI), transfer entropy (TE) and active information storage (AIS) to analyze the MNIST learning dynamics of NWNs. We find that MI with respect to classes is maximized after network feature extraction and that TE is largest when MNIST digit boundaries are processed, while AIS is strongest when areas with lower pixel values are presented. Overall, these results suggest the information processing capabilities of neuromorphic NWNs make them promising candidates for complex learning applications.

Modern applications of artificial intelligence (AI) are generally algorithmic in nature and implemented using either general-purpose or application-specific hardware systems that have high power requirements. In the present study, physical (in-materio) reservoir computing (RC) implemented in hardware was explored as an alternative to software-based AI. The device, made up of a random, highly interconnected network of nonlinear Ag2Se nanojunctions, demonstrated the requisite characteristics of an in-materio reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. As a hardware reservoir, the devices successfully performed waveform generation tasks, where tasks conducted at elevated network temperatures were found to be more stable than those conducted at room temperature. Finally, a comparison of voice classification, with and without the network device, showed that classification performance increased in the presence of the network device.

Disruptive technology in computational devices is required as the universal computing machines approach quantum mechanical limits. Integration of state-of-the-art memristive devices provides optimal scaling of current technologies beyond this limit through the adoption of neuromorphic models. Universal computing machines pioneered by Alan Turing are strictly based on top-down intelligent design. Neuromorphic models instead engage in bottom-up programmability by emulating mammalian brain design and characteristics. Here we show the design, characterization, and implementation of a massively parallel memristor neuromorphic network based on metal chalcogenide atomic switch network (ASN) systems with key characteristics such as short- and long-term potentiation, power-law dynamics, and scale-free topology.

Biological neural networks, unlike artificial neural networks (ANNs), can process information from data that is inherently noisy, unstructured, sparse and dynamic. How is this possible? And how can we replicate this? A crucial clue comes from neuroscience: the brain is a complex physical system and the network topology of its neural circuitry is a determinant of its emergent collective properties. Indeed, as the brain's neural network is already entrained in its physical hardware, it clearly does not require an ANN software add-on to learn from natural data. Self-assembled nanowire networks with memristive junctions represent arguably the closest hardware architecture to real biological neural networks and are thus uniquely placed to demonstrate genuinely neuromorphic information processing. Here, we present preliminary results on polymer-coated silver nanowire networks. Their neuromorphic architecture (densely structured network topology, memristive switch junctions and efficient interconnect) gives rise to a rich repertoire of collective nonlinear dynamics manifested through adaptive current transport pathways. The potential for associative learning is demonstrated in a test protocol in which a nanowire network is stimulated by multiple electrodes mapped to different spatial patterns. The capacity to process information in the temporal domain is demonstrated via simulations of a reservoir computing implementation in which nanowire networks are shown to perform tasks such as time series prediction and handwritten digit recognition. Overall, their unique properties and neuromorphic information processing capabilities make nanowire networks promising candidates for emerging applications in cognitive devices in particular, at the edge.

We present simulations based on a model of self– assembled nanowire networks with memristive junctions and neural network–like topology. We analyze the dynamical voltage distribution in response to an applied bias and explain the network conductance fluctuations observed in our previous experimental studies. We then demonstrate the potential of neuromorphic nanowire networks as a physical reservoir by performing benchmark reservoir computing tasks. The tasks include sine wave nonlinear transformation, sine wave auto– generation and forecasting the Mackey–Glass chaotic time series.

BackgroundConcomitant exposure to environmental/occupational toxicants such as aflatoxin B1 (AFB1) and arsenic in some regions of the world has been well reported. Therefore, this calls for the assessment of the efficacy of agents such as phytochemicals, which are already known for their ethno-medicinal uses in prophylaxis/remediation. We investigated the possible cytotoxic bio-interactions between AFB1 and sodium arsenite (SA) in urinary bladder cells. We also assessed the cytoprotective effects of curcumin and the ethanol stem bark extract of Khaya senegalensis (K2S).MethodsThe cells were exposed to graded levels of AFB1, SA, curcumin, and K2S for 24, 48, and 72 h. Subsequently, using optimum toxic concentrations of AFB1 and SA, respectively, the influence of non-toxic levels of curcumin and/or K2S was tested on exposure of the cells to AFB1 and/or SA. Hoechst 33342/propidium iodide staining technique was used to determine the end-points due to cytotoxicity with changes in adenosine triphosphate (ATP) levels determined using Promega’s CellTiter-Glo luminescent assay.ResultsCo-treatment of the cells with AFB1 and SA resulted in synergy in cytotoxic effects. Cytotoxicity was reduced by 3.5- and 2.9-fold by pre-treatment of the cells with curcumin and K2S before treatment with AFB1, while post-treatment resulted in 1.1- and 2.6-fold reduction, respectively. Pre-exposure of the cells with curcumin and K2S before treatment with SA ameliorated cytotoxicity by 3.8- and 3.0-fold, but post-treatment caused a 1.2- and 1.3-fold reduction, respectively.ConclusionsPre-treatment of the cells with either curcumin or K2S exhibited cytoprotective effects by ameliorating AFB1- and SA-induced cytotoxicity with inferred tendencies to prevent carcinogenesis.

Cancer cell mechanotype changes are newly recognized cancer phenotypic events, whereas metastatic cancer cells show decreased cell stiffness and increased deformability relative to normal cells. To further examine how cell mechanotype changes in early stages of cancer transformation and progression, anin vitromulti-step human urothelial cell carcinogenic model was used to measure cellular Young’s modulus, deformability, and transit time using single-cell atomic force microscopy, microfluidic-based deformability cytometry, and quantitative deformability cytometry, respectively. Measurable cell mechanotype changes of stiffness, deformability, and cell transit time occur early in the transformation process. As cells progress from normal, to preinvasive, to invasive cells, Young’s modulus of stiffness decreases and deformability increases gradually. These changes were confirmed in three-dimensional cultured microtumor masses and urine exfoliated cells directly from patients. Using gene screening and proteomics approaches, we found that the main molecular pathway implicated in cell mechanotype changes appears to be epithelial to mesenchymal transition.

Artificial realizations of the mammalian brain alongside their integration into electronic components are explored through neuromorphic architectures, neuroarchitectectonics, on CMOS compatible platforms. Exploration of neuromorphic technologies continue to develop as an alternative computational paradigm as both capacity and capability reach their fundamental limits with the end of the transistor-driven industrial phenomenon of Moore’s law. Here, we consider the electronic landscape within neuromorphic technologies and the role of the atomic switch as a model device. We report the fabrication of an atomic switch network (ASN) showing critical dynamics and harness criticality to perform benchmark signal classification and Boolean logic tasks. Observed evidence of biomimetic behavior such as synaptic plasticity and fading memory enable the ASN to attain a cognitive capability within the context of artificial neural networks.

The atomic switch technology can be used to emulate the synaptic plasticity underlying short-term and long-term memory functions in the human brain. These functions are realized by transport of metal ions or oxygen ions in sulfide or oxide matrices. The change in conductance of these devices is considered analogous to the change in strength of biological synapses that varies depending on the strength, frequency, and number of stimulating input pulses. The devices also exhibit sensitivity to the moisture in and temperature of the ambient environment, and configurable multifunction including rectification and synaptic plasticity under different electroforming conditions. These observations indicate that the atomic switch technology has great potential for use as an essential building block for neural computing systems.

Numerous studies suggest critical dynamics may play a role in information processing and task performance in biological systems. However, studying critical dynamics in these systems can be challenging due to many confounding biological variables that limit access to the physical processes underpinning critical dynamics. Here we offer a perspective on the use of abiotic, neuromorphic nanowire networks as a means to investigate critical dynamics in complex adaptive systems. Neuromorphic nanowire networks are composed of metallic nanowires and possess metal-insulator-metal junctions. These networks self-assemble into a highly interconnected, variable-density structure and exhibit nonlinear electrical switching properties and information processing capabilities. We highlight key dynamical characteristics observed in neuromorphic nanowire networks, including persistent fluctuations in conductivity with power law distributions, hysteresis, chaotic attractor dynamics, and avalanche criticality. We posit that neuromorphic nanowire networks can function effectively as tunable abiotic physical systems for studying critical dynamics and leveraging criticality for computation.

Lung cancers are documented to have remarkable intratumoral genetic heterogeneity. However, little is known about the heterogeneity of biophysical properties, such as cell motility, and its relationship to early disease pathogenesis and micrometastatic dissemination. In this study, we identified and selected a subpopulation of highly migratory premalignant airway epithelial cells that were observed to migrate through microscale constrictions at up to 100-fold the rate of the unselected immortalized epithelial cell lines. This enhanced migratory capacity was found to be Rac1-dependent and heritable, as evidenced by maintenance of the phenotype through multiple cell divisions continuing more than 8 weeks after selection. The morphology of this lung epithelial subpopulation was characterized by increased cell protrusion intensity. In a murine model of micrometastatic seeding and pulmonary colonization, the motility-selected premalignant cells exhibit both enhanced survival in short-term assays and enhanced outgrowth of premalignant lesions in longer-term assays, thus overcoming important aspects of “metastatic inefficiency.” Overall, our findings indicate that among immortalized premalignant airway epithelial cell lines, subpopulations with heritable motility-related biophysical properties exist, and these may explain micrometastatic seeding occurring early in the pathogenesis of lung cancer. Understanding, targeting, and preventing these critical biophysical traits and their underlying molecular mechanisms may provide a new approach to prevent metastatic behavior. Cancer Prev Res; 10(9); 514–24. ©2017 AACR. See related editorial by Hynds and Janes, p. 491

BackgroundEpidemiological and experimental evidences have shown cancer as a leading cause of death worldwide. Although the folklore use of plants as a reliable source of health-restoring principles is well-documented, the search for more of such plants that are active against diseases, such as cancer, continues. We report here a laboratory-based evidence of the relevance of an ethanol leaf extract of Anogeissus leiocarpus (A2L) in comparison with resveratrol, a natural polyphenol, in cancer therapy.MethodsThe quantitative assessment of flavonoid and phenolic contents involved quercetin and gallic acid as standards, respectively were determined using spectrophotometry. Cytotoxicity was determined fluorometrically using propidium-iodide-staining method. Antioxidant status, adenosine triphosphate (ATP) levels, caspase activities and mitochondrial integrity were assessed using fluorometry/luminometry.ResultsThe antioxidant assay demonstrated that A2L possesses a strong antioxidant capacity as compared with the reference compounds, ascorbic acid and butylated hydroxytoluene. This is further buttressed by the significantly high level of phenolics obtained in the quantitative assessment of the extract. A 72-h post-treatment examination indicated that both A2L and resveratrol modulate the proliferation of HepG2 liver carcinoma cells in a time- and concentration-dependent manner. Determination of the total nuclei area, propidium-iodide negative and positive nuclei areas all further buttress the modulation of cell proliferation by A2L and resveratrol with the indication that the observed cell death is due to apoptosis and necrosis at lower and higher concentrations of treatments respectively. At lower concentrations (0.39-3.13μg/mL), resveratrol possesses higher tendencies to activate caspases 3 and 7. Bioenergetically, both resveratrol and A2L do not adversely affect the cells at lower concentrations (0.39-6.25μg/mL for resveratrol and 12.5-100.0μg/mL for A2L) except at higher concentrations (12.5-25.0μg/mL for resveratrol and 200-800μg/mL for A2L) which are more pronounced in A2L-treated cells. Furthermore, the antioxidant status of HepG2 cells is not perturbed by resveratrol as compared with A2L. Assessment of 24-h post-treatment mitochondrial function shows that resveratrol is not mitotoxic as compared with A2L which exhibits mitotoxicity at its highest concentration.ConclusionsTaken together, findings from this study showed that A2L possesses strong antiproliferative activity and its prospect in the management of hepatocellular carcinoma deserves further investigation.

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Palpable thyroid lesions are common, and although mostly benign, lethal malignant nodules do occur and may be difficult to differentiate. Here, we introduce the use of a piezoelectric system called Smart-touch fine needle (or STFN) mounted directly onto conventional biopsy needles, to evaluate abnormal tissues, through quantitative real-time measurements of variations in tissue stiffness as the needle penetrates tissue. Using well-characterized biomaterials of known stiffness and explanted animal tissue models, we first established experimental protocols for STFN measures on biological tissues, as well as optimized device design for high signal-to-noise ratio. Freshly excised patient thyroids with varying fibrotic and malignant potential revealed discrete variations in STFN based tissue stiffness/stiffness heterogeneity and correlated well with final histopathology. Our piezoelectric needle sensor reveals mechanical heterogeneity in thyroid tissue lesions and provides a foundation for the design of hand-held tools for the rapid, mechano-profiling of malignant lesions in vivo while performing fine needle aspiration (FNA).

Cancer cell metastasis is a leading cause of mortality whereby cancer cells migrate from a tumor and spread to distant sites in the body. Understanding metastasis requires a deeper understanding of biomechanics and mechanobiology at the cellular level. We have established the use of Atomic Force Microscopy to infer the mechanical properties of single cells in cultures by measurement of their Young’s modulus. Here we discuss the main advantages, challenges, technological limitations and applicability of AFM based cell mechanics studies along with other emerging high throughput techniques for the development of single cell mechanical based clinical assays for cancer detection and management.