A Label-Free Platform for Identification of Exosomes from Different Sources.

Exosomes contain cell- and cell-state-specific cargos of proteins, lipids, and nucleic acids and play significant roles in cell signaling and cell-cell communication. Current research into exosome-based biomarkers has relied largely on analyzing candidate biomarkers, i.e., specific proteins or nucleic acids. However, this approach may miss important biomarkers that are yet to be identified. Alternative approaches are to analyze the entire exosome system, either by "omics" methods or by techniques that provide "fingerprints" of the system without identifying each individual biomolecule component. Here, we describe a platform of the latter type, which is based on surface-enhanced Raman spectroscopy (SERS) in combination with multivariate analysis, and demonstrate the utility of this platform for analyzing exosomes derived from different biological sources. First, we examined whether this analysis could use exosomes isolated from fetal bovine serum using a simple, commercially available isolation kit or necessitates the higher purity achieved by the "gold standard" ultracentrifugation/filtration procedure. Our data demonstrate that the latter method is required for this type of analysis. Having established this requirement, we rigorously analyzed the Raman spectral signature of individual exosomes using a unique, hybrid SERS substrate made of a graphene-covered Au surface containing a quasi-periodic array of pyramids. To examine the source of the Raman signal, we used Raman mapping of low and high spatial resolution combined with morphological identification of exosomes by scanning electron microscopy. Both approaches suggested that the spectra were collected from single exosomes. Finally, we demonstrate for the first time that our platform can distinguish among exosomes from different biological sources based on their Raman signature, a promising approach for developing exosome-based fingerprinting. Our study serves as a solid technological foundation for future exploration of the roles of exosomes in various biological processes and their use as biomarkers for disease diagnosis and treatment monitoring

Self-supervised 6D Object Pose Estimation for Robot Manipulation

To teach robots skills, it is crucial to obtain data with supervision. Since annotating real world data is time-consuming and expensive, enabling robots to learn in a self-supervised way is important. In this work, we introduce a robot system for self-supervised 6D object pose estimation. Starting from modules trained in simulation, our system is able to label real world images with accurate 6D object poses for self-supervised learning. In addition, the robot interacts with objects in the environment to change the object configuration by grasping or pushing objects. In this way, our system is able to continuously collect data and improve its pose estimation modules. We show that the self-supervised learning improves object segmentation and 6D pose estimation performance, and consequently enables the system to grasp objects more reliably. A video showing the experiments can be found at https://youtu.be/W1Y0Mmh1Gd8.Comment: Accepted to International Conference on Robotics and Automation (ICRA), 202

Ultrasensitive amyloid β-protein quantification with high dynamic range using a hybrid graphene-gold surface-enhanced Raman spectroscopy platform.

Surface enhanced Raman spectroscopy (SERS) holds great promise in biosensing because of its single-molecule, label-free sensitivity. We describe here the use of a graphene-gold hybrid plasmonic platform that enables quantitative SERS measurement. Quantification is enabled by normalizing analyte peak intensities to that of the graphene G peak. We show that two complementary quantification modes are intrinsic features of the platform, and that through their combined use, the platform enables accurate determination of analyte concentration over a concentration range spanning seven orders of magnitude. We demonstrate, using a biologically relevant test analyte, the amyloid β-protein (Aβ), a seminal pathologic agent of Alzheimer's disease (AD), that linear relationships exist between (a) peak intensity and concentration at a single plasmonic hot spot smaller than 100 nm, and (b) frequency of hot spots with observable protein signals, i.e. the co-location of an Aβ protein and a hot spot. We demonstrate the detection of Aβ at a concentration as low as 10-18 M after a single 20 μl aliquot of the analyte onto the hybrid platform. This detection sensitivity can be improved further through multiple applications of analyte to the platform and by rastering the laser beam with smaller step sizes

On the kinetic barriers of graphene homo-epitaxy

The diffusion processes and kinetic barriers of individual carbon adatoms and clusters on graphene surfaces are investigated to provide fundamental understanding of the physics governing epitaxial growth of multilayer graphene. It is found that individual carbon adatoms form bonds with the underlying graphene whereas the interaction between graphene and carbon clusters, consisting of 6 atoms or more, is very weak being van der Waals in nature. Therefore, small carbon clusters are quite mobile on the graphene surfaces and the diffusion barrier is negligibly small (∼6 meV). This suggests the feasibility of high-quality graphene epitaxial growth at very low growth temperatures with small carbon clusters (e.g., hexagons) as carbon source. We propose that the growth mode is totally different from 3-dimensional bulk materials with the surface mobility of carbon hexagons being the highest over graphene surfaces that gradually decreases with further increase in cluster size

Endogenous and exogenous galectin-3 promote the adhesion of tumor cells with low expression of MUC1 to HUVECs through upregulation of N-cadherin and CD44

Tumor cell-endothelial adhesion is one of the key steps in tumor cell haematogenous dissemination in metastasis and was previously shown to be mediated by interaction of galectin-3 with the transmembrane mucin protein MUC1. In this study, the effect of exogenous as well as endogenous galectin-3 on adhesion of two cell lines (low MUC1-expressing human prostate cancer PC-3M cells and non-small-cell lung cancer A549 cells) to monolayer of umbilical vein endothelial cells (HUVECs) was investigated. We found that suppression of endogenous galectin-3 expression reduced tumor cell adhesion to HUVECs and also decreased cell invasion and migration. Exogenous galectin-3 promoted tumor cell adhesion to HUVECs by entering cells. Both exogenous and endogenous galectin-3 upregulated the expression of β-catenin and increased β-catenin nuclear accumulation, and subsequently upregulated the expression of N-cadherin and CD44. We deduced that both exogenous as well as endogenous galectin-3 promoted low MUC1-expressing cancer cell adhesion to HUVECs by increasing the expression of N-cadherin and CD44 via an increase of nuclear β-catenin accumulation. These results were confirmed further by using a β-catenin/TCF transcriptional activity inhibitor, N-cadherin or CD44 siRNAs. Taken together, our results suggest a new molecular mechanism of galectin-3-mediated cell adhesion in cancer metastasis