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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2019)

Early diagnosis of Alzheimer’s disease (AD) is critical for disease prevention and cure, but no method to do so has yet been developed that had the required sensitivity and specificity. Computational methods are increasingly being applied in these efforts. One such method is "deep learning." We propose here a convolutional neural network-based AD diagnosis approach using surface enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). To realize the testing, we further prove the reliability of a SERS hybrid platform from its quantification capability and orientation dependence. Analysis using Amyloid beta (Aβ) to prove the biological feasibility and test the specificity of the platform is also done.

We report results demonstrating the reproducibility and accuracy of this novel SERS data analysis platform. We have achieved 100% reproducibility in double blind experiments and 92% accuracy in disease diagnosis. Comparison of the SERS-neural network approach with single biomarker tests shows it is more accurate, thus it may have substantial value in the differential diagnosis of AD as well as other neurodegenerative disorders.

We also show here that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) readily distinguishes small biological differences: Aβ40 and Aβ42. We show further, through comparison of assembly-dependent changes in secondary structure and morphology, that the SERS/PCA approach readily and unambiguously differentiates closely related assembly stages not readily differentiable by circular dichroism spectroscopy, electron microscopy, or other techniques.

To test the substrate feature, 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 A protein and a hot spot. We demonstrate the detection of Aβ at a concentration as low as 10-18 M after a single 20 �l aliquot of the analyte onto the hybrid platform.

Orientation dependence is also proved by analyzing the standard deviation of spectral feature. The standard deviation in the intensity of individual Raman peaks diminishes for protein size larger than 13 amino acids. Secondary structure of protein (such as protein-protein interaction) remains unchanged regardless of protein orientation. Numerical simulation studies corroborate the experimental observation in that the SERS spectral features of biomedically relevant protein (of larger than 13 amino acids in size, which represent all human protein types) are not affected by the orientation of amino acids randomly dispersed on SERS-active surfaces.

    Cover page: Alzheimer's Disease Diagnosis Using SERS and Advanced Data Analysis
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2022)
    Early diagnosis of Alzheimer's Disease (AD) is critical for disease prevention and cure. However, currently, techniques with the required high sensitivity and specificity are lacking. Recently, with the advances and increased accessibility of data analysis tools, such as machine learning, research efforts have increasingly focused on using these computational methods to solve this challenge. Here, we demonstrate a convolutional neural network (CNN)-based AD diagnosis approach using the surface-enhanced Raman spectroscopy (SERS) fingerprints of human cerebrospinal fluid (CSF). SERS and CNN were combined for biomarker detection to analyze disease-associated biochemical changes in the CSF. We achieved very high reproducibility in double-blind experiments for testing the feasibility of our system on human samples. We achieved an overall accuracy of 92% (100% for normal individuals and 88.9% for AD individuals) based on the clinical diagnosis. Further, we observed an excellent correlation coefficient between our test score and the Clinical Dementia Rating (CDR) score. Our findings offer a substantial indication of the feasibility of detecting AD biomarkers using the innovative combination of SERS and machine learning. We are hoping that this will serve as an incentive for future research in the field.
      Cover page: The Feasibility of Early Alzheimer’s Disease Diagnosis Using a Neural Network Hybrid Platform
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2014)
      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.
        Cover page: On the kinetic barriers of graphene homo-epitaxy
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2020)
        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.
          Cover page: Ultrasensitive amyloid β-protein quantification with high dynamic range using a hybrid graphene-gold surface-enhanced Raman spectroscopy platform.
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2018)
          Amyloid β-protein (Aβ) self-association is one process linked to the development of Alzheimer's disease (AD). Aβ peptides, including its most abundant forms, Aβ40 and Aβ42, are associated with the two predominant neuropathologic findings in AD, vascular and parenchymal amyloidosis, respectively. Efforts to develop therapies for AD often have focused on understanding and controlling the assembly of these two peptides. An obligate step in these efforts is the monitoring of assembly state. We show here that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) readily distinguishes Aβ40 and Aβ42. We show further, through comparison of assembly dependent changes in secondary structure and morphology, that the SERS/PCA approach unambiguously differentiates closely related assembly stages not readily differentiable by circular dichroism spectroscopy, electron microscopy, or other techniques. The high discriminating power of SERS/PCA is based on the rich structural information present in its spectra, which comprises not only on interatomic resonances between covalently associated atoms and hydrogen bond interactions important in controlling secondary structure, but effects of protein orientation relative to the substrate surface. Coupled with the label-free, single molecule sensitivity of SERS, the approach should prove useful for determining structure activity relationships, suggesting target sites for drug development, and for testing the effects of such drugs on the assembly process. The approach also could be of value in other systems in which assembly dependent changes in protein structure correlate with the formation of toxic peptide assemblies.
            Cover page: Surface enhanced Raman spectroscopy distinguishes amyloid Β‐protein isoforms and conformational states
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2019)
            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.
              Cover page: A Label-Free Platform for Identification of Exosomes from Different Sources.
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2015)
              Aluminum nitride (AlN) films have been deposited on AlGaN/GaN heterostructure substrates by plasma enhanced atomic layer deposition (PEALD). Different annealing treatments were adopted to change film structure and improve performance. Chemical composition, crystallinity, and electrical properties were studied for AlN films. The results show that some crystal grains appear in the films after annealing at a temperature of over 800 °C. The film crystalline quality increases as the annealing temperature rises. The N-O-Al bond decomposes during the high temperature annealing in N2, and some new N-Al bonds are formed in the AlN films. Annealing promotes the elemental interdiffusion between the films and the substrates. High-temperature annealing at 1000 °C in a nitrogen atmosphere can effectively promote complete nitridation of the AlN film, reduce the nitrogen vacancies, and cause the AlN film to form a semiconductor-like structure.
                Cover page: Effects of rapid thermal annealing on the properties of AlN films deposited by PEALD on AlGaN/GaN heterostructures
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