Plant biotechnology and biofuel research is critical in addressing increasing global demands for energy. Further understanding of biomass producing associated metabolic pathways in plants can be used to exploit and increase the production of biomass for energy purposes. In vivo detection of biomarkers associated with plant growth for bioenergy has proved to be limited due to complex sample preparation required by traditional methods. In addition, genetic transformation and biomolecule monitoring inside plant cells is regulated by diameter and size exclusion limits of the plant cell wall (5 - 20 nm). Currently limited methods exist for enabling direct entry into plant cells. Moreover, these methods, such as biolistic particle delivery and electroporation use mechanical force that causes damages to the plant tissue. Nanoparticles could serve as promising platforms for probes to characterize intercellular and intracellular plant biomarkers and pathways. Bi-metallic nanostars are a plasmonics-active nanoplatform capable of high surface-enhanced Raman scattering (SERS) which can enter plant cells and have the future potential for nucleic acid sensing. Imaging technologies such as SERS mapping, confocal imaging, X-ray fluorescence imaging, multi-photon imaging, and transmission electron microscopy have been utilized to determine the compartmentalization and location of the SERS iMS biosensors inside Arabidopsis plants.
Surface-enhanced Raman spectroscopy (SERS) has wide applications in chemical and biosensing as well as imaging. Raman spectra obtained from SERS exhibit characteristic narrow peaks that allow higher degrees of multiplexing than possible with fluorescence imaging. The nanorattle is a bimetallic nanoparticle which can be loaded with different dyes to produce SERS for multiplexed mRNA detection assays and in vivo imaging. But as multiplexing degree increases, so does spectral complexity, making analysis difficult. Machine learning has been applied for SERS-based chemical recognition and quantification. However, multiplexed, assays using SERS labels or imaging using SERS-labeled materials rarely utilize machine learning. Since the spectral shapes of each multiplexed label is known, analysis is easy when multiplexing <4 dyes given the computational tradeoff. Here we demonstrate and compare the use of spectral decomposition, support vector regression, and convolutional neural network (CNN) for “spectral unmixing” of SERS spectra obtained from a highly multiplexed mixture of 7 SERS-active nanorattles. Training data was simulated by combining individual nanorattle spectra by linear scaling and addition. We show that CNN performed the best in determining relative contributions of each distinct dye-loaded nanorattle.
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