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In JoVE (1)
Other Publications (5)
Articles by Pradeep Perera in JoVE
JoVE General
Label-free in situ Imaging of Lignification in Plant Cell Walls
1Energy Biosciences Institute, University of California, Berkeley, 2Molecular Foundry, Lawrence Berkeley National Laboratory, 3Physical Biosciences Division, Lawrence Berkeley National Laboratory
A method based on confocal Raman microscopy is presented that affords label-free visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species.
Other articles by Pradeep Perera on PubMed
Solute-induced Perturbations of Solvent-shell Molecules Observed Using Multivariate Raman Curve Resolution
Raman spectral features associated with the reorganization of solvent molecules around a solute are obtained using multivariate curve resolution. Spectra collected from solutions of variable concentration are resolved into unperturbed and perturbed components assuming only that the spectra and concentrations of each component are non-negative (with no peak-fitting or constraints on the shapes of either the perturbed or unperturbed spectral features). The capabilities of the method are demonstrated using solutions of acetonitrile, acetone, and pyridine in water as well as acetonitrile and cyclohexane in 1,2-dichloroethane (DCE). The results reveal vibrational spectra of solvation-shell molecules that are perturbed by each solute. The perturbed solvent-shell water molecules are found to have different OH stretch bands (of higher frequency and narrower width than bulk water), and the gauche-trans equilibrium of solvent-shell DCE molecules are perturbed in opposite directions by the polar and nonpolar solutes.
Quantification of Isotope Encoded Proteins in 2-D Gels Using Surface Enhanced Resonance Raman
A strategy for quantification of multiple protein isoforms from a complex sample background is demonstrated, combining isotopomeric rhodamine 6G (R6G) labels and surface-enhanced Raman in polyacrylamide matrix. The procedure involves isotope-encoding by lysine-labeling with (R6G) active ester reagents, isoform separation by 2-DGE, fluorescence quantification using internal standardization to water, and silver nanoparticle deposition followed by surface-enhanced Raman detection. R6G sample encoding and standardization enabled the determination of total protein concentration and the distribution of specific isoforms using the combined detection approach of water-referenced fluorescence spectral imaging and ratiometric quantification. A detection limit of approximately 13.5 picomolar R6G-labeled protein was determined for the surface-enhanced Raman in a gel matrix (15-fold lower than fluorescence). High quantification accuracies for small differences in protein populations at low nanogram abundance were demonstrated for human GMP synthetase (hGMPS) either as purified protein samples in a single-point determination mode (3% relative standard deviation, RSD%) or as HCT116 human cancer cellular lysate in an imaging application (with 16% RSD%). These results represent a prototype for future applications of isotopic surface-enhanced resonance Raman scatter to quantification of protein distributions.
Perturbations of Water by Alkali Halide Ions Measured Using Multivariate Raman Curve Resolution
Polarized Raman spectroscopy, combined with multivariate curve resolution (MCR), is used to measure the influence of dilute alkali halide ions on the OH stretch vibrational band of water. The frequency and integrated intensity of the resulting hydration shell OH bands are found to increase with increasing anion size. Comparisons of results obtained from salt solutions in H(2)O and HOD/D(2)O imply that ion-water interactions reduce the influence of resonance coupling on the OH stretch band of H(2)O. Polarized Raman results indicate that the hydration shell of F(-) gives rise to a nearly perfectly polarized OH stretch band, while large anions produce larger depolarization.
Blind Image Analysis for the Compositional and Structural Characterization of Plant Cell Walls
A new image analysis strategy is introduced to determine the composition and the structural characteristics of plant cell walls by combining Raman microspectroscopy and unsupervised data mining methods. The proposed method consists of three main steps: spectral preprocessing, spatial clustering of the image and finally estimation of spectral profiles of pure components and their weights. Point spectra of Raman maps of cell walls were preprocessed to remove noise and fluorescence contributions and compressed with PCA. Processed spectra were then subjected to k-means clustering to identify spatial segregations in the images. Cell wall images were reconstructed with cluster identities and each cluster was represented by the average spectrum of all the pixels in the cluster. Pure components spectra were estimated by spectral entropy minimization criteria with simulated annealing optimization. Two pure spectral estimates that represent lignin and carbohydrates were recovered and their spatial distributions were calculated. Our approach partitioned the cell walls into many sublayers, based on their composition, thus enabling composition analysis at subcellular levels. It also overcame the well known problem that native lignin spectra in lignocellulosics have high spectral overlap with contributions from cellulose and hemicelluloses, thus opening up new avenues for microanalyses of monolignol composition of native lignin and carbohydrates without chemical or mechanical extraction of the cell wall materials.
Raman-spectroscopy-based Noninvasive Microanalysis of Native Lignin Structure
A new robust, noninvasive, Raman microspectroscopic method is introduced to analyze the structure of native lignin. Lignin spectra of poplar, Arabidopsis, and Miscanthus were recovered and structural differences were unambiguously detected. Compositional analysis of 4-coumarate-CoA ligase suppressed transgenic poplar showed that the syringyl-to-guaiacyl ratio decreased by 35% upon the mutation. A cell-specific compositional analysis of basal stems of Arabidopsis showed similar distributions of S and G monolignols in xylary fiber cells and interfascicular cells.
