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Laser tweezers Raman spectroscopy (LTRS) was used to characterize the Raman fingerprints of the metabolic states of Escherichia coli (E. coli) cells and to determine the spectral changes associated with cellular response to the antibiotic Cefazolin. The Raman spectra of E. coli cells sampled at different time points in the bacterial growth curve exhibited several spectral features that enabled direct identification of the growth phase of the bacteria. Four groups of Raman peaks were identified based on similarities in the time-dependent behavior of their intensities over the course of the growth curve. These groupings were also consistent with the different biochemical species represented by the Raman peaks. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, while protein-specific Raman vibrations increased at different rates. The adenine ring-breathing mode at 729 and the 1245 [cm.sup.-1] vibration peaked in intensity within the first 10 h and decreased afterward. Application of principal component analysis (PCA) to the Raman spectra enabled accurate identification of the different metabolic states of the bacterial cells. The Raman spectra of cells exposed to Cefazolin at the end of log phase exhibited a different behavior. The 729 and 1245 cm-1 Raman peaks showed a slight decrease in intensity from 4 to 10 h after inoculation. Moreover, a shift in the spectral position of the adenine ring-breathing mode from 724 to 729 [cm.sup.-1], which was observed during normal bacterial growth, was inhibited during antibiotic drug treatment. These results suggest that potential Raman markers exist that can be used to identify E. coli cell response to antibiotic drug treatment. 10.1021/ac902351a

Specific, quantitative, and sensitive protein detection with minimal sample preparation is an enduring need in biology and medicine. Protein detection assays ideally provide quick, definitive measurements that use only small amounts of material. Fluorescence cross-correlation spectroscopy (FCCS) has been proposed and developed as a protein detection assay for several years. Here, we combine several recent advances in FCCS apparatus and analysis to demonstrate it as an important method for sensitive, quantitative, information-rich protein detection with low false positives. The addition of alternating laser excitation (ALEX) to FCCS along with a method to exclude signals from occasional aggregates leads to a very low rate of false positives, allowing the detection and quantification of the concentrations of a wide variety of proteins. We detect human chorionic gonadotropin (hCG) using an antibody-based sandwich assay and quantitatively compare our results with calculations based on binding equilibrium equations. Furthermore, using our aggregate exclusion method, we detect smaller oligomers of the prion protein PrP by excluding bright signals from large aggregates.

Currently, a combination of technologies is typically required to assess the malignancy of cancer cells. These methods often lack the specificity and sensitivity necessary for early, accurate diagnosis. Here we demonstrate using clinical samples the application of laser trapping Raman spectroscopy as a novel approach that provides intrinsic biochemical markers for the noninvasive detection of individual cancer cells. The Raman spectra of live, hematopoietic cells provide reliable molecular fingerprints that reflect their biochemical composition and biology. Populations of normal T and B lymphocytes from four healthy individuals and cells from three leukemia patients were analyzed, and multiple intrinsic Raman markers associated with DNA and protein vibrational modes have been identified that exhibit excellent discriminating power for cancer cell identification. A combination of two multivariate statistical methods, principal component analysis (PCA) and linear discriminant analysis (LDA), was used to confirm the significance of these markers for identifying cancer cells and classifying the data. The results indicate that, on average, 95% of the normal cells and 90% of the patient cells were accurately classified into their respective cell types. We also provide evidence that these markers are unique to cancer cells and not purely a function of differences in their cellular activation.

We present the development of nanoscale pH sensors based on functionalized silver nanoparticles and surface-enhanced Raman scattering (SERS). The SERS spectrum from individual silver nanoparticle (50-80 nm in diameter) clusters functionalized with 4-mercaptobenzoic acid shows a characteristic response to the pH of the surrounding solution and is sensitive to pH changes in the range of 6-8. Measurements from nanoparticles incorporated in living Chinese hamster ovary cells demonstrate that the nanoparticle sensors retain their robust signal and sensitivity to pH when incorporated into a cell.