Current methods for identifying neoplastic cells and discerning them from their normal counterparts are often nonspecific, slow, biologically perturbing, or a combination thereof. Here, we show that single-cell micro-Raman spectroscopy averts these shortcomings and can be used to discriminate between unfixed normal human lymphocytes and transformed Jurkat and Raji lymphocyte cell lines based on their biomolecular Raman signatures. We demonstrate that single-cell Raman spectra provide a highly reproducible biomolecular fingerprint of each cell type. Characteristic peaks, mostly due to different DNA and protein concentrations, allow for discerning normal lymphocytes from transformed lymphocytes with high confidence (p << 0.05). Spectra are also compared and analyzed by principal component analysis to demonstrate that normal and transformed cells form distinct clusters that can be defined using just two principal components. The method is shown to have a sensitivity of 98.3% for cancer detection, with 97.2% of the cells being correctly classified as belonging to the normal or transformed type. These results demonstrate the potential application of confocal micro-Raman spectroscopy as a clinical tool for single cancer cell detection based on intrinsic biomolecular signatures, therefore eliminating the need for exogenous fluorescent labeling.

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.

ABSTRACT. Background. Previous research investigating the relationship between the time of admission and mortality rates has yielded inconsistent results and has not been conducted in the pediatric intensive care unit (PICU) patient population.Objective. To determine whether an association between the time of admission (weekday versus weekend and daytime versus evening) and the risk of death exists among pediatric patients included in a cohort of children admitted to a national sample of PICUs.Design/Methods. We analyzed retrospectively a cohort of consecutive admissions to 15 PICUs included in the Pediatric Intensive Care Unit Evaluations database. The odds of death were analyzed by using mixed-effects, multivariate, logistic regression, with clustering at the hospital level. The primary independent variables were admission to the PICU on a weekend and admission to the PICU during evening hours. The severity of illness was adjusted by using the Pediatric Risk of Mortality III probability of death score.Patients. T o improve the quality of care, discrepancies in health care delivery and their effects on patient outcomes must be identified. Recent research has demonstrated that, among large patient cohorts, differences in time of admission are associated with differences in patient outcomes. Bell and Redelmeier 1 identified a significantly higher mortality rate among adult patients admitted on the weekend, compared with similar patients admitted on weekdays. Barnett et al, 2 in a study of Ͼ150 000 adult patients admitted to 38 intensive care units (ICUs), demonstrated both a higher risk-adjusted odds of in-hospital death and a longer ICU length of stay for patients admitted on the weekend, compared with patients admitted on weekdays. Similarly, neonatal mortality rates were shown to be slightly higher among infants born on weekends and during evening hours, compared with those born on weekdays and during daytime hours, respectively. [3][4][5][6][7][8] Whether an association exists between patient outcomes and time of admission in the pediatric patient population is unknown.To address this question, the Pediatric Intensive Care Unit Evaluations (PICUEs) database, representing a cohort of 34 993 pediatric ICU (PICU) patients from 15 institutions in the United States, was analyzed. 9 The objectives of this study were to compare the risk-adjusted mortality rates for weekend admissions versus weekday admissions and the risk-adjusted mortality rates for evening admissions versus daytime admissions. METHODS PatientsData for analysis were obtained from the most current available PICUEs database, Research Dataset 20 -02. 9 This database contains patient-level data from 34 993 admissions to 15 PICUs in the United States. Details of the site-selection procedures for the 15 study sites, general data-collection methods, and other analyses of this data set have been published. 9,10 Briefly, a minimum of 67 demographic, diagnostic physiologic, laboratory, and outcome data points were collected for all patients within 24 hours a...

Laser tweezers Raman spectroscopy (LTRS) was used to acquire the Raman spectra of leukemic T lymphocytes exposed to the chemotherapy drug doxorubicin at different time points over 72 hours. Changes observed in the Raman spectra were dependent on drug exposure time and concentration. The sequence of spectral changes includes an intensity increase in lipid Raman peaks, followed by an intensity increase in DNA Raman peaks, and finally changes in DNA and protein (phenylalanine) Raman vibrations. These Raman signatures are consistent with vesicle formation, cell membrane blebbing, chromatin condensation, and the cytoplasm of dead cells during the different stages of drug-induced apoptosis. These results suggest the potential of LTRS as a real-time single cell tool for monitoring apoptosis, evaluating the efficacy of chemotherapeutic treatments, or pharmaceutical testing.

Laser tweezers Raman spectroscopy (LTRS) was used to characterize the effect of different chemical fixation procedures on the Raman spectra of normal and leukemia cells. Individual unfixed, paraformaldehyde-fixed, and methanol-fixed normal and transformed lymphocytes from three different cell lines were analyzed with LTRS. When compared to the spectra of unfixed cells, the fixed cell spectra show clear, reproducible changes in the intensity of specific Raman markers commonly assigned to DNA, RNA, protein, and lipid vibrations (e.g. 785, 1230, 1305, 1660 cm(-1)) in mammalian cells, many of which are important markers that have been used to discriminate between normal and cancer lymphocytes. Statistical analyses of the Raman data and classification using principal component analysis and linear discriminant analysis indicate that methanol fixation induces a greater change in the Raman spectra than paraformaldehyde. In addition, we demonstrate that the spectral changes as a result of the fixation process have an adverse effect on the accurate Raman discrimination of the normal and cancer cells. The spectral artifacts created by the use of fixatives indicate that the method of cell preparation is an important parameter to consider when applying Raman spectroscopy to characterize, image, or differentiate between different fixed cell samples to avoid potential misinterpretation of the data.

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(-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(-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.

Laser tweezers Raman spectroscopy was used to detect the cellular response of Escherichia coli cells to penicillin G-streptomycin and cefazolin. Time-dependent intensity changes of several Raman peaks at 729, 1,245, and 1,660 cm ؊1 enabled untreated cells and cells treated with the different antibiotic drugs to be distinguished.

Total body irradiation (TBI)-based conditioning regimens for pediatric patients with acute myelogenous leukemia (AML) beyond first complete remission (CR1) are controversial. Because the long-term morbidity of busulfan (Bu)-based regimens appears to be lower, determining efficacy is critical. We retrospectively evaluated 151 pediatric patients with AML beyond CR1, comparing outcomes in 90 patients who received a TBI-based conditioning regimen and 61 patients who received a Bu-based conditioning regimen. There were no differences between the 2 groups with respect to age, sex, duration of CR1, time from most recent remission to transplantation, or donor source. The probability of relapse at 2 years also did not differ between the 2 groups (26% and 27%, respectively; P=.93). No significant difference in event-free survival (EFS) (P=.29) or overall survival (OS) (P=.11) was noted between the 2 groups. These findings were supported by a multivariate analysis in which TBI was not associated with improved EFS (hazard ratio [HR]=1.17; 95% confidence interval [CI]=0.66-2.10; P=.58) or OS (HR=1.42; 95% CI=0.76-2.64; P=.27). Shorter CR1 and receiving an HLA-mismatched transplant adversely affected EFS and OS in this cohort. Our study provides no evidence of an advantage to using TBI in children with AML beyond CR1. A prospective, randomized study is needed to confirm these results.