Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Diagnosis of colorectal cancer using Raman spectroscopy of laser-trapped single living epithelial cells

A single-cell diagnostic technique for epithelial cancers is developed by utilizing laser trapping and Raman spectroscopy to differentiate cancerous and normal epithelial cells. Single-cell suspensions were prepared from surgically removed human colorectal tissues following standard primary culture protocols and examined in a near-infrared laser-trapping Raman spectroscopy system, where living epithelial cells were investigated one by one. A diagnostic model was built on the spectral data obtained from 8 patients and validated by the data from 2 new patients. Our technique has potential applications from epithelial cancer diagnosis to the study of cell dynamics of carcinogenesis. (c) 2006 Optical Society of America.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Co-expression of CD173 (H2) and CD174 (Lewis Y) with CD44 suggests that fucosylated histo-blood group antigens are markers of breast cancer-initiating cells

Histo-blood group antigens CD173 (H2) and CD174 (Lewis Y) are known to be developmentally regulated carbohydrate antigens which are expressed to a varying degree on many human carcinomas. We hypothesized that they might represent markers of cancer-initiating cells (or cancer stem cells, CSC). In order to test this hypothesis, we examined the co-expression of CD173 and CD174 with stem cell markers CD44 and CD133 by flow cytometry analysis, immunocytochemistry, and immunohistochemistry on cell lines and tissue sections from breast cancer. In three breast cancer cell lines, the percentage of CD173(+)/CD44(+) cells ranged from 17% to > 60% and of CD174(+)/CD44(+) from 21% to 57%. In breast cancer tissue sections from 15 patients, up to 50% of tumor cells simultaneously expressed CD173, CD174, and CD44 antigens. Co-expression of CD173 and CD174 with CD133 was also observed, but to a lesser percentage. Co-immunoprecipitation and sandwich ELISA experiments on breast cancer cell lines suggested that CD173 and CD174 are carried on the CD44 molecule. The results show that in these tissues CD173 (H2) and CD174 (LeY) are associated with CD44 expression, suggesting that these carbohydrate antigens are markers of cancer-initiating cells or of early progenitors of breast carcinomas.

Excretion pattern investigation of urinary normal and modified nucleosides of breast cancer patients by RP-HPLC and factor analysis method

Modified nucleosides, formed post-transcriptionally in RNA by a number of modification enzymes, are excreted in abnormal levels in the urine of patients with malignant tumors. To test their usefulness as tumor markers, and to compare them with the conventional tumor markers, a reversed-phase high-performance liquid chromatographic (RP-HPLC) method and a factor analysis method have been used to study the excretion pattern of nucleosides of breast cancer patients. A clear cut differentiation of the breast cancer group and the healthy individuals in two clusters without overlapping was obtained. Copyright (C) 2000 John Wiley & Sons, Ltd.

Artificial neural network applied to diagnosis of lung cancer

Artificial neural network(ANN) approach was applied to classification of normal persons and lung cancer patients based on the metal content of hair and serum samples obtained by inductively coupled plasma atomic emission spectrometry (ICP-AES) for the two groups. This method was verified with independent prediction samples and can be used as an aiding means of the diagnosis of lung cancer. The case of predictive classification with one element missing in the prediction samples was studied in details, The significance of elements in hair and serum samples for classification prediction was also investigated.

Incidence of diverse integrons and beta-lactamase genes in environmental Enterobacteriaceae isolates from Jiaozhou Bay, China

Environmental microbiology investigation was performed to determine the molecular diversity of beta-lactamase genes among ampicillin-resistant bacteria from Jiaozhou Bay. beta-lactamase genes were detected in 93.8% of the bacterial isolates identified as Enterobacteriaceae. The most frequently detected gene was bla(TEM), followed by bla(SHV), bla(OAX-1), bla(MOX) and bla(CMY). Most of the isolates (68.8%) were positive for the intI1 integrase gene, and two isolates were also found for the intI2 gene. The dfr and aadA gene cassettes were predominant. Anthropogenic contamination from onshore sewage processing plants might contribute predominantly to the beta-lactamase gene reservoir in the studied coastal waters. Environmental antibiotic-resistant bacteria and resistance genes may serve as bioindicators of coastal environmental quality or biotracers of the potential contamination sources. This is the first report of the prevalence and characterization of beta-lactamase genes and integrons in coastal Enterobacteriaceae from China.