Clinical Chemistry · 2006 · 23 citations · 24 references
EngineeringCell BiologyMolecular BiologyBiomemsCell SignalingMolecular OncologyCell Culture MicrosystemsCell-surface LabelingFluorescence MicroscopyMicrofluidic DevicesMicrofluidic SystemsMicrofluidicsCell Surface ProteinsCell-surface Protein LabelingImmunofluorescence ImagingImmunofluorescence StainingMicrofluidic FlowMicrofluidic PlatformsMicrofluidic Single-cell AnalysisSignal Quantification
BACKGROUND: The analysis of single cells obtained from needle aspirates of tumors is constrained by the need for processing. To this end, we investigated two microfluidic approaches to measure the expression of surface proteins in single cancer cells or in small populations (<50 cells). METHODS: One approach involved indirect fluorescence labeling of cell-surface proteins and channeling of cells in a microfluidic device past a fluorescence detector for signal quantification and analysis. A second approach channeled cells in a microfluidic device over detection zones coated with ligands to surface proteins and measured rates of passage and of retardation based on transient interactions between surface proteins and ligands. RESULTS: The fluorescence device detected expression of integrin alpha5 induced by basic fibroblast growth factor (FGF-2) treatment in MCF-7 cells and that of Her-2/neu in SK-BR-3 cells compared with controls. Experiments measuring passage retardation showed significant differences in passage rates between FGF-2-treated and untreated MCF-7 cells over reaction regions coated with fibronectin and antibody to integrin alpha5beta1 compared with control regions. Blocking peptides reversed the retardation, demonstrating specificity. CONCLUSIONS: Immunofluorescence detection in a microfluidic channel demonstrates the potential for assaying surface protein expression in a few individual cells and will permit the development of future iterations not requiring cell handling. The flow retardation device represents the first application of this technology for assessing cell-surface protein expression in cancer cells and may provide a way for analyzing expression profiles of single cells without preanalytical manipulation.
24
Molecular portraits of human breast tumours
Charles M. Perou, Thérese Sørlie, Michael B. Eisen et al. · Nature · 2000 · 16.3K citations
Gene expression profiling predicts clinical outcome of breast cancer
Laura van ‘t Veer, Hongyue Dai, Marc J. van de Vijver et al. · Nature · 2002 · 9.6K citations · Full text
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
Marc J. van de Vijver, Yudong D. He, Laura van ‘t Veer et al. · New England Journal of Medicine · 2002 · 6.5K citations · Full text
Medicine, Bioinformatics, Oncology +17