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1.
Methods Mol Biol ; 740: 179-89, 2011.
Article in English | MEDLINE | ID: mdl-21468979

ABSTRACT

The number of techniques to identify, quantify and characterise cell death is rapidly increasing as more is known about the complex mechanisms underlying this process. However, most of these techniques are invasive and require preparation steps such as cell fixation, staining or protein extractions. Non-invasive analysis of living cells represents a key point in cell biology, e.g. in toxicology studies or in tissue engineering. In this chapter, we report the usefulness of Raman spectroscopy as a non-invasive method to distinguish cells at different stages of cell cycle and living cells from dead cells. Throughout two examples, we show the performance and the use of Raman spectroscopy as a new non-invasive method to assess cell viability.


Subject(s)
Spectrum Analysis, Raman/methods , Apoptosis/drug effects , Cell Adhesion/drug effects , Cell Line, Tumor , Cell Survival/drug effects , DNA, Neoplasm/metabolism , Etoposide/pharmacology , Humans , Neoplasm Proteins/metabolism , Octoxynol/pharmacology , Time Factors
2.
Analyst ; 135(12): 3205-12, 2010 Dec.
Article in English | MEDLINE | ID: mdl-20953516

ABSTRACT

Confocal Raman micro-spectroscopy (CRMS) was used to measure spectral images of immunological synapse formation between dendritic and T cells without using molecular labels or other invasive procedures. The purpose-built inverted CRMS instrument integrated an environmental enclosure and a near-infrared laser to allow measurements on live cells maintained under physiological conditions. The integration of the wide-field fluorescence also enabled viability assays and direct comparison between Raman spectral images and gold-standard immuno-fluorescence images for specific molecules. Raman spectral images of nucleus and proteins were built by fuzzy c-mean clustering method. The Raman images were found to be in good correspondence with the immuno-fluorescence images of DNA and actin. These results indicate that actin is a main contributor to the Raman spectrum of the cytoplasm of dendritic and T cells. While for control cells the Raman spectral images of proteins indicated a more homogeneous distribution of proteins in the cytoplasm of dendritic cells, they indicated a higher accumulation of proteins at the immunological synapses when dendritic cells were pre-treated with laminin. These conclusions were also supported by confocal immuno-fluorescence imaging after cell fixation and labelling. This study demonstrates the potential of CRMS for label-free non-invasive imaging of junctions between live cells. Therefore, this technique may become a useful tool for studying cellular processes in live cells and where non-invasive molecular specific imaging is desirable, such as cell-cell interactions.


Subject(s)
Dendritic Cells/chemistry , Immunological Synapses/chemistry , Microscopy, Confocal/methods , Spectrum Analysis, Raman/methods , T-Lymphocytes/chemistry , Actins/chemistry , Cells, Cultured , Cluster Analysis , Coculture Techniques , Dendritic Cells/ultrastructure , Humans , Immunological Synapses/ultrastructure , Laminin/chemistry , T-Lymphocytes/ultrastructure
3.
J Biomed Opt ; 14(5): 054031, 2009.
Article in English | MEDLINE | ID: mdl-19895133

ABSTRACT

We investigate the potential of Raman microspectroscopy (RMS) for automated evaluation of excised skin tissue during Mohs micrographic surgery (MMS). The main aim is to develop an automated method for imaging and diagnosis of basal cell carcinoma (BCC) regions. Selected Raman bands responsible for the largest spectral differences between BCC and normal skin regions and linear discriminant analysis (LDA) are used to build a multivariate supervised classification model. The model is based on 329 Raman spectra measured on skin tissue obtained from 20 patients. BCC is discriminated from healthy tissue with 90+/-9% sensitivity and 85+/-9% specificity in a 70% to 30% split cross-validation algorithm. This multivariate model is then applied on tissue sections from new patients to image tumor regions. The RMS images show excellent correlation with the gold standard of histopathology sections, BCC being detected in all positive sections. We demonstrate the potential of RMS as an automated objective method for tumor evaluation during MMS. The replacement of current histopathology during MMS by a "generalization" of the proposed technique may improve the feasibility and efficacy of MMS, leading to a wider use according to clinical need.


Subject(s)
Algorithms , Artificial Intelligence , Carcinoma, Basal Cell/diagnosis , Diagnosis, Computer-Assisted/methods , Skin Neoplasms/diagnosis , Spectrum Analysis, Raman/methods , Female , Humans , Reproducibility of Results , Sensitivity and Specificity
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