Identification and differentiation of single cells from peripheral blood by Raman spectroscopic imaging

J Biophotonics. 2010 Aug;3(8-9):579-87. doi: 10.1002/jbio.201000020.


Medical diagnosis can be improved significantly by fast, highly sensitive and quantitative cell identification from easily accessible body fluids. Prominent examples are disseminated tumor cells circulating in the peripheral blood of cancer patients. These cells are extremely rare and therefore difficult to detect. In this contribution we present the Raman spectroscopic characterization of different cells that can be found in peripheral blood such as leukocytes, leukemic cells and solid tumor cells. Leukocytes were isolated from the peripheral blood from healthy donors. Breast carcinoma derived tumor cells (MCF-7, BT-20) and myeloid leukaemia cells (OCI-AML3) were prepared from cell cultures. Raman images were collected from dried cells on calcium fluoride slides using 785 nm laser excitation. Unsupervised statistical methods (hierarchical cluster analysis and principal component analysis) were used to visualize spectral differences and cluster formation according to the cell type. With the help of supervised statistical methods (support vector machines) a classification model with 99.7% accuracy rates for the differentiation of the cells was built. The model was successfully applied to identify single cells from an independent mixture of cells based on their vibrational spectra. The classification was confirmed by fluorescence staining of the cells after the Raman measurement.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Blood Cell Count / methods*
  • Blood Cells / classification*
  • Data Interpretation, Statistical
  • Humans
  • Spectrum Analysis, Raman / methods*