Circulating tumor cells (CTCs) that can be extracted from body fluids offer new prospects in cancer diagnostics. An
overview about our recent achievements is presented to use Raman-based methodologies to distinguish cancer cells from
normal blood cells. In a first approach, a microfluidic chip was developed to collect Raman spectra from optically
trapped cells. Whereas sensitivities and specificities were promising, the throughput was not compatible with the
expected low number of CTCs per million white blood cells. A second strategy immobilized up to 200,000 cells onto a
microhole array made of silicon nitride. Rapid microscopic screening can be applied to pre-select a subset of cells from
which Raman spectra are collected for specific CTC identification. As this approach is compatible with living cells and
Raman spectroscopy with 785 nm excitation is non-destructive, a robotic arm can select positively identified CTCs for
in-depth biochemical assessment. Finally, an in vivo approach directly collects CTCs from the blood stream. This way
reduces the cell number to a manageable size that is subjected to Raman spectroscopy for cell typing and enumeration.
An integrated acquisition mode was introduced to further increase the throughput and robustness of single cell
Cell identification by Raman spectroscopy has evolved to be an attractive complement to established optical techniques.
Raman activated cell sorting (RACS) offers prospects to complement the widely applied fluorescence activated cell
sorting. RACS can be realized by combination with optical traps and microfluidic devices. The progress of RACS is
reported for a cellular model system that can be found in peripheral blood of tumor patients. Lymphocytes and
erythrocytes were extracted from blood samples. Breast carcinoma derived tumor cells (MCF-7, BT-20) and acute
myeloid leukemia cells (OCI-AML3) were grown in cell cultures. First, Raman images were collected from dried cells
on calcium fluoride slides. Support vector machines (SVM) classified 99.7% of the spectra to the correct cell type.
Second, a 785 nm laser was used for optical trapping of single cells in aqueous buffer and for excitation of the Raman
spectrum. SVM distinguished 1210 spectra of tumor and normal cells with a sensitivity of >99.7% and a specificity of
>99.5%. Third, a microfluidic glass chip was designed to inject single cells, modify the flow speed, accommodate fibers
of an optical trap and sort single cells after Raman based identification with 514 nm for excitation. Forth, the
microfluidic chip was fabricated by quartz which improved cell identification results with 785 nm excitation. Here,
partial least squares discriminant analysis gave classification rates of 98%. Finally, a Raman-on-chip approach was
developed that integrates fibers for trapping, Raman excitation and signal detection in a single compact unit.
As a molecular probe of tissue composition, infrared spectroscopic imaging serves as an adjunct to histopathology
in detecting and diagnosing disease. In the past it was demonstrated that the IR spectra of brain tumors can be
discriminated from one another according to their grade of malignancy. Although classification success rates up
to 93% were observed one problem consists in the variation of the models depending on the number of samples
used for the development of the classification model. In order to open the path for clinical trials the classification
has to be validated. A series of classification models were built using a k-fold cross validation scheme and
the classification predictions from the various models were combined to provide an aggregated prediction. The
validation highlights instabilities in the models, error rates, sensitivity as well as specificity of the classification
and allows the determination of confidence intervals. Better classification models could be achieved by an
aggregated prediction. The validation shows that brain tumors can be classified by infrared spectroscopy and
the grade of malignancy corresponds reasonably to the histopathological assignment.