Medical diagnosis of biopsies performed by fine needle aspiration has to be very reliable. Therefore, pathologists/cytologists need additional biochemical information on single cancer cells for an accurate diagnosis. Accordingly, we applied three different classification models for discriminating various features of six breast cancer cell lines by analyzing Raman microspectroscopic data. The statistical evaluations are implemented by linear discriminant analysis (LDA) and support vector machines (SVM). For the first model, a total of 61,580 Raman spectra from 110 single cells are discriminated at the cell-line level with an accuracy of 99.52% using an SVM. The LDA classification based on Raman data achieved an accuracy of 94.04% by discriminating cell lines by their origin (solid tumor versus pleural effusion). In the third model, Raman cell spectra are classified by their cancer subtypes. LDA results show an accuracy of 97.45% and specificities of 97.78%, 99.11%, and 98.97% for the subtypes basal-like, HER2+/ER− , and luminal, respectively. These subtypes are confirmed by gene expression patterns, which are important prognostic features in diagnosis. This work shows the applicability of Raman spectroscopy and statistical data handling in analyzing cancer-relevant biochemical information for advanced medical diagnosis on the single-cell level.
In the last years the identification of microorganisms by means of different IR and Raman spectroscopic techniques
has become quite popular. Most of the studies however apply the various vibrational spectroscopic methods to bulk
samples which require at least a short cultivation time of several hours. Nevertheless, bulk identification methods
achieve high classification rates which enable even the discrimination between closely related strains or the distinction
between resistance capabilities.
However, applying micro-Raman spectroscopy with visible excitation wavelengths enables for the detection of
single microorganisms. Especially for time critical process like the fast diagnosis of severe diseases or the identification
of bacterial contamination on food samples or pharmaceuticals, a cultivation-free identification of bacteria is required.
In doing so, we established different isolation techniques in combination with Raman spectroscopic identification.
Isolating bacteria from different matrixes always has an impact on the Raman spectroscopic identification capability.
Therefore, these isolation techniques have to be specially designed to fulfill the spectroscopic requirements. In total the
method should enable the identification of pathogens within the first 3 hours.
Pathogen detection is essential without time delay especially for severe diseases like sepsis. Here, the survival rate is
dependent on a prompt antibiosis. For sepsis three hours after the onset of shock the survival rate of the patient drops
below 60 %. Unfortunately, the results from standard diagnosis methods like PCR or microbiology can normally be
received after 12 or 36 h, respectively. Therefore diagnosis methods which require less cultivation or even no cultivation
at all have to be established for medical diagnosis. Here, Raman spectroscopy, as a vibrational spectroscopic method, is a
very sensitive and selective approach and monitors the biochemical composition of the investigated sample. Applying
micro-Raman spectroscopy allows for a spatial resolution below 1 μm and is therefore in the size range of bacteria.
Raman spectra of bacteria depend on the physiological status. Therefore, the databases require the inclusion of the
necessary environmental parameters such as temperature, pH, nutrition, etc. Such large databases therefore require a
specialized chemometric approach, since the variation between different strains is small. In this contribution we will
demonstrate the capability of Raman spectroscopy to identify pathogens without cultivation even from real
environmental or medical samples.
This contribution will present a variety of applications of lab-on-a-chip surface enhanced Raman spectroscopy in the
field of bioanalytic. Beside the quantification and online monitoring of drugs and pharmaceuticals, determination of
enzyme activity and discrimination of bacteria are successfully carried out utilizing LOC-SERS. The online-monitoring
of drugs using SERS in a microfluidic device is demonstrated for nicotine. The enzyme activity of thiopurine
methyltransferase (TPMT) in lysed red blood cells is determined by SERS in a lab-on-a-chip device. To analyse the
activity of TPMT the metabolism of 6-mercaptopurine to 6-methylmercaptopurine is investigated. The discrimination of
bacteria on strain level is carried out with different E. coli strains. For the investigations, the bacteria are busted by ultra
sonic to achieve a high information output. This sample preparation provides the possibility to detect SERS spectra
containing information of the bacterial cell walls as well as of the cytoplasm. This contribution demonstrates the great
potential of LOC-SERS in the field of bioanalytics.
The observation of agent concentrations is of major importance for a lot of areas such as medicine, process and
environmental analysis. The aim of this research work is the development of an analytical tool with the potential to
online-monitor concentration changes. For this purpose the combination of surface enhanced Raman spectroscopy
(SERS) and a microfluidic device seems to be a promising approach. This approach is capable for a qualitative as well
as quantitative analysis. In summary the great potential of surface enhanced Raman spectroscopy in combination with a
microfluidic device for a quantitative analysis will be shown.
Here we present our latest results concerning the application of Raman microspectroscopy in combination with
innovative chemometrics to characterize biological cells. The first part of this manuscript deals with the application of
micro-Raman spectroscopy to identify microbial contaminations while the main focus within the second part of this
presentation is concerned with Raman studies on eukaryotic cells where we will report about the development of an
algorithm to differentiate between breast cancer cells and normal epithelial cells.
Biophotonics is a new and highly interdisciplinary scientific discipline comprising the application of
light (i.e. innovative photonic tools) in life sciences. It is non exaggerated to say Biophotonics is on the way to
solve the most important problems in biomedicine. In particular Raman microspectroscopy allows one to derive
detailed and specific information on a molecular level which other photonic methods methods can only provide by
a limited extent. Here we will present latest results of our own research dealing with the application and
development of innovative Raman spectroscopic techniques for biomedical applications.
Here we will present challenges to be met in connection with the application of spectroscopic
imaging and in particular Raman microspectroscopy for life sciences and biomedicine. We start with an
introduction of a combinatorial approach between florescence imaging, Raman microspectroscopy and
innovative statistical Raman data analysis methods for rapid diagnosis and prevention of infectious
diseases. Furthermore we will report about the multimodal application of Raman and CARS imaging for
an early diagnosis of cancer.
The application of surface enhanced Raman spectroscopy in combination with a microfluidic device and an isotopeedited
internal standard seems to be a promising way for a new approach for quantitative SERS measurements. An
innovative lab on a chip system offers the possibility for reproducible, quantitative online SERS measurements based on
the application of isotope labelled internal standards and liquid/liquid segmented flow based flow-through Raman
detection. Errors caused by the used method can be compensated by using an internal standard.
Microorganisms can be found everywhere e.g. in food both as useful ingredients or harmful contaminations causing
food spoilage. Therefore, a fast and easy to handle analysis method is needed to detect bacteria in different kinds of
samples like meat, juice or air to decide if the sample is contaminated by harmful microorganisms. Conventional
identification methods in microbiology require always cultivation and therefore are time consuming.
In this contribution we present an analysis approach to identify fluorescence stained bacteria on strain level by means of
Raman spectroscopy. The stained bacteria are highlighted and can be localized easier against a complex sample
environment e.g. in food. The use of Raman spectroscopy in combination with chemometrical methods allows the
identification of single bacteria within minutes.
The identification of bacteria is necessary as fast as possible e.g. to provide an appropriate therapy for patients. Here the
cultivation time should be kept to a minimum. Beside microbiological identification methods Raman spectroscopy is a
valuable tool for bacteria identification. UV-resonance Raman spectroscopy enables selective monitoring of the cellular
DNA/RNA content and allows for a genotaxonomic classification of the bacteria. Since UV excitation may lead to
sample destruction the measurements are performed on rotated bacterial films.
For a faster identification avoiding the cultivation step single bacteria analysis is necessary. Using micro-Raman
spectroscopy a spatial resolution in the size range of the bacteria can be achieved. With this Raman excitation the
chemical components of the whole cell are measured which leads to a phenotypic classification. For localization of
bacteria inside complex matrices fluorescence labeling is achieved.
The detection of single bacteria should be improved by lowering the acquisition time via the application of SERS
(surface enhanced Raman spectroscopy). Nano structured colloids or surfaces consisting of gold or silver can be used as
SERS active substrates. However, for biological applications mostly gold is used as SERS active substrate since silver is
toxic for bacterial cells. Furthermore, the application of gold as a SERS-active substrate allows the usage of Raman
excitation wavelengths in the red part of the electromagnetic spectrum.
For the SERS investigations on bacteria different colloids (purchased and self prepared, preaggregated and non-aggregated)
are chosen as SERS active substrates. The application of different gold colloids under gently mixing
conditions to prevent the bacterial damage allowed the recording of reproducible SERS spectra of bacteria. The SERS
spectra of B. pumilus are dominated by contributions of ingredients of the outer cell wall, e.g. the peptidoglycan layer.
SEM images of the coated bacteria demonstrate the incomplete adsorption most probably due to variations within the
binding affinities between different outer cell components and the gold colloids.
A fast and unambiguous identification of microorganisms is necessary not only for medical purposes but also in technical processes such as the production of pharmaceuticals. Conventional microbiological identification methods are based on the morphology and the ability of microbes to grow under different conditions on various cultivation media depending on their biochemical properties. These methods require pure cultures which need cultivation of at least 6 h but normally much longer. Recently also additional methods to identify bacteria are established e.g. mass spectroscopy, polymerase chain reaction (PCR), flow cytometry or fluorescence spectroscopy. Alternative approaches for the identification of microorganisms are vibrational spectroscopic techniques. With Raman spectroscopy a spectroscopic fingerprint of the microorganisms can be achieved. Using UV-resonance Raman spectroscopy (UVRR) macromolecules like DNA/RNA and proteins are resonantly enhanced. With an excitation wavelength of e.g. 244 nm it is possible to determine the ratio of guanine/cytosine to all DNA bases which allows a genotypic identification of microorganisms. The application of UVRR requires a large amount of microorganisms (> 106 cells) e.g. at least a micro colony. For the analysis of single cells micro-Raman spectroscopy with an excitation wavelength of 532 nm can be used. Here, the obtained information is from all type of molecules inside the cells which lead to a chemotaxonomic identification. In this contribution we show how wavelength dependent Raman spectroscopy yields significant molecular information applicable for the identification of microorganisms on a single cell level.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.