In this research we introduce a plasmonic nanoparticle based optical biosensor for monitoring of molecular binding events. The sensor utilizes spotted gold nanoparticle arrays as sensing platform. The nanoparticle spots are functionalized with capture DNA sequences complementary to the analyte (target) DNA. Upon incubation with the target sequence, it will bind on the respectively complementary functionalized particle spot. This binding changes the local refractive index, which is detected spectroscopically as the resulting changes of the localized surface plasmon resonance (LSPR) peak wavelength. In order to increase the signal, a small gold nanoparticle label is introduced. The binding can be reversed using chemical means (10 mM HCl). It is demonstrated that a multiplexed detection and identification of several fungal pathogen DNA sequences subsequently on one sensor array is possible by this approach.
Neutrophils
are important cells of the innate immune system and
the major leukocyte subpopulation in blood. They are responsible for
recognizing and neutralizing invading pathogens, such as bacteria
or fungi. For this, neutrophils are well equipped with pathogen recognizing
receptors, cytokines, effector molecules, and granules filled with
reactive oxygen species (ROS)-producing enzymes. Depending on the
pathogen type, different reactions are triggered, which result in
specific activation states of the neutrophils. Here, we aim to establish
a label-free method to indirectly detect infections and to identify
the cause of infection by spectroscopic characterization of the neutrophils.
For this, isolated neutrophils from human peripheral blood were stimulated
in an in vitro infection model with heat-inactivated
Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacterial pathogens as well as with heat-inactivated and viable
fungi (Candida albicans). Label-free
and nondestructive Raman spectroscopy was used to characterize neutrophils
on a single cell level. Phagocytized fungi could be visualized in
a few high-resolution false color images of individual neutrophils
using label-free Raman spectroscopic imaging. Using a high-throughput
screening Raman spectroscope (HTS-RS), Raman spectra of more than
2000 individual neutrophils from three different donors were collected
in each treatment group, yielding a data set of almost 20 000
neutrophil spectra. Random forest classification models were trained
to differentiate infected and noninfected cells with high accuracy
(90%). Among the neutrophils challenged with pathogens, even the cause
of infection, bacterial or fungal, was predicted correctly with 92%
accuracy. Therefore, Raman spectroscopy enables reliable neutrophil
phenotyping and infection diagnosis in a label-free manner. In contrast
to the microbiological diagnostic standard, where the pathogen is
isolated in time-consuming cultivation, this Raman-based method could
potentially be blood-culture independent, thus saving precious time
in bloodstream infection diagnostics.
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