Detection and Differentiation of Bacterial and Fungal Infection of Neutrophils from Peripheral Blood Using Raman Spectroscopy

Abstract

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.

Biochemical Analysis of Leukocytes after In Vitro and In Vivo Activation with Bacterial and Fungal Pathogens Using Raman Spectroscopy

“…The balanced accuracy of the Raman model reaches only 59% ( Figure 2 D), which might be caused by the heterogeneous immune cell activation through bacteria or fungi. The neutrophil’s responses to pathogens are complex and known to be microbe-dependent [ 33 ], also reflected in different Raman signals [ 26 ]. In addition, donor-to-donor variations might have an influence.…”

“…The current Raman model faces challenges in discriminating neutrophils infected with Gram-positive and Gram-negative bacteria that might result from the heterogeneity between donors as visualized in the PCA plot ( Supplementary Figure S2A ) and the small numbers of cells investigated. However, another recent Raman study, wherein almost 20,000 neutrophils were analyzed after one hour of pathogen contact, provided reasonably good differentiation [ 26 ].…”

“…[ 14 , 15 , 16 , 17 , 18 ] Further Raman spectroscopy detected immune cell activation and apoptosis. [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ] Recently, in vitro stimulation studies with neutrophils pointed to the potential of Raman spectroscopy to differentiate the cause of infection [ 26 ]. Also, a first study highlighted the translational potential of Raman spectroscopic leukocyte characterization for patient stratification [ 27 ].…”

Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification

“…This paper builds on recent work in the development of robust Raman cytology platforms that are fully automated and high-speed. Schie et al have published several papers that describe a high-throughput automated Raman cytology platform with several applications [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. The basic platform [ 15 , 16 ] has several important features, including a defocused laser spot to excite a large cell area with high power, thereby facilitating shorter exposure times, and a LabVIEW-based automaton process, which identifies cell position using image processing and controls a translation stage to align the cells with the laser spot.…”

Automated Raman micro-spectroscopy of epithelial cells for the high-throughput classification

“…[20] Although, not a target application of the system proposed here, the study of pathogens, and in particular bacteria, has been another key application area of automated Raman cytology systems in recent years. The automated system described in the previous paragraph has been adapted to record spectra of single isolated neutrophils from human peripheral blood [19], which were stimulated via an in-vitro infection model with heat-inactivated bacterial and fungi pathogens; the system captured 20000 neutrophil spectra, across various treatment groups, originating from three donors. Another system developed by Douet et.…”