Characterization of bacteria, antibiotics of the fluoroquinolone type and their biological targets DNA and gyrase utilizing the unique potential of vibrational spectroscopy

Im Rahmen dieser Arbeit wurden verschiedene Schwingungsspektroskopische Techniken (IR-Absorptions-, Mikro-Raman-, UV-Resonanz-Raman-, oberflächenverstärkte und spitzenverstärkte Raman-Spektroskopie) dazu verwandt, Bakterien zu charakterisieren. Einen besonderen Einblick in die Zusammensetzung und Dynamik der äußeren Bakterienschicht bietet die erstmalige Anwendung der spitzenverstärkte Raman- Spektroskopie auf komplexe biologische Systeme wie Bakterien. Die Technik erlaubt die Gewinnung detaillierter chemischer Informationen mit hoher Ortsauflösung (wenige 10 nm). Ebenso konnten die Änderungen der chemischen Zusammensetzung während des Bakterienwachstums in Abwesenheit und in Gegenwart von Antibiotika aus der Gruppe der Fluorochinolone mit Hilfe schwingungsspektroskopischer Methoden verfolgt werden. Die Fluorochinolone greifen als biologische Zielstrukturen das Enzym Gyrase und die bakterielle DNA an, was schließlich zum Zelltod führt. Die am Wirkmechanismus beteiligten Komponenten Wirkstoff, DNA und Gyrase wurden zunächst in In-vitro-Experimenten schwingungsspektroskopisch charakterisiert und die Ergebnisse schließlich zur Interpretation der In-vivo-Experimente mit Bakterien verwandt. Mit Hilfe statistischer Auswertemethoden konnten die durch das Antibiotikum hervorgerufenen Änderungen in den Bakterienspektren auf Veränderungen an den Protein- und DNA-Bausteinen zurückgeführt werden, was den angenommenen Wirkmechanismus der Fluorochinolone unterstützt

A Computational Pipeline for Sepsis Patients’ Stratification and Diagnosis

Sepsis is still a little acknowledged public health issue, despite its increasing incidence and the growing mortality rate. In addition, a clear diagnosis can be lengthy and complicated, due to highly variable symptoms and non-specific criteria, causing the disease to be diagnosed and treated too late. This paper presents the HemoSpec platform, a decision support system which, by collecting and automatically processing data from several acquisition devices, can help in the early diagnosis of sepsis

3-Step flow focusing enables multidirectional imaging of bioparticles for imaging flow cytometry

Multidirectional imaging flow cytometry (mIFC) extends conventional imaging flow cytometry (IFC) for the image-based measurement of 3D-geometrical features of particles. The innovative core is a flow rotation unit in which a vertical sample lamella is incrementally rotated by 90 degrees into a horizontal lamella. The required multidirectional views are generated by guiding all particles at a controllable shear flow position of the parabolic velocity profile of the capillary slit detection chamber. All particles pass the detection chamber in a two-dimensional sheet under controlled rotation while each particle is imaged multiple times. This generates new options for automated particle analysis. In an experimental application, we used our system for the accurate classification of 15 species of pollen based on 3D-morphological information. We demonstrate how the combination of multi directional imaging with advanced machine learning algorithms can improve the accuracy of automated bio-particle classification. As an additional benefit, we significantly decrease the number of false positives in the classification of foreign particles,i.e.those elements which do not belong to one of the trained classes by the 3D-extension of the classification algorithm. © The Royal Society of Chemistry 2020

Comparability of Raman Spectroscopic Configurations: A LargeScale Cross-Laboratory Study

The variable configuration of Raman spectroscopic platforms is one ofthe major obstacles in establishing Raman spectroscopy as a valuable physicochemicalmethod within real-world scenarios such as clinical diagnostics. For such real worldapplications like diagnostic classification, the models should ideally be usable to predictdata from different setups. Whether it is done by training a rugged model with data frommany setups or by a primary-replica strategy where models are developed on a‘primary’setup and the test data are generated on‘replicate’setups, this is only possible if the Raman spectra from different setups are consistent, reproducible, and comparable.However, Raman spectra can be highly sensitive to the measurement conditions, and they change from setup to setup even if thesame samples are measured. Although increasingly recognized as an issue, the dependence of the Raman spectra on the instrumentalconfiguration is far from being fully understood and great effort is needed to address the resulting spectral variations and to correctfor them. To make the severity of the situation clear, we present a round robin experiment investigating the comparability of 35Raman spectroscopic devices with different configurations in 15 institutes within seven European countries from the COST(European Cooperation in Science and Technology) action Raman4clinics. The experiment was developed in a fashion that allowsvarious instrumental configurations ranging from highly confocal setups tofibre-optic based systems with different excitationwavelengths. We illustrate the spectral variations caused by the instrumental configurations from the perspectives of peak shifts,intensity variations, peak widths, and noise levels. We conclude this contribution with recommendations that may help to improvethe inter-laboratory studie

New perspectives for viability studies with high-content analysis Raman spectroscopy (HCA-RS)

Raman spectroscopy has been widely used in clinical and molecular biological studies, providing high chemical specificity without the necessity of labels and with little-to-no sample preparation. However, currently performed Raman-based studies of eukaryotic cells are still very laborious and time-consuming, resulting in a low number of sampled cells and questionable statistical validations. Furthermore, the approach requires a trained specialist to perform and analyze the experiments, rendering the method less attractive for most laboratories. In this work, we present a new high-content analysis Raman spectroscopy (HCA-RS) platform that overcomes the current challenges of conventional Raman spectroscopy implementations. HCA-RS allows sampling of a large number of cells under different physiological conditions without any user interaction. The performance of the approach is successfully demonstrated by the development of a Raman-based cell viability assay, i.e., the effect of doxorubicin concentration on monocytic THP-1 cells. A statistical model, principal component analysis combined with support vector machine (PCA-SVM), was found to successfully predict the percentage of viable cells in a mixed population and is in good agreement to results obtained by a standard cell viability assay. This study demonstrates the potential of Raman spectroscopy as a standard high-throughput tool for clinical and biological applications

Label-Free Characterization of Macrophage Polarization Using Raman Spectroscopy

Macrophages are important cells of the innate immune system that play many different roles in host defense, a fact that is reflected by their polarization into many distinct subtypes. Depending on their function and phenotype, macrophages can be grossly classified into classically activated macrophages (pro-inflammatory M1 cells), alternatively activated macrophages (anti-inflammatory M2 cells), and non-activated cells (resting M0 cells). A fast, label-free and non-destructive characterization of macrophage phenotypes could be of importance for studying the contribution of the various subtypes to numerous pathologies. In this work, single cell Raman spectroscopic imaging was applied to visualize the characteristic phenotype as well as to discriminate between different human macrophage phenotypes without any label and in a non-destructive manner. Macrophages were derived by differentiation of peripheral blood monocytes of human healthy donors and differently treated to yield M0, M1 and M2 phenotypes, as confirmed by marker analysis using flow cytometry and fluorescence imaging. Raman images of chemically fixed cells of those three macrophage phenotypes were processed using chemometric methods of unmixing (N-FINDR) and discrimination (PCA-LDA). The discrimination models were validated using leave-one donor-out cross-validation. The results show that Raman imaging is able to discriminate between pro- and anti-inflammatory macrophage phenotypes with high accuracy in a non-invasive, non-destructive and label-free manner. The spectral differences observed can be explained by the biochemical characteristics of the different phenotypes

Predictive Modeling of Antibiotic Susceptibility in E. Coli Strains Using the U-Net Network and One-Class Classification

The antibiotic resistance of bacterial pathogens has become one of the most serious global health issues due to misusing and overusing of antibiotics. Recently, different technologies were developed to determine bacteria susceptibility towards antibiotics; however, each of these technologies has its advantages and limitations in clinical applications. In this contribution, we aim to assess and automate the detection of bacterial susceptibilities towards three antibiotics; i.e. ciprofloxacin, cefotaxime and piperacillin using a combination of image processing and machine learning algorithms. Therein, microscopic images were collected from different E. coli strains, then the convolutional neural network U-Net was implemented to segment the areas showing bacteria. Subsequently, the encoder part of the trained U-Net was utilized as a feature extractor, and the U-Net bottleneck features were utilized to predict the antibiotic susceptibility of E. coli strains using a one-class support vector machine (OCSVM). This one-class model was always trained on images of untreated controls of each bacterial strain while the image labels of treated bacteria were predicted as control or non-control images. If an image of treated bacteria is predicted as control, we assume that these bacteria resist this antibiotic. In contrast, the sensitive bacteria show different morphology of the control bacteria; therefore, images collected from these treated bacteria are expected to be classified as non-control. Our results showed 83% area under the receiver operating characteristic (ROC) curve when OCSVM models were built using the U-Net bottleneck features of control bacteria images only. Additionally, the mean sensitivities of these one-class models are 91.67% and 86.61% for cefotaxime and piperacillin; respectively. The mean sensitivity for the prediction of ciprofloxacin is only 59.72% as the bacteria morphology was not fully detected by the proposed method

Correlation of crystal violet biofilm test results of Staphylococcus aureus clinical isolates with Raman spectroscopic read-out

Biofilm-related infections occur quite frequently in hospital settings and require rapid diagnostic identification as they are recalcitrant to antibiotic therapy and make special treatment necessary. One of the standard microbiological in vitro tests is the crystal violet test. It indirectly determines the amount of biofilm by measuring the optical density (OD) of the crystal violet-stained biofilm matrix and cells. However, this test is quite time-consuming, as it requires bacterial cultivation up to several days. In this study, we correlate fast Raman spectroscopic read-out of clinical Staphylococcus aureus isolates from 47 patients with different disease background with their biofilm-forming characteristics. Included were low (OD  20) biofilm performers as determined by the crystal violet test. Raman spectroscopic analysis of the bacteria revealed most spectral differences between high and low biofilm performers in the fingerprint region between 750 and 1150 cm−1. Using partial least square regression (PLSR) analysis on the Raman spectra involving the three categories of biofilm formation, it was possible to obtain a slight linear correlation of the Raman spectra with the biofilm OD values. The PLSR loading coefficient highlighted spectral differences between high and low biofilm performers for Raman bands that represent nucleic acids, carbohydrates, and proteins. Our results point to a possible application of Raman spectroscopy as a fast prediction tool for biofilm formation of bacterial strains directly after isolation from the infected patient. This could help clinicians make timely and adapted therapeutic decision in future

Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress

Analyzing the cells in various body fluids can greatly deepen the understanding of the mechanisms governing the cellular physiology. Due to the variability of physiological and metabolic states, it is important to be able to perform such studies on individual cells. Therefore, we developed an optofluidic system in which we precisely manipulated and monitored individual cells of Escherichia coli. We tested optical micromanipulation in a microfluidic chamber chip by transferring individual bacteria into the chambers. We then subjected the cells in the chambers to antibiotic cefotaxime and we observed the changes by using time-lapse microscopy. Separately, we used laser tweezers Raman spectroscopy (LTRS) in a different micro-chamber chip to manipulate and analyze individual cefotaxime-treated E. coli cells. Additionally, we performed conventional Raman micro-spectroscopic measurements of E. coli cells in a micro-chamber. We found observable changes in the cellular morphology (cell elongation) and in Raman spectra, which were consistent with other recently published observations. The principal component analysis (PCA) of Raman data distinguished between the cefotaxime treated cells and control. We tested the capabilities of the optofluidic system and found it to be a reliable and versatile solution for this class of microbiological experiments