title: Chemical effects on blood studied using label-free nanoscale analytics
Creators: name: Bergaglio, Talia
Publisher: name: Universität Bern
Year: 2024
Subjects: subject: 570 Life sciences; biology
subject: 610 Medicine &amp; health

description: The analysis of biofluids plays a pivotal role in the identification and tracking of disease-related biomarkers. Among these biofluids, blood is of particular interest due to its complex composition of cellular and molecular constituents, which offer insights into both organ-specific health (e.g., heart, liver, brain) and an individual's overall well-being. Furthermore, the minimally-invasive nature of blood collection makes it an ideal source for health information. Recent advancements in the digitization of diagnostic pathology and data analysis tools have paved the way for the automation of blood-based analytical tests, reducing the need for time-consuming and labor-intensive procedures. Leveraging high-throughput and automated techniques, driven by digital imaging and machine learning-based data analytics, holds great potential for improving the efficiency of blood sample processing and analysis, thereby expediting patient screening and monitoring. In the first part of the thesis, we show the development of the image and analysis framework for blood screening, based on digital holo-tomographic microscopy (DHTM). First, we optimized the best practices for blood collection and blood dilution in order to achieve high-resolution images. We subsequently developed the image processing pipeline, including noise filtering, background removal, image segmentation, feature recognition, and data extraction. For the morphological assessment of blood cells, we trained a machine learning classifier to automatically classify red blood cells (RBCs) based on their shape. Finally, we validated the morphological and chemical parameters obtained from DHTM- and atomic force microscopy (AFM)-based measurements in the context of RBC rheological properties and blood coagulation dynamics. In order to investigate the chemical effects on blood, we demonstrated the capability of DHTM to perform real-time, label-free monitoring of ibuprofen's concentration-dependent and time-dependent effects on red blood cells (RBCs) from a healthy donor. To do so, we first validated our imaging and analysis framework for the screening of RBCs to identity shape changes between healthy and sickle cell disease donors. Additionally, we tested our methodology for the real-time monitoring of cell shape changes upon variations to the chemical environment, with the addition of urea and hydrogen peroxide-induced oxidative stress. We then applied our DHTM-based approach for the label-free detection and quantification of ibuprofen-induced RBC shape changes. Here, we propose the employment of our DHTM-based technique for drug monitoring and we highlight the importance of taking into account RBC rheological properties when assessing safety levels for dose-dependent drug intake. The enduring health repercussions of the COVID-19 pandemic and its ongoing long-term effects have initiated extensive research efforts aimed at unraveling the pathogenic mechanisms and comprehending the heterogeneous nature of symptoms. We extended the application of our DHTM- based imaging and analysis framework for the detection and characterization of microclots by screening plasma samples of COVID-19 donors. In order to delve into the nanoscale analysis of micrometer-sized blood clots and to elucidate the mechanisms by which chemicals and medications affect clot dissolution, we employed a combinatorial imaging platform, including DHTM and AFM analysis, to resolve and quantify the morphological parameters of synthetically-prepared blood clot fragments in aqueous solution. The proposed nanoscale investigation of fibrin-rich clots could provide comprehensive insights into the role of blood clot morphology and composition in the development of targeted treatment strategies for thrombotic diseases. In the transition from cell to protein characterization using nanoscale analytics, we sought to investigate the dose dependent effect of Levodopa treatment on α-Synuclein aggregation dynamics using AFM. In view of the identification of protein aggregates implicated in neurodegenerative disorders, like Alzheimer's and Parkinson's disease, in blood, it is crucial to determine the role of such medications in dissolving these aggregates and the potential dose-dependent adverse effects on blood rheology. Altogether, this thesis introduces an innovative imaging and analysis platform for clinical level screening of blood-related pathologies and drug-induced cytotoxic effect. As digital diagnostic hematology continues to progress, our point-of-care analytical tool could help develop tailored therapeutic approaches to optimize treatment outcomes and to mitigate the risk of drug-related side effects, thereby playing a pivotal role in advancing the field of personalized medicine.