Extracellular Vesicles (EVs), particularly exosomes, are of considerable interest as tumor biomarkers since tumor-derived EVs contain a broad array of information about tumor pathophysiology including its metabolic and metastatic status. However, current EV based assays cannot distinguish between EV biomarker changes by altered secretion of EVs during diseased conditions like cancer, inflammation, etc. that express a constant level of a given biomarker, stable secretion of EVs with altered biomarker expression, or a combination of these two factors. This issue was addressed by developing a nanoparticle and dye-based fluorescent immunoassay that can distinguish among these possibilities by normalizing EV biomarker level(s) to EV abundance, revealing average expression levels of EV biomarker under observation. In this approach, EVs are captured from complex samples (e.g. serum), stained with a lipophilic dye and hybridized with antibody-conjugated quantum dot probes for specific EV surface biomarkers. EV dye signal is used to quantify EV abundance and normalize EV surface biomarker expression levels. EVs from malignant (PANC-1) and nonmalignant pancreatic cell lines (HPNE) exhibited similar staining, and probe-to-dye ratios did not change with EV abundance, allowing direct analysis of normalized EV biomarker expression without a separate EV quantification step. This EV biomarker normalization approach markedly improved the ability of serum levels of two pancreatic cancer biomarkers, EV EpCAM, and EV EphA2, to discriminate pancreatic cancer patients from nonmalignant control subjects. The streamlined workflow and robust results of this assay are suitable for rapid translation to clinical applications and its flexible design permits it to be rapidly adapted to quantitate other EV biomarkers by the simple swapping of the antibody-conjugated quantum dot probes for those that recognize a different disease-specific EV biomarker utilizing a workflow that is suitable for rapid clinical translation.

Cellular heterogeneity is a key factor in various cellular processes as well as in disease development, especially associated with immune response and cancer progression. Cell-to-cell variability is considered to be one of the major obstacles in early detection and successful treatment of cancer. Most present technologies are based on bulk cell analysis, which results in averaging out the results acquired from a group of cells and hence missing important information about individual cells and their behavior. Understanding the cellular behavior at the single-cell level can help in obtaining a complete profile of the cell and to get a more in-depth knowledge of cellular processes. For example, measuring transmembrane fluxes oxygen can provide a direct readout of the cell metabolism.

The goal of this thesis is to design, optimize and implement a device that can measure the oxygen consumption rate (OCR) of live single cells. A microfluidic device has been designed with the ability to rapidly seal and unseal microchambers containing individual cells and an extracellular optical oxygen sensor for measuring the OCR of live single cells. The device consists of two parts, one with the sensor in microwells (top half) and the other with channels and cells trapped in Pachinko-type micro-traps (bottom half). When the two parts of the device are placed together the wells enclose each cell. Oil is flown in through the channels of the device to produce isolated and sealed microchamber around each cell. Different fluids can be flowed in and out of the device, alternating with oil, to rapidly switch between sealed and unsealed microenvironment around each cell. A fluorescent ratiometric dual pH and oxygen sensor is placed in each well. The thesis focuses on measuring changes in the oxygen consumption rate of each cell within a well. Live and dead cells are identified using a fluorescent live/dead cell assay. Finally, the technology is designed to be scalable for high-throughput applications by controlling the flow rate of the system and increasing the cell array density.