Small extracellular vesicles (sEVs, < 200nm) have been found in bodily fluids such as plasma, saliva, and urine, and hold immense potential as a diagnostic marker for cancer. By analyzing the molecular composition of sEVs, researchers can detect the presence of cancer and monitor its progression. This innovative diagnostic technique has the potential to transform cancer diagnosis by enabling the development of non-invasive or minimally invasive methods for detection, prognosis, and monitoring of cancer patients. To this end, we propose the development of a plasmofluidic chip capable of directly isolating sEVs from plasma, followed by the use of an integrated surface plasmon resonance-based sensor to detect cancer.

Building a cell from scratch has been a study hot point for decades. Microfluidics offers a potential way to build cell-like structures with specific functions. This could help people efficiently understand what happened in a cell. In this project, the student will play with the vesicle to build the artificial cell in steps. Different chip structures will be designed depending on applied physic fields. Simulation and experiment will be combined to get a deep understanding of building artificial cells.

To join this project, you should at least equipped with basic simulation skill, like Comsol. Also, basic chemistry and biology knowledge is required for this project. It would be better if you also have a strong background working on protein or lipids.

Microrobots manipulation by different physic fields have been widely studied by researchers in last decades, and it is still a hot point. The ability microrobots can be precisely controlled at microscopic scale has been demonstrated. However, how to scale up the fabrication of the microrobots and bring them close to the real application is still a problem. Microfluidics could be a solution, since it is born with the virtue to product tiny scale particles in high throughput. Here, we aim to combine magnetic microrobots with microfluidic fabrication to make them be able to be used for some applications. In this project, the student will learn how to fabricate this microrobot and develop the potential functions of them.

To join this project, you should know or have work experience on cells, basic simulations (molecular dynamics or magnetic field simulation). An strong ability in simulation or biology (protein/lipids) would be more helpful to this project.

What? Digital diagnostics are a relatively new technology where tens of thousands of independent tests are done in tiny constrained areas and read as either a true or false and then interpreted together using Poisson statistics to infer disease markers like viral load. They are ultra-sensitive and have lower limits of detection than other technologies.

Why? Diagnosis is a key step in the treatment process, and improving diagnostic technology has shown outsized positive effects in personal health and population health (a lesson underscored by the COVID-19 pandemic). However, recent gains require robust infrastructure and thus have disproportionately benefited developed countries.

How? This project maximizes impact by focusing on the translation of digital diagnostic techniques to paper-based microfluidic substrates for use in resource-constrained locations. Specifically, the focus is on the protein micropatterning to make the individual “compartments” which are key to the digital analysis. The student will extend work which was already done with streptavidin patterning to other proteins, testing both the pattern stamping parameters as well as the capacity of the deposited proteins to be functionalized for the biological assay.

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In this work, we propose using a microfluidic probe (MFP) to quantify heterogeneity in tissue sections by periodic sampling and spatial mapping of the tissue section.

Tumors, as all biological organisms, provide a wide range of variability in their structure and expression. This variability manifests itself in the macro scale – the morphology itself, and also in the micro-scale – the difference in molecular expression. These molecular variations are expressed as inter-tumor and intra-tumor heterogeneities. Traditional gold standard technique of tumor analysis – immunohistochemistry (IHC) provided an elegant staining method but is limited by being an end-point assay and is used to provide one data point for the whole tissue. Averaging out all heterogeneity information in the entire tissue section leads to loss of important diagnostic information. A recently developed workflow, called GeneScape (Voithenberg et. al., Small, 2021), allows localized analysis while preserving spatial information.

We propose to extend the workflow to parallelize sample collection and subsequent analysis. Adapting sample collection techniques to existing workflows will further allow easy acceptance and adoption of the proposed technique in general practice. The application of spatial information in tumor heterogeneity will be in basic research and clinical use to adapt tumor therapy based on molecular heterogeneity.