Studying cellular heterogeneity provides insights into the diverse cell populations within tumors, contributing to cancer progression, adaptability, and resistance to treatment. Single-cell genome sequencing is a powerful tool for revealing cell-to-cell heterogeneity in many complex biological systems and identifying stochastic genomic changes in time and position, including copy number variation (CNV), single-nucleotide variations (SNVs), and structural variations. However, tens to hundreds of single cells are not enough to discover rare variants that may evolve into significant disease progression. To address this challenge, we developed a high-throughput microfluidic strategy from cells to library to realize massive single-cell genome sequencing with high coverage and accuracy. In this project, you will play with advanced microfluidic controls, explore colorful molecular processes, and create beautiful gene maps.

Welcome to join the project. It would be great if you could have a basic knowledge of bioinformatics, chemistry, and biology.

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.

More details see here

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.