Photodynamic therapy (PDT), wherein light sensitive non-toxic agents are locally and selectively activated using light, has emerged as an appealing alternative to traditional cancer chemotherapy. Yet to date, PDT efficacy has been mostly characterized using 2D cultures. Compared to 2D cultures, 3D sphere culture generates unique spatial distributions of nutrients and oxygen for the cells that better mimics the in-vivo conditions. Using a novel polyHEMA (non-adherent polymer) fabrication process, we developed a microfluidic sphere formation platform that can (1) generate 1,024 uniform (size variation <10%) cancer spheres within a 2 cm by 2 cm core area, (2) culture spheres for more than 2 weeks and (3) allow the retrieval of spheres. Using the presented platform, we have successfully characterized the different responses in 2D and 3D cell culture to PDT. Furthermore, we investigated the treatment resistance effect in cancer cells induced by tumor associated fibroblasts (CAF). Although the CAFs can enhance the resistance to traditional chemotherapy agents, no significant difference in PDT was observed. The preliminary results suggest that the PDT can be an attractive alternative cancer therapy, which is less affected by the therapeutic resistance induced by cancer associated cells.

We enriched migratory breast cancer cells with enhanced tumor formation and metastasis capability using microfluidics and performed single-cell RNA-sequencing to identify unique EMT and CSC signature of migratory cells.

DNA damage often induces heterogeneous cell-fate responses, such as cell-cycle arrest and apoptosis. Through single-cell RNA sequencing (scRNA-seq), we characterize the transcriptome response of cultured colon cancer cell lines to 5-fluorouracil (5FU)-induced DNA damage. After 5FU treatment, a single population of colon cancer cells adopts three distinct transcriptome phenotypes, which correspond to diversified cell-fate responses: apoptosis, cell-cycle checkpoint, and stress resistance. Although some genes are regulated uniformly across all groups of cells, many genes showed group-specific expression patterns mediating DNA damage responses specific to the corresponding cell fate. Some of these observations are reproduced at the protein level by flow cytometry and are replicated in cells treated with other 5FU-unrelated genotoxic drugs, camptothecin and etoposide. This work provides a resource for understanding heterogeneous DNA damage responses involving fractional killing and chemoresistance, which are among the major challenges in current cancer chemotherapy.

Isolation of tumor-initiating cells currently relies on markers that do not reflect essential biologic functions of these cells. We proposed to overcome this limitation by isolating tumor-initiating cells based on enhanced migration, a function tightly linked to tumor-initiating potential through epithelial-to-mesenchymal transition (EMT). We developed a high-throughput microfluidic migration platform with automated cell tracking software and facile recovery of cells for downstream functional and genetic analyses. Using this device, we isolated a small subpopulation of migratory cells with significantly greater tumor formation and metastasis in mouse models. Whole transcriptome sequencing of migratory versus non-migratory cells from two metastatic breast cancer cell lines revealed a unique set of genes as key regulators of tumor-initiating cells. We focused on phosphatidylserine decarboxylase (PISD), a gene downregulated by 8-fold in migratory cells. Breast cancer cells overexpressing PISD exhibited reduced tumor-initiating potential in a high-throughput microfluidic mammosphere device and mouse xenograft model. PISD regulated multiple aspects of mitochondria, highlighting mitochondrial functions as therapeutic targets against cancer stem cells. This research establishes not only a novel microfluidic technology for functional isolation of tumor-initiating cells regardless of cancer type, but also a new approach to identify essential regulators of these cells as targets for drug development.

Cell migratory direction and speed are predicted based on morphological features using computer vision and machine learning algorithms.