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.

Cancer heterogeneity is a notorious hallmark of this disease, and it is desirable to tailor effective treatments for each individual patient. Drug combinations have been widely accepted in cancer treatment for better therapeutic efficacy as compared to a single compound. However, experimental complexity and cost grow exponentially with more target compounds under investigation. The primary challenge remains to efficiently perform a large‐scale drug combination screening using a small number of patient primary samples for testing. Here, a scalable, easy‐to‐use, high‐throughput drug combination screening scheme is reported, which has the potential of screening all possible pairwise drug combinations for arbitrary number of drugs with multiple logarithmic mixing ratios. A “Christmas tree mixer” structure is introduced to generate a logarithmic concentration mixing ratio between drug pairs, providing a large drug concentration range for screening. A three‐layer structure design and special inlets arrangement facilitate simple drug loading process. As a proof of concept, an 8‐drug combination chip is implemented, which is capable of screening 172 different treatment conditions over 1032 3D cancer spheroids on a single chip. Using both cancer cell lines and patient‐derived cancer cells, effective drug combination screening is demonstrated for precision medicine.

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.

Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a process elevating cell migration and metastasis. Marker-based approaches are limited by inconsistences among patients, types of cancer, and partial EMT states. Alternatively, we analyzed cancer cell migration behavior using computer vision. Using microfluidic single-cell migration chip and high-content imaging, we extracted morphological features and recorded migratory direction and speed of breast cancer cells. By applying Random Decision Forest (RDF) and Artificial Neural Network (ANN), we achieved over 99% accuracy for cell movement direction prediction and 91% for speed prediction. Unprecedentedly, we identified highly motile cells and non-motile cells based on microscope images and machine learning model, and pinpointed and validated morphological features determining cell migration, including not only known features related to cell polarization but also novel ones that can drive future mechanistic studies. Predicting cell movement by computer vision and machine learning establishes a ground-breaking approach to analyze cell migration and metastasis.

Considerable evidence suggests that cancer stem-like cells (CSCs) are critical in tumor pathogenesis, but their rarity and transience has led to much controversy about their exact nature. Although CSCs can be functionally identified using dish-based tumorsphere assays, it is difficult to handle and monitor single cells in dish-based approaches; single cell-based microfluidic approaches offer better control and reliable single cell derived sphere formation. However, like normal stem cells, CSCs are heavily regulated by their microenvironment, requiring tumor-stromal interactions for tumorigenic and proliferative behaviors. To enable single cell derived tumorsphere formation within a stromal microenvironment, we present a dual adherent/suspension co-culture device, which combines a suspension environment for single-cell tumorsphere assays and an adherent environment for co-culturing stromal cells in close proximity by selectively patterning polyHEMA in indented microwells. By minimizing dead volume and improving cell capture efficiency, the presented platform allows for the use of small numbers of cells (<100 cells). As a proof of concept, we co-cultured single T47D (breast cancer) cells and primary cancer associated fibroblasts (CAF) on-chip for 14 days to monitor sphere formation and growth. Compared to mono-culture, co-cultured T47D have higher tumorigenic potential (sphere formation rate) and proliferation rates (larger sphere size). Furthermore, 96-multiplexed single-cell transcriptome analyses were performed to compare the gene expression of co-cultured and mono-cultured T47D cells. Phenotypic changes observed in co-culture correlated with expression changes in genes associated with proliferation, apoptotic suppression, tumorigenicity and even epithelial-to-mesechymal transition. Combining the presented platform with single cell transcriptome analysis, we successfully identified functional CSCs and investigated the phenotypic and transcriptome effects induced by tumor-stromal interactions.