B cell non-Hodgkin lymphomas (bNHL) are the most common lymphoma subtype representing >85% of all NHLs. bNHL are typically treated the anti-CD20 antibody (e.g., rituximab) alone or in combination with chemotherapy. There are currently, however, no biological methods or markers to predict the sensitivity or resistance to rituximab (or any other) antibody therapy. A key feature of antibody activity occurs through natural killer (NK) cell-mediated killing of antibody-coated target tumor cells, however, antitumor activity and subsequent resistance, is poorly understood. In this application, we propose to develop and validate a high throughput droplet based microfluidic platform to investigate the key features of NK cells associated with rapid, slow or inactive tumor killing kinetics in NHL. We will first adapt a novel approach and integrate the biocompatible acoustofluidic droplet sorter during the droplet microarray formation to determine the phenotypes of immune-target cell interaction in microfluidic droplets. We will validate a droplet-based microfluidic device to interrogate single-cell dynamic responses and cell-cell interactions within intact droplets. Next, we will demonstrate a high-purity (>95%), high-throughput (>10,000 events/s), four-channel acoustofluidic droplet sorter to integrate with droplet analysis array. The downstream 4-channel sorting will allow, after establishing the kinetic profiles of interactions, to identify and sort droplets containing active lymphocytes into a distinctive pool; separate basal lymphocytes into another pool based on fluorescence. A unique function of selecting sorting criteria based on imaging analysis can be provided by the combination of droplet imaging array and acoustofluidic droplet sorters, which is unachievable for conventional fluorescence activated droplet sorters (FADS) since imaging tracking is inherently tricky in high-speed flow. Thus, our approach serves as a ?bottom-up? method of classification, by first identifying distinct functional categories and then probing the content of the individual cell category to determine the key factors for the molecular classification of heterogeneous immune functions of NK cells related to target cell kill. In addition, we will identify NK cell heterogeneity and bio-functional characteristics to discover novel drug combinations for NK cell dependent immunotherapy via an integrated acoustofluidic droplet sorting platform. We will demonstrate that the accuracy of phenotype identification of our device and its suitability for clinical applications by monitoring and classifying NK/NHL single cell interactions in the presence of monoclonal antibodies and performing biochemical secretome assay from ?hyperactive?, ?basal? and non-responsive pools. By combing these findings with drug screening and identification of phenotype altering drugs, we will demonstrate the applicability of this technology for personalized medicine and rational clinical immunotherapeutic applications. We envision our platform may be leveraged in a variety of single-cell analysis applications in immunotherapy and it will provide high value to the bioengineering, biomedical, and therapeutic research communities.
We envision our platform being employed in a variety of single-cell analysis applications and being of great value to the cancer, bioengineering, biomedical, and pharmaceutical research communities. The integrated acoustofluidic droplet-sorting platform developed in this proposal will be wide-spread in biomedical fields where working with single cells, pairs of cells, or multi-cellular assemblies in a precise, biocompatible, and high- throughput way is desirable. We will leverage these results to discover immunomodulatory drugs that could potentially switch NK phenotype from basal to hyperactive in cancer.
| Chen, Chuyi; Zhang, Steven Peiran; Mao, Zhangming et al. (2018) Three-dimensional numerical simulation and experimental investigation of boundary-driven streaming in surface acoustic wave microfluidics. Lab Chip 18:3645-3654 |
| Ozcelik, Adem; Rufo, Joseph; Guo, Feng et al. (2018) Acoustic tweezers for the life sciences. Nat Methods 15:1021-1028 |
