An integrated acoustofluidic droplet-sorting platform for classification of lymphocyte activity and functional phenotyping at single cell level

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Abstract The outcome of many pathological diseases such as infection and cancer is determined by the interaction of diseased cells with various immune cell subsets, both of which are phenotypically and functionally diverse. Induced resistance to chemo- and immuno-therapeutic drugs remain one of the main challenges in modern medicine. Moreover, there exists significant inter-patient and even intra-patient variability in response to well- established drug regimens, making it difficult to predict a patient's response to applied treatments. Single-cell analysis techniques have great potential in revealing, and ultimately utilizing, patient-specific cellular information to devise a more personalized approach to therapeutic regimens. Thus, developing a rapid screening system for assessing target-immune cell interactions, which are modulated by immunogenic treatments, will allow clinicians to predict the patient's response to a treatment, streamline treatment protocols and improve the efficacy of immunotherapeutic strategies in patient-specific manner. Here, we propose to develop an integrated microfluidic droplet based platform that permits quantitative analysis of the efficiency of immune reactions (responsive vs. non-responsive and fast vs. slow kinetics) and subsequent sorting and recovery of functionally distinct subsets for molecular (transcriptomic) characterization. We propose to integrate large-scale droplet microfluidic arrays with dual acoustofluidic sorters that achieve a level of throughput (>18,000 events/sec) and controllability (four-channel sorting) that no microfluidic droplet sorter has been able to achieve. Collectively, the sorters and the droplet docking arrays will allow for selection, enrichment and visualization of droplets of interest containing effector immune and target cells at different cell ratios, thereby allowing dynamic analysis of cell-cell interaction to identify predictive phenotypic classifiers. The sorted cells will then be analyzed by global transcriptomic profiling and unbiased systems biology approaches to identify the correlation between key molecular signatures and functional heterogeneity. This multifunctional analytical platform will further enable screening and validation of targeted therapies at single-cell level by sequentially exposing the encapsulated effector/target cells to first and second generation drugs via an integrated droplet merging junction. This approach will greatly enhance the identification of drug candidates prior to therapeutic administration to assist in disease - and patient-specific treatment decisions. We will validate our platform by functionally classifying lymphoma-Natural Killer (NK) cell interaction in the presence of approved anti-CD20 immunotherapy to (1) determine optimal immune activity required to achieve highly effective response to anti-CD20 immunotherapy; and (2) obtain critical insights into the development of resistance to anti-CD20 therapy in cancer. We envision this platform ultimately being employed in a variety of single-cell analysis applications and being of high value to the biomedical, and pharmaceutical research communities.