Statistical Modeling of Single Target Cell Encapsulation

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems.Wallace H. Coulter Foundation (Young Investigator in Bioengineering Award)National Institutes of Health (U.S.) (Grant R01AI081534)National Institutes of Health (U.S.) (Grant R21AI087107

Living Bacterial Sacrificial Porogens to Engineer Decellularized Porous Scaffolds

Decellularization and cellularization of organs have emerged as disruptive methods in tissue engineering and regenerative medicine. Porous hydrogel scaffolds have widespread applications in tissue engineering, regenerative medicine and drug discovery as viable tissue mimics. However, the existing hydrogel fabrication techniques suffer from limited control over pore interconnectivity, density and size, which leads to inefficient nutrient and oxygen transport to cells embedded in the scaffolds. Here, we demonstrated an innovative approach to develop a new platform for tissue engineered constructs using live bacteria as sacrificial porogens. E.coli were patterned and cultured in an interconnected three-dimensional (3D) hydrogel network. The growing bacteria created interconnected micropores and microchannels. Then, the scafold was decellularized, and bacteria were eliminated from the scaffold through lysing and washing steps. This 3D porous network method combined with bioprinting has the potential to be broadly applicable and compatible with tissue specific applications allowing seeding of stem cells and other cell types

Assessment of stored red blood cells through lab-on-a-chip technologies for precision transfusion medicine

Abstract Transfusion of red blood cells (RBCs) is one of the most valuable and widespread treatments in modern medicine. Lifesaving RBC transfusions are facilitated by the cold storage of RBC units in blood banks worldwide. Currently, RBC storage and subsequent transfusion practices are performed using simplistic workflows. More specifically, most blood banks follow the “first-in-first-out” principle to avoid wastage, whereas most healthcare providers prefer the “last-in-first-out” approach simply favoring chronologically younger RBCs. Neither approach addresses recent advances through -omics showing that stored RBC quality is highly variable depending on donor-, time-, and processing-specific factors. Thus, it is time to rethink our workflows in transfusion medicine taking advantage of novel technologies to perform RBC quality assessment. We imagine a future where lab-on-a-chip technologies utilize novel predictive markers of RBC quality identified by -omics and machine learning to usher in a new era of safer and precise transfusion medicine