Efficient intracellular delivery of target macromolecules remains a major obstacle in cell engineering and other biomedical applications. We discovered a unique cell biophysical phenomenon of transient cell volume exchange by using microfluidics to rapidly and repeatedly compress cells. This behavior consists of brief, mechanically induced cell volume loss followed by rapid volume recovery. We harness this behavior for high-throughput, convective intracellular delivery of large polysaccharides (2000 kDa), particles (100 nm), and plasmids while maintaining high cell viability. Successful proof of concept experiments in transfection and intracellular labeling demonstrated potential to overcome the most prohibitive challenges in intracellular delivery for cell engineering.

Microfluidics are routinely used to study cellular properties, including the efficient quantification of single-cell biomechanics and label-free cell sorting based on the biomechanical properties, such as elasticity, viscosity, stiffness, and adhesion. Both quantification and sorting applications require optimal design of the microfluidic devices and mathematical modeling of the interactions between cells, fluid, and the channel of the device. As a first step toward building such a mathematical model, we collected video recordings of cells moving through a ridged microfluidic channel designed to compress and redirect cells according to cell biomechanics. We developed an efficient algorithm that automatically and accurately tracked the cell trajectories in the recordings. We tested the algorithm on recordings of cells with different stiffness, and showed the correlation between cell stiffness and the tracked trajectories. Moreover, the tracking algorithm successfully picked up subtle differences of cell motion when passing through consecutive ridges. The algorithm for accurately tracking cell trajectories paves the way for future efforts of modeling the flow, forces, and dynamics of cell properties in microfluidics applications.

Background: In systems biology, the dynamics of biological networks are often modeled with ordinary differential equations (ODEs) that encode interacting components in the systems, resulting in highly complex models. In contrast, the amount of experimentally available data is almost always limited, and insufficient to constrain the parameters. In this situation, parameter estimation is a very challenging problem. To address this challenge, two intuitive approaches are to perform experimental design to generate more data, and to perform model reduction to simplify the model. Experimental design and model reduction have been traditionally viewed as two distinct areas, and an extensive literature and excellent reviews exist on each of the two areas. Intriguingly, however, the intrinsic connections between the two areas have not been recognized. Results: Experimental design and model reduction are deeply related, and can be considered as one unified framework. There are two recent methods that can tackle both areas, one based on model manifold and the other based on profile likelihood. We use a simple sum-of-two-exponentials example to discuss the concepts and algorithmic details of both methods, and provide Matlab-based code and implementation which are useful resources for the dissemination and adoption of experimental design and model reduction in the biology community. Conclusions: From a geometric perspective, we consider the experimental data as a point in a high-dimensional data space and the mathematical model as a manifold living in this space. Parameter estimation can be viewed as a projection of the data point onto the manifold. By examining the singularity around the projected point on the manifold, we can perform both experimental design and model reduction. Experimental design identifies new experiments that expand the manifold and remove the singularity, whereas model reduction identifies the nearest boundary, which is the nearest singularity that suggests an appropriate form of a reduced model. This geometric interpretation represents one step toward the convergence of experimental design and model reduction as a unified framework.

Studying cell behavior within 3D material niches is key to understanding cell biology in health and disease, and developing biomaterials for regenerative medicine applications. Current approaches to study these cell-material niches are low throughput and can only analyze few replicates per experiment resulting in reduced measurement assurance and analytical power. Here we report 3D Material Cytometry (3DMaC), a novel high-throughput method based on microfabricated, shape-specific 3D cell-material niches and imaging cytometry. 3DMaC achieves rapid and highly multiplexed analyses of very high replicate numbers (“n” of 104-106) of 3D biomaterial constructs. 3DMaC overcomes current limitations of low “n”, low-throughput, “noisy” assays, to provide rapid, simultaneous analyses of potentially hundreds of parameters in 3D biomaterial cultures. The method is demonstrated here for a set of 85,000 events containing twelve distinct cell-biomaterial micro-niches along with robust, customized computational methods for high-throughput analytics with potentially unprecedented statistical power.