Parametric control of collision rates and capture rates in geometrically enhanced differential immunocapture (GEDI) microfluidic devices for rare cell capture

Abstract

The enrichment and isolation of rare cells from complex samples, such as circulating tumor cells (CTCs) from whole blood, is an important engineering problem with widespread clinical applications. One approach uses a microfluidic obstacle array with an antibody surface functionalization to both guide cells into contact with the capture surface and to facilitate adhesion; geometrically enhanced differential immunocapture is a design strategy in which the array is designed to promote target cell–obstacle contact and minimize other interactions (Gleghorn et al., 2010; Kirby et al., 2012). We present a simulation that uses capture experiments in a simple Hele-Shaw geometry (Santana et al., 2012) to inform a target-cell-specific capture model that can predict capture probability in immunocapture microdevices of any arbitrary complex geometry. We show that capture performance is strongly dependent on the array geometry, and that it is possible to select an obstacle array geometry that maximizes capture efficiency (by creating combinations of frequent target cell–obstacle collisions and shear stress low enough to support capture), while simulatenously enhancing purity by minimizing non-specific adhesion of both smaller contaminant cells (with infrequent cell–obstacle collisions) and larger contaminant cells (by focusing those collisions into regions of high shear stress).

Enhancing sensitivity and specificity in rare cell capture microdevices with dielectrophoresis

“…We have adapted our previously reported simulations 28,29 to calculate cell transport within a microfluidic obstacle array caused by fluid advection and DEP forcing, predicting the cell trajectories in a range of geometries and applied electric fields. These trajectories are then used in a Monte Carlo simulation, informed by experimentally measured capture parameters, to calculate the probability of capturing different cells within each geometry.…”

“…Finally, a shear-and hF DEP i-dependent cell capture simulation was developed in MATLAB, based on the model that we have previously reported; 28 this model leverages our previous characterization experiments to inform cell adhesion parameters specific to each cell-antibody system. The model was incorporated into a Monte Carlo simulation and used to calculate the probability of capturing a given cell type with a particular geometry, DEP forcing, and antibody combination.…”

“…We have previously reported on a reduced-order model as an engineering tool for the optimization of rare cell capture microdevices; 28 here, we have adapted that model to include hF DEP i-dependent cell capture parameters. Briefly, we begin with an exponential model developed by Decuzzi and Ferrari 41 and used to study the capture of cancer cells in a microfluidic device by Wan et al 42 This model predicts P capture , the probability of capturing a given cell rolling along a surface, as…”

“…1, right inset), attracting our target cells using pDEP to those regions, where shear stress is low and the residence time is long; we have previously shown that this is where capture is most likely to occur. 28 The electric field magnitude is minimized at the obstacles' shoulder; although the resulting pDEP force repels our target cells, the high shear stress and short residence time minimize the role the shoulder region plays in capture. Likewise, cells experiencing nDEP are repelled from regions where capture is likely (i.e., the obstacles' leading edge) and attracted to regions where capture is unlikely (i.e., the obstacles' shoulder).…”

“…Once a cell is in contact with the antibodyfunctionalized obstacle surface, the probability that said contact results in cell capture is a function of the cell-antibody system, the distance and duration of contact, and the shear stress experienced by the cell (s). 28 We have previously reported particle advection and cell capture simulations that predict transport, collision behavior, and capture probability in these obstacle arrays due to fluid advection and immunocapture; 11,28,29 these simulations have been adapted to study a range of target cells. 30,31 In this study, we expand those existing numerical simulations to also include the effect of DEP forcing, identifying geometries and applied AC electric fields that are optimized for DEP-immunocapture.…”