Surfaces that Adhesively Discriminate Breast Epithelial Cell Lines and Lymphocytes in Buffer and Human Breast Milk.

Abstract

We report new surface coatings that adhesively distinguish three breast epithelial cell lines (MCF-10A, MCF-7, and TMX2-28) when cell suspensions in buffer or breast milk are flowed over the coatings. We also report the selective capture of epithelial cells and rejection of Jurkat lymphocytes, with average selectivities exceeding 60 and captured cell purities often exceeding >99%. The surfaces achieve the dual goals of selective cell capture and resistance to fouling by proteins and other components of breast milk. The coatings do not rely on antibody targeting of cell surface markers but instead contain polycation chains embedded within a layer of end-tethered poly(ethylene glycol) (PEG) chains. The PEG, somewhat shielding the polycations, prevents surface fouling by proteins, nondesired cells, and other milk components, while the polycations produce electrostatic attractions that are heterogeneous on nanoscopic length scales. These electrostatic heterogeneities on the engineered coating, shown to produce curvature-selective particle capture in other studies, produce cell selectivity here. The ability of the engineered surfaces to discriminate these cell lines via an electrostatic driving force is remarkable, as the cells are of very similar surface charge as evidenced by their nearly identical ζ-potentials. The current surfaces, which likely distinguish cells based on their electrostatic surface landscape combined with other factors, adhesively distinguish cell lines that may differ only slightly in their expression of a surface marker, or cancer cells that minimally express EpCAM but which have different distributions of electrostatic charge on their surfaces. These surfaces are among the first to be documented for the compatibility of a polymer brush with human breast milk and may find use in technologies that capture cells from human breast milk or other complex fluids for cancer risk assessment.