Microfluidic Platform for Profiling of Extracellular Vesicles from Single Breast Cancer Cells

Abstract

Extracellular vesicles (EVs) are considered as valuable biomarkers to discriminate healthy from diseased cells such as cancer. Passing cytosolic and plasma membranes before their release, EVs inherit the biochemical properties of the cell. Here, we determine protein profiles of single EVs to understand how much they represent their cell of origin. We use a microfluidic platform which allows to immobilize EVs from completely isolated single cells, reducing heterogeneity of EVs as strongly seen in cell populations. After immunostaining, we employ four-color total internal reflection fluorescence microscopy to enumerate EVs and determine their biochemical fingerprint encoded in membranous or cytosolic proteins. Analyzing single cells derived from pleural effusions of two different human adenocarcinoma as well as from human embryonic kidney (SkBr3, MCF-7 and HEK293, respectively), we observed that a single cell secretes enough EVs to extract the respective tissue fingerprint. We show that overexpressed integral plasma membrane proteins are also found in EV membranes, which together with populations of colocalized proteins, provide a cell-specific, characteristic pattern. Our method highlights the potential of EVs as a diagnostic marker and can be directly employed for fundamental studies of EV biogenesis.

Figure 1

Cells secrete a heterogeneous population of EVs, e.g., with different proteins in the membrane and lumen. In this study, we determine the phenotypic fingerprint of single-cell derived EVs from different breast cancer cells. Image created with BioRender.com.

Figure 2

Microfluidic platform for single-cell isolation and EV detection. (A) The three-layer microfluidic device comprises two pressure layers (gray, black) and a fluid layer (blue). (B) Circular cell chambers contain a central hydrodynamic cell trap, which is surrounded by two concentric pneumatic valves in the top (gray) and bottom pressure layer (black), which are separated by pillars. (C) Actuation of the pneumatic results in volumetric separation and allows for specific and spatially restricted surface coatings. (D) Micrograph of one chamber (scale bar: 100 μm). (E) Bright-field images of single trapped cells in the central chamber (black arrows). Occasionally, we have cell clusters of several cells in the chamber (right image, scale bar: 50 μm).

Figure 3

Workflow for detecting and identifying EV populations form single cells. (A) Using fluorescently conjugated mAbs and four-color TIRFM, we image immobilized EVs (B) in the direct neighborhood of the cells, which are prevented from cross-contamination with EVs from other cells. (C) Multicolor TIRFM images of secreted and immobilized EVs. (D) Customized Matlab imaging processing allows for quantifying and identifying EVs per image. Note that the histograms display raw data (detected signals per image).

Figure 4

EVs represent the phenotype of the cell of origin. (A) Immunocytochemical staining showing distinct expression profiles in SkBr-3, MCF-7, and HEK293 for HER2, of ERα, CD81, and HSP70. Scale bar 100 μm. (B) Dot plots depicting the absolute number of detected EVs per image when immobilized on anti-CD63 antibody. Separated by cell type, we detect EVs from wells with single, two, or three and more cells for all four investigated markers.

Figure 5

Phenotype analysis of EVs secreted from single cells. Data points (black) and resulting dot plots (in gray) depicting the absolute number of detected EVs, grouped by the cell type and the 15 possible phenotypes, when immobilized on anti-CD63 antibody.

Figure 6

Correlation analysis of analyzed markers on detected EV populations. Correlation analysis, showing a weak positive association of HSP70 to integral ERα. Overexpressed membrane proteins, like CD81, HER2, and ERα show negative correlation with most other proteins due to its abundant integration into membranes. Protein overexpression disguises protein colocalization in EV membranes.