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Data from: Identifying extracellular vesicles from single cells


Dittrich, Petra; Nikoloff, Jonas; Kling, André; Saucedo-Espinosa, Mario (2021), Data from: Identifying extracellular vesicles from single cells, Dryad, Dataset,


Extracellular vesicles (EVs) are constantly secreted from both eukaryotic and prokaryotic cells. EVs, including those referred to as exosomes, may have an impact on cell signaling and an incidence in diseased cells. In this manuscript, a platform to capture, quantify, and phenotypically classify the EVs secreted from single cells is introduced. Microfluidic chambers of about 300 pL are employed to trap and isolate individual cells. The EVs secreted within these chambers are then captured by surface-immobilized monoclonal antibodies (mAbs), irrespectively of their intracellular origin. Immunostaining against both plasma-membrane and cytosolic proteins was combined with highly sensitive, multi-color total internal reflection fluorescence microscopy (TIRFM) to characterize the immobilized vesicles. The data analysis of high-resolution images allowed the assignment of each detected EV to one of 15 unique populations, and demonstrated the presence of highly-heterogeneous phenotypes even at the single-cell level. The analysis also revealed that each mAb isolates phenotypically-different EVs, and more vesicles were effectively immobilized when CD63 was targeted instead of CD81. Finally, we demonstrate how an heterogeneous suppression in the secreted vesicles is obtained when the enzyme neutral sphingomyelinase is inhibited.


Images of immobilized extracellular vesicles on a microfluidic device were recorded by use of multi-color total internal fluorescence microscopy (TIRFM) as described in the manuscript. Images were analyzed by a Matlab script as described in the data file "live_script_01.pdf".

The compressed file "figure-excel-files" contains the results used to create the final graphs in figures 3, 4, 5 and 6.

The compressed file "matlab_analysis_script" contains the image analysis file and example figures.


European Research Council, Award: 681587