Extracellular vesicles (EVs) are lipid membrane enclosed nano-sized structures released into the extracellular environment by all cell types. EV constituents include proteins, lipids and nucleic acids that reflect the cell from which they originated. The molecular profile of cancer cells is distinct as compared to healthy cells of the same tissue type, and this distinct profile should be reflected by the EVs they release. This makes EVs desirable candidates for blood-based biopsy diagnosis of cancer. EVs can be time consuming to isolate therefore, a technology that can analyze EVs in complex biological samples in a high throughput manner is in demand. Here nanoscale flow cytometry is used to analyze EVs in whole, unpurified, plasma samples from healthy individuals and breast cancer patients. A known breast cancer marker, mammaglobin-a, was evaluated as a potential candidate for expression on EVs and increased levels in breast cancer. Mammaglobin-a particles were abundantly detected in plasma by nanoscale flow cytometry but only a portion of these particles were validated as bona fide EVs. EVs could be distinguish and characterized from small protein clusters and platelets based on size, marker composition, and detergent treatment. Mammaglobin-a positive EVs were characterized and found to be CD42a/CD41-positive platelet EVs, and the number of these EVs present was dependent upon plasma preparation protocol. Different plasma preparation protocols influenced the total number of platelet EVs and mammaglobin-a was found to associate with lipid membranes in plasma. When comparing plasma samples prepared by the same protocol, mammaglobin-a positive EVs were more abundant in estrogen receptor (ER) positive as compared to ER negative breast cancer patient plasma samples. This study demonstrates the capabilities of nanoscale flow cytometry for EV and small particle analysis in whole, unpurified, plasma samples, and highlights important technical challenges that need to be addressed when developing this technology as a liquid biopsy platform.