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. 2019 Jun 20;9:8991. doi: 10.1038/s41598-019-45473-9

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Proteomic profiling of EV lysates and plasma. (a) Heatmaps of all detected proteins in EV lysates (n = 52) and plasma (n = 92) across all samples. Unit variance scaling is applied to proteins and individuals are clustered using Manhattan distance and average linkage. (b) Principal component analysis based on all proteins detected in EV lysates and plasma. Plots show the first two principal components and their relative contribution to overall variance. Prediction ellipses are such that with a probability 0.95, a new observation from the same group will fall inside the ellipse.