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. 2022 Jul 1;20:227. doi: 10.1186/s12916-022-02415-z

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — The four main aspects of our analysis (A–D). A Using paired t-tests, we compared levels/abundance (log2 RFUs) of proteins between the uterine vein (UV) and radial artery (RA) in 4-vessel samples to define placental uptake and release on the maternal side of the placenta. B A machine learning algorithm (the variable selector elastic net with stability selection) identified placenta-derived proteins that can predict gestational age, a “placental proteomic clock.” C We performed another set of comparisons to define placenta-specific released proteins (AV = antecubital vein). D Placenta-specific released proteins (based on C) were further characterized by the gestational changes in maternal protein levels (log2 RFU) using samples from the STORK cohort. Created with BioRender.com