Human Plasma Proteome during Normal Pregnancy

Adi L. Tarca, Roberto Romero, Gaurav Bhatti, Francesca Gotsch, Bogdan Done, Dereje W. Gudicha, Dahiana M. Gallo, Eunjung Jung, Roger Pique-Regi, Stanley M. Berry, Tinnakorn Chaiworapongsa, Nardhy Gomez-Lopez

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The human plasma proteome is underexplored despite its potential value for monitoring health and disease. Herein, using a recently developed aptamer-based platform, we profiled 7288 proteins in 528 plasma samples from 91 normal pregnancies (Gene Expression Omnibus identifier GSE206454). The coefficient of variation was <20% for 93% of analytes (median 7%), and a cross-platform correlation for selected key angiogenic and anti-angiogenic proteins was significant. Gestational age was associated with changes in 953 proteins, including highly modulated placenta- and decidua-specific proteins, and they were enriched in biological processes including regulation of growth, angiogenesis, immunity, and inflammation. The abundance of proteins corresponding to RNAs specific to populations of cells previously described by single-cell RNA-Seq analysis of the placenta was highly modulated throughout gestation. Furthermore, machine learning-based prediction of gestational age and of time from sampling to term delivery compared favorably with transcriptomic models (mean absolute error of 2 weeks). These results suggested that the plasma proteome may provide a non-invasive readout of placental cellular dynamics and serve as a blueprint for investigating obstetrical disease.

Original languageEnglish
Pages (from-to)2687-2702
Number of pages16
JournalJournal of Proteome Research
Volume21
Issue number11
DOIs
StatePublished - Nov 4 2022

Keywords

  • aptamer
  • biomarker
  • machine learning
  • proteomic standards
  • single-cell RNA signature

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