Human Plasma Proteome During Normal Pregnancy

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.

Keywords: aptamer; biomarker; machine learning; proteomic standards; single-cell RNA signature.

Figure 1:. Agreement between ELISA and SomaScan^® measurements for key angiogenic and anti-angiogenic proteins.

Protein abundance for 515 samples are shown, with one dot for each sample. The SomaScan relative fluorescence units in (log, base 2) (y-axis) is shown vs. ELISA based concentrations (log, base 2) (x-axis) for sFlt-1 (A), PlGF (B) and sEng (C). ρ is the Spearman’s correlation coefficient. Correlation test p<0.001 for all three proteins.

Figure 2:. Principal component analysis of 6,277 proteins and correlation with gestational age and maternal characteristics.

Protein abundance in relative fluorescence units (log, base 2) was analyzed using principal components (PC) analysis. The resulting principal components can be understood as meta-proteins. A) The % of variance explained by each principal component is shown as a scree plot. (B) The top 10 principal components were correlated with maternal age, parity, gestational age (GA) and body mass index (BMI). The heatmap shows Pearson correlation coefficients between PC and covariates (significance levels: *<0.05, **<0.01, ***<0.001). The correlation between PC4 (C) and PC6 (D) with gestational age is also shown, with each dot representing one sample. Similar correlations are shown for PC3 (E) and PC4 (F) in relation with BMI.

Figure 3:. Proteomic changes with advancing gestation in normal pregnancy.

The volcano plot (A) shows the significance (y-axis) vs. magnitude of change (x-axis) for each protein. Protein with significant modulation (adjusted p-value, q <0.1 and fold change >1.25, N=953 proteins) are shown in red. The names of a select set of most significant proteins are also displayed. The correlation between fold changes (log, base 2) from 10 weeks to 32 weeks of gestation and similar results based on Romero et al. 2017 study is shown for 255 proteins deemed significant in this study and profiled in Romero et al. 2017 using SomaScan^® platform v3. The gene symbols of the top increased and decreased proteins with concordant direction of change between studies are listed in the figure. The same is true for top three proteins with the most discordant fold change between studies.

Figure 4:. Maternal plasma proteomic trajectories throughout gestation.

The figure shows three clusters of proteins with steady increase (A), slow increase or decrease early in pregnancy followed by an increase later in pregnancy (B), and overall decreasing trend (C). The 50 proteins most representative of each cluster are shown, with names displayed representing corresponding gene symbols. See Supplementary File 2 for a depiction of the raw data used to derive the protein trajectories for selected proteins.

Figure 5:. Biological processes associated with maternal plasma protein modulation with gestational age.

The Venn diagram shows the overlap in biological processes significantly associated with differentially modulated proteins for each cluster illustrated in Figure 4. See also Supplementary File 3 for the full list of biological processes associated with gestational age modulation. The list of the top 10 biological processes (ranked by enrichment p-value) for each Venn diagram category is shown in a table.

Figure 6:. Maternal plasma proteomic trajectories of single-cell signatures throughout gestation.

For each placental single-cell signature the average Z-score of member proteins is shown as a function of gestational age. The gene symbols corresponding to each signature are: Synciotrophoblasts (KISS1, CSH1, TFPI2, CGA, GH2, PSG3, PSG2, PSG1, HOPX, CRH, GDF15, S100P, PSG11), B cells (CD79A, CD74, RPS5), Extravillous trophoblast (AOC1, PRG2, IGF2, NOTUM, FSTL3, FLT1, EBI3, PAPPA2, HPGD, HLA-G, PAPPA, ITM2B, KRT19, SERPINE2, MFAP5, HEXB, QSOX1, TPM1, TNFSF10), Stromal 1 (TIMP1, DLK1, COL3A1, COL1A1, TGFBI, IL1RL1, COL6A2, IGFBP3, DCN, COMP, SERPINE2, COL6A1), Stromal 2 (CXCL14, EGFL6, PTGDS, APOD, TCF21, DLK1, IGFBP3, COL3A1, PLA2G2A, COL1A1, C7, GPC3, LUM, CTHRC1, SERPINF1, RARRES2), hematopoietic stem cell (SPARCL1, ENPP2, EDN1, IGFBP7, CRIP2, A2M, SOCS3, ID1), Monocytes (S100A8, LYZ, S100A9, IL1B, S100A12, CXCL2, BCL2A1, CCL3, CCL20, CXCL3, G0S2, PLAUR, FCN1, SOD2, C15orf48, EREG, IL1RN), Decidual (IGFBP1, LUM, DKK1, IGFBP2, DCN, RBP1, IGFBP4, PRL, IGFBP5, HSD11B1, IGFBP6, CD248, TIMP3, CFD), Dendritic/Macrophage 1 (APOE, APOC1, CCL18, CD74, SPP1, C1QC, FTL, RNASE1, CXCL3, CTSZ), Cytotrophoblasts (PAGE4, CGA, TINAGL1, SPINT1, SPINT2, LDHB).

Figure 7:. Prediction of gestational age and of time from sample to delivery using proteomic data.

Prediction of gestational age (A) and of time from sample to a spontaneous term delivery (B) is shown. Each dot corresponds to a sample. Random forest predictions are obtained via cross-validation, in which, data from all samples of a given patient are left out when selecting predictor proteins and training the model. ρ: correlation coefficient.

Figure 8:. Functionality implemented in the SomaPreg package.

Sample annotation data paired with proteomic data (RFU) (5 proteins of 7288 are shown) are used as input (A) to determine the expected proteomic abundance (B) and to calculate multiple of the mean values (MoM) for gestational age and maternal characteristics (C).