Research ArticleSYSTEMS IMMUNOLOGY

An immune clock of human pregnancy

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Science Immunology  01 Sep 2017:
Vol. 2, Issue 15, eaan2946
DOI: 10.1126/sciimmunol.aan2946
  • Fig. 1 Experimental workflow and analytical approach.

    (A) Eighteen women who delivered at term gestation were initially studied. Ten additional women were subsequently enrolled as a validation cohort. A whole-blood sample was obtained at three time points (early, mid, and late) during pregnancy and 6 weeks postpartum (PP). (B) Aliquots were either left unstimulated (to quantify cell frequency and endogenous intracellular signaling activity) or stimulated with a panel of receptor-specific ligands, including IFN-α, a cocktail of ILs containing IL-2 and IL-6 (IL), and LPS. Immune cells were stained with surface and intracellular antibodies and analyzed with mass cytometry. (C) The assay produced three categories of immune features, providing information about cell frequencies (Fq) measured in 24 manually gated immune cell subsets (purple bar), cell type–specific signaling capacity to respond to exogenous ligands (IFN-α, yellow bar; IL, red bar; LPS, green bar), and endogenous signaling activity (blue bar). The number of immune features contained within each data category is indicated in parentheses. The analyses used the variability in sample collection time to define a continuous variable (gestational age at time of sampling in weeks) distributed across the course of pregnancy (left, black circles).

  • Fig. 2 A prospectively validated csEN model accurately predicts dynamic changes of the maternal immune system over the course of pregnancy.

    (A) Correlation network revealing the relationships between immune features within and across mass cytometry data categories (Spearman’s coefficient). (B) Cell signaling–based penalization matrix that allowed prioritizing canonical, receptor-specific signaling responses (see Materials and Methods). Signaling responses amenable to prioritizing are highlighted in blue (endogenous), yellow (IFN-α), red (IL), or green (LPS). (C) Cross-validated csEN model predicting gestational age at time of sampling. Red/blue dots highlight model components that trended upward/downward during pregnancy. Dot size indicates the correlation between model component and gestational age (Spearman’s coefficient). (D and E) Training cohort. (D) csEN model prediction of the gestational age at time of sampling (R = 0.89, P = 2.2 × 10−16, n = 18, cross-validation). (E) Line plots depicting csEN model values for each patient during pregnancy and for the postpartum samples. Red lines and red shadow represent median and 95% confidence interval of EN components. (F and G) Validation cohort. (F) csEN model prediction of the gestational age at time of sampling (R = 0.62, P = 2.4 × 10−4, n = 10, Spearman’s coefficient). (G) Line plots depicting csEN model values for each patient (validation cohort).

  • Fig. 3 Comparison of the csEN algorithm to existing predictive methods and model reduction.

    (A and B) Comparison of the predictive power of existing algorithms for the estimation of gestational age at time of sampling in the training cohort (A) (n = 18) and the validation cohort (B) (n = 10). Algorithms included Support Vector Machine (SVM), EN, LASSO, randomForest, and k-nearest neighbors (KNN). (C) The dot plot depicts the number of csEN model components versus the P value of the csEN model for predicting gestational age. Red lines indicate the piece-wise regression fit for identification of a breakpoint indicating that 25 features are required for highest statistical stringency. (D) Location of the 25 features in the correlation network.

  • Fig. 4 csEN components reveal precisely timed cellular programs that characterize the dynamic changes of the peripheral immune system over the course of pregnancy.

    (A) The correlation network segregated into 20 communities containing correlated immune features that changed in synchronicity during pregnancy. (B) The 20 communities were annotated on the basis of immune feature attributes (cell subset, stimulation, or signaling property) most commonly represented within each community. (C) Communities containing csEN components most predictive of gestational age are highlighted (red dots) and shown in table S3. Community numbers are indicated in red. (D to I) The five communities containing the most informative EN components of the csEN model (see fig. S2 and table S4). Communities are represented on the left. Graphs on the right depict csEN components (black lines represent each patient; red lines and red shadow represent median and 95% confidence interval, n =18).

  • Fig. 5 Correlation between endogenous STAT5ab signaling and circulating plasma factors.

    (A) Heat map depicting the correlation between the plasma concentrations (relative fluorescence unit) of known activators of STAT5ab and immune features contained in community 7 (endogenous pSTAT5ab signaling in innate and adaptive cell subsets). Scale proportional to Spearman’s correlation P values (yellow indicates lower P values). (B to D) Correlations between the EN component “endogenous pSTAT5ab in naïve CD4+ T cells” and PRL (B), IL-3 (C), and IL-2 (D) plasma concentrations. The strongest correlation was observed between IL-2 and endogenous pSTAT5ab in naïve CD4+ T cells (R = 0.56, P = 5.4 × 10−8, n = 17). (E) Box plot depicting IL-2 plasma concentrations at each trimester (T1, T2, and T3) compared with their levels for the postpartum samples. IL-2 concentration increased during pregnancy and was significantly higher at T1, T2, and T3 compared with the postpartum time points (T1, P = 0.001; T2, P = 3.0 × 10−7; T3, P = 4.0 × 10−6, unpaired t test). Median, interquartile range, and 5th to 95th percentiles are shown.

  • Table 1 Patient and pregnancy characteristics.
    Training cohort
    (n = 18)
    Validation cohort
    (n = 10)
    Demographics
    Age (years, mean ± SD)31.9 ± 3.432.8 ± 5.1
    Body mass index (kg/m2,
    mean ± SD)
    23.8 ± 6.526.3 ± 2.2
    Gravity [median (interquartile
    range)]
    1.5 (1, 7)2 (1, 5)
    Parity (% nulliparous)5030
    Race (%)
      Caucasian15 (83)7 (70)
      Others (Asian, African, or
    American Indian)
    2 (11)3 (30)
      Unknown1 (6)0
    Ethnicity (% Hispanic)3 (11)2 (20)
    Married (%)14 (78)8 (80)
    Level of education (%)
      ≤High school2 (11)1 (10)
      >High school16 (89)9 (90)
    Current pregnancy parameters
    Gestational age at delivery
    (weeks, mean ± SD)
    38.9 ± 1.439.5 ± 1.3
    Mode of delivery (%)
      Spontaneous13 (72)5 (50)
      Cesarian section5 (18)5 (50)
    Birth weight (kg, mean ± SD)3.2 ± 0.43.7 ± 0.4
    Five-minute Apgar score [media
    (range)]
    9 (8–9)9 (8–9)

Supplementary Materials

  • immunology.sciencemag.org/cgi/content/full/2/15/eaan2946/DC1

    Materials and Methods

    Fig. S1. Gating strategy of immune cell subsets.

    Fig. S2. Reduced csEN model components.

    Fig. S3. Time-dependent changes in csEN model components are reflected across communities of interrelated immune features.

    Table S1. Antibody panel used for mass cytometry analysis.

    Table S2. Signaling responses prioritized in the signaling-based penalization matrix by a 5:1 margin.

    Table S3. Features excluded from the csEN model as compared with the non–signaling-based EN model.

    Table S4. Reduced csEN model components.

    References (4049)

  • Supplementary Materials

    Supplementary Material for:

    An immune clock of human pregnancy

    Nima Aghaeepour, Edward A. Ganio, David Mcilwain, Amy S. Tsai, Martha Tingle, Sofie Van Gassen, Dyani K. Gaudilliere, Quentin Baca, Leslie McNeil, Robin Okada, Mohammad S. Ghaemi, David Furman, Ronald J. Wong, Virginia D. Winn, Maurice L. Druzin, Yaser Y. El-Sayed, Cecele Quaintance, Ronald Gibbs, Gary L. Darmstadt, Gary M. Shaw, David K. Stevenson, Robert Tibshirani, Garry P. Nolan, David B. Lewis, Martin S. Angst, Brice Gaudilliere*

    *Corresponding author. Email: gbrice@stanford.edu

    Published 1 September 2017, Sci. Immunol. 2, eaan2946 (2017)
    DOI: 10.1126/sciimmunol.aan2946

    This PDF file includes:

    • Materials and Methods
    • Fig. S1. Gating strategy of immune cell subsets.
    • Fig. S2. Reduced csEN model components.
    • Fig. S3. Time-dependent changes in csEN model components are reflected across communities of interrelated immune features.
    • Table S1. Antibody panel used for mass cytometry analysis.
    • Table S2. Signaling responses prioritized in the signaling-based penalization matrix by a 5:1 margin.
    • Table S3. Features excluded from the csEN model as compared with the non? signaling-based EN model.
    • Table S4. Reduced csEN model components.
    • References (40–49)

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