A Peripheral Immune Signature of Labor Induction

Abstract

Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8⁺ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.

Keywords: induction of labor; labor; machine learning; mass cytometry (CyTOF); parturition; pregnancy; systems immunology.

Figure 1

Experimental workflow and analytical approach. (A) Whole blood samples were collected at indicated time points (T1 to T5) from 15 women with term pregnancies undergoing induction of labor. (B) A mass cytometry immune-assay was employed to measure the frequency, and single-cell intracellular signaling activities in all major immune cell populations at basal level (unstimulated) and after 15 min stimulation with LPS. (C) The high-dimensional data set was used in a multivariate modeling approach to predict the time since induction (TSI) from the correlation relationship between all immune features across sampling time points. See also Table 1.

Figure 2

Systemic immune responses in maternal blood after labor induction. (A) Correlation network showing the relationships between immune features within and across mass cytometry data categories. Features and communities are annotated based on stimulation condition, and functional marker. (B–D) Correlation networks depicting immune feature changes 1 hr post-induction (no cervical change since admission, T2, teal), in the latent phase of labor (T3, yellow), and with active labor (T5, purple), compared to baseline (pre-induction) (T1). Node color indicates increase/decrease (red/blue) compared to baseline (T1). Node size represents p-value (adjusted for multiple comparisons) associated with paired (B) and unpaired (C, D) T tests compared to baseline (univariate comparisons). (E) Heatmap showing Z-scored fold change feature behavior across time points [post-induction (T2, teal), in the latent phase of labor (T3, yellow), and with active labor (T5, purple)] compared to baseline (T1). Shown are features significantly different from baseline (p < 0.05, Wilcoxon signed-rank test for T1 vs. T2 or rank-sum test for T1 vs. T3-5). See also Supplementary Figure 1 and Supplementary Table 2.

Figure 3

A regression model accurately predicts dynamic changes of the maternal immune response throughout the latent phase of labor. (A) Regression of predicted vs. true time since induction (TSI) derived from the LASSO model (Spearman R = 0.86, cross-validation, p-value = 6.7e-15, RMSE = 277 min, N = 15 patients). (B) Top informative feature ranking derived from occurrences in a bootstrap analysis with 1,000 iterations. (C) Correlation network of cross-validated LASSO model predicting TSI at time of sampling. Red color highlights features top informative for the prediction model. Blue features did not inform the model and were not included. Dot size indicates the bootstrap count per feature (square-rooted). Top model features selected by bootstrap analyses are circled. (D–G) Dynamics of individual top model features across time points during latent labor. See also Supplementary Figure 2 and Supplementary Table 3.

Figure 4

Differences in immune trajectories between complicated and uncomplicated labor. (A) Prediction deviation from the true TSI [residual error (predicted – true TSI)] stratified by absence (open circles) or presence (triangles) of labor complications. (B) PCA analysis identified two components that explain 36.2% (PC1) and 10.9% (PC2) of the variation across phases of latent labor, where complicated labor (triangles) majorly clusters along the axis of PC2. (C) Representative features among the top predictive features which follow different trajectories in patients with (triangles) or without (open circles) complications after the induction of labor (lines represent splines with 4 knots). See also Supplementary Table 3.