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Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset


Stelzer, Ina et al. (2021), Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset, Dryad, Dataset,


Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10−40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10−7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.


Blood was collected into EDTA tubes, kept on ice, and centrifuged (1500 x g, 20 min) at 4 ̊C within 60 min. Separated plasma was stored at 80°C until further processing. The 200μL plasma samples were analyzed by the Genome Technology Access Center (St. Louis, MO) using a highly multiplexed, aptamer-based platform capturing 1310 proteins (SomaLogic, Inc., Boulder, CO). The assay quantifies proteins over a wide dynamic range (> 8 log) using chemically modified aptamers with slow off-rate kinetics (SOMAmer reagents). Each SOMAmer reagent is a unique, high-affinity, single-strand DNA endowed with functional groups mimicking amino acid side chains. In brief, samples were incubated on 96-well plates with a mixture of SOMAmer reagents. Two sequential bead-based immobilization and washing steps were used to eliminate nonspecifically-bound proteins, unbound proteins, and unbound SOMAmer reagents from protein target-bound reagents. After eluting SOMAmer reagents from the target proteins, the fluorescently-labeled reagents were quantified on an Agilent hybridization array (Agilent Technologies, Santa Clara, CA). Data were normalized in 4 specific steps and according to assay data quality control procedures defined in the good laboratory practice quality system of SomaLogic, Inc. Normalization steps control for signal intensity biases introduced by differential hybridization efficiencies and the overall brightness of plates, collection protocol artifacts, and batch effects between different plates.

Usage Notes

The data files contain the normalized plasma protein data, expressed as relative fluorescence units (RFU). Please refer to the ReadMe file ('Onset of labor proteomics') for additional information.


National Institutes of Health, Award: 2RM1HG00773506

Doris Duke Charitable Foundation, Award: 2018100

March of Dimes Foundation, Award: 22FY19343

American Heart Association, Award: 18IPA34170507

Evergreen State College Foundation, Award: STE2757/1-1

Stanford Maternal and Child Health Research Institute

Deutsche Forschungsgemeinschaft, Award: STE2757/1-1

NIH, Award: R01AG058417

NIH, Award: R01HL13984401

NIH, Award: R21DE02772801

NIH, Award: R61NS114926

NIH, Award: R01HL13984403

Gates Foundation, Award: OPP1189911

NIH, Award: 2RM1HG00773506

NIH, Award: R35GM138353

NIH, Award: R35GM137936