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///2018 Abstract Details
2018 Abstract Details2019-08-02T15:57:01-05:00

Identification of immunological events that predict the onset of spontaneous labor

Abstract Number: BP-2
Abstract Type: Original Research

Kazuo Ando M.D, PhD1 ; Xiayuan Han PhD2; Sajjad Ghaemi PhD3; Nima Aghaeepour PhD4; Brendan Carvalho MBBCh, FRCA5; Brice Gaudilliere M.D, PhD6

Introduction: Maintenance of pregnancy relies on finely tuned immunological mechanisms of feto-maternal tolerance that prevent premature rejection of the allogenic fetus. Our group recently reported on the precise timing of immunological adaptations during pregnancy using a high dimensional mass cytometry (CyTOF) approach (Science Immunology, 2017). The aim of this study was to combine the mass cytometry analysis of peripheral immune cell subsets with the high content analysis of circulating plasma factors to identify maternal immunological events that predict the onset of labor.

Methods: Eighteen women with spontaneous delivery at term (≥37 weeks of gestation) were enrolled this study (Fig. 1A). The frequency and intracellular signaling responses of all major innate and adaptive peripheral immune cells were analyzed using a 42-parameter CyTOF assay in whole-blood samples collected at 3 time points during pregnancy. Proteomics analysis of plasma samples collected concurrently was performed using the SomaLogic platform. A novel cell-signaling Elastic Net (csEN) algorithm was applied to derive an integrated cytomic and proteomic model predicting the time from sample collection to onset of labor.

Results: A total of 8,300 mass cytometry and plasma proteomic features were extracted from each blood sample including the frequencies and intracellular signaling activities all major innate and adaptive immune cell subsets as well as the concentration of over 7,000 circulating proteins (Fig. 1B). The csEN algorithm applied to the high-dimensional immunological dataset identified a multivariate model that predicted the onset of labor (R = 0.91, cross-validated p = 7.07×10-21, Fig. 1C). The most predictive components of the csEN model included the endogenous signal transducer and activator of transcription 5 (STAT5) activity in naive CD4+ T cells and the circulating factor Chorionic Somatomammotropin hormone 1 (CSH1).

Conclusion: This study demonstrates the utility of an integrated mass cytometry and plasma proteomic analysis of peripheral blood samples to accurately predict the onset of labor in a normal term pregnancy.The results provide the analytical framework for future prospective studies to identify immunological events associated with preterm onset of labor.



SOAP 2018