Deconvolution of a Large Cohort of Placental Microarray Data Reveals Clinically Distinct Subtypes of Preeclampsia

Abstract

It has been well established that the dysfunctional placenta plays an important role in the pathogenesis of preeclampsia (PE), a hypertensive disorder in pregnancy. However, it is not well understood how individual cell types in the placenta are involved in placenta dysfunction because of limited single-cell studies of placenta with PE. Given that a high-resolution single-cell atlas in the placenta is now available, deconvolution of publicly available bulk PE transcriptome data may provide us with the opportunity to investigate the contribution of individual placental cell types to PE. Recent benchmark studies on deconvolution have provided suggestions on the strategy of marker gene selection and the choice of methodologies. In this study, we experimented with these suggestions by using real bulk data with known cell-type proportions and established a deconvolution pipeline using CIBERSORT. Applying the deconvolution pipeline to a large cohort of PE placental microarray data, we found that the proportions of trophoblast cells in the placenta were significantly different between PE and normal controls. We then predicted cell-type-level expression profiles for each sample using CIBERSORTx and found that the activities of several canonical PE-related pathways were significantly altered in specific subtypes of trophoblasts in PE. Finally, we constructed an integrated expression profile for each PE sample by combining the predicted cell-type-level expression profiles of several clinically relevant placental cell types and identified four clusters likely representing four PE subtypes with clinically distinct features. As such, our study showed that deconvolution of a large cohort of placental microarray provided new insights about the molecular mechanism of PE that would not be obtained by analyzing bulk expression profiles.