A multi-dataset associative analysis of single-cell and bulk transcriptomic profiles in preeclampsia
Bei Liu, Xuena Cui, Xiaofei Wang, Min Zhao, Lin XuAbstract
Objectives
Preeclampsia is a multifactorial disease involving diverse pathological mechanisms and signaling pathways. However, the specific mechanism of PE is not yet completely elucidated.
Methods
The scRNA-seq dataset GSE173193 and the bulk transcriptomic dataset GSE74341 were obtained from the Gene Expression Omnibus and analyzed separately as complementary datasets. The single-cell dataset was used to explore cellular heterogeneity, inferred intercellular communication, pathway activity, candidate regulons, and transcriptional-state trajectories. The bulk dataset was analyzed using CIBERSORT, GSEA, and GSVA to estimate immune-cell fractions and assess their associations with candidate regulators. Cross-dataset findings were interpreted as complementary association-level evidence rather than direct validation.
Results
Within the analyzed datasets, macrophages/monocytes exhibited relatively high inferred communication activity. FOLR2 + macrophages and APOBEC3A + monocytes were identified as candidate subpopulations of interest, while EVX1 and KLF9 were identified as computationally predicted candidate regulators associated with immune and metabolic pathways. In the bulk transcriptomic dataset, CIBERSORT using the blood-derived LM22 reference matrix estimated higher fractions of dendritic cells and resting mast cells and a lower fraction of activated CD4 + memory T cells in the preeclampsia samples. These estimates may not fully represent specialized placental or decidual immune-cell populations and should not be interpreted as directly measured changes in placental immune-cell abundance. Given the limited number of biological samples and the lack of detailed clinical metadata, these observations should be regarded as exploratory dataset-level associations that may be influenced by inter-individual, gestational, or other clinical heterogeneity.
Conclusions
This study employed complementary analyses of independent single-cell and bulk transcriptomic datasets to identify exploratory immune-cell and transcriptional associations in preeclampsia. The identified cell populations and candidate regulators provide hypotheses for future investigation but require confirmation in independent cohorts and experimental studies.