2026-10-11T11:30:38Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33904964 2026-09-17T17:38:59Z category_12 category_13 category_272 category_46 category_734 category_61 portal_5 item_type_3 month_year_09_2026

<p>Patient statistics.</p> Tae Lyun Ko (25038740) Jaesub Park (5452244) Dongju Leem (25038743) Junho Kim (758076) Jae won Han (25038746) Jin Sol Park (25038749) Sung Ki Lee (6889607) Hyojung Paik (4040747) Cell Biology Genetics Environmental Sciences not elsewhere classified Immunology Biological Sciences not elsewhere classified Developmental Biology underlying immunological mechanisms remain incompletely understood nominates candidate targets network centrality analysis computational drug repurposing based foundation model immune activation genes immune tolerance breakdown driven approach characterizes cell rna sequencing although immune tolerance maternal immune signatures maternal immune landscape rpl decidual tissues decidual tissues immune subsets fetal tolerance driven decoding complex landscape cell dysregulation associated signatures trimester controls transcriptional module scgpt ). rigorous origin pregnancy success population carrying performed single multifactorial condition identified cxcr4 euploid pregnancies employed genotype cellular drivers <div><p>Repeated pregnancy loss (RPL) is a multifactorial condition in which the underlying immunological mechanisms, particularly the disruption of maternal-fetal tolerance, remain incompletely understood. Although immune tolerance is critical for pregnancy success, the specific immune dysregulations contributing to RPL, particularly in euploid pregnancies, have been difficult to characterize. To address this, we performed single-cell RNA sequencing of decidual tissues from RPL patients and first-trimester controls. Our analysis initially revealed elevated expression of a transcriptional module of immune activation genes in RPL decidual tissues. To dissect the cellular drivers of this complex landscape, we employed genotype-based origin analysis coupled with a supervised machine learning model and a transformer-based foundation model (scGPT). This hierarchical approach prioritized maternal T cells over other immune subsets as the population carrying the most distinct and generalizable RPL-associated signatures. Through the convergence of computational drug repurposing, network centrality analysis, and a rigorous origin-controlled expression filtering strategy, we identified CXCR4 and JUN as druggable molecular candidates strongly associated with this T-cell dysregulation. Collectively, our machine learning–driven approach characterizes the maternal immune landscape of euploid RPL in the context of immune tolerance breakdown, and nominates candidate targets for future functional investigation.</p></div> 2026-09-17T17:59:39Z Dataset Dataset 10.1371/journal.pcbi.1014806.s006 https://figshare.com/articles/dataset/_p_Patient_statistics_p_/33904964 CC BY 4.0