Nabhan, M. | bioRxiv Science

bioRxiv2024

Spatial proteomics and transcriptomics of placenta accreta spectrum

In severe Placenta Accreta Spectrum (PAS), trophoblasts gain deep access in the myometrium (placenta increta). This study investigated alterations at the fetal-maternal interface in PAS cases using a systems biology approach consisting of immunohistochemistry, spatial transcriptomics and proteomics. We identified spatial variation in the distribution of CD4+, CD3+ and CD8+ T-cells at the maternal-interface in placenta increta cases. Spatial transcriptomics identified transcription factors involved in promotion of trophoblast invasion such as AP-1 subunits ATF-3 and JUN, and NFKB were upregulated in regions with deep myometrial invasion. Pathway analysis of differentially expressed genes demonstrated that degradation of extracellular matrix (ECM) and class 1 MHC protein were increased in increta regions, suggesting local tissue injury and immune suppression. Spatial proteomics demonstrated that increta regions were characterised by excessive trophoblastic proliferation in an immunosuppressive environment. Expression of inhibitors of apoptosis such as BCL-2 and fibronectin were increased, while CTLA-4 was decreased and increased expression of PD-L1, PD-L2 and CD14 macrophages. Additionally, CD44, which is a ligand of fibronectin that promotes trophoblast invasion and cell adhesion was also increased in increta regions. We subsequently examined ligand receptor interactions enriched in increta regions, with interactions with ITG{beta}1, including with fibronectin and ADAMS, emerging as central in increta. These ITG{beta}1 ligand interactions are involved in activation of epithelial-mesenchymal transition and remodelling of ECM suggesting a more invasive trophoblast phenotype. In PAS, we suggest this is driven by fibronectin via AP-1 signalling, likely as a secondary response to myometrial scarring. Overall, this study suggests the biological processes leading to deep trophoblast invasion in the myometrium in placenta increta are as a result of upregulation of transcription factors and subsequent genes and proteins which promote trophoblast invasion. This occurs in a locally immune suppressed environment, with increased ECM degradation suggesting these findings are secondary to iatrogenic uterine injury. Significance statementPlacenta Accreta Spectrum (PAS) is a rare pregnancy complication, where the placenta fails to separate from the womb resulting in severe bleeding, which is associated with significant maternal morbidity and mortality. As Caesarean section rates increase, the incidence of PAS is increasing. The underlying pathophysiology of PAS is poorly understood. Here, we apply a spatial multi-omic approach to explore the biologic changes at the maternal-fetal interface in severe PAS (placenta increta). Using spatial transcriptomics and proteomics, we identified genes and proteins that are dysregulated in severe PAS involving processes such as extracellular matrix degradation, local immune suppression and promotion of epithelial-mesenchymal transition. This study provides new insights into the biological changes and underlying pathophysiology leading to placenta increta.

bioinformatics↗

bioRxiv2022

Small gene networks can delineate immune cell states and characterize immunotherapy response in melanoma

BackgroundSingle-cell sequencing studies have elucidated some of the underlying mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are difficult to implement as a general strategy or in a clinical diagnostic setting. In contrast, bulk RNAseq is now routine for both research and clinical applications. Therefore, our analysis extracts small transcription factor-directed co-expression networks (regulons) from single-cell RNA-seq data and uses them to deconvolute immune functional states from bulk RNA-seq data to characterize patient responses. MethodsRegulons were inferred in pre-treatment CD45+ cells from metastatic melanoma samples (n=19) treated with first-line ICI therapy (discovery dataset). A logistic regression-based classifier identified immune cell states associated with response, which were characterized according to differentially active, cell-state specific regulons. The complexity of these regulons was reduced and scored in bulk RNAseq melanoma samples from four independent studies (n=209, validation dataset). Patients were clustered according to their regulon scores, and the associations between cluster assignment, response, and survival were determined. Intercellular communication analysis of cell states was performed, and the resulting effector genes were analyzed by trajectory inference. ResultsRegulons preserved the information of gene expression data and accurately delineated immune cell phenotypes, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells, were associated with therapeutic responses in the discovery dataset. The cell states were characterized by seven differentially active and specific regulons that showed low specificity in non-immune cells. Four clusters with significantly different response outcomes (P <0.001) were identified in the bulk RNAseq validation cohort. An intercellular link between exhausted T cells and monocyte lineage cells was established, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. Analysis of ligand - receptor expression suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. ConclusionsRegulon-based characterization of cell states provides robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

immunology↗