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[Types of Preeclampsia According to Placental Single Cell RNA Sequencing, Type 1 and Type 2 Levels - Insights and Clinical Implications].
INTRODUCTION: Preeclampsia, one of the Great Obstetrical Syndromes, remains a major contributor to maternal and fetal morbidity worldwide. Its clinical heterogeneity, classically reflected in "early" versus "late" presentations, underscores the need for biologically grounded sub-classifications to guide diagnosis and management. OBJECTIVES: To characterize the cellular and molecular mechanisms underlying preeclampsia subtypes using advanced single-cell and single-nuclei transcriptomic approaches, and to evaluate their alignment with a proposed integrated model distinguishing Type I (placental dysfunction) from Type II (maternal cardiovascular maladaptation) preeclampsia. METHODS: We performed single-cell and single-nuclei RNA sequencing on approximately 90,000 placental cells, encompassing 46 distinct cell populations, including trophoblast, lymphoid, myeloid, stromal, and endothelial lineages. Comparative transcriptomic analyses were conducted between early-onset and late-onset preeclampsia and control pregnancies. RESULTS: Profound cellular dysregulation was identified in early-onset preeclampsia across multiple placental cell types, consistent with widespread placental dysfunction, angiogenic imbalance, inflammatory response and apoptosis. In contrast, late-onset preeclampsia exhibited comparatively subtle transcriptional changes, suggestive of a distinct pathophysiological process driven by maternal cardiovascular maladaptation, placental aging, cellular senescence and oxidative stress. These findings support the classification of preeclampsia into Type I (placental-dominant, early-onset) and Type II (maternal-dominant, late-onset) forms, each characterized by specific risk factors, biomarkers, and therapeutic targets. CONCLUSIONS: This placental molecular atlas integrates single-cell and single-nuclei transcriptomic data with clinical phenotypes, providing mechanistic support for the Type I and Type II preeclampsia framework. These insights inform the development of targeted interventions, including RNA-based therapies to modulate placental angiogenic pathways in early-onset disease, and maternal cardiovascular optimization strategies in late-onset disease.
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A spatiotemporal transcriptomic atlas of porcine (Sus scrofa) female early gonadal development.
The early gonads of mammals contain primordial germ cells (PGCs) and gonadal somatic cells. In females, the gonadal somatic cells promote the specification of PGCs into oogonia and their entry into meiosis, which is crucial for oogenesis. Although single-cell transcriptome sequencing technology holds significant advantages in cell type identification, its inability to resolve spatial positional information substantially hampers research on communication mechanisms between germ cells and adjacent somatic cells. Here, we utilized high-resolution spatial transcriptomic technology to dissect the spatial dynamics of various cell types during the specification of PGCs into oogonia and their entry into meiosis in porcine gonads. We clarified the spatial localization of two waves of granulosa cells in the supporting cell lineage and their roles in regulating germ cell development. Furthermore, we found that interstitial and endothelial cells were predominantly located in the medullary region of the early gonads. Notably, cell-cell communication analysis revealed the critical role of BMP signaling (BMP2, BMP4 and GDF5) in driving the specification of PGCs into oogonia and their subsequent entry into meiosis. Our study provides a spatially resolved understanding of the PGC-to-oogonia specification in vivo and offers critical resources for reconstituting oogenesis in vitro.
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PRECISE: A Prognostic Thyrocyte-Derived Gene Signature for Papillary Thyroid Carcinoma.
PURPOSE: Papillary thyroid carcinoma (PTC) exhibits heterogeneous behavior, underscoring the need for effective biomarkers and improved risk stratification methods. We developed a thyrocyte-derived gene signature, Prognostic RNA Expression Cell-specific Integrated Signature (PRECISE), using single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) and evaluated its prognostic utility across cohorts. EXPERIMENTAL DESIGN: PRECISE was developed using a discovery cohort of patients with PTC [MD Anderson Cancer Center (MDACC), n = 109, median follow-up = 14 years]. Within the cohort, 11 PTC tumors and 4 normal thyroid samples were successfully sequenced using snRNA-seq. Differentially expressed genes were integrated with previously identified thyrocyte-associated genes from scRNA-seq. Prognostic significance was assessed using bulk RNA-seq in the discovery cohort and validated in two additional cohorts [Vanderbilt University Medical Center (VUMC), n = 65; The Cancer Genome Atlas (TCGA), n = 370]. A rank-based single-sample method was used for score calculation. Associations between PRECISE and progression-free survival (PFS) and disease-specific survival (DSS) were evaluated using multivariate Cox models, and predictive models were compared using Harrell's C-statistic and likelihood ratio tests. RESULTS: PRECISE is comprised of 41 epithelial genes downregulated in PTC tumor cells. Higher PRECISE was significantly associated with shorter PFS across all three cohorts [MDACC: hazard ratio (HR) = 1.64, P = 0.002; VUMC: HR = 2.54, P < 0.001; TCGA: HR = 1.63, P = 0.012] and remained significant after tumor-node-metastasis stage adjustment in two cohorts with ≥5 years of follow-up [MDACC adjusted HR (aHR) = 1.42, P = 0.038; VUMC aHR = 2.12, P = 0.024]. PRECISE was also associated with DSS in these cohorts (MDACC: HR = 4.16, P < 0.001; VUMC: HR = 2.23, P = 0.010). Incorporating PRECISE significantly improved predictive performance for PFS and DSS beyond stage-based models. CONCLUSIONS: PRECISE is a thyroid epithelial gene signature with independent prognostic value in PTC.
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Decoding plant cell heterogeneity and dynamics across responses, development, to evolution with single-cell technologies.
Single-cell technologies are redefining plant cell identity. Traditional classifications based on position, morphology, and a few marker genes yielded static, coarse cell categories. In contrast, single-cell and single-nucleus RNA sequencing reveal hidden cellular heterogeneity and reconstruct developmental trajectories in ostensibly well-characterized plant tissues including vasculature and mesophyll. Environmental cues such as pathogen attack, drought, and wounding generate transient, spatially restricted cell states that bulk profiling masks, and these dynamics are best resolved by integrating single-cell data with spatial transcriptomics and live imaging. Comparative single-cell analyses extend these insights across evolution, revealing conserved core cell-type groups, lineage-specific innovations, and rapid transcriptomic rewiring in particular cell types. Emerging computational strategies mitigate orthology issues caused by genome duplications, enabling robust cross-species atlas alignment. These advances demonstrate that plant cell identity is dynamic, context-dependent, and distributed along continuous spectra. We argue that future frameworks should balance discrete cell-type labels with flexible state-based descriptions and integrate multiomic and spatial information to capture the full plasticity of plant cells, from ephemeral stress responses to millennial evolutionary changes.
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Single-cell transcriptomic and chromatin accessibility atlas of peripheral blood mononuclear cells reveals immune cell heterogeneity and breed-specific characteristics in Duroc and Meishan pigs.
BACKGROUND: Pig is an important model for biomedical studies, yet the immune cell heterogeneity, the subtle relationship between immune cells and the inter-breed immune variation in pigs remains poorly characterized. Here, we generate single-cell transcriptomes for the Peripheral Blood Mononuclear Cells (PBMCs) of two pig breeds with distinct immune traits including Duroc and Meishan pigs – with the latter have better immune capacity. RESULTS: Dozens of cell types are annotated, with γδ T lymphocytes making up one-fifth of all PBMCs. A list of putative novel markers is identified in each cell type, such as GATA3, SYTL3 and C9H1orf186 which are enriched in γδ T lymphocytes. The cellular composition and immune gene expression abundance are compared across breeds, showing that Meishan pigs have higher proportion of monocytes and their monocytes have enhanced expression of immune-related genes. Re-clustering analysis further identified the sub-populations of T, B and monocytes, with their inter-breed differences further characterized. We also generate single-cell chromatin maps for PBMCs of Duroc pigs and identify a list of transcription factors associated with each immune lineage. Comparison with human data demonstrates that while most cell populations and the corresponding markers match between pig and human, CD4+CD8+ DPTCs and γδ T cells are absent in human and one monocyte subset are absent in pigs. CONCLUSIONS: Overall, the present study examined the transcription levels and constructed the single-cell maps of blood immune cells between Duroc and Meishan pigs, which revealed the heterogeneity of immune cells in pigs and their differential characteristics among Duroc and Meishan pigs. This observation would be valuable for better understanding of the immune heterogeneity and immune traits in pigs.
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scInterpreter: a knowledge-regularized generative model for interpretably integrating scRNA-seq data.
BACKGROUND: The rapid emergence of single-cell RNA-seq (scRNA-seq) data presents remarkable opportunities for broad investigations through integration analyses. However, most integration models are black boxes that lack interpretability or are hard to train. RESULTS: To address the above issues, we propose scInterpreter, a deep learning-based interpretable model. scInterpreter substantially outperforms other state-of-the-art (SOTA) models in multiple benchmark datasets. In addition, scInterpreter is extensible and can integrate and annotate atlas scRNA-seq data. We evaluated the robustness of scInterpreter in a variety of situations. Through comparison experiments, we found that with a knowledge prior, the training process can be significantly accelerated. Finally, we conducted interpretability analysis for each dimension (pathway) of cell representation in the embedding space. CONCLUSIONS: The results showed that the cell representations obtained by scInterpreter are full of biological significance. Through weight sorting, we found several new genes related to pathways in PBMC dataset. In general, scInterpreter is an effective and interpretable integration tool. It is expected that scInterpreter will bring great convenience to the study of single-cell transcriptomics.
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Single-cell transcriptome analysis reveals the key genes associated with macrophage polarization in liver cancer.
BACKGROUND: The aim of this study was to reveal the key genes associated with macrophage polarization in liver cancer. METHODS: Data were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas databases (TCGA). R package Seurat 4.0 was used to preprocess the downloaded single-cell sequencing data, principal component analysis, and clustering. R package SingleR was used to annotate cell types and calculate macrophage polarization scores. Spearman correlation analysis was performed to obtain key genes highly correlated with macrophage polarization in liver cancer. The Tumor IMmune Estimation Resource algorithm was used to analyze the correlation between genes and the infiltration level of macrophages. Finally, the prognostic model was constructed based on 6 macrophage polarization-related genes by multivariate Cox regression analysis. Kaplan-Meier curves and receiver operating characteristic curves validated the prognostic value of the prognostic model. RESULTS: Two thousand highly variable genes were obtained after the normalization of single-cell profiles. In all, 16 principal components and 15 cell clusters were obtained. Monocytes and macrophages were the main immune cells in the microenvironment of liver cancer tissues. Macrophage polarization scores showed that cluster 5 had the highest degree of polarization. Spearman analysis yielded that a total of 6 key genes associated with macrophage polarization (CD53, TGFBI, S100A4, pyruvate kinase M, LSP1, SPP1), and Tumor IMmune Estimation Resource analysis showed that 6 key genes were significantly positively correlated with macrophage infiltration levels. The model constructed by 6 key genes could effectively evaluate the prognosis of patients with liver cancer. CONCLUSIONS: The key genes associated with macrophage polarization, namely CD53, TGFBI, S100A4, pyruvate kinase M, LSP1, and SPP1, may be potential therapeutic targets for liver cancer.
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Exploring phenotype-related single-cells through attention-enhanced representation learning.
BACKGROUND: Atlas-level single-cell investigations elucidate disease pathogenesis and progression. Accurate interpretation of phenotype-related single-cell data necessitates pre-defining cell subtypes and identifying their abundance variations. However, batch correction and clustering resolution biases can impact this interpretation. To overcome these challenges, an end-to-end integrative approach that combines both cell- and gene-level information is needed to more accurately connect single-cell characteristics to clinical phenotypes. METHODS: We developed scPhase, a deep learning framework using attention-based multiple instance learning (AMIL). It treats each patient sample as a bag of single cells, learning a comprehensive representation from their gene expression profiles. By incorporating a Mixture-of-Experts (MoE) aggregation layer, it predicts clinical phenotypes that generalize across patient cohorts. Furthermore, it includes an interpretability framework that uses cellular attention and gene attribution scores to pinpoint the key cell profiles that drive its predictions. RESULTS: We evaluated scPhase across diverse single-cell disease atlases, covering COVID-19 infection, aging, neurodegeneration, and oncology, using single-cell data from peripheral blood mononuclear cells (PBMCs), brain, and tumor tissues. The model consistently outperforms baselines in classifying diverse clinical phenotypes, achieving area under the curve (AUC) scores of 0.895 for COVID-19, 0.840 for Alzheimer’s disease, and 0.951 and 0.962 for lung and colorectal cancers. It shows robust performance in age regression with a Pearson correlation coefficient (PCC) of 0.87. The model’s interpretability framework effectively pinpointed clinically relevant cell populations, enhancing its utility in identifying disease-specific cellular signatures. CONCLUSIONS: scPhase offers an interpretable supervised learning framework for single-cell data, accurately predicting sample-level clinical phenotypes while uncovering key biological mechanisms. Furthermore, it can be readily adapted for broader atlas-level clinical phenotype analyses.
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Single-cell RNA sequencing reveals transcriptional profiles of endometrial epithelial remodeling and identifies candidate genes regulating glandular cell development in Chinese Meishan pigs.
The porcine endometrium undergoes dynamic cellular remodeling across the estrous cycle, yet epithelial heterogeneity and differentiation trajectories remain incompletely characterized. In this study, a single-cell transcriptomic atlas of the endometrial epithelium during the follicular and luteal phases in Meishan pigs was generated using high-resolution single-cell RNA sequencing. Canonical epithelial cell types were identified, including glandular epithelium (GE), secretory glandular epithelium (secretory GE), luminal epithelium (LE), ciliated epithelium, as well as an epithelial stem cell subpopulation marked by ALDH1A3 and LGR5 and a progenitor subpopulation marked by ALDH1A1. During the follicular phase, the epithelium was primarily composed of ALDH1A3+LGR5+ stem cells and ALDH1A1+ progenitor cells, whereas GE, secretory GE, and LE predominated during the luteal phase. Integrated trajectory inference and RNA velocity analyses suggested a continuous differentiation trajectory from ALDH1A3+LGR5+ stem cells through ALDH1A1+ progenitors toward GE. Gene expression patterns indicated that glandular epithelial formation was associated with activation of transcriptional regulatory programs and increased mitochondrial respiratory activity. In contrast, differentiation toward the secretory glandular epithelial lineage was characterized by early activation of stress-response pathways, secretion-related genes, and specific metabolic programs. Several transcription factors, including ZFP36L1, TRPS1, and KLF6, were associated with glandular differentiation, whereas MBD4, CREB3L4, ZNF524, SPDEF, USF1, XBP1, and MLX were predicted to contribute to glandular secretory function. These genes showed significant associations with sow reproductive traits in a phenome-wide association study. Overall, this study provides a single-cell framework for understanding epithelial dynamics in the porcine endometrium across the estrous cycle.
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Spatial transcriptomics in ovarian biology technologies: computational challenges, and biological insights.
In brief: This review synthesizes the technical landscape of current spatial transcriptomic platforms, addresses computational challenges unique to ovarian tissue, and surveys biological discoveries across ovarian development, aging, follicle dynamics, and cancer, providing a practical framework to guide platform selection and analytical strategy in reproductive biology. Abstract: The ovary is a structurally complex organ whose function depends on precisely coordinated interactions among multiple cell types. Spatially resolved transcriptomics (ST) has emerged as a powerful complement to single-cell RNA sequencing (scRNA-seq), enabling gene expression profiling within intact tissue and preserving the spatial context that dissociation-based methods inherently lack. This review provides a comprehensive overview of the major ST platforms, including sequencing-based technologies (Visium, Visium HD, Stereo-seq, and GeoMx) and imaging-based technologies (Xenium, MERSCOPE, and CosMx), with a focus on their distinct technical features, resolution trade-offs, and suitability for ovarian research. We survey 40 published studies applying ST to ovarian biology, spanning atlases, ovarian aging, follicle development and ovulation, and ovarian cancer. We also discuss typical computational analyses as well as their challenges specific to ovary, including cell segmentation of morphologically diverse cell populations, deconvolution of mixed-cell capture spots in sequencing-based platforms, quality control, batch correction, and spatially aware downstream analyses encompassing trajectory inference, cell-cell interaction modeling, gene regulatory network reconstruction, and more. Across these biological contexts, multimodal integration, pairing ST with scRNA-seq, spatial proteomics, or chromatin accessibility profiling, has proven increasingly valuable for resolving the full molecular complexity of ovarian biology. Nevertheless, some challenges persist, and no single platform is universally optimal for all research questions. Thoughtful alignment between biological objectives, tissue scale, and platform capability will be critical for advancing ST from descriptive mapping toward mechanistic and clinically translatable discovery.