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Bridging the dimensional gap from planar spatial transcriptomics to 3D cell atlases.
Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation demonstrates that SpatialZ accurately preserves cell identities, gene expression patterns and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells. This dense atlas enables new capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, 3D mapping of query tissue sections, and discovery of 3D spatial molecular architectures through new synthesized views. To demonstrate its extensibility beyond transcriptomics, we applied SpatialZ to imaging mass cytometry data from human breast cancer, successfully deciphering 3D spatial gradients within the tumor microenvironment. Our approach generates cell atlases that provide previously unattainable 3D resolution of spatial molecular landscapes.
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Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics.
Similarities between stem cells and cancer cells have implicated mammary stem cells in breast carcinogenesis. Recent evidence suggests that normal breast stem cells exist in multiple phenotypic states: epithelial, mesenchymal, and hybrid epithelial/mesenchymal (E/M). Hybrid E/M cells in particular have been implicated in breast cancer metastasis and poor prognosis. Mounting evidence also suggests that stem cell phenotypes change throughout the life course, for example, through embryonic development and pregnancy. The goal of this study was to use single cell RNA-sequencing to quantify cell state distributions of the normal mammary (NM) gland throughout developmental stages and when perturbed into a stem-like state in vitro using conditional reprogramming (CR). Using machine learning based dataset alignment, we integrate multiple mammary gland single cell RNA-seq datasets from human and mouse, along with bulk RNA-seq data from breast tumors in the Cancer Genome Atlas (TCGA), to interrogate hybrid stem cell states in the normal mammary gland and cancer. CR of human mammary cells induces an expanded stem cell state, characterized by increased expression of embryonic stem cell associated genes. Alignment to a mouse single-cell transcriptome atlas spanning mammary gland development from in utero to adulthood revealed that NM cells align to adult mouse cells and CR cells align across the pseudotime trajectory with a stem-like population aligning to the embryonic mouse cells. Three hybrid populations emerge after CR that are rare in NM: KRT18+/KRT14+ (hybrid luminal/basal), EPCAM+/VIM+ (hybrid E/M), and a quadruple positive population, expressing all four markers. Pseudotime analysis and alignment to the mouse developmental trajectory revealed that E/M hybrids are the most developmentally immature. Analyses of single cell mouse mammary RNA-seq throughout pregnancy show that during gestation, there is an enrichment of hybrid E/M cells, suggesting that these cells play an important role in mammary morphogenesis during lactation. Finally, pseudotime analysis and alignment of TCGA breast cancer expression data revealed that breast cancer subtypes express distinct developmental signatures, with basal tumors representing the most "developmentally immature" phenotype. These results highlight phenotypic plasticity of normal mammary stem cells and provide insight into the relationship between hybrid cell populations, stemness, and cancer.
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Evaluation of the impact of glycolysis-related gene signatures on prognosis and therapeutic targeting in lung adenocarcinoma.
Abnormal glycolysis is one of the hallmarks of cancer and plays a significant role in its progression. This study investigates the association between glycolysis genes and the progression of lung adenocarcinoma (LUAD). Utilizing various bioinformatics techniques, the research explores the heterogeneity of glycolysis genes in different LUAD cell types, identifies glycolysis-related prognostic signatures (GRPS). We obtained one training set for model construction from the Cancer Genome Atlas (TCGA) database, and also obtained four LUAD gene expression datasets as validation sets from the Gene Expression Omnibus (GEO) database. The single-cell RNA sequencing (scRNA seq) data also comes from the GEO database. Firstly, the "limma" R package was used to identify differentially expressed glycolysis related genes, and a machine learning computational framework composed of multiple combinations was used to preliminarily screen for glycolysis related prognostic markers (GRPS) in LUAD. Based on these GRPS, prognostic features were developed and validated through survival analysis, column chart development, and ROC curve analysis. The ssGSEA algorithm, ESTIMATE algorithm, and seven integrated computational algorithms from the TIMER 2.0 database were used to analyze the immune cell infiltration patterns of different risk groups. Analyze scRNA seq data to evaluate the distribution of GRPS and intercellular communication among various cell types, and further determine the core GRPS through the "hdWGCNA" and "ConstructNetwork" packages. In addition, we also evaluated the responsiveness of high and low-risk groups to 198 drugs using the "OncoPredict" software package. Result: We found that the glycolytic activity score of tumor tissue was significantly higher than that of normal tissue, and a total of 49 upregulated genes and 15 downregulated genes were selected from the total. Based on a machine learning computational framework, a total of 8 GRPS were screened, which constitute the prognostic features of LUAD patients. This feature demonstrates strong prognostic value, as confirmed by univariate and multivariate Cox regression analysis. Significant differences in tumor microenvironment (TME) immune infiltration were observed between high and low-risk groups. ScRNA seq revealed the distribution and expression of cell type specific GRPS, particularly in T cells, epithelial cells, and fibroblasts, while also revealing the strong cell-cell communication ability of the high GRPS group. The hdWGCNA analysis ultimately identified five core GRPS, namely DDIT4, FKBP4, CHPF, EFNA3, and B3GNT3. In addition, there are significant differences in sensitivity to most drugs between high-risk and low-risk cohorts, with WIKI4 and Lapatinib negatively correlated with risk scores, while Doramapimod and Niraparib positively correlated with risk scores. This study established a GRPS based risk feature for LUAD, demonstrating strong predictive power for prognosis assessment. The drug sensitivity results also provide drug guidance for the clinical application of this feature, all of which provide important clinical utility for the prognosis of LUAD. At the same time, the intercellular communication network was plotted based on the GRPS score, providing insights into the pathogenesis of LUAD and offering new ideas for developing targeted therapies and precision medicine methods.
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Multiomic analysis defines the first microRNA atlas across all small intestinal epithelial lineages and reveals novel markers of almost all major cell types.
MicroRNA-mediated regulation is critical for the proper development and function of the small intestinal (SI) epithelium. However, it is not known which microRNAs are expressed in each of the cell types of the SI epithelium. To bridge this important knowledge gap, we performed comprehensive microRNA profiling in all major cell types of the mouse SI epithelium. We used flow cytometry and fluorescence-activated cell sorting with multiple reporter mouse models to isolate intestinal stem cells, enterocytes, goblet cells, Paneth cells, enteroendocrine cells, tuft cells, and secretory progenitors. We then subjected these cell populations to small RNA-sequencing. The resulting atlas revealed highly enriched microRNA markers for almost every major cell type (https://sethupathy-lab.shinyapps.io/SI_miRNA/). Several of these lineage-enriched microRNAs (LEMs) were observed to be embedded in annotated host genes. We used chromatin-run-on sequencing to determine which of these LEMs are likely cotranscribed with their host genes. We then performed single-cell RNA-sequencing to define the cell type specificity of the host genes and embedded LEMs. We observed that the two most enriched microRNAs in secretory progenitors are miR-1224 and miR-672, the latter of which we found is deleted in hominin species. Finally, using several in vivo models, we established that miR-152 is a Paneth cell-specific microRNA.NEW & NOTEWORTHY In this study, first, microRNA atlas (and searchable web server) across all major small intestinal epithelial cell types is presented. We have demonstrated microRNAs that uniquely mark several lineages, including enteroendocrine and tuft. Identification of a key marker of mouse secretory progenitor cells, miR-672, which we show is deleted in humans. We have used several in vivo models to establish miR-152 as a specific marker of Paneth cells, which are highly understudied in terms of microRNAs.
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Peripheral lncRNA-IL1RAP Dysregulation in Schizophrenia: A Multi-Omics Bridge Between Immunity and Diagnosis.
BACKGROUND: Schizophrenia (SCZ) is frequently accompanied by peripheral immune dysregulation, yet robust and reproducible blood-based molecular markers remain limited. OBJECTIVE: We sought a compact long non-coding RNA (lncRNA) signal in peripheral blood leukocytes (PBL) and examined how it aligns with immune-linked transcriptional programs and cellular compartments, using bulk RNA sequencing (RNA-seq), targeted validation, and a healthy-donor peripheral blood mononuclear cell (PBMC) single-cell reference for localization. METHODS: PBL from 50 first-episode or unmedicated SCZ patients and 50 matched controls underwent RNA-seq. We performed reference-guided transcript annotation and generated lncRNA and mRNA count matrices. Differential expression was assessed with edgeR (TMM normalization). Candidate lncRNAs were prioritized using sample-level lncRNA-mRNA co-variation, followed by qRT-PCR validation in an independent cohort and evaluation with a two-feature logistic regression model using repeated 10-fold cross-validation. Pathway-scale analyses and weighted gene co-expression network analysis (WGCNA) summarized coordinated programs. Single-cell data from healthy donors were used for expression localization only, not case-control testing. RESULTS: We observed a small set of dysregulated lncRNAs alongside broader mRNA changes. Two candidates at the CCR3 and IL1RAP loci (TCONS_00134168/lncRNA-CCR3 and TCONS_00138311/lncRNA-IL1RAP) showed consistent case-control directionality and were supported by qRT-PCR. The two-lncRNA model showed strong internal discrimination (AUC = 0.933) but weaker, uncertain performance in a small external qRT-PCR set (AUC = 0.656). Enrichment analyses highlighted synapse-related annotations, RNA processing/translation, and immune signaling, with recurrent involvement of IL1RAP-linked IL-1 branches. WGCNA placed lncRNA-IL1RAP and IL1RAP within diagnosis-inversely associated co-expression programs, whereas lncRNA-CCR3 showed a more transcript-specific pattern. In the healthy-reference single-cell atlas, IL1B/IL1RAP/HSF1 signals were most prominent in the monocyte/macrophage compartment. CONCLUSION: Together, these findings support an exploratory two-lncRNA candidate marker concept, while underscoring the need for larger multi-center validation and targeted mechanistic follow-up without implying causality.
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A single-cell transcriptomic atlas of the periventricular proliferative zone in the late gestation fetal brain in the pigtail macaque.
BACKGROUND: The fetal brain undergoes rapid cellular and structural changes in late gestation, when waves of neurogenesis and gliogenesis shape cortical circuitry. The ventricular zone (VZ), subventricular zone (SVZ), periventricular white matter (PVWM), and deep white matter (DWM) are enriched in neuroprogenitor cells, newborn neurons, and interneurons, which are regions challenging to study in the third-trimester human fetal brain. The nonhuman primate (NHP) provides a powerful translational model to overcome this limitation, given its close similarity to human neurodevelopmental trajectories. The study objective was to construct a single-cell RNA-Seq (scRNA-Seq) atlas of the late-gestation fetal brain of the pigtail macaque (Macaca nemestrina), focusing on cells in the periventricular proliferative zone. METHODS: A sample of the lateral ventricular wall, subventricular zone, and overlying white/gray matter was dissociated into single cells and processed through 10X Genomics sequencing, SoupX removal of ambient RNA, empty droplet removal, doublet exclusion, and Seurat's pipeline which consists of aggregation, unsupervised clustering, and cluster annotation to create a single-cell RNA-Seq (scRNA-Seq) atlas. We also investigated developmental trajectories using scVelo and CellRank2. RESULTS: This analysis captured diverse populations of neuroprogenitors, newborn neurons, developing lineages of excitatory and inhibitory neurons, oligodendrocytes, astrocytes, epithelial and vascular cells. CONCLUSIONS: Single-cell populations from the third-trimester nonhuman primate fetal brain are highly similar to those in the human fetus at the level of major lineages. This late-gestation single-cell atlas of the periventricular proliferative zone provides a unique reference for progenitor, neuronal, glial, vascular, and immune cell states during a critical window of primate neurodevelopment, enabling mechanistic interrogation of how inflammatory, infectious, or hypoxic insults disrupt vulnerable neurogenic niches.
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Multi-Omics Identification of UBE2C as a Prognostic Biomarker and Therapeutic Target Linked to Topotecan Sensitivity in Cervical Cancer.
Objectives: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) necessitate the discovery of novel biomarkers for prognostic and therapeutic advancement. This study aims to evaluate the clinical significance of ubiquitin-conjugating enzyme E2C (UBE2C) and its association with the tumor microenvironment (TME) in CESC. Methods: We meticulously sourced CESC data from renowned repositories such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO), leveraging cutting-edge techniques including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and pharmacogenomics. Through multifaceted data analysis, we endeavored to unravel the intricate role and potential value of UBE2C in CESC tumorigenesis and progression. Results: Analysis of public datasets confirms UBE2C elevation in CESC tumors, correlating with advanced stages, metastasis, and poor disease-free survival (DFS). Dependency screens and functional enrichment highlight UBE2C's critical role in cell viability and DNA replication. Notably, multi-omics and spatial transcriptomics reveal a strong link between UBE2C expression and macrophage infiltration (CD63+) in tumor regions. Finally, pharmacogenomic profiling and molecular docking identified Topotecan as a potent therapeutic agent with high UBE2C binding affinity. Conclusion: In conclusion, UBE2C expression is associated with cervical cancer progression and correlates with an immunosuppressive macrophage-enriched microenvironment, making it a promising candidate for further investigation in therapeutic intervention.
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Prognostic value and potential mechanism of cellular senescence and tumor microenvironment in hepatocellular carcinoma: Insights from bulk transcriptomics and single-cell sequencing analysis.
The high mortality rate and postoperative recurrence of hepatocellular carcinoma (HCC) contribute to the burden on society and healthcare. The prognostic value and underlying mechanisms of cellular senescence and tumor microenvironment (TME) in HCC remain unclear. Bulk transcriptomic data were obtained from 368 HCC samples in The Cancer Genome Atlas-liver hepatocellular carcinoma cohort and 64 samples from the GSE116174 dataset. Single-cell RNA sequencing (scRNA-seq) data of HCC were obtained from the GSE149614 dataset, including 18 tumor samples from 10 patients. Prognosis-related cellular senescence genes and immune cells were identified through univariate analysis. Least absolute shrinkage and selection operator regression analysis was performed to construct the CellAge score and TME score, both of which were identified as independent prognostic factors for HCC based on multivariate Cox analysis. The combined CellAge and TME scores showed improved prognostic stratification for HCC patients, as confirmed by multivariate Cox analysis (p < .001). The gene set enrichment analysis (GSEA) revealed enrichment of the extracellular matrix receptor interaction signaling pathway in the group with high CellAge scores and low TME scores, which exhibited a worse prognosis. Single-cell sequencing results revealed higher expression activity of the cAMP response element modulator (CREM) extended transcription factor in HCC cells and most immune cells, indicating its involvement in TME remodeling. Finally, the tumor immune dysfunction and exclusion (TIDE) analysis demonstrated that the combined scores could predict the outcomes of immune therapy in patients with HCC. In conclusion, cellular senescence contributes to TME remodeling in HCC, and the developed CellAge and TME scores serve as independent prognostic factors and predictors of immune therapy in HCC.
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Single-cell exploration of gonadal somatic cell lineage specification during human sex determination.
Gonad development is an exciting model to study cell fate commitment. A better understanding of sex determination requires the identification of all involved cell types and their dynamic expression programs. Here, we present an atlas of 128,000 single cells from human gonads between 5 and 12 post-conceptional weeks. A focused analysis of somatic cells uncovered a population of bipotential progenitors derived from the coelomic epithelium of both testes and ovaries, which may have the capacity to commit to either a steroidogenic or a supporting fate. Moreover, our analyses suggest that early supporting cells, prior to differentiation into Sertoli or pre-granulosa cells, also give rise to the rete testis/ovarii and that the ovary retains the capacity to feed the supporting cell pool for an extended period of time, directly from the surface epithelium. Finally, the potential involvement of the gonadotropin releasing hormone (GnRH) signaling pathway in regulating testis differentiation was assessed ex vivo.
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Integrative multi-omics reveals the POSTN+ CAF-APOE+ macrophage axis drives immunosuppression and progression in prostate cancer.
BACKGROUND: The progression of prostate cancer to lethal castration-resistant (CRPC) and metastatic (mCRPC) stages is driven by a profoundly remodeled tumor microenvironment (TME). However, the identity of key stromal cell populations, their developmental dynamics, and their precise crosstalk with immune cells remain incompletely understood, limiting our ability to target the TME therapeutically. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data from 222,529 cells across 10 studies, encompassing normal prostate, primary tumors, CRPC, and mCRPC. Fibroblast heterogeneity was resolved using unsupervised clustering, trajectory inference (Monocle2), and regulon analysis (pySCENIC). Intercellular communication was deciphered using CellChat. Spatial transcriptomic data were integrated via CellTrek and SpaGene for validation. Clinical associations were evaluated in multiple bulk transcriptomic cohorts (e.g., TCGA-PRAD, IMvigor210) and extended to a pan-cancer atlas of 11 tumor types. RESULTS: We identified a distinct POSTN+ CAF subset that was progressively enriched in advanced disease. Trajectory analysis positioned POSTN+ CAFs as a progenitor-like state potentially transitioning toward inflammatory or contractile subtypes, with STAT1-centered regulon activity associated with this process. POSTN+ CAFs created an extracellular matrix (ECM)-remodeled, immune-excluded niche, correlated with elevated T-cell exclusion scores. We further uncovered a predicted communication axis where POSTN+ CAFs are computationally inferred to interact with M2-like, immunoregulatory APOE+ macrophages via the MDK-NCL ligand-receptor pair. The co-occurrence of POSTN+ cancer-associated fibroblasts and APOE+ macrophages correlated with worse patient outcomes. In the IMvigor210 urothelial carcinoma cohort (as indirect cross-cancer evidence), high co-infiltration was associated with diminished anti-PD-L1 response, although this finding requires validation in prostate cancer-specific cohorts. Exploratory pan-cancer analysis suggested conserved enrichment and adverse prognostic impact of this stromal-immune axis across diverse tumor types, though tissue-specific context should be considered. CONCLUSIONS: Our integrated single-cell atlas defines a critical POSTN+ CAF-APOE+ macrophage unit that is associated with a fibrotic and immunosuppressive TME in advanced prostate cancer. If functionally validated, targeting this stromal-immune crosstalk axis could represent a promising therapeutic strategy to remodel the TME and overcome treatment resistance, although this remains speculative at present.