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DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.
Differential network analysis has become an important approach in identifying driver genes in development and disease. However, most studies capture only local features of the underlying gene-regulatory network topology. These approaches are vulnerable to noise and other changes which mask driver-gene activity. Therefore, methods are urgently needed which can separate the impact of true regulatory elements from stochastic changes and downstream effects. We propose the differential network flow (DNF) method to identify key regulators of progression in development or disease. Given the network representation of consecutive biological states, DNF quantifies the essentiality of each node by differences in the distribution of network flow, which are capable of capturing comprehensive topological differences from local to global feature domains. DNF achieves more accurate driver-gene identification than other state-of-the-art methods when applied to four human datasets from The Cancer Genome Atlas and three single-cell RNA-seq datasets of murine neural and hematopoietic differentiation. Furthermore, we predict key regulators of crosstalk between separate networks underlying both neuronal differentiation and the progression of neurodegenerative disease, among which APP is predicted as a driver gene of neural stem cell differentiation. Our method is a new approach for quantifying the essentiality of genes across networks of different biological states.
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Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region.
The hypothalamus controls essential social behaviors and homeostatic functions. However, the cellular architecture of hypothalamic nuclei-including the molecular identity, spatial organization, and function of distinct cell types-is poorly understood. Here, we developed an imaging-based in situ cell-type identification and mapping method and combined it with single-cell RNA-sequencing to create a molecularly annotated and spatially resolved cell atlas of the mouse hypothalamic preoptic region. We profiled ~1 million cells, identified ~70 neuronal populations characterized by distinct neuromodulatory signatures and spatial organizations, and defined specific neuronal populations activated during social behaviors in male and female mice, providing a high-resolution framework for mechanistic investigation of behavior circuits. The approach described opens a new avenue for the construction of cell atlases in diverse tissues and organisms.
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A spatially resolved single-cell landscape of colorectal cancer liver metastasis reveals a stromal-tumor glycolytic signaling interaction.
BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer mortality, with liver metastasis being the principal determinant of poor prognosis, but the spatial mechanisms orchestrating metastatic niches remain elusive. METHOD: To dissect the molecular and spatial dynamics of CRC progression, we constructed an integrative atlas using 35 single-cell RNA-seq datasets and spatial transcriptomics from primary tumors, liver metastases, and matched normal tissues. Malignant epithelial subpopulations were stratified via inferCNV and CytoTRACE analyses. Stromal-tumor interactions were dissected using CellChat and NicheNet, with functional validation through in vitro co-culture and immunohistochemistry. RESULT: We identified a transcriptionally distinct epithelial subpopulation, termed high-malignancy CRC (High-M CRC), enriched in metastatic lesions and characterized by enhanced stemness, MYC-driven transcriptional activity, and glycolytic reprogramming. Stromal-tumor interaction analyses revealed that cancer-associated fibroblasts (CAFs), particularly matrix CAFs (mCAFs), promote malignant progression via the HGF-MET-MYC signaling axis. Spatial transcriptomic mapping confirmed the physical proximity and molecular co-localization of High-M CRC cells and mCAFs, along with enriched glycolysis and MYC expression at the cell-cell interface. In vitro functional validation demonstrated that CAF-derived HGF activates MET-MYC signaling in CRC cells, enhancing their invasion and proliferation-effects reversible by MET knockdown. CONCLUSION: We unveil a spatially organized metabolic niche driven by stromal-tumor HGF-MET-MYC signaling. These findings offer novel insights into the stromal-tumor interaction and suggest actionable targets for therapeutic intervention in CRC.
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Integrative multi-omic profiling of adult mouse brain endothelial cells and potential implications in Alzheimer's disease.
The blood-brain barrier (BBB) is primarily manifested by a variety of physiological properties of brain endothelial cells (ECs), but the molecular foundation for these properties remains incompletely clear. Here, we generate a comprehensive molecular atlas of adult brain ECs using acutely purified mouse ECs and integrated multi-omics. Using RNA sequencing (RNA-seq) and proteomics, we identify the transcripts and proteins selectively enriched in brain ECs and demonstrate that they are partially correlated. Using single-cell RNA-seq, we dissect the molecular basis of functional heterogeneity of brain ECs. Using integrative epigenomics and transcriptomics, we determine that TCF/LEF, SOX, and ETS families are top-ranked transcription factors regulating the BBB. We then validate the identified brain-EC-enriched proteins and transcription factors in normal mouse and human brain tissue and assess their expression changes in mice with Alzheimer's disease. Overall, we present a valuable resource with broad implications for regulation of the BBB and treatment of neurological disorders.
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Integrative multi-omics and Mendelian randomization analysis reveal SPP1+ tumor-associated macrophage-driven prognostic signature for hepatocellular carcinoma.
BACKGROUND: The SPP1+ tumor-associated macrophages (TAMs) have been implicated in tumor metastasis and immune evasion. However, the prognostic significance of SPP1+ TAMs in hepatocellular carcinoma (HCC) remains largely unexplored. This study aimed to identify SPP1+ TAMs-related genes and construct a model to predict overall survival (OS) in HCC patients. METHODS: Single-cell RNA sequencing (scRNA-seq) datasets from HCC patients were analyzed to identify SPP1+ TAMs. SPP1+ TAMs-related risk score (STRS) was developed using Mendelian randomization (MR) analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression. HCC patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were stratified into high- and low-STRS groups based on STRS. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and functional enrichment analysis were performed to assess the prognostic value of STRS. RESULTS: SPP1+ TAMs exhibited strong associations with immunosuppressive functions. 16 SPP1+ TAMs-related genes were used to construct STRS. Patients in the high-STRS group had significantly worse OS than those in the low-STRS group (p < 0.001). ROC analysis demonstrated robust predictive power, with AUC values ranging from 0.685 to 0.748 for 1-year OS, 0.717 to 0.739 for 2-year OS, and 0.719 to 0.738 for 3-year OS. The STRS model also exhibited strong predictive capability for the distinction of drug resistance. CONCLUSION: This study identified SPP1+ TAMs-related genes as key prognostic indicators in HCC. The STRS model provides an effective tool for predicting patient survival and may facilitate personalized treatment strategies for HCC. These findings enhance the understanding of TAMs-driven immune modulation in HCC and highlight potential therapeutic targets for improving patient outcomes.
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Massively parallel single-nucleus RNA-seq with DroNc-seq.
Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.
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Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types.
BACKGROUND: In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors. METHODS: Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index. RESULTS: In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index. CONCLUSIONS: The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.
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BDCD: a comprehensive Brain Disease Cell-cell communication Database.
Dysregulated cell-cell communication (CCC) is increasingly recognized as a driver of brain disease pathology, contributing to neuroinflammation, synaptic dysfunction, and neurodegeneration. Nevertheless, existing resources remain limited in brain specificity, regional coverage, and functional annotation. To address this gap, we develop the Brain Disease Cell-cell communication Database (BDCD), the first comprehensive resource focused on CCC networks across major brain diseases. BDCD integrates 38 manually curated datasets, comprising 8 519 425 single cells from single-cell RNA-seq studies and 140 744 spots from spatial transcriptomic maps, spanning 14 brain regions and 13 canonical cell types covering Alzheimer's disease, Parkinson's disease, schizophrenia, bipolar disorder, and multiple sclerosis. BDCD reconstructs more than 495 000 ligand-receptor interaction events and links them to structural features, genetic associations, pathways, and therapeutic modulators, including 6100 single nucleotide polymorphisms from genome-wide association studies, 3350 drugs, and 72 477 allosteric modulators. This comprehensive atlas enables cross-disease comparison and supports dynamic hypothesis generation, providing a foundation for mechanistic insights and therapeutic discovery. BDCD is publicly available at https://bioinfo.uth.edu/bdcd/. Database URL: https://bioinfo.uth.edu/bdcd/.
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Cell-Type-Specific and Variety-Specific Responses to Salt Stress in Wheat Root Revealed by Single-Cell Transcriptomics.
Soil salinization threatens the yield and quality of wheat. Roots are critical for the salt stress response, yet their cell-type-specific mechanisms remain unclear. We conducted the first single-nucleus RNA sequencing analysis of wheat root tips from salt-sensitive (CS) and -tolerant (DK) varieties under salt stress, profiling 188 270 high-quality root cells. Seventeen cell types were identified and validated using reported marker genes and reference atlases. Cell-type-specific analyses found that root hair cells exhibited the strongest association with salt stress, with variety-specific transcriptional changes potentially contributing to DK's superior salt tolerance. Comparative gene profiling of root hair cells highlighted that CS prioritized rapid stress signaling and osmolyte accumulation, whereas DK emphasized metabolic reprogramming and cellular repair mechanisms. Pseudotime analysis pinpointed TaGSTU1-5B as a key candidate for wheat salt tolerance, and it was validated as improving salt tolerance by mediating ROS scavenging by overexpression. Additionally, subgenomic analysis revealed that salt stress enhanced asymmetric expression of the homeologs in polyploid wheat roots, and A- and D-dominant homologs were more related to salt response. This study reports the first single-cell atlas of salt-stressed wheat roots, uncovering cell-type-specific and variety-specific salt responses, providing novel insights into the molecular basis of salt adaptation and tolerance in wheat.
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A core stemness-associated module reveals PLK1, NUF2, KIF23, CDCA8, TOP2A, CENPF, AURKA, and ASPM as key genes in rectal cancer.
BACKGROUND: Rectal cancer (RC) presents significant clinical challenges due to a high risk of recurrence and therapy resistance, which are largely driven by cancer stem cells (CSCs). To address the critical need for RC-specific CSC biomarkers and targets, this study aimed to leverage single-cell transcriptomics and high-dimensional co-expression network analysis (hdWGCNA) to define a core stemness-associated signature and identify potential therapeutic vulnerabilities. MATERIALS AND METHODS: We built a single-cell atlas (GSE199726) and applied hdWGCNA to the CSC-like compartment, then validated findings across TCGA-READ and GEO GSE90627 cohorts. Pathway/immune features were assessed by clusterProfiler, GSEA, CIBERSORT, and ESTIMATE. Drug candidates were nominated by Enrichr/DSigDB and molecular docking. Wet-lab assays included qRT-PCR (SW620/Caco-2 vs HIEC-6), PLK1 knockdown (si-PLK1#1/#2), wound healing, transwell, and CCK-8 at different time courses (0/24/48/72 h). RESULTS: Our analysis identified a CSC-enriched subpopulation from the single-cell atlas of RC, from which hdWGCNA derived four co-expression modules. This yielded an eight-gene panel (PLK1, NUF2, KIF23, CDCA8, TOP2A, CENPF, AURKA, ASPM) that was consistently upregulated in tumors across TCGA and GEO cohorts, correlated with G2/M checkpoint and E2F/MYC pathways, and associated with an immune-altered microenvironment. Crucially, this yielded an eight-gene panel that demonstrated exceptional diagnostic accuracy (AUC > 0.8) and significant prognostic value. In vitro, all eight genes were elevated in cancer cells; si-PLK1 efficiently reduced PLK1 expression and curtailed migration, invasion, and proliferation across time. DISCUSSION: By integrating single-cell atlasing with hdWGCNA, we identified an eight-gene rectal CSC signature that captures a G2/M-stemness program, which generalized across cohorts and was functionally substantiated through PLK1 knockdown in vitro. This tractable vulnerability, anchored by kinases PLK1/AURKA, provides a compact biomarker readout for risk stratification and preclinical therapeutic screening, warranting further in vivo and clinical validation. CONCLUSIONS: An eight-gene CSC signature captures a stemness-linked G2/M program that generalizes across cohorts, relates to the microenvironment, and is therapeutically tractable via kinase targeting, providing a compact readout for risk stratification and preclinical screening.