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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.
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FANCI promotes esophageal squamous cell carcinoma progression and cell cycle regulation and interacts with FANCD2.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with poor clinical outcomes, and reliable molecular biomarkers and therapeutic targets remain limited. Fanconi anemia group I protein (FANCI) is a core component of the Fanconi anemia (FA) pathway, but its expression pattern, clinical significance, and functional role in ESCC have not been comprehensively defined. This study aimed to investigate FANCI expression and prognostic value in ESCC, assess its effects on malignant cellular phenotypes and tumor growth, and explore its potential mechanistic relationship with Fanconi anemia group D2 protein (FANCD2) and cell-cycle regulation. METHODS: Multi-cohort analyses were performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, together with ESCC single-cell RNA sequencing (RNA-seq) data. FANCI functions were assessed by bidirectional gain- and loss-of-function experiments in vitro (proliferation, colony formation, migration, invasion, apoptosis, and cell-cycle assays) and by xenograft models in vivo. Mechanistic studies included protein-protein interaction (PPI) analyses, co-immunoprecipitation (Co-IP), and immunofluorescence (IF) colocalization. RESULTS: FANCI was consistently upregulated in ESCC across bulk transcriptomic datasets and was further supported by quantitative polymerase chain reaction (qPCR), Western blotting, and immunohistochemistry (IHC). FANCI discriminated ESCC from normal tissues in TCGA-ESCC and was independently validated in GSE53624 [area under the curve (AUC) =0.940 and 0.975, respectively]. FANCI was associated with poorer overall survival (OS) and shorter disease-free interval (DFI), and these findings were validated in an independent GEO cohort. Functionally, FANCI promoted ESCC cell proliferation, migration, and invasion, while inhibiting apoptosis; FANCI knockdown suppressed tumor growth in vivo and induced G2/M cell-cycle arrest. Mechanistically, FANCI physically interacted with FANCD2, colocalized with FANCD2 in the nucleus, and was associated with altered FANCD2 protein abundance, consistent with cell-cycle and DNA repair-related programs. Single-cell analysis indicated that FANCI was enriched in epithelial cells and associated with higher activity of malignant functional programs. In TCGA-ESCC, FANCI-high tumors showed distinct mutation profiles, a trend toward increased tumor mutation burden (TMB), and altered immune-associated signatures. CONCLUSIONS: FANCI is upregulated in ESCC and is associated with diagnostic and prognostic value. It promotes malignant phenotypes and tumor growth, potentially through a FANCI-FANCD2-linked cell-cycle/DNA repair program, supporting FANCI as a candidate biomarker and therapeutic target in ESCC.
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Distinct cell type-specific mechanisms underlie cognitive dysfunction during persistent integrated stress response activation.
Persistent activation of the integrated stress response (ISR) is a central driver of cognitive decline in both neurodevelopmental and neurodegenerative disorders. However, the cell type-specific mechanisms underlying these deficits remain poorly understood. By integrating single-cell RNA-seq and single-cell assay for transposase-accessible chromatin sequencing, we generated a brain ISR atlas using Ppp1r15bR658C mice, a clinically relevant model of intellectual disability characterized by selective and persistent ISR activation. We find that distinct brain cell types differentially engage transcriptional and chromatin remodeling programs. Notably, selective deletion of the major ISR downstream effector ATF4 in GABAergic neurons, but not in glutamatergic neurons, exacerbates ISR-mediated cognitive decline in Ppp1r15bR658C mice, demonstrating that different neuronal subtypes rely on distinct ISR effectors. We define a molecular single-cell signature of persistent ISR activation that serves as a metric of ISR-mediated cellular vulnerability and as a biomarker for cognitive dysfunction across human cognitive disorders. These findings demonstrate that cell type-specific responses drive cognitive dysfunction during persistent ISR activation.
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Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity.
Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies. VIDEO ABSTRACT.
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Structure-based virtual screening identifies ethyl rosmarinate as a SLC1A6-targeted therapeutic candidate for bladder cancer.
OBJECTIVE: SLC1A6 remains poorly characterized in bladder cancer. This study investigated its expression pattern, clinical relevance, cellular localization, functional implications, and potential pharmacological modulation. METHODS: Bulk transcriptomic cohorts, clinical data, single-cell RNA-seq data, and spatial transcriptomic data were analyzed to characterize SLC1A6 in bladder cancer. We evaluated SLC1A6 expression, prognosis, clinicopathological associations, immune microenvironment features, metabolic programs, and predicted drug sensitivity. Single-cell and spatial analyses were used to identify the main cellular source and tissue localization of SLC1A6. Virtual knockout analysis was used to estimate pathway changes after SLC1A6 depletion. SLC1A6 expression was validated using Human Protein Atlas immunohistochemistry images, paired clinical tissue Western blotting, and qPCR/Western blotting in bladder cancer cell lines. Structure-based virtual screening and molecular docking were performed, followed by functional testing of ethyl rosmarinate in T24 and UM-UC-3 bladder cancer cells. RESULTS: SLC1A6 was upregulated in bladder cancer tissues and cell lines and was associated with poor survival, advanced clinicopathological features, immunosuppressive characteristics, metabolic remodeling, and poorer survival outcomes in immunotherapy-treated cohorts. Single-cell and spatial analyses showed that SLC1A6 was mainly enriched in epithelial tumor cells and epithelial-dominant tumor regions. SLC1A6-high epithelial cells showed enhanced lipid- and glycan-related metabolic activity. Virtual knockout analysis predicted broad pathway alterations after SLC1A6 depletion. Virtual screening prioritized ethyl rosmarinate as a candidate SLC1A6-interacting compound, and CETSA further supported its cellular target engagement with SLC1A6. In T24 cells, ethyl rosmarinate reduced SLC1A6 protein expression and suppressed migration, colony formation, and invasion. Consistent phenotypic effects on migration, clonogenic growth, and invasion were further observed in UM-UC-3 cells. CONCLUSION: SLC1A6 is an epithelial-enriched, clinically relevant molecule associated with malignant progression, immune suppression, and metabolic remodeling in bladder cancer. Ethyl rosmarinate may represent a candidate compound for further investigation in SLC1A6-related bladder cancer models.
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A single-cell level comparison of human inner ear organoids with the human cochlea and vestibular organs.
Inner ear disorders are among the most common congenital abnormalities; however, current tissue culture models lack the cell type diversity to study these disorders and normal otic development. Here, we demonstrate the robustness of human pluripotent stem cell-derived inner ear organoids (IEOs) and evaluate cell type heterogeneity by single-cell transcriptomics. To validate our findings, we construct a single-cell atlas of human fetal and adult inner ear tissue. Our study identifies various cell types in the IEOs including periotic mesenchyme, type I and type II vestibular hair cells, and developing vestibular and cochlear epithelium. Many genes linked to congenital inner ear dysfunction are confirmed to be expressed in these cell types. Additional cell-cell communication analysis within IEOs and fetal tissue highlights the role of endothelial cells on the developing sensory epithelium. These findings provide insights into this organoid model and its potential applications in studying inner ear development and disorders.
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An atlas of cell-type-specific interactome networks across 44 human tumor types.
BACKGROUND: Biological processes are controlled by groups of genes acting in concert. Investigating gene-gene interactions within different cell types can help researchers understand the regulatory mechanisms behind human complex diseases, such as tumors. METHODS: We collected extensive single-cell RNA-seq data from tumors, involving 563 patients with 44 different tumor types. Through our analysis, we identified various cell types in tumors and created an atlas of different immune cell subsets across different tumor types. Using the SCINET method, we reconstructed interactome networks specific to different cell types. Diverse functional data was then integrated to gain biological insights into the networks, including somatic mutation patterns and gene functional annotation. Additionally, genes with prognostic relevance within the networks were also identified. We also examined cell-cell communications to investigate how gene interactions modulate cell-cell interactions. RESULTS: We developed a data portal called CellNetdb for researchers to study cell-type-specific interactome networks. Our findings indicate that these networks can be used to identify genes with topological specificity in different cell types. We also found that prognostic genes can deconvolved into cell types through analyzing network connectivity. Additionally, we identified commonalities and differences in cell-type-specific networks across different tumor types. Our results suggest that these networks can be used to prioritize risk genes. CONCLUSIONS: This study presented CellNetdb, a comprehensive repository featuring an atlas of cell-type-specific interactome networks across 44 human tumor types. The findings underscore the utility of these networks in delineating the intricacies of tumor microenvironments and advancing the understanding of molecular mechanisms underpinning human tumors.
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Copper-binding protein modelling by single-cell transcriptome and Bulk transcriptome to predict overall survival in lung adenocarcinoma patients.
Background: Copper and copper-binding proteins are key components of tumour progression as they play an important role in tumour invasion and migration, and abnormal accumulation of copper (Cu) may be intimately linked to with lung adenocarcinoma (LUAD). Methods: Data on lung adenocarcinoma were sourced from the Cancer Genome Atlas (TCGA) database and the National Centre for Biotechnology Information (GEO). 10x scRNA sequencing, which is from Bischoff P et al, was used for down-sequencing clustering and subgroup identification using TSNE. The genes for Copper-binding proteins (CBP) were acquired from the MSigDB database. LASSO-Cox analysis was subsequently used to construct a model for copper-binding proteins (CBPRS), which was then compared to lung adenocarcinoma models developed by others. External validation was carried out in the GSE31210 and GSE50081 cohorts. The effectiveness of immunotherapy was evaluated using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and the immune microenvironment between different risk groups were investigated. The CBPRS's key regulatory genes were screened using ROC diagnostic and KM survival curves. The differential expression of these genes was then verified by RT-qPCR. Results: The six CBP genes were identified as highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. The CBPRS demonstrated superior predictive ability compared to 11 previously published models. We constructed a column-line graph that includes CBPRS and clinical characteristics, which exhibits high predictive performance. Additionally, we observed significant differences in biological functions, mutational landscapes, and immune cell infiltration in the tumour microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both the high- and low-risk groups. These results suggest that the model has good predictive efficacy. Conclusions: The CBP model demonstrated good predictive performance, revealing characteristics of the tumour microenvironment. This provides a new method for assessing the efficacy of pre-immunisation and offers a potential strategy for future treatment of lung adenocarcinoma.
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Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data.
The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods. The user-friendly open-source software implementation has extensive documentation (https://elihei2.github.io/segger_dev/), requires little manual intervention, integrates seamlessly into existing workflows, and enables atlas-scale applications.