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Single-cell transcriptome atlas unveils transcriptional regulation networks of banana root tips in response to Fusarium oxysporum infection.
Fusarium wilt caused by Fusarium oxysporum (Foc) is one of the most destructive diseases in global banana production. The response of root system to Foc infection through gene expression in multiple cell types is crucial for understanding the disease resistance mechanism in banana. Here, we reported a single-cell transcriptional landscape of banana root tips in response to Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) infection. We characterized 10 major cell types from 19 cell clusters. We explored differentiation trajectories of meristematic cells, root cap cells, and pericycle cells through pseudotime analysis, and identified pericycle cell as the dominant root cell type under Foc TR4 infection. Moreover, we identified 11 co-expression regulatory networks, of which eight were significantly associated with Foc TR4 response. Specifically, MaKAN4 was co-expressed with two Zn 2+-dependent genes (MaACA7 and MaADH3) in M5 module, which was associated with pericycle cell type and responded to Foc TR4 infection. Further analysis demonstrated that MaKAN4 protein could interact with the promoters of MaACA7 and MaADH3 to promote their expression levels, highlighting a crucial role of MaKAN4 in banana disease resistance by regulating the Zn 2+-dependent MaACA7/MaADH3 module. These findings provide a comprehensive view of cell fate determination in banana root tips and highlight novel insights into the regulatory mechanisms of major cell types in response to Foc TR4 infection, laying a useful foundation for developing disease-resistant banana cultivars.
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Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes.
OBJECTIVE: Although glucagon-secreting α-cells and insulin-secreting β-cells have opposing functions in regulating plasma glucose levels, the two cell types share a common developmental origin and exhibit overlapping transcriptomes and epigenomes. Notably, destruction of β-cells can stimulate repopulation via transdifferentiation of α-cells, at least in mice, suggesting plasticity between these cell fates. Furthermore, dysfunction of both α- and β-cells contributes to the pathophysiology of type 1 and type 2 diabetes, and β-cell de-differentiation has been proposed to contribute to type 2 diabetes. Our objective was to delineate the molecular properties that maintain islet cell type specification yet allow for cellular plasticity. We hypothesized that correlating cell type-specific transcriptomes with an atlas of open chromatin will identify novel genes and transcriptional regulatory elements such as enhancers involved in α- and β-cell specification and plasticity. METHODS: We sorted human α- and β-cells and performed the "Assay for Transposase-Accessible Chromatin with high throughput sequencing" (ATAC-seq) and mRNA-seq, followed by integrative analysis to identify cell type-selective gene regulatory regions. RESULTS: We identified numerous transcripts with either α-cell- or β-cell-selective expression and discovered the cell type-selective open chromatin regions that correlate with these gene activation patterns. We confirmed cell type-selective expression on the protein level for two of the top hits from our screen. The "group specific protein" (GC; or vitamin D binding protein) was restricted to α-cells, while CHODL (chondrolectin) immunoreactivity was only present in β-cells. Furthermore, α-cell- and β-cell-selective ATAC-seq peaks were identified to overlap with known binding sites for islet transcription factors, as well as with single nucleotide polymorphisms (SNPs) previously identified as risk loci for type 2 diabetes. CONCLUSIONS: We have determined the genetic landscape of human α- and β-cells based on chromatin accessibility and transcript levels, which allowed for detection of novel α- and β-cell signature genes not previously known to be expressed in islets. Using fine-mapping of open chromatin, we have identified thousands of potential cis-regulatory elements that operate in an endocrine cell type-specific fashion.
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Single-cell sequencing reveals novel cellular heterogeneity in uterine leiomyomas.
STUDY QUESTION: What are the cellular composition and single-cell transcriptomic differences between myometrium and leiomyomas as defined by single-cell RNA sequencing? SUMMARY ANSWER: We discovered cellular heterogeneity in smooth muscle cells (SMCs), fibroblast and endothelial cell populations in both myometrium and leiomyoma tissues. WHAT IS KNOWN ALREADY: Previous studies have shown the presence of SMCs, fibroblasts, endothelial cells and immune cells in myometrium and leiomyomas. However, there is no information on the cellular heterogeneity in these tissues and the transcriptomic differences at the single-cell level between these tissues. STUDY DESIGN, SIZE, DURATION: We collected five leiomyoma and five myometrium samples from a total of eight patients undergoing hysterectomy. We then performed single-cell RNA sequencing to generate a cell atlas for both tissues. We utilized our single-cell sequencing data to define cell types, compare cell types by tissue type (leiomyoma versus myometrium) and determine the transcriptional changes at a single-cell resolution between leiomyomas and myometrium. Additionally, we performed MED12-variant analysis at the single-cell level to determine the genotype heterogeneity within leiomyomas. PARTICIPANTS/MATERIALS, SETTING, METHODS: We collected five MED12-variant positive leiomyomas and five myometrium samples from a total of eight patients. We then performed single-cell RNA sequencing on freshly isolated single-cell preparations. Histopathological assessment confirmed the identity of the samples. Sanger sequencing was performed to confirm the presence of the MED12 variant in leiomyomas. MAIN RESULTS AND ROLE OF CHANCE: Our data revealed previously unknown heterogeneity in the SMC, fibroblast cell and endothelial cell populations of myometrium and leiomyomas. We discovered the presence of two different lymphatic endothelial cell populations specific to uterine leiomyomas. We showed that both myometrium and MED12-variant leiomyomas are relatively similar in cellular composition but differ in cellular transcriptomic profiles. We found that fibroblasts influence the leiomyoma microenvironment through their interactions with endothelial cells, immune cells and SMCs. Variant analysis at the single-cell level revealed the presence of both MED12 variants as well as the wild-type MED12 allele in SMCs of leiomyomatous tissue. These results indicate genotype heterogeneity of cellular composition within leiomyomas. LARGE SCALE DATA: The datasets are available in the NCBI Gene Expression Omnibus (GEO) using GSE162122. LIMITATIONS, REASONS FOR CAUTION: Our study focused on MED12-variant positive leiomyomas for single-cell RNA sequencing analyses. Leiomyomas carrying other genetic rearrangements may differ in their cellular composition and transcriptomic profiles. WIDER IMPLICATIONS FOR THE FINDINGS: Our study provides a cellular atlas for myometrium and MED12-variant positive leiomyomas as defined by single-cell RNA sequencing. Our analysis provides significant insight into the differences between myometrium and leiomyomas at the single-cell level and reveals hitherto unknown genetic heterogeneity in multiple cell types within human leiomyomas. Our results will be important for future studies into the origin and growth of human leiomyomas. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by funding from the National Institute of Child Health and Human Development (HD098580 and HD088629). The authors declare no competing interests.
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Deeper insights into transcriptional features of cancer-associated fibroblasts: An integrated meta-analysis of single-cell and bulk RNA-sequencing data.
Cancer-associated fibroblasts (CAFs) have long been known as one of the most important players in tumor initiation and progression. Even so, there is an incomplete understanding of the identification of CAFs among tumor microenvironment cells as the list of CAF marker genes varies greatly in the literature, therefore it is imperative to find a better way to identify reliable markers of CAFs. To this end, we summarized a large number of single-cell RNA-sequencing data of multiple tumor types and corresponding normal tissues. As a result, for 9 different types of cancer, we identified CAF-specific gene expression signatures and found 10 protein markers that showed strongly positive staining of tumor stroma according to the analysis of IHC images from the Human Protein Atlas database. Our results give an insight into selecting the most appropriate combination of cancer-associated fibroblast markers. Furthermore, comparison of different approaches for studying differences between cancer-associated and normal fibroblasts (NFs) illustrates the superiority of transcriptome analysis of fibroblasts obtained from fresh tissue samples. Using single-cell RNA sequencing data, we identified common differences in gene expression patterns between normal and cancer-associated fibroblasts, which do not depend on the type of tumor.
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Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens.
Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. Finally, we demonstrate how our Perturb-seq derived gene lists can be used to precisely infer changes in signalling pathway activation for in vivo and in situ samples. Our work enhances our understanding of signalling regulators and their targets, and lays a computational framework towards the data-driven inference of an 'atlas' of perturbation signatures.
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A nine-gene nicotine-metabolism signature predicts prognosis and characterizes the immune landscape in colon adenocarcinoma.
BACKGROUND: Smoking is a preventable cause of colorectal cancer (CRC), and nicotine metabolism may promote tumorigenesis. We aimed to investigate the prognostic significance of nicotine metabolism‑related genes (NRGs) in colon adenocarcinoma (COAD) and to develop a gene signature for patient stratification. METHODS: Using the Cancer Genome Atlas COAD cohort, we performed differential expression analysis and weighted gene co-expression network analysis to identify NRGs. Functional enrichment, Cox regression, and least absolute shrinkage and selection operator (LASSO) modelling were applied to build a multigene signature, which was validated in external cohorts. Single-cell transcriptomic data and immune infiltration analyses were used to evaluate gene expression patterns and tumor microenvironmental features. RESULTS: We identified 767 differentially expressed NRGs enriched in extracellular matrix (ECM) organization and signaling pathways. A nine‑gene signature (FOXC1, TRIP6, NRCAM, TIMP1, TSPAN11, STC2, CST2, SIX2, GPRASP1) was derived; this risk model independently predicted overall survival and showed robust performance across multiple datasets. Single‑cell analyses confirmed cell‑type‑specific expression of these genes. High‑risk scores were associated with altered immune cell infiltration, distinct mutation profiles, and differential drug sensitivity patterns. CONCLUSIONS: We propose a novel NRG-based prognostic signature that accurately predicts outcomes in COAD. The model highlights the interplay between nicotine metabolism, ECM remodeling, and immune responses, highlighting genes and pathways that may inform future therapeutic stratification.
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Overexpression of PLXDC2 in Stromal Cell-Associated M2 Macrophages Is Related to EMT and the Progression of Gastric Cancer.
The tumor microenvironment (TME) comprises distinct cell types, including stromal types such as fibroblast cells and macrophage cells, which have recently become a critical factor in tumor development and progression. Here, we identified the TME-related gene, plexin domain containing 2 (PLXDC2), in a high-stromal-score population. And we revealed that this gene was related to poor survival and advanced (tumor-node-metastasis) stage in gastric cancer (GC) patients from The Cancer Genome Atlas database. An integrated gene profile and functional analysis of the proportions of tumor-infiltrating immune cells revealed that the expression of the M2 macrophages cell marker CD163 was positively correlated with PLXDC2 expression. In addition, the M2 macrophages gene signature and high PLXDC2 expression were associated with the inflammatory signaling pathway and the epithelial-to-mesenchymal transition (EMT)-related gene signature. Single-cell study of GC identified PLXDC2 was enriched specifically in fibroblasts and monocytes/macrophages populations, which supported its important role in the stroma. Furthermore, according to a tissue microarray immunohistochemistry analysis, the expression of PLXDC2 elevated in human GC stromal specimens compared to tumor tissue specimens. Moreover, PLXDC2 overexpression in the stromal compartment was associated with CD163-positive regulatory M2 macrophages, and its functions were related to the pathogenesis of GC. Multiplexed immunohistochemistry verified PLXDC2's correlation with EMT markers. Our data suggested that PLXDC2 was expressed in stromal cells and that its crosstalk with tumor-associated macrophages could contribute to cancer biology by inducing the EMT process.
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Pan-cancer analysis of AIM2 inflammasomes with potential implications for immunotherapy in human cancer: A bulk omics research and single cell sequencing validation.
BACKGROUND: The absent in melanoma 2 (AIM2) inflammasome is a multi-protein platform that recognizes aberrant cytoplasmic double-stranded DNA(dsDNA) and induces cytokine maturation, release, and pyroptosis. Some studies found that the AIM2 inflammasome was a double-edged sword in many cancers. However, there have been fewer studies on AIM2 inflammasomes in pan-cancer. METHODS: Gene expression was analyzed using The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) database. Immunohistochemistry (IHC) was used to validate the expression of the AIM2. We used the survival curve to explore the prognostic significance of the AIM2 inflammasomes in pan-cancer. Mutations and methylation of AIM2 inflammasome-related genes (AIM2i-RGs) were also comprehensively analyzed. Single sample gene set enrichment analysis was used to calculate the AIM2 inflammasomes score and explore the correlation of the AIM2 inflammasomes score with immune-related genes and immune infiltrations. The function of AIM2 inflammasomes in pan-cancer was analyzed at the single-cell level. Single-cell transcriptome sequencing (scRNA-seq) data was used to assess the activation state of the AIM2 inflammasomes in the tumor microenvironment. RESULTS: We found that AIM2i-RGs were aberrantly expressed in tumors and were strongly associated with prognosis. In pan-cancer, the expression of AIM2i-RGs was positively associated with copy number variation and negatively associated with methylation. In AIM2i-RGs, missense mutations were the predominant type of single nucleotide polymorphism. Moreover, we found that the drugs dimethyloxallyl glycine (DMOG) and Z-LNle-CHO may be sensitive to the AIM2 inflammasomes. The AIM2 inflammasomes score was significantly and positively correlated with the tumor immunity score and the stroma score. In most tumors, the AIM2 inflammasomes score was significantly and positively correlated with CD8+ T cell abundance in the tumor microenvironment. Additionally, the AIM2 inflammasomes score was significantly correlated with immune checkpoint genes in pan-cancer as well as immune checkpoint therapy-related markers including tumor mutational burden (TMB), microsatellite instability(MSI), and tumor immune dysfunction and exclusion(TIDE). scRNA-seq analysis suggested that AIM2 inflammasomes differ significantly among different cells in the tumor microenvironment. IHC confirmed low expression of AIM2 in colorectal cancer. DISCUSSION: AIM2 inflammasomes may be a new target for future tumor therapy It is likely involved in tumor development, and its high expression may serve as a predictor of tumor immunotherapy efficacy.
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Integrating single-cell and bulk RNA sequencing data establishes a cuproptosis-related gene predictive signature in breast cancer.
Breast cancer (BRCA) remains the most common malignancy and the leading cause of cancer-related death among women worldwide. Recent studies have highlighted dysregulated copper homeostasis as a contributor to tumorigenesis, with cuproptosis-a copper-dependent form of regulated cell death-emerging as a potential therapeutic target. In this study, we systematically evaluated the prognostic significance of cuproptosis-related genes (CRGs) in BRCA by integrating multi-omics data from TCGA and GEO cohorts. Through weighted gene co-expression network analysis (WGCNA), we identified four key CRGs (CCDC24, TMEM65, XPOT, and NUDCD1) to construct a prognostic signature. The resulting risk score effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse overall survival, higher TP53 mutation frequency, and features of an immunosuppressive tumor microenvironment (TME). Functional enrichment analyses further implicated these genes in immune evasion and metabolic reprogramming pathways. Single-cell RNA sequencing (scRNAseq) confirmed heterogeneous expression of the signature genes across distinct cell populations, supporting their involvement in both tumor biology and immune modulation. Clinically, this CRG-based model provides a promising tool for individualized risk assessment and treatment planning. High-risk patients may benefit from intensified therapies or immunomodulatory strategies, while low-risk individuals could be considered for treatment de-escalation. In summary, our findings suggest that cuproptosis may play a regulatory role in BRCA progression and offer a novel, clinically actionable framework for prognostic stratification.
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Decoding gene networks controlling hypothalamic and prethalamic neuron development.
The hypothalamus and prethalamus regulate diverse physiological and behavioral processes, yet the gene regulatory networks guiding their development remain poorly defined. Using single-cell RNA and ATAC sequencing, we profile over 660,000 cells in the developing mouse hypothalamus and prethalamus between embryonic day 11 and postnatal day 8. This resource maps key transcriptional and chromatin dynamics underlying regionalization, neurogenesis, and neuronal subtype differentiation. We identify distinct neurogenic progenitor populations and uncover gene regulatory networks controlling their spatial and temporal identity. Integration with genome-wide association study data reveals that transcription factors active in supramammillary and prethalamic lineages are associated with metabolic and neuropsychiatric traits. Cross-repressive interactions among regional transcription factors reinforce hypothalamic boundaries. Functional analysis of Dlx1/2 shows that their loss disrupts GABAergic neuron specification, leading to impaired thalamic inhibition and hyperactivity. This study provides a foundational atlas of hypothalamic and prethalamic development and highlights the importance of early gene regulatory programs in health and disease.