Yan Niu’s research while affiliated with Yangzhou University and other places

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Publications (2)


Characterization of thalamic hemorrhage by snRNA-seq. (A) The PCA plot demonstrates the variability within the dataset, highlighting cell clusters based on gene expression profiles. (B) The scatter plot identifies 13 distinct cell clusters. (C) The 13 cell clusters are annotated as 8 cell types: oligodendrocytes, neurons, astrocytes, microglia, NPCs, monocytes, endothelial cells, and T cells. (D) The dot plot displays the expression levels of specific marker genes for each cell type. (E) The proportion of each cell type within the samples, illustrating the cellular composition in the hemorrhagic thalamic environment. snRNA-seq, single-nucleus RNA sequencing; PCA, principal component analysis; NPCs, neural progenitor cells
CellPhone analysis of the different cell types. (A) Heatmap showing the number of ligand‒receptor interactions responsible for intercellular communication. (B-I) Numbers of receptor-ligand interactions between (B) astrocytes, (C) endothelial cells, (D) microglia, (E) monocytes, (F) T cells, (G) NPCs, (H) neurons and (I) oligodendrocytes with other cell types. NPCs, neural progenitor cells
Screening of DEGs in neurons and their functional analysis. (A) Volcano plot displaying DEGs between the control and thalamic hemorrhage groups. (B) Heatmap illustrating the expression differences of DEGs in neurons. (C) The top 10 enriched GO terms. (D) The top 10 enriched KEGG pathways. (E) PPI network of DEGs, emphasizing the complex signaling interactions altered in the disease state. (F) The top 5 genes with the highest connectivity. DEGs, differentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes
Pseudotime analysis of neurons. (A) Pseudotime analysis projects all neurons onto seven branches, with colors ranging from light blue to dark blue, indicating the developmental trajectory from the initial to the mature state. (B) Distribution of neuronal states, with different colors representing the progression of seven states along the pseudotime trajectory. (C) Differences in distribution along the pseudotime trajectory between the Control group and the ICH group. (D) Distribution of each sample (C1, C2, M1, M2) along the pseudotime. (E) Relative expression levels of genes (such as Cacna1c, Dlgap1, Gapdh, Gfap, and Hspb1) along the pseudotime. Note: The numbers (1, 2, 3) within the black circles indicate key branching points in the pseudotime trajectory. ICH: intracerebral hemorrhage
Expression Levels of Key DEGs. (A–E) Expression levels of the key genes Cacna1c, Dlgap1, Gapdh, Gria2, and Hsp90ab1. (F) Combined expression profile of these genes. Note: Each group consists of nuclei pooled from six mice, combined into two samples with three mice each (n = 2 per group)

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Decoding neuronal genes in stroke-induced pain: insights from single-nucleus sequencing in mice
  • Article
  • Full-text available

November 2024

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3 Reads

BMC Neurology

Yan Niu

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Xiaoping Chen

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[...]

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Tianfeng Huang

Background The role of neurons in central post-stroke pain (CPSP) following thalamic hemorrhage remains unclear. This study aimed to identify key genes associated with post-thalamic hemorrhage pain and to explore their functions in neurons. Single-nucleus RNA sequencing (snRNA-seq) data from a mouse model was used for this analysis. Methods First, snRNA-seq data were analyzed to identify cell types associated with CPSP induced by thalamic hemorrhage. Differentially expressed genes (DEGs) in neurons were then screened between control and model groups, followed by the construction of a protein-protein interaction (PPI) network for the DEGs. CytoNCA was used to assess node connectivity in the PPI network, and the top 5 key genes were identified. Subsequently, transcription factor (TF)-mRNA and miRNA-mRNA networks were constructed, and small-molecule drugs potentially targeting these key genes were predicted. Finally, the expression differences of key genes in neurons were compared between the model and control groups. Results A total of 13 cell clusters were identified, categorized into 8 cell types: T cells, endothelial cells, monocytes, neural progenitor cells (NPCs), microglia, astrocytes, neurons, and oligodendrocytes. A total of 228 DEGs were detected in neurons when comparing the model group with the control group. The PPI network of the DEGs consisted of 126 nodes and 209 edges, identifying the top 5 key genes: Dlgap1, Cacna1c, Gria2, Hsp90ab1, and Gapdh. The miRNA-mRNA network included 68 miRNA-mRNA pairs, 62 miRNAs, and 5 mRNAs, while the TF-mRNA network consisted of 66 TF-mRNA pairs, 56 TFs, and 5 mRNAs. Drug prediction identified 110 small-molecule drugs (e.g., purpurogallin, nifedipine, and novobiocin) potentially targeting these key genes. Additionally, Cacna1c were significantly upregulated in model mice. Conclusion This study identified the role of key genes in thalamic hemorrhage-induced CPSP through snRNA-seq, providing a scientific basis for further exploration of the molecular mechanisms underlying CPSP.

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The Cytidine N-Acetyltransferase NAT10 Promotes Thalamus Hemorrhage-Induced Central Poststroke Pain by Stabilizing Fn14 Expression in Thalamic Neurons

September 2024

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37 Reads

Molecular Neurobiology

The recognition of RNA N4-acetylcytidine (ac4C) modification as a significant type of gene regulation is growing; nevertheless, whether ac4C modification or the N-acetyltransferase 10 protein (NAT10, the only ac4C “writer” that is presently known) participates in thalamus hemorrhage (TH)-induced central poststroke pain (CPSP) is unknown. Here, we observed NAT10 was primarily located in the neuronal nuclei of the thalamus of mice, with Fn14 and p65. An increase of NAT10 mRNA and protein expression levels in the ipsilateral thalamus was observed from days 1 to 14 after TH. Inhibition of NAT10 by several different approaches attenuated Fn14 and p65 upregulation of TH mice, as well as tissue injury in the thalamus on the ipsilateral side, and the development and maintenance of contralateral nociceptive hypersensitivities. NAT10 overexpression increased Fn14 and p65 expression and elicited nociceptive hypersensitivities in naïve mice. Our findings suggest that ac4C modification and NAT10 participate in TH-induced CPSP by activating the NF-κB pathway through upregulating Fn14 in thalamic neurons. NAT10 could serve as a promising new target for CPSP treatment.