Yun Tian’s research while affiliated with Jinan University and other places

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Publications (3)


Lycium barbarum glycopeptide ameliorates aging phenotypes and enhances cardiac metabolism by activating the PINK1/Parkin-mediated mitophagy pathway in D-galactose-induced mice
  • Article

January 2025

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6 Reads

Experimental Gerontology

Tianchan Peng

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Jian Xiang

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Yun Tian

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[...]

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Single-cell transcriptomic profiling elucidates the cellular heterogeneity of R/R AML patients. A UMAP visualization of scRNA-seq data of BM samples from AML patients and HCs. Each dot represents a single cell, colored by cell cluster. The clusters include hematopoietic stem cells/multipotent progenitors (HSC/MPPs), granulocyte-monocyte progenitors (GMPs), megakaryocyte-erythroid progenitors (MEPs), eosinophil, basophil, and mast cell progenitors (EBMs), erythrocytes (early, mid, and late Erys), monocytes, B cells, T cells, natural killer (NK) cells, conventional dendritic cells (cDCs), and plasmacytoid dendritic cells (pDCs). B Dot plot of average gene expression for cell type-specific genes. The average expression of selected marker genes is shown across different cell types. The color intensity represents the average expression level, and the size of the dot indicates the percentage of cells expressing the gene. All samples are used in cluster annotation based on cell types corresponding to A. C Proportion of distinct cell types in AML and healthy controls (HCs) samples. Bar plots display the proportion of each cell type in AML (purple) and HCs (blue) samples, highlighting the differences in cell type distribution. D Stacked bar plots displaying the composition of cell types in AML and HCs samples, with each color representing a different cell type, depicting the distribution within each sample type. E. UMAP visualization of cells from AML patients. The cells were categorized by patients with complete remission (CR) and non-response (NR) after chemotherapy. Cells from CR patients are highlighted in pink (top panel), while cells from NR patients are highlighted in purple (bottom panel), depicting the distribution and clustering of cells based on treatment response. F. Cell absolute counts (top panel) and relative abundances (bottom panel) of all cell types in AML patients. Different colors represent CR (pink) and NR (purple) samples. The bars in the bottom panel represent log2 odds ratios (Fisher exact test, P-value after Bonferroni correction; n.s., not significant; ***P < .0001)
Identification of malignant cells and comparison with normal hematopoietic cell lineages. A UMAP plots illustrating the distribution of malignant cells (red) and normal cells (gray) across different sample types (HC, CR, and NR). B Statistical comparison between malignant cells and normal cells. The top panel shows the absolute cell counts for cell types predicted to have more than 20% malignant cells, compared to the normal group. The bottom panel shows the relative abundances. Different colors represent the malignant cell (pink) and control (purple) groups. The bar plots in the bottom panel depict the log2 odds ratios (Fisher exact test, P-value after Bonferroni correction; n.s., not significant; ***P < .0001) for the different cell types. C Heatmap showing the scaled expression levels of upregulated genes in malignant cells compared to the normal group. The genes are grouped by their functional categories, including cell stemness related, glycolysis, myeloid maturation, and erythroid related. D Radar plot showing the Gene Set Variation Analysis (GSVA) scores for selected signal pathways, including stem cell proliferation, differentiation, migration, maintenance of stemness, glycolysis, and oxidative phosphorylation, in normal and malignant HSC/MPPs cells. The plot illustrates the differential enrichment of these stem cell-related pathways between the HSC/MPP-normal and HSC/MPP-malignant cell states. E Radar plot showing the GSVA scores for selected signaling pathways, including myeloid cell proliferation, differentiation, activation, leukemia suppressor signaling, glycolysis, and oxidative phosphorylation, in the two cell states of normal and malignant GMPs cells. The plot illustrates the differential enrichment of these myeloid-related pathways between the GMP-normal and GMP-malignant cell states. F Inferred activated (red) and repressed (blue) regulatory proteins in malignant cells compared to control group. The arrows indicate the distribution of activated (red) and repressed (blue) targets for different regulatory proteins, with their positions sorted based on the differential expression between malignant group and the control group (leftmost: most upregulated in malignant cells, rightmost: most downregulated in malignant cells). The P-values are shown on the left side of each row. G Gene regulatory network (GRN) visualization of differentially active regulatory proteins (ENO1, TCF4, ID1, and SREBF1) and their target differentially expressed genes (DEGs) in malignant cells
ENO1 involvement in differentiation blockage of AML malignant Cells. A Cell fate trajectory analysis of the four major myeloid cell lineages, including HSC/MPPs, GMPs, monocytes, and DCs. Scatter plots show the distribution of these cell types in normal (left) and malignant (right) samples, where each dot represents a single cell colored by cell type. B Comparison of the myeloid differentiation pseudotime (calculated by Monocle) between malignant cells and normal cells using HSC/MPPs as the starting point. Box plot (top) and density plot (bottom) depicting the pseudotime distribution of myeloid differentiation in malignant cells (red) and the normal cells (blue). C BEAM heatmap visualization of gene expression levels, where the expression profiles are correlated with the pseudotime trajectory. Proportions of cells mapped to each trajectory branch from the normal and malignant groups are shown underneath the heatmap. D Density plot showing the distribution of cells highly expressing ENO1 across pseudotime for CR (left) and NR (right) patients, with cell types (HSC/MPPs, GMPs, Monocyte, and DCs) indicated by distinct colors. E & F Violin plots comparing the expression levels of ENO1 in different cell types (HSC/MPPs, GMPs, Monocyte, and DCs) between CR and NR conditions (E) and between malignant and the normal cells (F). For the embedded boxplots, the bottom and top of the box are located at the 25th and 75th percentiles, respectively. The bars represent values more than 1.5 times the interquartile range from the border of each box. The same applies to the rest of paper. The P-values were calculated using the Wilcoxon rank-sum test (two-sided; ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05, ns: not significant). G. Violin plot showing the ENO1 expression levels in HC (blue), CR (pink), and NR (purple) groups. The bottom and top of the box are located at the 25th and 75th percentiles, respectively. The Kruskal–Wallis test (P < 2.2e-16) indicates a significant difference among the groups. H Beeswarm plots comparing the differences in ENO1 mRNA expression levels measured by qPCR between HCs and AML patients (left panel), as well as between CR and NR patients (right panel). The P-values from t-tests indicate that there are significant differences in both comparisons (P < 0.0001 and P = 0.0006). I Survival curves stratifying the AML patients into two groups based on the optimal cut-off value, showing that the high ENO1 expression group had significantly poorer prognosis compared to the low expression group (Log-rank test, P = 0.0038)
High expression of ENO1 promotes self-renewal and chemoresistance of LSCs. A UMAP plot showing the distribution of HSC/MPP subpopulations (HSC/MPP-1, HSC/MPP-2, HSC/MPP-3, HSC/MPP-4) in the dataset. Each subpopulation is color-coded as indicated. B UMAP plot showing the distribution of malignant cells (red) and normal cells (gray) among the HSC/MPP cells. C Bar plot showing the proportion of each HSC/MPP subpopulation in HC, CR, and NR. Each condition is color-coded as indicated. D Heatmap showing the expression levels of DEGs across HSC/MPP subpopulations. Each column represents a cell, and each row represents a gene. The color scale indicates relative expression levels. E. Violin plots comparing the stemness, differentiation, proliferation, and chemotaxis scores across HSC/MPP subpopulations (HSC/MPP-1, HSC/MPP-2, HSC/MPP-3 and HSC/MPP-4). F UMAP plots showing the cell fate trajectory of HSC/MPP-1, HSC/MPP-2, and HSC/MPP-3, primarily composed of malignant cells, with cell clusters information mapping (top) and pseudotime inferred by Monocle3 (bottom). G UMAP plots showing the expression levels of ENO1 in HSC/MPP split by different groups (HC, CR, and NR). Color intensity represents expression level, with deeper colors indicating higher expression levels. H UMAP plots showing the distribution of HSC/MPP subpopulations before and after treatment. Subpopulations are color-coded as indicated. I Bar chart showing the abundance of each HSC/MPP cell subtype before (purple) and after (yellow) treatment. J Violin plots comparing ENO1 expression levels in HSC/MPP subpopulations before (purple) and after (yellow) treatment. The P-values were calculated using the Wilcoxon rank-sum test (two-sided; ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05, ns: not significant)
Impact of ENO1 knockdown on leukemic cell survival and apoptosis. A Western blot results showing ENO1 protein levels in MOLM-13 cells transduced with control shRNA or three different ENO1-targeting shRNAs (shRNA1, shRNA2 and shRNA3). β-Actin served as a loading control. B Bar graph depicting the relative expression levels of ENO1 mRNA in MOLM-13 cells transducted with control shRNA or ENO1-targeting shRNAs (shRNA1, shRNA2 and shRNA3), measured by qPCR. ***P < 0.001. C Line graph showing the cellular viability of MOLM-13 cells transducted with control shRNA or ENO1-targeting shRNAs (shRNA1, shRNA2 and shRNA3) after 72 hours, as measured by OD450. Error bars in bar plots represent the means ± SE. P-values was calculated using Wilcoxon test (two-sided; ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05, ns: not significant). D Line plot (left panel) and heatmap (right panel) showing the clustering of DEGs in MOLM-13 cells with ENO1 knockdown (ENO1_KD) compared to the control group, as analyzed by RNA-seq. Gene symbols of selected genes with significant alteration are shown. E Heatmap of GSVA scores of shared and specific GO terms enriched for ENO1_KD and control group. F Scatter plot showing the activation and quiescence scores of long-term hematopoietic stem cells (LT-HSCs) in the ENO1_KD group and control group. G Line graph illustrating the cellular viability of MOLM-13 cells after treatment with different concentrations of an ENO1-inhibitor (AP3-III-a4) for different time, as assessed by OD450. Statistical significance was determined by one-way ANOVA (****P < 0.0001)
Single-cell dissection reveals promotive role of ENO1 in leukemia stem cell self-renewal and chemoresistance in acute myeloid leukemia
  • Article
  • Full-text available

October 2024

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36 Reads

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1 Citation

Stem Cell Research & Therapy

Background Quiescent self-renewal of leukemia stem cells (LSCs) and resistance to conventional chemotherapy are the main factors leading to relapse of acute myeloid leukemia (AML). Alpha-enolase (ENO1), a key glycolytic enzyme, has been shown to regulate embryonic stem cell differentiation and promote self-renewal and malignant phenotypes in various cancer stem cells. Here, we sought to test whether and how ENO1 influences LSCs renewal and chemoresistance within the context of AML. Methods We analyzed single-cell RNA sequencing data from bone marrow samples of 8 relapsed/refractory AML patients and 4 healthy controls using bioinformatics and machine learning algorithms. In addition, we compared ENO1 expression levels in the AML cohort with those in 37 control subjects and conducted survival analyses to correlate ENO1 expression with clinical outcomes. Furthermore, we performed functional studies involving ENO1 knockdown and inhibition in AML cell line. Results We used machine learning to model and infer malignant cells in AML, finding more primitive malignant cells in the non-response (NR) group. The differentiation capacity of LSCs and progenitor malignant cells exhibited an inverse correlation with glycolysis levels. Trajectory analysis indicated delayed myeloid cell differentiation in NR group, with high ENO1-expressing LSCs at the initial stages of differentiation being preserved post-treatment. Simultaneously, ENO1 and stemness-related genes were upregulated and co-expressed in malignant cells during early differentiation. ENO1 level in our AML cohort was significantly higher than the controls, with higher levels in NR compared to those in complete remission. Knockdown of ENO1 in AML cell line resulted in the activation of LSCs, promoting cell differentiation and apoptosis, and inhibited proliferation. ENO1 inhibitor can impede the proliferation of AML cells. Furthermore, survival analyses associated higher ENO1 expression with poorer outcome in AML patients. Conclusions Our findings underscore the critical role of ENO1 as a plausible driver of LSC self-renewal, a potential target for AML target therapy and a biomarker for AML prognosis.

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Single‐cell RNA sequencing gene signatures for classifying and scoring exhausted CD8 + T cells in B‐cell acute lymphoblastic leukaemia

November 2023

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9 Reads

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1 Citation

Citations (1)


... Changes in subset components may have important implications in the prognosis and treatment response of patients with hematological malignancies. Our previous study has demonstrated a skewed distribution for the four discrete stages of exhausted CD8 + T cells in different disease statuses and highlighted that a higher proportion of progenitor exhausted CD8 + T cells may be associated with a more favorable outcome for B-ALL patients [48]. Importantly, Li et al. demonstrated that the combination of the demethylating agent decitabine and anti-PD-1 antibody can promote the activation and expansion of progenitor exhausted CD8 + T cells, effectively suppressing tumor growth in mice [21]. ...

Reference:

BRD4 inhibitor reduces exhaustion and blocks terminal differentiation in CAR-T cells by modulating BATF and EGR1
Single‐cell RNA sequencing gene signatures for classifying and scoring exhausted CD8 + T cells in B‐cell acute lymphoblastic leukaemia
  • Citing Article
  • November 2023