Qiuyue Feng’s research while affiliated with Jinan University and other places

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Publications (3)


Identification of T‐ALL cells in PBMCs and BMMCs from T‐ALL patients by scRNA‐Seq and scTCR‐Seq. (A) Study design schematics. (B) UMAP (Uniform Manifold Approximation and Projection) plot of PBMC scRNA‐seq datasets from two T‐ALL patients and two healthy donors, colour‐coded by five distinct cell types. (C) Dot plot of marker genes for each cell type in PBMCs. Colour scale shows the average normalised expression of marker genes in each cell type, and dot size indicates the percentage of cells within each cell cluster expressing the marker gene. The same applies to all other dot plots in this paper. (D) Same as (B), but colour‐coded by sample origin. P1: Patient1; P2: Patient2; HD1: Healthy donor 1; HD2: Healthy donor 2. (E) Stacked bar chart for summarising the proportion of cell types in PBMCs of each T‐ALL patient and normal donor, according to the scRNA‐Seq datasets. (F) Heatmap of expression levels of selected transcription factors associated with blood malignancy. (G) Same as (B), but colour‐coded by individual TCR clonotype. Patient origin, clonotype ID, clonotype frequency and actual TCRαβ clonotype are shown on top of each UMAP. (H) UMAP plot of BMMC scRNA‐seq dataset from two T‐ALL patients pre‐ and post‐treatment, colour‐coded by six distinct cell types. (I) Dot plot of marker genes for each cell type in BMMCs. (J) Same as (H), but colour‐coded by patient origin and treatment condition. Pre: Pre‐treatment; Post: Post‐treatment. (K) Stacked bar chart for summarising the proportion of cell types in BMMCs of each T‐ALL patient and treatment condition. (L) Comparison of the proportions of T‐ALL cells with two distinct TCR clonotype from Patient2 (P2), pre‐ and post‐treatment.
Characterisation of T‐ALL cells in PBMC and BMMC of T‐ALL patients by scATAC‐Seq. (A) UMAP plot of PBMC scATAC‐seq datasets from two T‐ALL patients and two healthy donors, colour‐coded by five identified cell types. (B) Projection of gene scores of selected marker genes on PBMC scATAC‐Seq UMAP visualisation. Gene score reflects predicted expression of the corresponding gene, higher score implies higher accessibility. (C) Track view of the PBMC scATAC‐Seq data for selected loci in distinct cell types. (D) Same as (A), but colour‐coded by sample origin. (E) Stacked bar chart for summarising the proportion of cell types in PBMCs of each T‐ALL patient and normal donor, according to the scATAC‐Seq datasets. (F) UMAP plot of BMMC scATAC‐seq datasets from two T‐ALL patients pre‐ and post‐treatment, colour‐coded by six identified cell types. (G) Projection of gene scores of selected marker genes on BMMC scATAC‐Seq UMAP visualisation. (H) Track view of the BMMC scATAC‐Seq data for selected loci in distinct cell types. (I) Same as (F), but colour‐coded by sample origin with different treatment condition. P1: Patient1; P2: Patient2; Pre: Pre‐treatment; Post: Post‐treatment. (J) Stacked bar chart for summarising the proportion of cell types in BMMCs of each T‐ALL patient pre‐ and post‐treatment, according to the scATAC‐Seq datasets.
T‐ALL cells specific gene regulatory programs. (A) TFs arranged based on DyNet scores calculated from separate gene regulatory networks in T‐ALL cells and normal T cells. Higher DyNet score implies higher rewiring score for the same regulator (TF or surface protein) in different cell types. (B) Circos plots showing exemplary regulons in normal T cell and T‐ALL cell, respectively. Normal T‐cell‐specific and T‐ALL cell‐specific TF is shown in blue and red, respectively, with links connecting to the target genes. Top 30 target genes are shown for each TF. Node size of target genes is proportional to regulation weight by the corresponding TF. (C) Boxplots comparing the expression levels of KLF3 (left) and TCF12 (right) between normal T cells and T‐ALL cells. For the boxplots, the outlines of the boxes represent the first and third quartiles, the line inside each box represents the median, and boundaries of the whiskers are found within 1.5 times the interquartile range, with dots representing outliers. The same applies to all other boxplots in this manuscript, except stated otherwise. (D) Cell surface proteins arranged based on DyNet scores calculated from separate gene regulatory networks in T‐ALL cells and normal T cells. (E) Comparison of KLF3, TCF12, TCF3, IKZF2 and ETS2 expression levels in healthy donor (HD) and T‐ALL patients. *p < 0.05, **p < 0.01, ***p < 0.001 (Wilcoxon rank‐sum test, two‐sided). (F) OS analysis of KLF3 and TCF3 in T‐ALL patients from JNU dataset. (G) EFS analysis of KLF3 and TCF3 in T‐ALL patients from TARGET database.
Gene regulatory programs associated changes in chromatin accessibility in T‐ALL cells. (A) Stacked barplot of different types of peaks in normal T cells and T‐ALL cells. (B) Ranking of TF binding motifs enriched in chromatin accessibility peaks identified in T‐ALL cells (left) and normal T cells (right). (C) Venn diagram indicating the overlap between differentially enriched TF binding motifs and rewired TFs identified by the DyNet algorithm between T‐ALL cells and normal T cells. (D) Pseudotime trajectory spanning from normal T cells to T‐ALL cells based on chromatin accessibility. (E) Heatmap displaying the changes of gene scores (left) and TF motif accessibility (right) along the pseudotime trajectory from normal T cells to T‐ALL cells. (F) Track view of scATAC‐Seq data, chromatin accessibility peaks and predicted peak‐to‐gene interaction of the KLF3 (left) and TCF12 locus (right).
TCR clonotype‐specific characteristics of T‐ALL cells. (A) Proportion of cell cycle phases in each TCR clonotype depicted by a stacked bar chart. (B) Heatmap illustrating heterogeneity of tumour‐associated signalling pathway activity. (C) Volcano plot for marker genes of the five different TCR clonotypes. (D) Enriched pathways for the upregulated genes in the T‐ALL cells with five different TCR clonotypes. (E) Dot plot depicting the mean expression levels and cell expression proportions of marker genes for thymocytes from different differentiation stages. (F) Comparison of the SPINK2 (top) or PTCRA (bottom) expression levels in each sample. ***p < 0.001 (Wilcoxon rank‐sum test, two‐sided). (G) Browser tracks showing the chromatin accessibility profile and peak‐to‐gene links at the SPINK2 locus (top) or PTCRA locus (bottom) across T‐ALL cells of P1 and P2. (H) Scatterplot showing the log2(Fold Change) of marker genes expression of the three different TCR clonotypes in P1 (top) or two different TCR clonotypes in P2 (bottom).

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Single‐Cell Multiomics Reveals TCR Clonotype‐Specific Phenotype and Stemness Heterogeneity of T‐ALL Cells
  • Article
  • Full-text available

December 2024

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23 Reads

Songnan Sui

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Xiaolei Wei

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Yue Zhu

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Oscar Junhong Luo

T‐cell acute lymphoblastic leukaemia (T‐ALL) is a heterogeneous malignant disease with high relapse and mortality rates. To characterise the multiomics features of T‐ALL, we conducted integrative analyses using single‐cell RNA, TCR and chromatin accessibility sequencing on pre‐ and post‐treatment peripheral blood and bone marrow samples of the same patients. We found that there is transcriptional rewiring of gene regulatory networks in T‐ALL cells. Some transcription factors, such as TCF3 and KLF3, showed differences in activity and expression levels between T‐ALL and normal T cells and were associated with the prognosis of T‐ALL patients. Furthermore, we identified multiple malignant TCR clonotypes among the T‐ALL cells, where the clonotypes consisted of distinct combinations of the same TCR α and β chain per patient. The T‐ALL cells displayed clonotype‐specific immature thymocyte cellular characteristics and response to chemotherapy. Remarkably, T‐ALL cells with an orphan TCRβ chain displayed the strongest stemness and resistance to chemotherapy. Our study provided transcriptome and epigenome characterisation of T‐ALL cells categorised by TCR clonotypes, which may be helpful for the development of novel predictive markers to evaluate treatment effectiveness for T‐ALL.

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