Single-cell sequencing technology to characterize stem T-cell subpopulations in acute T-lymphoblastic leukemia and the role of stem T-cells in the disease process

Yan Li; Zhenwei Jia; Xiaoyan Liu; Hongbo Zhao; Guirong Cui; Jianmin Luo; Xiaoyang Kong

doi:10.18632/aging.206123

Research Paper Volume 16, Issue 20 pp 13117—13131

Single-cell sequencing technology to characterize stem T-cell subpopulations in acute T-lymphoblastic leukemia and the role of stem T-cells in the disease process

Yan Li^1, , Zhenwei Jia^1, , Xiaoyan Liu^1, , Hongbo Zhao^1, , Guirong Cui^1, , Jianmin Luo^2, , Xiaoyang Kong^1, ,

¹ Department of Hematology, Handan First Hospital, Handan, Hebei 056001, China
² Department of Hematology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, China

Received: March 1, 2024 Accepted: July 17, 2024 Published: October 17, 2024

https://doi.org/10.18632/aging.206123
How to Cite

Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Precursor T-cell acute lymphoblastic leukemia (Pre-T ALL) is a malignant neoplastic disease in which T-cells proliferate in the bone marrow. Single-cell sequencing technology could identify characteristic cell types, facilitating the study of the therapeutic mechanisms in Pre-T ALL.

Methods: The single-cell sequencing data (scRNA-seq) of Pre-T ALL were obtained from public databases. Key immune cell subpopulations involved in the progression of Pre-T ALL were identified by clustering and annotating the cellular data using AUCell. Next, pseudo-temporal analysis was performed to identify the differentiation trajectories of immune cell subpopulations using Monocle. Copy number mutation landscape of cell subpopulations was characterized by inferCNV. Finally, cellphoneDB was used to analyze intercellular communication relationships.

Results: A total of 10 cellular subpopulations were classified, with Pre-T ALL showing a higher proportion of NK/T cells. NK/T cells were further clustered into two subpopulations. Stem T cells showed a high expression of marker genes related to hematopoietic stem cells, Naive T cells had a high expression of CCR7, CCR7, RCAN3, and NK cells high-expressed KLRD1, TRDC. The cell proliferation was reduced and the activation of T cell was increased during the differentiation of stem T cells to Naive T cells. We observed interaction between stem T cells with dendritic cells such as CD74-COPA, CD74-MIF as well as co-inhibition-related interactions such as LGALS9-HAVCR2, TGFB1-TGFBR3.

Conclusion: Stem T cells were involved in the development of Pre-T-ALL through the regulatory effects of transcription factors (TFs) KLF2 and FOS and multiple ligand-receptor pairs.

Introduction

Acute lymphoblastic leukemia (ALL) is a type of blood cancer that results from the overgrowth and accumulation of precursor cells of the immune system in the bone marrow or at the site other than bone marrow. ALL exhibits T-cell and B-cell phenotypic subpopulations [1]. More than half of new B-cell ALL cases occur in children. In recent decades, the treatment for pediatric ALL has been significantly improved, with contemporary chemotherapy regimens reaching 90% cure rate in numerous subpopulations [2]. Primary lymphocytes proliferate and accumulate abnormally in the bone marrow, which inhibits hematopoietic function, causes infection and anemia, and invades tissues outside the bone marrow [3]. Acute T-lymphoblastic leukemia (T-ALL) accounts for 15–25% of ALL cases, with a median onset age of o18 years old. T-ALL often occurs to males than females with a lower complete remission rate and a median survival of 11–17 months [4, 5]. T-ALL could be subdivided into pre-T-cellular (Pre-T-ALL) and mature T-cellular, however, the prognosis of Pre-T-ALL is not as favorable as that of the latter [6]. Currently, chemotherapeutic drugs are commonly used in clinical practice to treat T-ALL, but a high toxicity and side effects including neurotoxicity and drug tolerance of the drugs could all contribute to a poor prognosis of T-ALL [7]. Thus, exploring the pathogenesis of T-ALL and Pre-T-ALL is of great significance for the development of targeted therapeutic strategies.

Cytotoxic T cells, Th1 cells, Th2, Th17 and regulatory T cells are the T cell subsets that influence the development of T-ALL [8]. Mechanistically, different T cell subsets influence ALL development through the release of cytokines, including myeloid cell development and maturation-associated factors ThPOK, IL-7, IL-3, GM-CSF, etc. [9]. Currently, therapeutic regimens using cytokines, especially interleukins, to treat T-cell-associated malignancies including T-ALL are now emerging [10, 11]. A study revealed that CD2 deficiency downregulates the anti-tumor capacity of CAR-T cells in a xenograft mouse model, whereas treatment with exogenous interleukins could effectively reverse the anti-tumor effector capacity of immune cells, which supports the adjuvant therapeutic role of T-cell factor release in T-ALL [12]. Abnormal levels of several cytokines together with Treg are involved in the pathogenesis of leukemia. Study found that the proportion of Treg and the levels of IL-10 and TGF-β are higher in the peripheral blood of leukemia patients as compared to those of healthy control subjects, and confirmed that IL-10 and TGF-β produced by Treg could inhibit the proliferation of effector T cell proliferation in vitro [13]. In addition, IL-35 level in the bone marrow and peripheral blood of newly diagnosed leukemia patients is significantly elevated as compared to healthy subjects and its level is closely associated with treatment remission and relapses, suggesting that IL-35 is involved in the pathogenesis and prognosis of AML [14]. However, the mechanisms through which these T-cell subsets interacted with each other and bound to other immune cells to regulate T-ALL progression have not been adequately investigated. Moreover, in-depth characterization of crucial T-cell subsets in the regulation of T-ALL as well as their interactions with immune cells may contribute to the development of potential therapeutic targets and improvement of clinical outcomes for patients.

Advances in single-cell sequencing technology [15] allow for more accurate identification of immune cell subsets closely associated with leukemia pathogenesis [16] and many other diseases [17, 18]. Previous study revealed the characteristics of the immune microenvironment in high-risk B-cell acute lymphoblastic leukemia using single-cell transcriptome analysis, and analyzed the functional activation of NK cells and DC cell subsets as well as the specific expression of NF-κB, CD19, and Bruton’s tyrosine kinase during B-ALL progression [19]. Immune cell subpopulations crosstalk with the tumor immune microenvironment through ligand-receptor interactions, further leading to the remodeling of tumor immune microenvironment [20, 21]. The development of single-cell sequencing technology has greatly facilitated the analysis of immune cell subpopulations and corresponding ligand-receptors, which was also employed in the present study.

In this study, we analyzed scRNA-seq data from re- T-ALL and healthy bone marrow samples to identify stem T cell subsets and key transcription factors and receptor/ligand molecules involved in interactions during Pre-T-ALL progression. In addition, we performed pseudo-temporal analysis to explore stem T-cell differentiation trajectories and to uncover potential mechanisms underlying the developmental process of Pre-T-ALL.

Materials and Methods

Data collection and processing

We downloaded 2 Pre-T ALL samples and 3 healthy pediatric bone marrow mononuclear cells from GSE132509 in the Gene Expression Omnibus (GEO) website (https://www.ncbi.nlm.nih.gov/geo/). After removing mitochondrial, ribosomal and hemoglobin genes, the Read10X function in the Seurat package was used to read the scRNA-seq data of each sample to collect cells with a gene number of 200–6000 and a proportion of mitochondrial genes <10% [22, 23]. Next, the SCTransform function was used for normalization, and the harmony package [24] was used to remove the batch effect among different samples after principal component analysis (PCA) dimensionality reduction. Unified surface approximation and projection (UMAP) dimensionality reduction was performed using the RunUMAP function. Clustering was conducted applying the FindNeighbors and FindClusters functions with the parameters of dims=1:20, resolution=0.2 (dims=1:15, resolution=0.1 for further subdivision of NK/T cells 1 and NK/T cells 2). Clustering analysis was conducted according to CellMarker2.0 (CellMarker or http://117.50.127.228/CellMarker/) database to annotate cell subpopulations with marker genes [25]. To explore the heterogeneity of gene expression patterns among cell subpopulations, the FindAllMarkers function was used to calculate the differentially expressed genes (DEGs) among each cell subpopulation with parameters of logfc.threshold=0.25, min.pct=0.25, only.pos=TRUE.

Functional enrichment analysis

The expression data of DEGs were imported to the DAVID database (https://david.ncifcrf.gov/) to explore the enriched biological processes of these genes among the cellular subpopulations (P < 0.05).

Pseudo-time analysis

Pseudo-time analysis was performed using Monocle, which allows for the construction of dynamic trajectories of cell differentiation to help reveal the transition process between cell states [26, 27]. Advanced algorithms of Monocle support the inference of cell fate from complex data, which is critical to our exploration of how stem T cells evolve into native T cells and participate in pathological processes. For Monocle 3, we used the new_cell_data_set function to create CDS objects. The reduce_dimension function was applied to perform PCA and UMAP dimensionality reduction. The trajectory was developed using learn_graph function by setting the subpopulation of stem T cells as the starting point of the trajectory. Finally, the plot_genes_ in_pesudotime function was used to present changes in the expression level of the genes of interest with pseudotime.

For monocle 2 [28], the newCellDataSet function was used to construct the object, the FindAllMarkers function was used to calculate the DEGs between stem T cells and Naive T cells 1 (avg_log2FC>0.25 and p_val_adj<0.01). Trajectories were downscaled and developed based on these genes applying the reduceDimension function (max_components=2, method=“DDRTree”). The orderCells function was employed to set the starting point of the trajectory with more states containing stem T cells. To identify the pseudotime-related genes (q-val < 0.01), the differentialGeneTest function (fullModelFormulaStr=“~sm.ns(Pseudotime)”) was used. Changes in the expression level of the genes of interest with the pseudotime were shown by the plot_pseudotime_heatmap function.

The inferCNV analysis

We used the inferCNV software to portray the landscape of copy number variation in genomic regions of stem T cells in the pre-T group [29], with B cells in the healthy control group as the reference. Other parameters were set to cluster_by_groups=TRUE, analysis_mode= “subclusters”, HMM_type=“i3”, denoise=TRUE, HMM_report_by=“subcluster”, HMM=TRUE.

SCENIC analysis

SCENIC is an innovative algorithm for GRNs developed specifically for single-cell data. SCENIC introduces gene co-expression networks inferred from the motif sequence validation statistics of transcription factors (TFs) to identify highly reliable TF-dominated GRNs [30]. Potential target genes for each TF were screened by the GENIE3 method, and the top 5 potential target genes were used to construct the TF regulatory networks. The TF-target gene relationship pairs were identified, and the AUCell function was used to calculate the degree of regulon activity in each cell. In this study, AUCell function was used for analysis due to its ability to efficiently assess the activity of specific sets of genes in individual cells, which helps determine the cells having active TF regulatory networks [31]. This was critical for understanding the role of stem cell T cells in Pre-T ALL. Finally, the SCENIC package was utilized to score the activity of each regulon in each cell.

Cellular subpopulation interactions

To explore the interactions between T cell subpopulations and other cell subpopulations in the Pre-T-ALL group, we first used cellphoneDB [32] to construct a network of ligand-receptor interactions between all cell subpopulations. Next, the ktplots package was used to show costimulatory, coinhibitory, chemokines, and regulatory-related ligand-receptor pairs between stem T cells and other cell subpopulations [32].

Statistical analysis

The R package employed in this study was obtained from the Bioconductor R project. Data analysis was performed by R software (version: 4.1.1). The data were pre-processed in the SangerBox 3.0 (http://sangerbox.com/home.html). For all the statistical tests, p < 0.05 was considered as statistically significant.

Data availability statement

The datasets generated and/or analyzed during the current study are available in the (GSE132509) repository, (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132509).

Results

Cell types in PT-ALL samples

The scRNA-seq analysis was performed to investigate the cellular subpopulations and related molecular characteristics of Pre-T-ALL samples (grouped as PRE-T) and healthy bone marrow samples (grouped as PBMMC). After cell data filtering, normalization, and downscaling and clustering analyses, a total of 24,505 cells were obtained and clustered into 10 cellular subpopulations (Figure 1A). The expression of the marker genes in cellular subpopulations were as follows: CLEC10A gene had a significantly high expression level and a high percentage expression in dendritic cells; IRF8 gene was also high-expressed in dendritic cells but with a slightly lower percentage of expression; CD34 gene was high-expressed in hematopoietic stem cells 1; AURKB gene was high-expressed in proliferating cells and had a higher percentage of expression; GZMA and KLRB1 genes were significantly expressed in the NK/T cell 1 subpopulation (Figure 1B, 1C). We found that the number of NK/T cells 1 and NK/T cells 2 were higher in the PRE-T group (Figure 1D, 1E). This suggested that NK/T cell function may be altered in pre-T ALL, and that these alterations could promote disease progression.

Single-cell mapping of pre-T childhood leukemia. (A) UMAP downscaling plot for clustering and annotation of pre-T leukemia and healthy control bone marrow mononuclear cells. (B) Bubble plots of marker gene expression profiles used for cell subpopulations. (C) Heatmap of marker gene expression profiles used to annotate cell subpopulations. (D) Proportion of each cell subpopulation between the two groups. (E) Proportion of each cell subpopulation within all cells.

Figure 1. Single-cell mapping of pre-T childhood leukemia. (A) UMAP downscaling plot for clustering and annotation of pre-T leukemia and healthy control bone marrow mononuclear cells. (B) Bubble plots of marker gene expression profiles used for cell subpopulations. (C) Heatmap of marker gene expression profiles used to annotate cell subpopulations. (D) Proportion of each cell subpopulation between the two groups. (E) Proportion of each cell subpopulation within all cells.

Stem T-cell subpopulation clustering in Pre-T-ALL

To characterize the heterogeneity of NK/T cells in Pre-T-ALL, NK/T cells 1 were further divided into two subpopulations (stem T cells and Naive T cells 1) in this study (Figure 2A). Specifically, stem T cells high-expressed hematopoietic stem cell-related marker genes ALDHA2, RUNX1, MSI2, MYB, and Naive T cells 1 high-expressed CCR7 (Figure 2B). Interestingly, 2,385 of all the stem T cells were found in the PRE-T group, while only 5 were found in the PBMMC group. The vast majority of stem T cells in the PRE-T group suggested a potential association between stem T and Pre-T-ALL pathogenesis and progression (Figure 2C). Next, we further divided NK/T cells 2 into two subpopulations (Figure 2D). Among them, Naive T cells 2 high-expressed CCR7 and RCAN3, while NK cells high-expressed marker genes such as KLRD1, TRDC, PRF1 (Figure 2E). Meanwhile, it was found that the proportion of Naive T cells 2 was higher in PRE-T group as compared to PBMMC (Figure 2F). These findings reveal potential distributional and functional differences between stem T cells and Naive T cells in leukemia, which could help improve the understanding of the complex microenvironment in leukemia.

Identification of stem T cell subsets within the pre-T group. (A) UMAP plot of further subdivision of NK/T cells 1. (B) Violin plot of marker gene expression levels of NK/T cells 1 subpopulations. (C) Proportion of NK/T cells 1 subpopulation within pre-T and control groups, respectively. (D) UMAP plot of further subdivision of NK/T cells 2 subpopulation. (E) Violin plot of marker gene expression levels in the NK/T cells 2 subpopulation. (F) Proportion of NK/T cells 2 subpopulation within pre-T and control groups, respectively.

Figure 2. Identification of stem T cell subsets within the pre-T group. (A) UMAP plot of further subdivision of NK/T cells 1. (B) Violin plot of marker gene expression levels of NK/T cells 1 subpopulations. (C) Proportion of NK/T cells 1 subpopulation within pre-T and control groups, respectively. (D) UMAP plot of further subdivision of NK/T cells 2 subpopulation. (E) Violin plot of marker gene expression levels in the NK/T cells 2 subpopulation. (F) Proportion of NK/T cells 2 subpopulation within pre-T and control groups, respectively.

Evolution of differentiation of stem T cells to Naive T cells

To further characterize the dynamic evolution of stem T cells to Naive T cells, we constructed cell differentiation trajectories using Monocle 3 and Monocle 2 (Figure 3A, 3C). It was found that the expression levels of genes related to cell proliferation such as CDKN2D, CDC25B, FGFR1, PSMB2, etc., were gradually downregulated during the differentiation of stem T cells to Naive T cells. The expression levels of T cell activation-related genes such as B2M, CD8B, CD74, CD44, TRAC, etc. were gradually upregulated (Figure 3B, 3D). These results indicated that the cell proliferation ability was gradually inhibited and the T cell activation and activation ability was promoted during the differentiation of stem T cells to Naive T cells, which was of great significance in revealing the pathogenesis of Pre-T-ALL.

Construction of a differentiation trajectory from Stem T cells to Naive T cells 1. (A) Differentiation trajectory from Stem T cells to Naive T cells 1 was constructed using Monocle 3. (B) Scatterplot of the expression levels of the genes of interest as a function of pseudotime. (C) Differentiation trajectory from Stem T cells to Naive T cells 1 constructed with Monocle 2. (D) Heatmap of the expression level of the gene of interest with pseudotime.

Figure 3. Construction of a differentiation trajectory from Stem T cells to Naive T cells 1. (A) Differentiation trajectory from Stem T cells to Naive T cells 1 was constructed using Monocle 3. (B) Scatterplot of the expression levels of the genes of interest as a function of pseudotime. (C) Differentiation trajectory from Stem T cells to Naive T cells 1 constructed with Monocle 2. (D) Heatmap of the expression level of the gene of interest with pseudotime.

Landscape of copy number variation within stem T cells

To further characterize the copy number variation of stem T cells in pre-T childhood leukemia, regions of amplification and deletion within 22 pairs of autosomes were identified using inferCNV. We found that stem T cells mainly had amplification of chr7q, chr11q, and chr12p and deletions of chr6p and chr22p (Figure 4A). Interestingly, the genes with the copy number amplification regions were all associated with cell cycle, proliferation, DNA replication, and cell growth (Figure 4B, 4C), while the genes with copy number deletion region were all related to T cell activation, apoptotic process, and T cell receptor signaling pathway (Figure 4D, 4E). These results suggested that enhanced proliferative capacity of stem T cells was associated with the amplification of chr7q, chr11q and chr12p and their declined immunoreactivity was associated with the deletion of chr6p and chr22p.

Inscription of copy number variation within Stem T cells. (A) Heatmap of copy number variation within Stem T cells analyzed by inferCNV. (B) BPs enriched for chr7p amplicons. (C) BPs enriched for chr11q and chr12p amplicons. (D) BPs enriched for chr6p deletions. (E) BPs enriched for chr22p deletions.

Figure 4. Inscription of copy number variation within Stem T cells. (A) Heatmap of copy number variation within Stem T cells analyzed by inferCNV. (B) BPs enriched for chr7p amplicons. (C) BPs enriched for chr11q and chr12p amplicons. (D) BPs enriched for chr6p deletions. (E) BPs enriched for chr22p deletions.

Alterations in transcription factor activity in stem T cells in Pre-T-ALL

To further identify the TFs in stem T cells associated with pre-T ontogeny, we constructed a TF regulatory network and used the AUCell algorithm to determine the degree of activity of each TF in each cell. The results showed higher activity scores for FOSB, KLF2, JUNB, JUN, KLF6 and YY1 in the pre-T group (Figure 5A). Among them, KLF2 was significantly enriched in negative regulation of apoptotic process, intracellular signal transduction, regulation of cell proliferation, positive regulation of cell migration, and Ras protein signal transduction, while FOS was significantly enriched in cell migration, regulation of cell proliferation, and regulation of GTPase activity, Ras protein signal transduction, regulation of cell growth, and JAK-STAT cascade biological functions (Figure 5B, 5C). Overall, these results suggested that these factors may play an important role in regulating disease-related biological processes.

SCENIC analysis within Stem T cells. (A) Degree of activity of each transcription factor within Stem T cells between pre-T and control. (B) Target genes of KLF2 enriched to BP. (C) Target genes of FOS enriched to BP.

Figure 5. SCENIC analysis within Stem T cells. (A) Degree of activity of each transcription factor within Stem T cells between pre-T and control. (B) Target genes of KLF2 enriched to BP. (C) Target genes of FOS enriched to BP.

Communication between stem T cells and other cells in the pre-T group

To investigate the mechanism of stem T cells on the pathogenesis and progression of Pre-T-ALL, the interactions between stem T cells and other cell subpopulations in the bone marrow were analyzed employing CellPhoneDB. The cellular communication network showed multiple ligand-receptor pair interactions between stem T cells and other cells, for example, co-stimulation-related interactions between stem T cells and dendritic cells such as CD74-COPA, CD74-MIF and co-inhibition-related interactions such as LGALS9-HAVCR2, TGFB1-TGFBR3 (Figure 6).

Figure 6. CellPhoneDB analysis of Stem T cells and other cells within the pre-T group.

Discussion

This study analyzed scRNA-seq data from Pre-T-ALL and healthy bone marrow samples. Based on single-cell profiles, we found that NK/T cells were significantly upregulated in Pre-T-ALL. Studies have shown an interactive relationship between NK cells and cancer cells, and that upregulation of activated receptors on NK cells can exert anti-cancer cell effects [33]. However, abnormal aggregation of NK cells in the bone marrow could impair the hematopoietic function of the organism, and NK cells have a direct regulatory effect on the carcinogenesis of hematopoietic stem cells through the secretion of a series of pro-inflammatory factors, such as TNF-α and IFN-γ, which in turn will also contribute to the progression of Pre-T-ALL [34]. In terms of immune escape mediation, NK cells secrete immunomodulatory factors such as TGF-β and IL-10 to promote the immune escape of malignant hematopoietic stem cells [35, 36]. In addition, NK cells interact with tumor-associated macrophages, tumor-associated fibroblasts and regulatory T cells in the immune microenvironment, leading to the formation of an immunosuppressive microenvironment to further enhance the proliferation of malignant hematopoietic stem cells [37]. High levels of T-cell infiltration in Pre-T-ALL are associated with the malignant progression of the disease, as leukemia cells originate from T-cell precursors, in particular, abnormal accumulation of immature T-cells in the bone marrow could promote disease progression through the secretion of pro-inflammatory factors and immunosuppressive molecules [37, 38]. The high infiltration of NK cells and T-cells is an indicator of malignancy of Pre-T-ALL, providing a basis for the subsequent analysis of this study.

NK/T cells were further clustered into Naive T cells and stem T cells. Specifically, several marker genes ALDHA2, RUNX1, MSI2, and MYB were upregulated and expressed in stem T cells, and these genes were closely related to the activation and proliferation of hematopoietic stem cells. As a class of stem cells with self-renewal ability in bone marrow and blood, stem T cells are mainly responsible for producing red blood cells, white blood cells and platelets [28]. The development of leukemia is often associated with abnormalities in hematopoietic stem cells, and genetic changes in hematopoietic stem cells during differentiation can cause imbalance to the regulation of cell proliferation and differentiation, thereby inducing the formation of malignant proliferating leukocytes [39]. ALDHA2 belongs to the aldehyde dehydrogenase (ALDH) family. The aberrant expression of ALDHA2 in stem T cells is mechanistically linked to the development of leukemia through elevating ROS activity, lipid peroxidation and platelet production. In particular, ALDHA2 promotes B-cell carcinogenesis and the development of multiple myeloma [40]. RUNX1 activates the AP-1/GATA2 axis in the bone marrow microenvironment, thereby leading to the aberrant proliferation of leukemia stem cells and the promotion of their immune escape [41]. The regulatory role of MSI2 is mainly reflected in its ability to further promote CD4+ and CD8+ T cell infiltration and inflammatory responses by mediating dendritic cell maturation and migration [42]. Overexpression of MYB is one of the factors that promotes the aberrant proliferation of certain lymphocyte precursors through enhancing the aberrant proliferation and accumulation of mature B cells [43]. These results suggested that marker genes aberrantly expressed by stem T cells in Pre-T-ALL played important roles in regulating the bone marrow microenvironment and disease progression, especially in modulating immune responses and promoting immune escape. Hence, future studies are encouraged to explore the development of antibodies or small molecule inhibitors against NK/T cell surface molecules to inhibit the proliferation and survival of stem T cells and suppress the progression Pre-T-ALL.

Immune cell interactions and interconversions in the bone marrow microenvironment play a regulatory role in leukemia progression. Previous study showed that monocytes in the bone marrow microenvironment mainly interact with monocyte precursor cells ST-HSC, MLP, and MDP, which are associated with processes such as cell migration, phosphorylation regulation, and inflammatory responses and will in turn affect leukemia progression [44]. In this study, the analysis of cellular communication relationships revealed dynamic evolution from stem T cells to Naive T cells, during which the expression levels of genes such as CDKN2D, CDC25B, FGFR1, and PSMB2 were gradually downregulated, while the expressions of genes such as B2M, CD8B, CD74, CD44, and TRAC were gradually upregulated. And most of these downregulated genes were related to cell proliferation, especially CDKN2D, a gene-encoding protein of the CDK inhibitor family. CDKN2D plays an important role in cell cycle regulation by inhibiting cell-cycle-dependent kinases to regulate cell proliferation to prevent uncontrolled cell division, particularly in the G1 phase [45]. In acute promyelocytic leukemia (APL), the fusion protein PML/RARα downregulates the expression of CDKN2D by suppressing its promoter activity, which in turn affects leukemia cell proliferation and differentiation [46]. Upregulated genes are closely related to the regulation of T cell activity. For example, previous study found that B2M recognizes and activates T cell-mediated immune responses by stabilizing the expression of MHC I molecules on the cell surface, affecting their activity in the clearance of leukemia cells [47]. Noticeably, TRAC gene plays a key role in regulating the mutual killing effect of CAR-T cells, therefore targeted silencing of this gene may promotes the therapeutic effect of CAR-T cells on leukemia [48, 49]. These results indicated that the pathogenesis of Pre-T-ALL is associated with a reduced cell proliferation capacity of Naive T cells and increased activation capacity of T cells, providing novel insights into the pathogenesis of Pre-T-ALL.

The present study further analyzed and elucidated the ligand-receptor relationship between stem T cells and dendritic cells, and found a strong CD74-COPA co-stimulatory interaction and a strong LGALS9-HAVCR2 co-inhibitory interaction between the two types of cells. CD74 is a type II transmembrane glycoprotein involved in the regulation of antigen presentation, formation of major histocompatibility complex class II proteins, and B-cell maturation and survival. CD74 is aberrantly expressed in acute myeloid leukemia and is associated with aberrant activation of lymphocytes and the pathogenesis of Pre-T-ALL [50, 51]. COPA affects cancer therapeutic response through the remodeling of immune microenvironment in a variety of cancers, and activation of CD74-APP/COPA/MIF on the surface of B cells facilitates the suppressive immunomodulatory effects of M2 macrophages and Tregs [52]. It is possible that the activation of COPA affects leukemia pathogenesis and progression through immunosuppressive effects. Studies have shown that LGALS9 is cytotoxic to leukemic stem cells derived from patients with ALL, and that the protein encoded by this gene does not show killing activity against normal stem cells of the organism, suggesting an important role of its high expression for the targeted treatment of ALL [53, 54]. HAVCR2 regulates T-cell depletion and acts as an immune checkpoint in immune escape and immunotherapy of leukemia [55]. Another study revealed an interactive relationship between LGALS9 and HAVCR2, which suppresses immune responses to induce immune tolerance by regulating T cell apoptosis or inhibiting their differentiation [56]. Taken together, the ligand-receptor relationship between stem T cells and dendritic cells promoted Pre-T-ALL progression by regulating the bone marrow microenvironment mainly through aberrant activation of T cells. The role of CD74 and COPA in regulating the immunosuppressive microenvironment as well as the function of LGALS9 and HAVCR2 in regulating T-cell responses signified that they may be potential targets for the treatment of acute T-cell lymphoblastic leukemia. A deeper understanding of these complex immunoregulatory processes could facilitate the development of more precise and effective treatments to improve patients’ survival and quality of life. However, our study still had certain limitations. For instance, the sample size should be expanded to enhance the generalizability and reliability of our findings. Additionally, to achieve a deeper understanding of the cellular interactions and their underlying molecular mechanisms, we planned to employ a series of bioinformatics methods to investigate key regulatory factors and signaling pathways. Finally, we aimed to design both in vitro and in vivo experiments to validate the roles of critical genes and signaling pathways as well as their specific functions in Pre-T ALL.

In conclusion, the present study performed single-cell technology analysis to reveal a cellular subpopulation potentially associated with the development of Pre-T-ALL. Our findings indicated that stem T cells had a potential promoting effect on Pre-T-ALL through the ligand-receptor interaction relationship. However, this study also had some limitations. Despite the discovery of potential cell subpopulations affecting Pre-T-ALL, we lacked cellular experiments and animal experiments to validate the results, which will be the focus of our subsequent research.

Abbreviations

Pre-T-ALL: Pre-T acute lymphoblastic leukemia; scRNA-seq: Single-cell transcriptome sequencing; PCA: Principal component analysis; UMAP: Uniform Manifold Approximation and Projection; GEO: Gene Expression Omnibus; NK: Natural killer; DC: Dendritic cell; PBMMC: Peripheral blood mononuclear cell.

Author Contributions

All authors contributed to this present work: (YL) and (ZWJ) designed the study, (XYL) and (HBZ) acquired the data, (GRC) and (JML) improved the figure quality. (XYK), (YL) and (XYL) drafted the manuscript, and (HBZ) and (YL) revised the manuscript. All authors read and approved the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Ethical Statement

Ethnic approval was not required for this study because it is not involved any human and animal experiments.

Funding

This study is funded by the Research Fund of the Health and Family Planning Commission of Hebei Province (20220496).

References

1. Franquiz MJ, Short NJ. Blinatumomab for the Treatment of Adult B-Cell Acute Lymphoblastic Leukemia: Toward a New Era of Targeted Immunotherapy. Biologics. 2020; 14:23–34. https://doi.org/10.2147/BTT.S202746 [PubMed]
2. Pui CH, Evans WE. Treatment of acute lymphoblastic leukemia. N Engl J Med. 2006; 354:166–78. https://doi.org/10.1056/NEJMra052603 [PubMed]
3. Yoon JH, Lee S. Diagnostic and therapeutic advances in adults with acute lymphoblastic leukemia in the era of gene analysis and targeted immunotherapy. Korean J Intern Med. 2024; 39:34–56. https://doi.org/10.3904/kjim.2023.407 [PubMed]
4. Testa U, Sica S, Pelosi E, Castelli G, Leone G. CAR-T Cell Therapy in B-Cell Acute Lymphoblastic Leukemia. Mediterr J Hematol Infect Dis. 2024; 16:e2024010. https://doi.org/10.4084/MJHID.2024.010 [PubMed]
5. Barrios-Palacios D, Organista-Nava J, Balandrán JC, Alarcón-Romero LDC, Zubillaga-Guerrero MI, Illades-Aguiar B, Rivas-Alarcón AA, Diaz-Lucas JJ, Gómez-Gómez Y, Leyva-Vázquez MA. The Role of miRNAs in Childhood Acute Lymphoblastic Leukemia Relapse and the Associated Molecular Mechanisms. Int J Mol Sci. 2023; 25:119. https://doi.org/10.3390/ijms25010119 [PubMed]
6. Walia Y, de Bock CE, Huang Y. The landscape of alterations affecting epigenetic regulators in T-cell acute lymphoblastic leukemia: Roles in leukemogenesis and therapeutic opportunities. Int J Cancer. 2024; 154:1522–36. https://doi.org/10.1002/ijc.34819 [PubMed]
7. DuVall AS, Wesevich A, Larson RA. Developing Targeted Therapies for T Cell Acute Lymphoblastic Leukemia/Lymphoma. Curr Hematol Malig Rep. 2023; 18:217–25. https://doi.org/10.1007/s11899-023-00706-7 [PubMed]
8. Summers RJ, Teachey DT, Hunger SP. How I treat early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) in children. Blood. 2024. [Epub ahead of print]. https://doi.org/10.1182/blood.2023023155 [PubMed]
9. Aminov S, Giricz O, Melnekoff DT, Sica RA, Polishchuk V, Papazoglu C, Yates B, Wang HW, Sahu S, Wang Y, Gordon-Mitchell S, Leshchenko VV, Schinke C, et al. Immunotherapy-resistant acute lymphoblastic leukemia cells exhibit reduced CD19 and CD22 expression and BTK pathway dependency. J Clin Invest. 2024; 134:e175199. https://doi.org/10.1172/JCI175199 [PubMed]
10. Chraa D, Naim A, Olive D, Badou A. T lymphocyte subsets in cancer immunity: Friends or foes. J Leukoc Biol. 2019; 105:243–55. https://doi.org/10.1002/JLB.MR0318-097R [PubMed]
11. Du W, Huang X, Liu R, Ye F, Li X, Sun B, Li H. Transcriptome analysis of tertiary lymphoid structures (TLSs)-related genes reveals prognostic value and immunotherapeutic potential in cancer. Oncologie. 2024; 26:287–300. https://doi.org/10.1515/oncologie-2023-0372
12. Xiang J, Devenport JM, Carter AJ, Staser KW, Kim MY, O' Neal J, Ritchey JK, Rettig MP, Gao F, Rettig G, Turk R, Lee BH, Cooper ML, DiPersio JF. An "off-the-shelf" CD2 universal CAR-T therapy for T-cell malignancies. Leukemia. 2023; 37:2448–56. https://doi.org/10.1038/s41375-023-02039-z [PubMed]
13. Szczepanski MJ, Szajnik M, Czystowska M, Mandapathil M, Strauss L, Welsh A, Foon KA, Whiteside TL, Boyiadzis M. Increased frequency and suppression by regulatory T cells in patients with acute myelogenous leukemia. Clin Cancer Res. 2009; 15:3325–32. https://doi.org/10.1158/1078-0432.CCR-08-3010 [PubMed]
14. Wang J, Tao Q, Wang H, Wang Z, Wu F, Pan Y, Tao L, Xiong S, Wang Y, Zhai Z. Elevated IL-35 in bone marrow of the patients with acute myeloid leukemia. Hum Immunol. 2015; 76:681–6. https://doi.org/10.1016/j.humimm.2015.09.020 [PubMed]
15. Shahrajabian MH, Sun W. Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application. Current Pharmaceutical Analysis. 2023; 19:267–81. https://doi.org/10.2174/1573412919666230406100948
16. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, Schwartz S, Yosef N, Malboeuf C, Lu D, Trombetta JJ, Gennert D, Gnirke A, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature. 2013; 498:236–40. https://doi.org/10.1038/nature12172 [PubMed]
17. Zulibiya A, Wen J, Yu H, Chen X, Xu L, Ma X, Zhang B. Single-Cell RNA Sequencing Reveals Potential for Endothelial-to-Mesenchymal Transition in Tetralogy of Fallot. Congenital Heart Disease. 2023; 18: 611–25. https://doi.org/10.32604/chd.2023.047689
18. Matsushita Y, Ono W, Ono N. Synergy of single-cell sequencing analyses and in vivo lineage-tracing approaches: A new opportunity for stem cell biology. Biocell. 2022; 46:1157–62. https://doi.org/10.32604/biocell.2022.018960 [PubMed]
19. Cai Y, Chen X, Lu T, Yu Z, Hu S, Liu J, Zhou X, Wang X. Single-cell transcriptome analysis profiles the expression features of TMEM173 in BM cells of high-risk B-cell acute lymphoblastic leukemia. BMC Cancer. 2023; 23:372. https://doi.org/10.1186/s12885-023-10830-5 [PubMed]
20. Lv M, Chen M, Zhang R, Zhang W, Wang C, Zhang Y, Wei X, Guan Y, Liu J, Feng K, Jing M, Wang X, Liu YC, et al. Manganese is critical for antitumor immune responses via cGAS-STING and improves the efficacy of clinical immunotherapy. Cell Res. 2020; 30:966–79. https://doi.org/10.1038/s41422-020-00395-4 [PubMed]
21. Du H, Xu T, Cui M. cGAS-STING signaling in cancer immunity and immunotherapy. Biomed Pharmacother. 2021; 133:110972. https://doi.org/10.1016/j.biopha.2020.110972 [PubMed]
22. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019; 177:1888–902.e21. https://doi.org/10.1016/j.cell.2019.05.031 [PubMed]
23. Kodous AS, Balaiah M, Ramanathan P. Single cell RNA sequencing – a valuable tool for cancer immunotherapy: a mini review. Oncologie. 2023; 25:635–9. https://doi.org/10.1515/oncologie-2023-0244
24. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019; 16:1289–96. https://doi.org/10.1038/s41592-019-0619-0 [PubMed]
25. Hu C, Li T, Xu Y, Zhang X, Li F, Bai J, Chen J, Jiang W, Yang K, Ou Q, Li X, Wang P, Zhang Y. CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023; 51:D870–6. https://doi.org/10.1093/nar/gkac947 [PubMed]
26. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014; 32:381–6. https://doi.org/10.1038/nbt.2859 [PubMed]
27. Cheng J, Smyth GK, Chen Y. Unraveling the timeline of gene expression: A pseudotemporal trajectory analysis of single-cell RNA sequencing data. F1000Res. 2023; 12:684. https://doi.org/10.12688/f1000research.134078.2 [PubMed]
28. Ahmadinia L, Rangrej SB, Miranda M, Shailer C, Ahmed W, Carvalho V, Rathore R. The Potential Role of NOD2/CARD15 Genotype as a Prognostic Indicator for Bone Marrow Transplantation Outcomes in Patients With Acute Lymphoblastic Leukemia and Acute Myeloid Leukemia: A Systematic Review. Cureus. 2024; 16:e52329. https://doi.org/10.7759/cureus.52329 [PubMed]
29. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt-Rosen O, Suvà ML, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014; 344:1396–401. https://doi.org/10.1126/science.1254257 [PubMed]
30. Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine JC, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts S. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017; 14:1083–6. https://doi.org/10.1038/nmeth.4463 [PubMed]
31. Van de Sande B, Flerin C, Davie K, De Waegeneer M, Hulselmans G, Aibar S, Seurinck R, Saelens W, Cannoodt R, Rouchon Q, Verbeiren T, De Maeyer D, Reumers J, et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat Protoc. 2020; 15:2247–76. https://doi.org/10.1038/s41596-020-0336-2 [PubMed]
32. Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. 2020; 15:1484–506. https://doi.org/10.1038/s41596-020-0292-x [PubMed]
33. Yang X, Li C, Yang H, Li T, Ling S, Zhang Y, Wu F, Liu X, Liu S, Fan C, Wang Q. Programmed Remodeling of the Tumor Milieu to Enhance NK Cell Immunotherapy Combined with Chemotherapy for Pancreatic Cancer. Nano Lett. 2024; 24:3421–31. https://doi.org/10.1021/acs.nanolett.4c00002 [PubMed]
34. Domagała D, Data K, Szyller H, Farzaneh M, Mozdziak P, Woźniak S, Zabel M, Dzięgiel P, Kempisty B. Cellular, Molecular and Clinical Aspects of Aortic Aneurysm-Vascular Physiology and Pathophysiology. Cells. 2024; 13:274. https://doi.org/10.3390/cells13030274 [PubMed]
35. Hou Y, Zhao X, Nie X. Enhancing the therapeutic efficacy of NK cells in the treatment of ovarian cancer (Review). Oncol Rep. 2024; 51:50. https://doi.org/10.3892/or.2024.8709 [PubMed]
36. Saadh MJ, Rasulova I, Khalil M, Farahim F, Sârbu I, Ciongradi CI, Omar TM, Alhili A, Jawad MJ, Hani T, Ali T, Lakshmaiya N. Natural killer cell-mediated immune surveillance in cancer: Role of tumor microenvironment. Pathol Res Pract. 2024; 254:155120. https://doi.org/10.1016/j.prp.2024.155120 [PubMed]
37. Trabolsi A, Arumov A, Schatz JH. Bispecific antibodies and CAR-T cells: dueling immunotherapies for large B-cell lymphomas. Blood Cancer J. 2024; 14:27. https://doi.org/10.1038/s41408-024-00997-w [PubMed]
38. Wang Y, Fan P, Feng Y, Yao X, Peng Y, Wang R. Clinical implication of naive and memory T cells in locally advanced cervical cancer: A proxy for tumor biology and short-term response prediction. Biocell. 2023; 47:1365–75. https://doi.org/10.32604/biocell.2023.027201
39. Reya T, Morrison SJ, Clarke MF, Weissman IL. Stem cells, cancer, and cancer stem cells. Nature. 2001; 414:105–11. https://doi.org/10.1038/35102167 [PubMed]
40. Barshidi A, Ardeshiri K, Ebrahimi F, Alian F, Shekarchi AA, Hojjat-Farsangi M, Jadidi-Niaragh F. The role of exhausted natural killer cells in the immunopathogenesis and treatment of leukemia. Cell Commun Signal. 2024; 22:59. https://doi.org/10.1186/s12964-023-01428-2 [PubMed]
41. Kellaway SG, Potluri S, Keane P, Blair HJ, Ames L, Worker A, Chin PS, Ptasinska A, Derevyanko PK, Adamo A, Coleman DJL, Khan N, Assi SA, et al. Leukemic stem cells activate lineage inappropriate signalling pathways to promote their growth. Nat Commun. 2024; 15:1359. https://doi.org/10.1038/s41467-024-45691-4 [PubMed]
42. Meng X, Na R, Peng X, Li H, Ouyang W, Zhou W, You X, Li Y, Pu X, Zhang K, Xia J, Wang J, Tang H, et al. Musashi-2 potentiates colorectal cancer immune infiltration by regulating the post-translational modifications of HMGB1 to promote DCs maturation and migration. Cell Commun Signal. 2024; 22:117. https://doi.org/10.1186/s12964-024-01495-z [PubMed]
43. Pieters T, Almeida A, T'Sas S, Lemeire K, Hochepied T, Berx G, Kentsis A, Goossens S, Van Vlierberghe P. Myb drives B-cell neoplasms and myeloid malignancies in vivo. Blood Adv. 2022; 6:2987–91. https://doi.org/10.1182/bloodadvances.2021005955 [PubMed]
44. Anderson D, Skut P, Hughes AM, Ferrari E, Tickner J, Xu J, Mullin BH, Tang D, Malinge S, Kees UR, Kotecha RS, Lassmann T, Cheung LC. The bone marrow microenvironment of pre-B acute lymphoblastic leukemia at single-cell resolution. Sci Rep. 2020; 10:19173. https://doi.org/10.1038/s41598-020-76157-4 [PubMed]
45. Kannan K, Coarfa C, Rajapakshe K, Hawkins SM, Matzuk MM, Milosavljevic A, Yen L. CDKN2D-WDFY2 is a cancer-specific fusion gene recurrent in high-grade serous ovarian carcinoma. PLoS Genet. 2014; 10:e1004216. https://doi.org/10.1371/journal.pgen.1004216 [PubMed]
46. Wang Y, Jin W, Jia X, Luo R, Tan Y, Zhu X, Yang X, Wang X, Wang K. Transcriptional repression of CDKN2D by PML/RARα contributes to the altered proliferation and differentiation block of acute promyelocytic leukemia cells. Cell Death Dis. 2014; 5:e1431. https://doi.org/10.1038/cddis.2014.388 [PubMed]
47. Li D, Zhang Q, Li L, Chen K, Yang J, Dixit D, Gimple RC, Ci S, Lu C, Hu L, Gao J, Shan D, Li Y, et al. β2-Microglobulin Maintains Glioblastoma Stem Cells and Induces M2-like Polarization of Tumor-Associated Macrophages. Cancer Res. 2022; 82:3321–34. https://doi.org/10.1158/0008-5472.CAN-22-0507 [PubMed]
48. Clottu AS, Humbel M, Fluder N, Karampetsou MP, Comte D. Innate Lymphoid Cells in Autoimmune Diseases. Front Immunol. 2022; 12:789788. https://doi.org/10.3389/fimmu.2021.789788 [PubMed]
49. Eyquem J, Mansilla-Soto J, Giavridis T, van der Stegen SJ, Hamieh M, Cunanan KM, Odak A, Gönen M, Sadelain M. Targeting a CAR to the TRAC locus with CRISPR/Cas9 enhances tumour rejection. Nature. 2017; 543:113–7. https://doi.org/10.1038/nature21405 [PubMed]
50. Su H, Na N, Zhang X, Zhao Y. The biological function and significance of CD74 in immune diseases. Inflamm Res. 2017; 66:209–16. https://doi.org/10.1007/s00011-016-0995-1 [PubMed]
51. Li QL, Tang J, Zhao L, Ruze A, Shan XF, Gao XM. The role of CD74 in cardiovascular disease. Front Cardiovasc Med. 2023; 9:1049143. https://doi.org/10.3389/fcvm.2022.1049143 [PubMed]
52. Jiang L, Zhao X, Li Y, Hu Y, Sun Y, Liu S, Zhang Z, Li Y, Feng X, Yuan J, Li J, Zhang X, Chen Y, Shen L. The tumor immune microenvironment remodeling and response to HER2-targeted therapy in HER2-positive advanced gastric cancer. IUBMB Life. 2024; 76:420–36. https://doi.org/10.1002/iub.2804 [PubMed]
53. Lv Y, Ma X, Ma Y, Du Y, Feng J. A new emerging target in cancer immunotherapy: Galectin-9 (LGALS9). Genes Dis. 2022; 10:2366–82. https://doi.org/10.1016/j.gendis.2022.05.020 [PubMed]
54. Ustun C, Miller JS, Munn DH, Weisdorf DJ, Blazar BR. Regulatory T cells in acute myelogenous leukemia: is it time for immunomodulation? Blood. 2011; 118:5084–95. https://doi.org/10.1182/blood-2011-07-365817 [PubMed]
55. Moonla C, Polprasert C, Komvilaisak P, Rattanathammethee T, Kongkiatkamon S, Wudhikarn K, Kobbuaklee S, Boonyabaramee P, Tangcheewinsirikul N, Pakakasama S, Rujkijyanont P, Choed-Amphai C, Phuakpet K, et al. Germline HAVCR2 mutations and their relation to the clinical spectrum of subcutaneous panniculitis-like T-cell lymphoma and hemophagocytic lymphohistiocytosis: results from a multicenter study and meta-analysis. Haematologica. 2023; 108:2743–52. https://doi.org/10.3324/haematol.2022.282419 [PubMed]
56. Yang R, Sun L, Li CF, Wang YH, Yao J, Li H, Yan M, Chang WC, Hsu JM, Cha JH, Hsu JL, Chou CW, Sun X, et al. Galectin-9 interacts with PD-1 and TIM-3 to regulate T cell death and is a target for cancer immunotherapy. Nat Commun. 2021; 12:832. https://doi.org/10.1038/s41467-021-21099-2 [PubMed]

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