Single-cell profiling of T cells uncovers a tissue-resident memory-like T-cell subset associated with bidirectional prognosis for B-cell acute lymphoblastic leukemia

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and receptor analysis, MIST (Multi-insight for T cell). MIST features three latent spaces: gene expression, TCR, and a joint latent space. Through analyses of antigen-specific T cells, and T cell datasets related to lung cancer immunotherapy and COVID19, we demonstrate MIST's interpretability and flexibility. MIST easily and accurately resolves cell function and antigen specificity by vectorizing and integrating transcriptome and TCR data of T cells. In addition, using MIST, we identified the heterogeneity of CXCL13+ subsets in lung cancer infiltrating CD8+ T cells and their association with immunotherapy, providing additional insights into the functional transition of CXCL13+ T cells related to anti-PD-1 therapy that were not reported in the original study.

Potential molecular mechanisms of ETV6-RUNX1-positive B progenitor cell cluster in acute lymphoblastic leukemia revealed by single-cell RNA sequencing

Aim

This study was to explore role of immune landscape and the immune cells in acute lymphoblastic leukemia (ALL) progression.

Background

The most prevalent genetic alteration in childhood ALL is the ETV6-RUNX1 fusion. The increased proliferation of B progenitor cells could expedite the disease's progression due to irregularities in the cell cycle. Nevertheless, the mechanisms by which particular cell clusters influence the cell cycle and promote the advancement of ALL are still not well understood.

Objective

This study was to explore role of immune landscape and the immune cells in ALL progression.

Methods

Single-cell RNA sequencing (scRNA-seq) data of ETV6-RUNX1 and healthy pediatric samples obtained from GSE132509 were clustered and annotated using the Seurat package, and differentially highly expressed genes identified in each cluster were analyzed using DAVID for pathway annotation. Chromosome amplification and deletion were analyzed using the inferCNV package. SCENIC evaluated the regulation of transcription factors and target gene formation in cells. cellphoneDB and CellChat were served to infer ligand-receptor pairs that mediate interactions between subpopulations. The role of the target gene in regulating ALL progression was assessed using RT-qPCR, Transwell and scratch healing assays.

Results

The bone marrow mononuclear cells (BMMCs) from ETV6-RUNX1 and healthy pediatric samples in GSE132509 were divided into 11 clusters, and B cell cluster 1 was identified as B progenitor cell, which was amplified on chromosome 6p. B progenitor cells were divided into seven clusters. Expression levels of amplified genes in chromosome 6p of B progenitor cell cluster 5 were the highest, and its specific highly expressed genes were annotated to pathways promoting cell cycle progression. Regulons formed in B progenitor cell cluster 5 were all involved in promoting cell cycle progression, so it was regarded as the B progenitor cell cluster that drives cell cycle progression. The key regulator of the B progenitor cell is E2F1, which promotes the migration and invasion ability of the cell line HAP1. The major ligand-receptor pairs that mediate the communication of B progenitor cell cluster 5 with cytotoxic NK/T cells or naive T cells included FAM3C-CLEC2D, CD47-SIRPG, HLAE-KLRC2, and CD47-KLRC2. HLAE-KLRC1 and TGFB1-(TGFBR1+TGFBR2).

Conclusion

This study outlined the immune cell landscape of ETV6-RUNX1 ALL and identified chromosome 6p amplification in B progenitor cells, described the major B progenitor cell cluster driving cell cycle progression and its potential regulatory mechanisms on NK cells and T cells, providing cellular and molecular insights into ETV6-RUNX1 ALL.

Unlocking the Complexity: Exploration of Acute Lymphoblastic Leukemia at the Single Cell Level

Acute lymphoblastic leukemia (ALL) is the most common cancer in children. ALL originates from precursor lymphocytes that acquire multiple genomic changes over time, including chromosomal rearrangements and point mutations. While a large variety of genomic defects was identified and characterized in ALL over the past 30 years, it was only in recent years that the clonal heterogeneity was recognized. Thanks to the latest advancements in single-cell sequencing techniques, which have evolved from the analysis of a few hundred cells to the analysis of thousands of cells simultaneously, the study of tumor heterogeneity now becomes possible. Different modalities can be explored at the single-cell level: DNA, RNA, epigenetic modifications, and intracellular and cell surface proteins. In this review, we describe these techniques and highlight their advantages and limitations in the study of ALL biology. Moreover, multiomics technologies and the incorporation of the spatial dimension can provide insight into intercellular communication. We describe how the different single-cell sequencing technologies help to unravel the molecular complexity of ALL, shedding light on its development, its heterogeneity, its interaction with the leukemia microenvironment and possible relapse mechanisms.

Higher frequency of peripheral blood CD103+CD8+ T cells with lower levels of PD-1 and TIGIT expression related to favorable outcomes in leukemia patients

Background

Leukemia is a prevalent pediatric life-threatening hematologic malignancy with a poor prognosis. Targeting immune checkpoints (ICs) to reverse T cell exhaustion is a potentially effective treatment for leukemia. Tissue resident memory T (TRM) cells have been found to predict the efficacy of programmed death receptor-1 inhibitor (anti-PD-1) therapy in solid tumors. However, the IC characteristics of TRM cells in leukemia and their relationship with prognosis remain unclear.

Methods

We employed multi-color flow cytometry to evaluate the frequencies of CD103+CD4+ and CD103+CD8+ T cells in the peripheral blood (PB) of patients with acute myeloid leukemia and B-cell acute lymphoblastic leukemia compared to healthy individuals. We examined the expression patterns of PD-1 and T cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT) within the circulating CD103+ T cell subsets affected by leukemia. To further elucidate the immunological landscape, we assessed the differentiation status of CD103+ T cells across various disease states in patients with leukemia.

Results

Our findings showed a significant increase in the frequency of CD103+CD8+ T cells in the PB of patients with leukemia who had achieved complete remission (CR) compared to those in the de novo (DN) and relapsed/refractory (RR) stages. This increase was accompanied by a notable decrease in the expression levels of PD-1 and TIGIT in CD103+CD8+ T cells in the CR stage. Additionally, our analysis revealed a higher proportion of CD103+CD8+ T cells in the central memory (TCM) and effector memory (TEM) subsets of the immune profile. Notably, the proportions of CD103+ naïve T cells, CD103+ TEM, and CD103+ terminally differentiated T cells within the CD8+ T cell population were significantly elevated in patients with CR compared to those in the DN/RR stages.

Conclusion

The data indicate that circulating higher frequency of CD103+CD8+ T cells with lower expression of PD-1 and TIGIT are associated with favorable outcomes in patients with leukemia. This suggests a potential role of TRM cells in leukemia prognosis and provides a foundation for developing targeted immunotherapies.

Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

T-cell acute lymphoblastic leukemia (T-ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.

Exploring Immune Cell Diversity in the Lacrimal Glands of Healthy Mice: A Single-Cell RNA-Sequencing Atlas

The lacrimal gland is responsible for maintaining the health of the ocular surface through the production of tears. However, our understanding of the immune system within the lacrimal gland is currently limited. Therefore, in this study, we utilized single-cell RNA sequencing and bioinformatic analysis to identify and analyze immune cells and molecules present in the lacrimal glands of normal mice. A total of 34,891 cells were obtained from the lacrimal glands of mice and classified into 18 distinct cell clusters using Seurat clustering. Within these cell populations, 26 different immune cell subpopulations were identified, including T cells, innate lymphocytes, macrophages, mast cells, dendritic cells, and B cells. Network analysis revealed complex cell-cell interactions between these immune cells, with particularly significant interactions observed among T cells, macrophages, plasma cells, and dendritic cells. Interestingly, T cells were found to be the main source of ligands for the Thy1 signaling pathway, while M2 macrophages were identified as the primary target of this pathway. Moreover, some of these immune cells were validated using immunohistological techniques. Collectively, these findings highlight the abundance and interactions of immune cells and provide valuable insights into the complexity of the lacrimal gland immune system and its relevance to associated diseases.