Human γδ T cell identification from single-cell RNA sequencing datasets by modular TCR expression

γδ T cells as critical anti-tumor immune effectors

While the effector cells that mediate anti-tumor immunity have historically been attributed to αβ T cells and natural killer cells, γδ T cells are now being recognized as a complementary mechanism mediating tumor rejection. γδ T cells possess a host of functions ranging from antigen presentation to regulatory function and, importantly, have critical roles in eliciting anti-tumor responses where other immune effectors may be rendered ineffective. Recent discoveries have elucidated how these differing functions are mediated by γδ T cells with specific T cell receptors and spatial distribution. Their relative resistance to mechanisms of dysfunction like T cell exhaustion has spurred the development of therapeutic approaches exploiting γδ T cells, and an improved understanding of these cells should enable more effective immunotherapies.

Systematic Analysis of Human Colorectal Cancer scRNA-seq Revealed Limited Pro-tumoral IL-17 Production Potential in Gamma Delta T Cells

Gamma delta (γδ) T cells play a crucial role in anti-tumor immunity due to their cytotoxic properties. However, the role and extent of γδ T cells in production of pro-tumorigenic interleukin- 17 (IL-17) within the tumor microenvironment (TME) of colorectal cancer (CRC) remains controversial. In this study, we re-analyzed nine published human CRC whole-tissue single-cell RNA sequencing (scRNA-seq) datasets, identifying 18,483 γδ T cells out of 951,785 total cells, in the neoplastic or adjacent normal tissue of 165 human CRC patients. Our results confirm that tumor-infiltrating γδ T cells exhibit high cytotoxicity-related transcription in both tumor and adjacent normal tissues, but critically, none of the γδ T cell clusters showed IL-17 production potential. We also identified various γδ T cell subsets, including Teff, TRM, Tpex, and Tex, and noted an increased expression of cytotoxic molecules in tumor-infiltrating γδ T cells compared to their normal area counterparts. Our work demonstrates that γδ T cells in CRC primarily function as cytotoxic effector cells rather than IL-17 producers, mitigating the concerns about their potential pro-tumorigenic roles in CRC, highlighting the importance of accurately characterizing these cells for cancer immunotherapy research and the unneglectable cross-species discrepancy between the mouse and human immune system in the study of cancer immunology.

AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification

MOTIVATION

Dropout events bring challenges in analyzing single-cell RNA sequencing data as they introduce noise and distort the true distributions of gene expression profiles. Recent studies focus on estimating dropout probability and imputing dropout events by leveraging information from similar cells or genes. However, the number of dropout events differs in different cells, due to the complex factors, such as different sequencing protocols, cell types, and batch effects. The dropout event differences are not fully considered in assessing the similarities between cells and genes, which compromises the reliability of downstream analysis.

RESULTS

This work proposes a hybrid Generative Adversarial Network with dropouts identification to impute single-cell RNA sequencing data, named AGImpute. First, the numbers of dropout events in different cells in scRNA-seq data are differentially estimated by using a dynamic threshold estimation strategy. Next, the identified dropout events are imputed by a hybrid deep learning model, combining Autoencoder with a Generative Adversarial Network. To validate the efficiency of the AGImpute, it is compared with seven state-of-the-art dropout imputation methods on two simulated datasets and seven real single-cell RNA sequencing datasets. The results show that AGImpute imputes the least number of dropout events than other methods. Moreover, AGImpute enhances the performance of downstream analysis, including clustering performance, identifying cell-specific marker genes, and inferring trajectory in the time-course dataset.

AVAILABILITY AND IMPLEMENTATION

The source code can be obtained from https://github.com/xszhu-lab/AGImpute.

The capability of heterogeneous γδ T cells in cancer treatment

γδ T cells, a specialized subset of T lymphocytes, have garnered significant attention within the realm of cancer immunotherapy. Operating at the nexus between adaptive and innate immunological paradigms, these cells showcase a profound tumor discernment repertoire, hinting at novel immunotherapeutic strategies. Significantly, these cells possess the capability to directly identify and eliminate tumor cells without reliance on HLA-antigen presentation. Furthermore, γδ T cells have the faculty to present tumor antigens to αβ T cells, amplifying their anti-tumoral efficacy.Within the diverse and heterogeneous subpopulations of γδ T cells, distinct immune functionalities emerge, manifesting either anti-tumor or pro-tumor roles within the tumor microenvironment. Grasping and strategically harnessing these heterogeneous γδ T cell cohorts is pivotal to their integration in tumor-specific immunotherapeutic modalities. The aim of this review is to describe the heterogeneity of the γδ T cell lineage and the functional plasticity it generates in the treatment of malignant tumors. This review endeavors to elucidate the intricate heterogeneity inherent to the γδ T cell lineage, the consequential functional dynamics in combating malignancies, the latest advancements from clinical trials, and the evolving landscape of γδ T cell-based oncological interventions, while addressing the challenges impeding the field.