Innate-like functions of natural killer T cell subsets result from highly divergent gene programs

Glycolipid antigen recognition by invariant natural killer T cells and its role in homeostasis and antimicrobial responses

Due to the COVID-19 pandemic, the importance of developing effective vaccines has received more attention than ever before. To maximize the effects of vaccines, it is important to select adjuvants that induce strong and rapid innate and acquired immune responses. Invariant natural killer T (iNKT) cells, which constitute a small population among lymphocytes, bypass the innate and acquired immune systems through the rapid production of cytokines after glycolipid recognition; hence, their activation could be used as a vaccine strategy against emerging infectious diseases. Additionally, the diverse functions of iNKT cells, including enhancing antibody production, are becoming more understood in recent years. In this review, we briefly describe the functional subset of iNKT cells and introduce the glycolipid antigens recognized by them. Furthermore, we also introduce novel vaccine development taking advantages of iNKT cell activation against infectious diseases.

SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

Promyelocytic leukemia zinc finger controls type 2 immune responses in the lungs by regulating lineage commitment and the function of innate and adaptive immune cells

Type 2 immune responses are critical for host defense, mediate allergy and Th2-high asthma. The transcription factor, promyelocytic leukemia zinc finger (PLZF), has emerged as a significant regulator of type 2 inflammation in the lung; however, its exact mechanism remains unclear. In this review, we summarized recent findings regarding the ability of PLZF to control the development and function of innate lymphoid cells (ILCs), iNKT cells, memory T cells, basophils, and other immune cells that drive type 2 responses. We discussed the important role of PLZF in the pathogenesis of Th2-high asthma. Collectively, prior studies have revealed the critical role of PLZF in the regulation of innate and adaptive immune cells involved in type 2 inflammation in the lung. Therefore, targeting PLZF signaling represents a promising therapeutic approach to suppress Th2-high asthma.

Kidins220 and Aiolos promote thymic iNKT cell development by reducing TCR signals

Development of T cells is controlled by the signal strength of the TCR. The scaffold protein kinase D-interacting substrate of 220 kilodalton (Kidins220) binds to the TCR; however, its role in T cell development was unknown. Here, we show that T cell-specific Kidins220 knockout (T-KO) mice have strongly reduced invariant natural killer T (iNKT) cell numbers and modest decreases in conventional T cells. Enhanced apoptosis due to increased TCR signaling in T-KO iNKT thymocytes of developmental stages 2 and 3 shows that Kidins220 down-regulates TCR signaling at these stages. scRNA-seq  indicated that the transcription factor Aiolos is down-regulated in Kidins220-deficient iNKT cells. Analysis of an Aiolos KO demonstrated that Aiolos is a downstream effector of Kidins220 during iNKT cell development. In the periphery, T-KO iNKT cells show reduced TCR signaling upon stimulation with α-galactosylceramide, suggesting that Kidins220 promotes TCR signaling in peripheral iNKT cells. Thus, Kidins220 reduces or promotes signaling dependent on the iNKT cell developmental stage.

Insights into the heterogeneity of iNKT cells: tissue-resident and circulating subsets shaped by local microenvironmental cues

Invariant natural killer T (iNKT) cells are a distinct subpopulation of innate-like T lymphocytes. They are characterized by semi-invariant T cell receptors (TCRs) that recognize both self and foreign lipid antigens presented by CD1d, a non-polymorphic MHC class I-like molecule. iNKT cells play a critical role in stimulating innate and adaptive immune responses, providing an effective defense against infections and cancers, while also contributing to chronic inflammation. The functions of iNKT cells are specific to their location, ranging from lymphoid to non-lymphoid tissues, such as the thymus, lung, liver, intestine, and adipose tissue. This review aims to provide insights into the heterogeneity of development and function in iNKT cells. First, we will review the expression of master transcription factors that define subsets of iNKT cells and their production of effector molecules such as cytokines and granzymes. In this article, we describe the gene expression profiles contributing to the kinetics, distribution, and cytotoxicity of iNKT cells across different tissue types. We also review the impact of cytokine production in distinct immune microenvironments on iNKT cell heterogeneity, highlighting a recently identified circulating iNKT cell subset. Additionally, we explore the potential of exploiting iNKT cell heterogeneity to create potent immunotherapies for human cancers in the future.

Quantifying the clusterness and trajectoriness of single-cell RNA-seq data

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.

Invariant natural killer T cells in autoimmune cholangiopathies: Mechanistic insights and therapeutic implications

Invariant natural killer T cells (iNKT cells) constitute a specialized subset of lymphocytes that bridges innate and adaptive immunity through a combination of traits characteristic of both conventional T cells and innate immune cells. iNKT cells are characterized by their invariant T cell receptors and discerning recognition of lipid antigens, which are presented by the non-classical MHC molecule, CD1d. Within the hepatic milieu, iNKT cells hold heightened prominence, contributing significantly to the orchestration of organ homeostasis. Their unique positioning to interact with diverse cellular entities, ranging from epithelial constituents like hepatocytes and cholangiocytes to immunocytes including Kupffer cells, B cells, T cells, and dendritic cells, imparts them with potent immunoregulatory abilities. Emergering knowledge of liver iNKT cells subsets enable to explore their therapeutic potential in autoimmne liver diseases. This comprehensive review navigates the landscape of iNKT cell investigations in immune-mediated cholangiopathies, with a particular focus on primary biliary cholangitis and primary sclerosing cholangitis, across murine models and human subjects to unravel the intricate involvements of iNKT cells in liver autoimmunity. Additionally, we also highlight the prospectives of iNKT cells as therapeutic targets in cholangiopathies. Modulation of the equilibrium between regulatory and proinflammatory iNKT subsets can be defining determinant in the dynamics of hepatic autoimmunity. This discernment not only enriches our foundational comprehension but also lays the groundwork for pioneering strategies to navigate the multifaceted landscape of liver autoimmunity.

A New Graph Autoencoder-Based Consensus-Guided Model for scRNA-seq Cell Type Detection

Single-cell RNA sequencing (scRNA-seq) technology is famous for providing a microscopic view to help capture cellular heterogeneity. This characteristic has advanced the field of genomics by enabling the delicate differentiation of cell types. However, the properties of single-cell datasets, such as high dropout events, noise, and high dimensionality, are still a research challenge in the single-cell field. To utilize single-cell data more efficiently and to better explore the heterogeneity among cells, a new graph autoencoder (GAE)-based consensus-guided model (scGAC) is proposed in this article. The data are preprocessed into multiple top-level feature datasets. Then, feature learning is performed by using GAEs to generate new feature matrices, followed by similarity learning based on distance fusion methods. The learned similarity matrices are fed back to the GAEs to guide their feature learning process. Finally, the abovementioned steps are iterated continuously to integrate the final consistent similarity matrix and perform other related downstream analyses. The scGAC model can accurately identify critical features and effectively preserve the internal structure of the data. This can further improve the accuracy of cell type identification.

Single-cell transcriptomics reveal different maturation stages and sublineage commitment of human thymic invariant natural killer T cells

Invariant natural killer T cells are a rare, heterogeneous T-cell subset with cytotoxic and immunomodulatory properties. During thymic development, murine invariant natural killer T cells go through different maturation stages differentiating into distinct sublineages, namely, invariant natural killer T1, 2, and 17 cells. Recent reports indicate that invariant natural killer T2 cells display immature properties and give rise to other subsets, whereas invariant natural killer T1 cells seem to be terminally differentiated. Whether human invariant natural killer T cells follow a similar differentiation model is still unknown. To define the maturation stages and assess the sublineage commitment of human invariant natural killer T cells during thymic development, in this study, we performed single-cell RNA sequencing analysis on human Vα24+Vβ11+ invariant natural killer T cells isolated from thymocytes. We show that these invariant natural killer T cells displayed heterogeneity, and our unsupervised analysis identified 5 clusters representing different maturation stages, from an immature profile with high expression of genes important for invariant natural killer T cell development and proliferation to a mature, fully differentiated profile with high levels of cytotoxic effector molecules. Evaluation of expression of sublineage-defining gene sets revealed mainly cells with an invariant natural killer T2 signature in the most immature cluster, whereas the more differentiated ones displayed an invariant natural killer T1 signature. Combined analysis with a publicly available single-cell RNA sequencing data set of human invariant natural killer T cells from peripheral blood suggested that the 2 main subsets exist both in thymus and in the periphery, while a third more immature one was restricted to the thymus. Our data point to the existence of different maturation stages of human thymic invariant natural killer T cells and provide evidence for sublineage commitment of invariant natural killer T cells in the human thymus.

Joint L2,p-norm and random walk graph constrained PCA for single-cell RNA-seq data

The development and widespread utilization of high-throughput sequencing technologies in biology has fueled the rapid growth of single-cell RNA sequencing (scRNA-seq) data over the past decade. The development of scRNA-seq technology has significantly expanded researchers' understanding of cellular heterogeneity. Accurate cell type identification is the prerequisite for any research on heterogeneous cell populations. However, due to the high noise and high dimensionality of scRNA-seq data, improving the effectiveness of cell type identification remains a challenge. As an effective dimensionality reduction method, Principal Component Analysis (PCA) is an essential tool for visualizing high-dimensional scRNA-seq data and identifying cell subpopulations. However, traditional PCA has some defects when used in mining the nonlinear manifold structure of the data and usually suffers from over-density of principal components (PCs). Therefore, we present a novel method in this paper called joint L 2 , p -norm and random walk graph constrained PCA (RWPPCA). RWPPCA aims to retain the data's local information in the process of mapping high-dimensional data to low-dimensional space, to more accurately obtain sparse principal components and to then identify cell types more precisely. Specifically, RWPPCA combines the random walk (RW) algorithm with graph regularization to more accurately determine the local geometric relationships between data points. Moreover, to mitigate the adverse effects of dense PCs, the L 2 , p -norm is introduced to make the PCs sparser, thus increasing their interpretability. Then, we evaluate the effectiveness of RWPPCA on simulated data and scRNA-seq data. The results show that RWPPCA performs well in cell type identification and outperforms other comparison methods.

Control of the Development, Distribution, and Function of Innate-Like Lymphocytes and Innate Lymphoid Cells by the Tissue Microenvironment

Recently, considerable attention has been directed toward innate-like T cells (ITCs) and innate lymphoid cells (ILCs) owing to their indispensable contributions to immune responses, tissue homeostasis, and inflammation. Innate-like T cells include NKT cells, MAIT cells, and γδ T cells, whereas ILCs include NK cells, type 1 ILCs (ILC1s), type 2 ILCs (ILC2s), and type 3 ILCs (ILC3s). Many of these ITCs and ILCs are distributed to specific tissues and remain tissue-resident, while others, such as NK cells and some γδ T cells, circulate through the bloodstream. Nevertheless, recent research has shed light on novel subsets of innate immune cells that exhibit characteristics intermediate between tissue-resident and circulating states under normal and pathological conditions. The local microenvironment frequently influences the development, distribution, and function of these innate immune cells. This review aims to consolidate the current knowledge on the functional heterogeneity of ITCs and ILCs, shaped by local environmental cues, with particular emphasis on IL-15, which governs the activities of the innate immune cells involved in type 1 immune responses.

Unique adipose tissue invariant natural killer T cell subpopulations control adipocyte turnover in mice

Adipose tissue invariant natural killer T (iNKT) cells are a crucial cell type for adipose tissue homeostasis in obese animals. However, heterogeneity of adipose iNKT cells and their function in adipocyte turnover are not thoroughly understood. Here, we investigate transcriptional heterogeneity in adipose iNKT cells and their hierarchy using single-cell RNA sequencing in lean and obese mice. We report that distinct subpopulations of adipose iNKT cells modulate adipose tissue homeostasis through adipocyte death and birth. We identify KLRG1+ iNKT cells as a unique iNKT cell subpopulation in adipose tissue. Adoptive transfer experiments showed that KLRG1+ iNKT cells are selectively generated within adipose tissue microenvironment and differentiate into a CX3CR1+ cytotoxic subpopulation in obese mice. In addition, CX3CR1+ iNKT cells specifically kill enlarged and inflamed adipocytes and recruit macrophages through CCL5. Furthermore, adipose iNKT17 cells have the potential to secrete AREG, and AREG is involved in stimulating adipose stem cell proliferation. Collectively, our data suggest that each adipose iNKT cell subpopulation plays key roles in the control of adipocyte turnover via interaction with adipocytes, adipose stem cells, and macrophages in adipose tissue.

Cytotoxic CD4+ tissue-resident memory T cells are associated with asthma severity

BACKGROUND

Patients with severe uncontrolled asthma represent a distinct endotype with persistent airway inflammation and remodeling that is refractory to corticosteroid treatment. CD4+ TH2 cells play a central role in orchestrating asthma pathogenesis, and biologic therapies targeting their cytokine pathways have had promising outcomes. However, not all patients respond well to such treatment, and their effects are not always durable nor reverse airway remodeling. This observation raises the possibility that other CD4+ T cell subsets and their effector molecules may drive airway inflammation and remodeling.

METHODS

We performed single-cell transcriptome analysis of >50,000 airway CD4+ T cells isolated from bronchoalveolar lavage samples from 30 patients with mild and severe asthma.

FINDINGS

We observed striking heterogeneity in the nature of CD4+ T cells present in asthmatics' airways, with tissue-resident memory T (TRM) cells making a dominant contribution. Notably, in severe asthmatics, a subset of CD4+ TRM cells (CD103-expressing) was significantly increased, comprising nearly 65% of all CD4+ T cells in the airways of male patients with severe asthma when compared to mild asthma (13%). This subset was enriched for transcripts linked to T cell receptor activation (HLA-DRB1, HLA-DPA1) and cytotoxicity (GZMB, GZMA) and, following stimulation, expressed high levels of transcripts encoding for pro-inflammatory non-TH2 cytokines (CCL3, CCL4, CCL5, TNF, LIGHT) that could fuel persistent airway inflammation and remodeling.

CONCLUSIONS

Our findings indicate the need to look beyond the traditional T2 model of severe asthma to better understand the heterogeneity of this disease.

FUNDING

This research was funded by the NIH.

scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising

Recently developed single-cell RNA-seq (scRNA-seq) technology has given researchers the chance to investigate single-cell level of disease development. Clustering is one of the most essential strategies for analyzing scRNA-seq data. Choosing high-quality feature sets can significantly enhance the outcomes of single-cell clustering and classification. But computationally burdensome and highly expressed genes cannot afford a stabilized and predictive feature set for technical reasons. In this study, we introduce scFED, a feature-engineered gene selection framework. scFED identifies prospective feature sets to eliminate the noise fluctuation. And fuse them with existing knowledge from the tissue-specific cellular taxonomy reference database (CellMatch) to avoid the influence of subjective factors. Then present a reconstruction approach for noise reduction and crucial information amplification. We apply scFED on four genuine single-cell datasets and compare it with other techniques. According to the results, scFED improves clustering, decreases dimension of the scRNA-seq data, improves cell type identification when combined with clustering algorithms, and has higher performance than other methods. Therefore, scFED offers certain benefits in scRNA-seq data gene selection.

Transcriptomes and metabolism define mouse and human MAIT cell populations

Mucosal-associated invariant T (MAIT) cells are a subset of T lymphocytes that respond to microbial metabolites. We defined MAIT cell populations in different organs and characterized the developmental pathway of mouse and human MAIT cells in the thymus using single-cell RNA sequencing and phenotypic and metabolic analyses. We showed that the predominant mouse subset, which produced IL-17 (MAIT17), and the subset that produced IFN-γ (MAIT1) had not only greatly different transcriptomes but also different metabolic states. MAIT17 cells in different organs exhibited increased lipid uptake, lipid storage, and mitochondrial potential compared with MAIT1 cells. All these properties were similar in the thymus and likely acquired there. Human MAIT cells in lung and blood were more homogeneous but still differed between tissues. Human MAIT cells had increased fatty acid uptake and lipid storage in blood and lung, similar to human CD8 T resident memory cells, but unlike mouse MAIT17 cells, they lacked increased mitochondrial potential. Although mouse and human MAIT cell transcriptomes showed similarities for immature cells in the thymus, they diverged more strikingly in the periphery. Analysis of pet store mice demonstrated decreased lung MAIT17 cells in these so-called "dirty" mice, indicative of an environmental influence on MAIT cell subsets and function.

TGF-β Promotes the Postselection Thymic Development and Peripheral Function of IFN-γ-Producing Invariant NKT cells

IFN-γ-producing invariant NKT (iNKT)1 cells are lipid-reactive innate-like lymphocytes that are resident in the thymus and peripheral tissues where they protect against pathogenic infection. The thymic functions of iNKT1 cells are not fully elucidated, but subsets of thymic iNKT cells modulate CD8 T cell, dendritic cell, B cell, and thymic epithelial cell numbers or function. In this study, we show that a subset of murine thymic iNKT1 cells required TGF-β-induced signals for their postselection development, to maintain hallmark TGF-β-induced genes, and for expression of the adhesion receptors CD49a and CD103. However, the residency-associated receptor CD69 was not TGF-β signaling-dependent. Recently described CD244+ c2 thymic iNKT1 cells, which produce IFN-γ without exogenous stimulation and have NK-like characteristics, reside in this TGF-β-responsive population. Liver and spleen iNKT1 cells do not share this TGF-β gene signature, but nonetheless TGF-β impacts liver iNKT1 cell phenotype and function. Our findings provide insight into the heterogeneity of mechanisms guiding iNKT1 cell development in different tissues and suggest a close association between a subset of iNKT1 cells and TGF-β-producing cells in the thymus that support their development.

Zeb2 regulates differentiation of long-lived effector of invariant natural killer T cells

After activation, some invariant natural killer T (iNKT) cells are differentiated into Klrg1+ long-lived effector NKT1 cells. However, the regulation from the effector phase to the memory phase has not been elucidated. Zeb2 is a zinc finger E homeobox-binding transcription factor and is expressed in a variety of immune cells, but its function in iNKT cell differentiation remains also unknown. Here, we show that Zeb2 is dispensable for development of iNKT cells in the thymus and their maintenance in steady state peripheral tissues. After ligand stimulation, Zeb2 plays essential roles in the differentiation to and maintenance of Klrg1+ Cx3cr1+GzmA+ iNKT cell population derived from the NKT1 subset. Our results including single-cell-RNA-seq analysis indicate that Zeb2 regulates Klrg1+ long-lived iNKT cell differentiation by preventing apoptosis. Collectively, this study reveals the crucial transcriptional regulation by Zeb2 in establishment of the memory iNKT phase through driving differentiation of Klrg1+ Cx3cr1+GzmA+ iNKT population.

Unconventional T cells in brain homeostasis, injury and neurodegeneration

The interaction between peripheral immune cells and the brain is an important component of the neuroimmune axis. Unconventional T cells, which include natural killer T (NKT) cells, mucosal-associated invariant T (MAIT) cells, γδ T cells, and other poorly defined subsets, are a special group of T lymphocytes that recognize a wide range of nonpolymorphic ligands and are the connection between adaptive and innate immunity. Recently, an increasing number of complex functions of these unconventional T cells in brain homeostasis and various brain disorders have been revealed. In this review, we describe the classification and effector function of unconventional T cells, review the evidence for the involvement of unconventional T cells in the regulation of brain homeostasis, summarize the roles and mechanisms of unconventional T cells in the regulation of brain injury and neurodegeneration, and discuss immunotherapeutic potential as well as future research goals. Insight of these processes can shed light on the regulation of T cell immunity on brain homeostasis and diseases and provide new clues for therapeutic approaches targeting brain injury and neurodegeneration.

NLRRC: A Novel Clustering Method of Jointing Non-Negative LRR and Random Walk Graph Regularized NMF for Single-Cell Type Identification

The development of single-cell RNA sequencing (scRNA-seq) technology has opened up a new perspective for us to study disease mechanisms at the single cell level. Cell clustering reveals the natural grouping of cells, which is a vital step in scRNA-seq data analysis. However, the high noise and dropout of single-cell data pose numerous challenges to cell clustering. In this study, we propose a novel matrix factorization method named NLRRC for single-cell type identification. NLRRC joins non-negative low-rank representation (LRR) and random walk graph regularized NMF (RWNMFC) to accurately reveal the natural grouping of cells. Specifically, we find the lowest rank representation of single-cell samples by non-negative LRR to reduce the difficulty of analyzing high-dimensional samples and capture the global information of the samples. Meanwhile, by using random walk graph regularization (RWGR) and NMF, RWNMFC captures manifold structure and cluster information before generating a cluster allocation matrix. The cluster assignment matrix contains cluster labels, which can be used directly to get the clustering results. The performance of NLRRC is validated on simulated and real single-cell datasets. The results of the experiments illustrate that NLRRC has a significant advantage in single-cell type identification.

Invariant natural killer T cells in lung diseases

Invariant natural killer T (iNKT) cells are a subset of T cells that are characterized by a restricted T-cell receptor (TCR) repertoire and a unique ability to recognize glycolipid antigens. These cells are found in all tissues, and evidence to date suggests that they play many immunological roles in both homeostasis and inflammatory conditions. The latter include lung inflammatory diseases such as asthma and infections: the roles of lung-resident iNKT cells in these diseases have been extensively researched. Here, we provide insights into the biology of iNKT cells in health and disease, with a particular focus on the role of pulmonary iNKT cells in airway inflammation and other lung diseases.

Defining iNKT Cell Subsets and Their Function by Flow Cytometry

This article discusses methods to assess invariant natural killer T (iNKT) cell subsets isolated from the thymus, as well as the spleen, the liver, and the lung. iNKT cells can be subdivided in distinct, functional subsets based on the transcription factors they express and the cytokines they produce to regulate the immune response. Basic Protocol 1 focuses on characterizing murine iNKT subsets ex vivo by flow cytometry by evaluating the expression of lineage-specifying transcription factors such as PLZF and RORγt. The Alternate Protocol describes a detailed approach to define subsets based on expression of surface markers. This approach can be very useful for maintaining the subsets alive, without fixing them, in order to isolate them for downstream molecular assays such as DNA/RNA isolation, genome-wide analysis to assess gene expression (such as RNA-seq), assessment of chromatin accessibility (for instance, by ATAC-seq), and assessment of DNA methylation by whole-genome bisulfite sequencing. Basic Protocol 2 describes the functional characterization of iNKT cells, which are activated in vitro with PMA and ionomycin for a short period of time and subsequently stained and characterized for production of cytokines, such as IFNγ and IL-4, by flow cytometry. Basic Protocol 3 describes the process of activating iNKT cells in vivo using α-galactosyl-ceramide, a lipid that can be recognized specifically by iNKT cells, allowing assessment of their functionality in vivo. Cells are then isolated and directly stained for cytokine secretion. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Identifying iNKT cell subsets based on transcription factor expression by flow cytometry Alternate Protocol: Identifying iNKT cell subsets based on surface marker expression by flow cytometry Basic Protocol 2: iNKT cell functional characterization based on in vitro activation and assessment of cytokine secretion Basic Protocol 3: iNKT cell in vivo activation and assessment of cytokine secretion by flow cytometry.

Mettl3-m6A-Creb1 forms an intrinsic regulatory axis in maintaining iNKT cell pool and functional differentiation

N6-methyladenosine (m6A) methyltransferase Mettl3 is involved in conventional T cell immunity; however, its role in innate immune cells remains largely unknown. Here, we show that Mettl3 intrinsically regulates invariant natural killer T (iNKT) cell development and function in an m6A-dependent manner. Conditional ablation of Mettl3 in CD4+CD8+ double-positive (DP) thymocytes impairs iNKT cell proliferation, differentiation, and cytokine secretion, which synergistically causes defects in B16F10 melanoma resistance. Transcriptomic and epi-transcriptomic analyses reveal that Mettl3 deficiency disturbs the expression of iNKT cell-related genes with altered m6A modification. Strikingly, Mettl3 modulates the stability of the Creb1 transcript, which in turn controls the protein and phosphorylation levels of Creb1. Furthermore, conditional targeting of Creb1 in DP thymocytes results in similar phenotypes of iNKT cells lacking Mettl3. Importantly, ectopic expression of Creb1 largely rectifies such developmental defects in Mettl3-deficient iNKT cells. These findings reveal that the Mettl3-m6A-Creb1 axis plays critical roles in regulating iNKT cells at the post-transcriptional layer.

Integrative scATAC-seq and scRNA-seq analyses map thymic iNKT cell development and identify Cbfβ for its commitment

Unlike conventional αβT cells, invariant natural killer T (iNKT) cells complete their terminal differentiation to functional iNKT1/2/17 cells in the thymus. However, underlying molecular programs that guide iNKT subset differentiation remain unclear. Here, we profiled the transcriptomes of over 17,000 iNKT cells and the chromatin accessibility states of over 39,000 iNKT cells across four thymic iNKT developmental stages using single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to define their developmental trajectories. Our study discovered novel features for iNKT precursors and different iNKT subsets and indicated that iNKT2 and iNKT17 lineage commitment may occur as early as stage 0 (ST0) by two distinct programs, while iNKT1 commitments may occur post ST0. Both iNKT1 and iNKT2 cells exhibit extensive phenotypic and functional heterogeneity, while iNKT17 cells are relatively homogenous. Furthermore, we identified that a novel transcription factor, Cbfβ, was highly expressed in iNKT progenitor commitment checkpoint, which showed a similar expression trajectory with other known transcription factors for iNKT cells development, Zbtb16 and Egr2, and could direct iNKT cells fate and drive their effector phenotype differentiation. Conditional deletion of Cbfβ blocked early iNKT cell development and led to severe impairment of iNKT1/2/17 cell differentiation. Overall, our findings uncovered distinct iNKT developmental programs as well as their cellular heterogeneity, and identified a novel transcription factor Cbfβ as a key regulator for early iNKT cell commitment.

Markers and makers of NKT17 cells

Invariant natural killer T (iNKT) cells are thymus-generated innate-like αβ T cells that undergo terminal differentiation in the thymus. Such a developmental pathway differs from that of conventional αβ T cells, which are generated in the thymus but complete their functional maturation in peripheral tissues. Multiple subsets of iNKT cells have been described, among which IL-17-producing iNKT cells are commonly referred to as NKT17 cells. IL-17 is considered a proinflammatory cytokine that can play both protective and pathogenic roles and has been implicated as a key regulatory factor in many disease settings. Akin to other iNKT subsets, NKT17 cells acquire their effector function during thymic development. However, the cellular mechanisms that drive NKT17 subset specification, and how iNKT cells in general acquire their effector function prior to antigen encounter, remain largely unknown. Considering that all iNKT cells express the canonical Vα14-Jα18 TCRα chain and all iNKT subsets display the same ligand specificity, i.e., glycolipid antigens in the context of the nonclassical MHC-I molecule CD1d, the conundrum is explaining how thymic NKT17 cell specification is determined. Mapping of the molecular circuitry of NKT17 cell differentiation, combined with the discovery of markers that identify NKT17 cells, has provided new insights into the developmental pathway of NKT17 cells. The current review aims to highlight recent advances in our understanding of thymic NKT17 cell development and to place these findings in the larger context of iNKT subset specification and differentiation.

Advanced omics techniques shed light on CD1d-mediated lipid antigen presentation to iNKT cells

Invariant natural killer T cells (iNKT cells) can be activated through binding antigenic lipid/CD1d complexes to their TCR. Antigenic lipids are processed, loaded, and displayed in complex with CD1d by lipid antigen presenting cells (LAPCs). The mechanism of lipid antigen presentation via CD1d is highly conserved with recent work showing adipocytes are LAPCs that, besides having a role in lipid storage, can activate iNKT cells and play an important role in systemic metabolic disease. Recent studies shed light on parameters potentially dictating cytokine output and how obesity-associated metabolic disease may affect such parameters. By following a lipid antigen's journey, we identify five key areas which may dictate cytokine skew: co-stimulation, structural properties of the lipid antigen, stability of lipid antigen/CD1d complexes, intracellular and extracellular pH, and intracellular and extracellular lipid environment. Recent publications indicate that the combination of advanced omics-type approaches and machine learning may be a fruitful way to interconnect these 5 areas, with the ultimate goal to provide new insights for therapeutic exploration.

Epigenetic control of CD1D expression as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas

Immune Checkpoint Therapies (ICT) have revolutionized the treatment of metastatic melanoma. However, only a subset of patients reaches complete responses. Deficient β2-microglobulin (β2M) expression impacts antigen presentation to T cells, leading to ICT resistance. Here, we investigate alternative β2M-correlated biomarkers that associate with ICT resistance. We shortlisted immune biomarkers interacting with human β2M using the STRING database. Next, we profiled the transcriptomic expression of these biomarkers in association with clinical and survival outcomes in the melanoma GDC-TCGA-SKCM dataset and a collection of publicly available metastatic melanoma cohorts treated with ICT (anti-PD1). Epigenetic control of identified biomarkers was interrogated using the Illumina Human Methylation 450 dataset from the melanoma GDC-TCGA-SKCM study. We show that β2M associates with CD1d, CD1b, and FCGRT at the protein level. Co-expression and correlation profile of B2M with CD1D, CD1B, and FCGRT dissociates in melanoma patients following B2M expression loss. Lower CD1D expression is typically found in patients with poor survival outcomes from the GDC-TCGA-SKCM dataset, in patients not responding to anti-PD1 immunotherapies, and in a resistant anti-PD1 pre-clinical model. Immune cell abundance study reveals that B2M and CD1D are both enriched in tumor cells and dendritic cells from patients responding to anti-PD1 immunotherapies. These patients also show increased levels of natural killer T (NKT) cell signatures in the tumor microenvironment (TME). Methylation reactions in the TME of melanoma impact the expression of B2M and SPI1, which controls CD1D expression. These findings suggest that epigenetic changes in the TME of melanoma may impact β2M and CD1d-mediated functions, such as antigen presentation for T cells and NKT cells. Our hypothesis is grounded in comprehensive bioinformatic analyses of a large transcriptomic dataset from four clinical cohorts and mouse models. It will benefit from further development using well-established functional immune assays to support understanding the molecular processes leading to epigenetic control of β2M and CD1d. This research line may lead to the rational development of new combinatorial treatments for metastatic melanoma patients that poorly respond to ICT.

KGLRR: A low-rank representation K-means with graph regularization constraint method for Single-cell type identification

Single-cell RNA sequencing technology provides a tremendous opportunity for studying disease mechanisms at the single-cell level. Cell type identification is a key step in the research of disease mechanisms. Many clustering algorithms have been proposed to identify cell types. Most clustering algorithms perform similarity calculation before cell clustering. Because clustering and similarity calculation are independent, a low-rank matrix obtained only by similarity calculation may be unable to fully reveal the patterns in single-cell data. In this study, to capture accurate single-cell clustering information, we propose a novel method based on a low-rank representation model, called KGLRR, that combines the low-rank representation approach with K-means clustering. The cluster centroid is updated as the cell dimension decreases to better from new clusters and improve the quality of clustering information. In addition, the low-rank representation model ignores local geometric information, so the graph regularization constraint is introduced. KGLRR is tested on both simulated and real single-cell datasets to validate the effectiveness of the new method. The experimental results show that KGLRR is more robust and accurate in cell type identification than other advanced algorithms.

JAML immunotherapy targets recently activated tumor-infiltrating CD8+ T cells

Junctional adhesion molecule-like protein (JAML) serves as a co-stimulatory molecule in γδ T cells. While it has recently been described as a cancer immunotherapy target in mice, its potential to cause toxicity, specific mode of action with regard to its cellular targets, and whether it can be targeted in humans remain unknown. Here, we show that JAML is induced by T cell receptor engagement, reveal that this induction is linked to cis-regulatory interactions between the CD3D and JAML gene loci. When compared with other immunotherapy targets plagued by low target specificity and end-organ toxicity, we find JAML to be mostly restricted to and highly expressed by tissue-resident memory CD8+ T cells in multiple cancer types. By delineating the key cellular targets and functional consequences of agonistic anti-JAML therapy in a murine melanoma model, we show its specific mode of action and the reason for its synergistic effects with anti-PD-1.

SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.

Current Developments in the Preclinical and Clinical use of Natural Killer T cells

Natural killer T (NKT) cells play a pivotal role as a bridge between the innate and the adaptive immune response and are instrumental in the regulation of homeostasis. In this review, we discuss the potential for NKT cells to serve as biodrugs in viral infections and in cancer. NKT cells are being investigated for their use as a prognostic biomarker, an immune adjuvant, and as a form of cellular therapy. Historically, the clinical utility of NKT cells was hampered by their low frequency in the blood, discrepancies in nomenclature, and challenges with ex vivo expansion. However, recent advances in the field have permitted the development of several NKT cell-based preclinical and clinical strategies. These new developments pave the way for the successful implementation of NKT cell-based approaches for the treatment of human disease.

TET Proteins in the Spotlight: Emerging Concepts of Epigenetic Regulation in T Cell Biology

Ten-eleven translocation (TET) proteins are dioxygenases that oxidize 5-methylcytosine to form 5-hydroxymethylcytosine and downstream oxidized modified cytosines. In the past decade, intensive research established that TET-mediated DNA demethylation is critical for immune cell development and function. In this study, we discuss major advances regarding the role of TET proteins in regulating gene expression in the context of T cell lineage specification, function, and proliferation. Then, we focus on open questions in the field. We discuss recent findings regarding the diverse roles of TET proteins in other systems, and we ask how these findings might relate to T cell biology. Finally, we ask how this tremendous progress on understanding the multifaceted roles of TET proteins in shaping T cell identity and function can be translated to improve outcomes of human disease, such as hematological malignancies and immune response to cancer.

IsoCell: An Approach to Enhance Single Cell Clustering by Integrating Isoform-Level Expression Through Orthogonal Projection

Single cell RNA sequencing (scRNA-seq) provides a powerful approach for profiling transcriptomes at single cell resolution. An essential application of scRNA-seq is the discovery of cell types with the aid of clustering analysis. Currently, existing single cell clustering methods are exclusively based on gene-level expression data, without considering alternative splicing information. It has been shown that alternative splicing has an important influence on biological processes such as cell differentiation and cell cycle. We therefore hypothesize that adding information about alternative splicing may help enhance single cell clustering. This motivates us to develop a way to integrate isoform-level expression and gene-level expression. We report an approach to enhance single cell clustering by integrating isoform-level expression through orthogonal projection. First, we construct an orthogonal projection matrix based on gene expression data. Second, isoforms are projected to the gene space to remove the redundant information between them. Third, isoform selection is performed based on the residual of the projected expression and the selected isoforms are combined with gene expression data for subsequent clustering. We applied our method to sixteen scRNA-seq datasets. We find that alternative splicing contains differential information among cell types and can be integrated to enhance single cell clustering. Compared with using only gene-level expression data, the integration of isoform-level expression leads to better clustering performances for most of the datasets. The integration of isoform-level expression also has potential in the detection of novel cell subgroups. Our study shows that integrating isoform and gene-level expression is a promising way to improve single cell clustering. The IsoCell R package is freely available at both Github (https://github.com/genemine/IsoCell) and Zenodo (https://zenodo.org/record/4395707).

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

PTEN directs developmental and metabolic signaling for innate-like T cell fate and tissue homeostasis

Phosphatase and tensin homologue (PTEN) is frequently mutated in human cancer, but its roles in lymphopoiesis and tissue homeostasis remain poorly defined. Here we show that PTEN orchestrates a two-step developmental process linking antigen receptor and IL-23-Stat3 signalling to type-17 innate-like T cell generation. Loss of PTEN leads to pronounced accumulation of mature IL-17-producing innate-like T cells in the thymus. IL-23 is essential for their accumulation, and ablation of IL-23 or IL-17 signalling rectifies the reduced survival of female PTEN-haploinsufficient mice that model human patients with PTEN mutations. Single-cell transcriptome and network analyses revealed the dynamic regulation of PTEN, mTOR and metabolic activities that accompanied type-17 cell programming. Furthermore, deletion of mTORC1 or mTORC2 blocks PTEN loss-driven type-17 cell accumulation, and this is further shaped by the Foxo1 and Stat3 pathways. Collectively, our study establishes developmental and metabolic signalling networks underpinning type-17 cell fate decisions and their functional effects at coordinating PTEN-dependent tissue homeostasis.

The multisensory regulation of unconventional T cell homeostasis

Unconventional T cells typically group γδ T cells, invariant Natural Killer T cells (NKT) and Mucosal Associated Invariant T (MAIT) cells. With their pre-activated status and biased tropism for non-lymphoid organs, they provide a rapid (innate-like) and efficient first line of defense against pathogens at strategical barrier sites, while they can also trigger chronic inflammation, and unexpectedly contribute to steady state physiology. Thus, a tight control of their homeostasis is critical to maintain tissue integrity. In this review, we discuss the recent advances of our understanding of the factors, from neuroimmune to inflammatory regulators, shaping the size and functional properties of unconventional T cell subsets in non-lymphoid organs. We present a general overview of the mechanisms common to these populations, while also acknowledging specific aspects of their diversity. We mainly focus on their maintenance at steady state and upon inflammation, highlighting some key unresolved issues and raising upcoming technical, fundamental and translational challenges.

New insights into iNKT cells and their roles in liver diseases

Natural killer T cells (NKTs) are an important part of the immune system. Since their discovery in the 1990s, researchers have gained deeper insights into the physiology and functions of these cells in many liver diseases. NKT cells are divided into two subsets, type I and type II. Type I NKT cells are also named iNKT cells as they express a semi-invariant T cell-receptor (TCR) α chain. As part of the innate immune system, hepatic iNKT cells interact with hepatocytes, macrophages (Kupffer cells), T cells, and dendritic cells through direct cell-to-cell contact and cytokine secretion, bridging the innate and adaptive immune systems. A better understanding of hepatic iNKT cells is necessary for finding new methods of treating liver disease including autoimmune liver diseases, alcoholic liver diseases (ALDs), non-alcoholic fatty liver diseases (NAFLDs), and liver tumors. Here we summarize how iNKT cells are activated, how they interact with other cells, and how they function in the presence of liver disease.

Identification of distinct functional thymic programming of fetal and pediatric human γδ thymocytes via single-cell analysis

Developmental thymic waves of innate-like and adaptive-like γδ T cells have been described, but the current understanding of γδ T cell development is mainly limited to mouse models. Here, we combine single cell (sc) RNA gene expression and sc γδ T cell receptor (TCR) sequencing on fetal and pediatric γδ thymocytes in order to understand the ontogeny of human γδ T cells. Mature fetal γδ thymocytes (both the Vγ9Vδ2 and nonVγ9Vδ2 subsets) are committed to either a type 1, a type 3 or a type 2-like effector fate displaying a wave-like pattern depending on gestation age, and are enriched for public CDR3 features upon maturation. Strikingly, these effector modules express different CDR3 sequences and follow distinct developmental trajectories. In contrast, the pediatric thymus generates only a small effector subset that is highly biased towards Vγ9Vδ2 TCR usage and shows a mixed type 1/type 3 effector profile. Thus, our combined dataset of gene expression and detailed TCR information at the single-cell level identifies distinct functional thymic programming of γδ T cell immunity in human.

Knowledge about the ontogeny of T cells in the thymus relies heavily on mouse studies because of difficulty to obtain human material. Here the authors perform a single cell analysis of thymocytes from human fetal and paediatric thymic samples to characterise the development of human γδ T cells in the thymus.

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

BACKGROUND

Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics.

RESULTS

Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods.

CONCLUSIONS

This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

Distinctive populations of CD4+T cells associated with vaccine efficacy

Memory T cells underpin vaccine-induced immunity but are not yet fully understood. To distinguish features of memory cells that confer protective immunity, we used single cell transcriptome analysis to compare antigen-specific CD4⁺T cells recalled to lungs of mice that received a protective or nonprotective subunit vaccine followed by challenge with a fungal pathogen. We unexpectedly found populations specific to protection that expressed a strong type I interferon response signature, whose distinctive transcriptional signature appeared unconventionally dependent on IFN-γ receptor. We also detected a unique population enriched in protection that highly expressed the gene for the natural killer cell marker NKG7. Lastly, we detected differences in TCR gene use and in Th1- and Th17-skewed responses after protective and nonprotective vaccine, respectively, reflecting heterogeneous Ifng- and Il17a-expressing populations. Our findings highlight key features of transcriptionally diverse and distinctive antigen-specific T cells associated with protective vaccine-induced immunity.

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Protective and nonprotective vaccines generate distinct T cells in fungal infection

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A strong type I interferon signal is seen among CD4 T cells in protective immunity

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Th1 bias is seen with protective immunity; Th17 bias with nonprotective immunity

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Nkg7-expressing CD4 T cells are enriched in protective immunity

METTL14-dependent m6A modification controls iNKT cell development and function

N⁶-methyladenosine (m⁶A), the most common form of RNA modification, controls CD4⁺ T cell homeostasis by targeting the IL-7/STAT5/SOCS signaling pathways. The role of m⁶A modification in unconventional T cell development remains unknown. Using mice with T cell-specific deletion of RNA methyltransferase METTL14 (T-Mettl14^−/−), we demonstrate that m⁶A modification is indispensable for iNKT cell homeostasis. Loss of METTL14-dependent m⁶A modification leads to the upregulation of apoptosis in double-positive thymocytes, which in turn decreases Vα14-Jα18 gene rearrangements, resulting in drastic reduction of iNKT numbers in the thymus and periphery. Residual T-Mettl14^−/− iNKT cells exhibit increased apoptosis, impaired maturation, and decreased responsiveness to IL-2/IL-15 and TCR stimulation. Furthermore, METTL14 knockdown in mature iNKT cells diminishes their cytokine production, correlating with increased Cish expression and decreased TCR signaling. Collectively, our study highlights a critical role for METTL14-dependent-m⁶A modification in iNKT cell development and function.

Cao et al. show that T cell-specific deletion of METTL14, a component of RNA m⁶A writer complex, leads to severe defects in iNKT cell development, survival, and function. Mechanistically, METTL14-dependent m⁶A modification controls iNKT cell development in a cell-intrinsic manner by regulating the apoptosis pathway and TCR signaling pathway.

Single-cell analysis reveals differences among iNKT cells colonizing peripheral organs and identifies Klf2 as a key gene for iNKT emigration

Invariant natural killer T cell (iNKT) subsets are differentially distributed in various immune organs. However, it remains unclear whether iNKT cells exhibit phenotypical and functional differences in different peripheral organs and how thymic iNKT cells emigrate to peripheral organs. Here, we used single-cell RNA-seq to map iNKT cells from peripheral organs. iNKT1 cells from liver, spleen, and lymph node appear to have distinct phenotypic profiles and functional capabilities. However, iNKT17 transcriptomes were comparable across peripheral organs. In addition, by integrating data with a thymic iNKT cell study, we uncovered a transient population of recent thymic emigrants, a cluster of peripheral iNKT cells with high expression of transcription factor Kruppel-like factor 2 (Klf2). Deletion of Klf2 led to a severe impairment of iNKT differentiation and migration. Our study revealed that iNKT subsets are uniquely distributed in peripheral organs with some inter-local tissue variation, especially for iNKT1 cell, and identified Klf2 as a rheostat for iNKT cell migration and differentiation.

A single-cell analysis of thymopoiesis and thymic iNKT cell development in pigs

Many aspects of the porcine immune system remain poorly characterized, which poses a barrier to improving swine health and utilizing pigs as preclinical models. Here, we employ single-cell RNA sequencing (scRNA-seq) to create a cell atlas of the early-adolescent pig thymus. Our data show conserved features as well as species-specific differences in cell states and cell types compared with human thymocytes. We also describe several unconventional T cell types with gene expression profiles associated with innate effector functions. This includes a cell census of more than 11,000 differentiating invariant natural killer T (iNKT) cells, which reveals that the functional diversity of pig iNKT cells differs substantially from the iNKT0/1/2/17 subset differentiation paradigm established in mice. Our data characterize key differentiation events in porcine thymopoiesis and iNKT cell maturation and provide important insights into pig T cell development.

Immunomodulatory functions of FXR

The Farnesoid-x-receptor (FXR) is a bile acids sensor activated in humans by primary bile acids. FXR is mostly expressed in liver, intestine and adrenal glands but also by cells of innate immunity, including macrophages, liver resident macrophages, the Kupffer cells, natural killer cells and dendritic cells. In normal physiology and clinical disorders, cells of innate immunity mediate communications between liver, intestine and adipose tissues. In addition to FXR, the G protein coupled receptor (GPBAR1), that is mainly activated by secondary bile acids, whose expression largely overlaps FXR, modulates chemical communications from the intestinal microbiota and the host's immune system, integrating epithelial cells and immune cells in the entero-hepatic system, providing a mechanism for development of a tolerogenic state toward the intestinal microbiota. Disruption of FXR results in generalized inflammation and disrupted bile acids metabolism. While FXR agonism in preclinical models provides counter-regulatory signals that attenuate inflammation-driven immune dysfunction in a variety of liver and intestinal disease models, the clinical relevance of these mechanisms in the setting of FXR-related disorders remain poorly defined.

Re-programming mouse liver-resident invariant natural killer T cells for suppressing hepatic and diabetogenic autoimmunity

Invariant NKT (iNKT) cells comprise a heterogeneous group of non-circulating, tissue-resident T lymphocytes that recognize glycolipids, including alpha-galactosylceramide (αGalCer), in the context of CD1d, but whether peripheral iNKT cell subsets are terminally differentiated remains unclear. Here we show that mouse and human liver-resident αGalCer/CD1d-binding iNKTs largely correspond to a novel Zbtb16+Tbx21+Gata3+MaflowRorc– subset that exhibits profound transcriptional, phenotypic and functional plasticity. Repetitive in vivo encounters of these liver iNKT (LiNKT) cells with intravenously delivered αGalCer/CD1d-coated nanoparticles (NP) trigger their differentiation into immunoregulatory, IL-10+IL-21-producing Zbtb16highMafhighTbx21+Gata3+Rorc– cells, termed LiNKTR1, expressing a T regulatory type 1 (TR1)-like transcriptional signature. This response is LiNKT-specific, since neither lung nor splenic tissue-resident iNKT cells from αGalCer/CD1d-NP-treated mice produce IL-10 or IL-21. Additionally, these LiNKTR1 cells suppress autoantigen presentation, and recognize CD1d expressed on conventional B cells to induce IL-10+IL-35-producing regulatory B (Breg) cells, leading to the suppression of liver and pancreas autoimmunity. Our results thus suggest that LiNKT cells are plastic for further functional diversification, with such plasticity potentially targetable for suppressing tissue-specific inflammatory phenomena.

Type 2 and Type 17 Invariant Natural Killer T Cells Contribute to Local Eosinophilic and Neutrophilic Inflammation and Their Function Is Regulated by Mucosal Microenvironment in Nasal Polyps

Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by heterogeneous inflammatory endotypes of unknown etiology. Invariant natural killer T (iNKT) cells are multifunctional innate T cells that exhibit Th1-, Th2-, and Th17-like characteristics. We investigated functional relationships between iNKT cells and inflammatory subtypes of CRSwNP. Eighty patients with CRSwNP and thirty-two control subjects were recruited in this study. Flow cytometry was used to analyze the frequencies and functions of iNKT cells and their subsets in peripheral blood mononuclear cells (PBMCs) and tissues. Polyp tissue homogenates were used to study the multifunctionality of iNKT cells. iNKT cells were significantly increased in polyps (0.41%) than in control mucosa (0.12%). iNKT cells were determined in the paucigranunlocytic (n=20), eosinophilic (n=22), neutrophilic (n=23), and mixed granulocytic (n=13) phenotypes of CRSwNP. The percentages of iNKT cells and HLA-DR⁺PD-1⁺ subsets were lower in eosinophilic or mixed granulocytic polyps than those of other phenotypes. iNKT cells and subsets were enriched in polyp tissues than in matched PBMCs. The evaluation of surface markers, transcription factors, and signature cytokines indicated that the frequencies of iNKT2 and iNKT17 subsets were significantly increased in eosinophilic and neutrophilic polyps, respectively, than in the paucigranulocytic group. Moreover, the production of type 2 (partially dependent on IL-7) and type 17 (partially dependent on IL-23) iNKT cells could be stimulated by eosinophilic and neutrophilic homogenates, respectively. Our study revealed that type 2 and type 17 iNKT cells were involved in eosinophilic and neutrophilic inflammation, respectively, in CRSwNP, while different inflammatory microenvironments could modulate the functions of iNKT cells, suggesting a role of iNKT cells in feedback mechanisms and local inflammation.

Deconstructing iNKT cell development at single-cell resolution

Invariant natural killer T (iNKT) cells are increasingly regarded as disease biomarkers and immunotherapeutic targets. However, a greater understanding of their biology is necessary to effectively target these cells in the clinic. The discovery of iNKT1/2/17 cell effector subsets was a milestone in our understanding of iNKT cell development and function. Recent transcriptomic studies have uncovered an even greater heterogeneity and challenge our understanding of iNKT cell ontogeny and effector differentiation. We propose a refined model whereby iNKT cells differentiate through a dynamic and continuous instructive process that requires the accumulation and integration of various signals within the thymus or peripheral tissues. Within this framework, we question the existence of true iNKT2 cells and discuss the parallels between mouse and human iNKT cells.