IL-20 controls resolution of experimental colitis by regulating epithelial IFN/STAT2 signalling.

OBJECTIVE: We sought to investigate the role of interleukin (IL)-20 in IBD and experimental colitis. DESIGN: Experimental colitis was induced in mice deficient in components of the IL-20 and signal transducer and activator of transcription (STAT)2 signalling pathways. In vivo imaging, high-resolution mini-endoscopy and histology were used to assess intestinal inflammation. We further used RNA-sequencing (RNA-Seq), RNAScope and Gene Ontology analysis, western blot analysis and co-immunoprecipitation, confocal microscopy and intestinal epithelial cell (IEC)-derived three-dimensional organoids to investigate the underlying molecular mechanisms. Results were validated using samples from patients with IBD and non-IBD control subjects by a combination of RNA-Seq, organoids and immunostainings. RESULTS: In IBD, IL20 levels were induced during remission and were significantly higher in antitumour necrosis factor responders versus non-responders. IL-20RA and IL-20RB were present on IECs from patients with IBD and IL-20-induced STAT3 and suppressed interferon (IFN)-STAT2 signalling in these cells. In IBD, experimental dextran sulfate sodium (DSS)-induced colitis and mucosal healing, IECs were the main producers of IL-20. Compared with wildtype controls, Il20-/-, Il20ra-/- and Il20rb-/- mice were more susceptible to experimental DSS-induced colitis. IL-20 deficiency was associated with increased IFN/STAT2 activity in mice and IFN/STAT2-induced necroptotic cell death in IEC-derived organoids could be markedly blocked by IL-20. Moreover, newly generated Stat2ΔIEC mice, lacking STAT2 in IECs, were less susceptible to experimental colitis compared with wildtype controls and the administration of IL-20 suppressed colitis activity in wildtype animals. CONCLUSION: IL-20 controls colitis and mucosal healing by interfering with the IFN/STAT2 death signalling pathway in IECs. These results indicate new directions for suppressing gut inflammation by modulating IL-20-controlled STAT2 signals.

SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition.

MOTIVATION: As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of 'pseudo-bulk' data, generated by aggregating single-cell RNA-seq expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists. RESULTS: We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modeling of cell-type-specific mRNA bias using experimentally derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods. AVAILABILITY AND IMPLEMENTATION: SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inducers of the endothelial cell barrier identified through chemogenomic screening in genome-edited hPSC-endothelial cells.

The blood-retina barrier and blood-brain barrier (BRB/BBB) are selective and semipermeable and are critical for supporting and protecting central nervous system (CNS)-resident cells. Endothelial cells (ECs) within the BRB/BBB are tightly coupled, express high levels of Claudin-5 (CLDN5), a junctional protein that stabilizes ECs, and are important for proper neuronal function. To identify novel CLDN5 regulators (and ultimately EC stabilizers), we generated a CLDN5-P2A-GFP stable cell line from human pluripotent stem cells (hPSCs), directed their differentiation to ECs (CLDN5-GFP hPSC-ECs), and performed flow cytometry-based chemogenomic library screening to measure GFP expression as a surrogate reporter of barrier integrity. Using this approach, we identified 62 unique compounds that activated CLDN5-GFP. Among them were TGF-ß pathway inhibitors, including RepSox. When applied to hPSC-ECs, primary brain ECs, and retinal ECs, RepSox strongly elevated barrier resistance (transendothelial electrical resistance), reduced paracellular permeability (fluorescein isothiocyanate-dextran), and prevented vascular endothelial growth factor A (VEGFA)-induced barrier breakdown in vitro. RepSox also altered vascular patterning in the mouse retina during development when delivered exogenously. To determine the mechanism of action of RepSox, we performed kinome-, transcriptome-, and proteome-profiling and discovered that RepSox inhibited TGF-ß, VEGFA, and inflammatory gene networks. In addition, RepSox not only activated vascular-stabilizing and barrier-establishing Notch and Wnt pathways, but also induced expression of important tight junctions and transporters. Taken together, our data suggest that inhibiting multiple pathways by selected individual small molecules, such as RepSox, may be an effective strategy for the development of better BRB/BBB models and novel EC barrier-inducing therapeutics.

Proteolytic Activity of the Paracaspase MALT1 Is Involved in Epithelial Restitution and Mucosal Healing.

The paracaspase MALT1 is a crucial regulator of immune responses in various cellular contexts. Recently, there is increasing evidence suggesting that MALT1 might represent a novel key player in mucosal inflammation. However, the molecular mechanisms underlying this process and the targeted cell population remain unclear. In this study, we investigate the role of MALT1 proteolytic activity in the context of mucosal inflammation. We demonstrate a significant enrichment of MALT1 gene and protein expression in colonic epithelial cells of UC patients, as well as in the context of experimental colitis. Mechanistically we demonstrate that MALT1 protease function inhibits ferroptosis, a form of iron-dependent cell death, upstream of NF-κB signaling, which can promote inflammation and tissue damage in IBD. We further show that MALT1 activity contributes to STAT3 signaling, which is essential for the regeneration of the intestinal epithelium after injury. In summary, our data strongly suggests that the protease function of MALT1 plays a critical role in the regulation of immune and inflammatory responses, as well as mucosal healing. Understanding the mechanisms by which MALT1 protease function regulates these processes may offer novel therapeutic targets for the treatment of IBD and other inflammatory diseases.

NKG2A is a late immune checkpoint on CD8 T cells and marks repeated stimulation and cell division.

The surface inhibitory receptor NKG2A forms heterodimers with the invariant CD94 chain and is expressed on a subset of activated CD8 T cells. As antibodies to block NKG2A are currently tested in several efficacy trials for different tumor indications, it is important to characterize the NKG2A+ CD8 T cell population in the context of other inhibitory receptors. Here we used a well-controlled culture system to study the kinetics of inhibitory receptor expression. Naïve mouse CD8 T cells were synchronously and repeatedly activated by artificial antigen presenting cells in the presence of the homeostatic cytokine IL-7. The results revealed NKG2A as a late inhibitory receptor, expressed after repeated cognate antigen stimulations. In contrast, the expression of PD-1, TIGIT and LAG-3 was rapidly induced, hours after first contact and subsequently down regulated during each resting phase. This late, but stable expression kinetics of NKG2A was most similar to that of TIM-3 and CD39. Importantly, single-cell transcriptomics of human tumor-infiltrating lymphocytes (TILs) showed indeed that these receptors were often coexpressed by the same CD8 T cell cluster. Furthermore, NKG2A expression was associated with cell division and was promoted by TGF-ß in vitro, although TGF-ß signaling was not necessary in a mouse tumor model in vivo. In summary, our data show that PD-1 reflects recent TCR triggering, but that NKG2A is induced after repeated antigen stimulations and represents a late inhibitory receptor. Together with TIM-3 and CD39, NKG2A might thus mark actively dividing tumor-specific TILs.

Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data.

SUMMARY: Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. AVAILABILITY AND IMPLEMENTATION: Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology.

MOTIVATION: The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. RESULTS: We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. AVAILABILITY AND IMPLEMENTATION: A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Correction to: Detect tissue heterogeneity in gene expression data with BioQC.

After the publication of this work [1], a mistake was noticed in the Eq. 1. Given an m × n expression matrix with m genes and samples of n tissues, the correct definition of the Gini index for gene i is.

Detect tissue heterogeneity in gene expression data with BioQC.

BACKGROUND: Gene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted. RESULTS: We introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies. Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity. CONCLUSIONS: Our experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.

Making mouse transcriptomics deconvolution accessible with immunedeconv.

Summary: Transcriptome deconvolution has emerged as a reliable technique to estimate cell-type abundances from bulk RNA sequencing data. Unlike their human equivalents, methods to quantify the cellular composition of complex tissues from murine transcriptomics are sparse and sometimes not easy to use. We extended the immunedeconv R package to facilitate the deconvolution of mouse transcriptomics, enabling the quantification of murine immune-cell types using 13 different methods. Through immunedeconv, we further offer the possibility of tweaking cell signatures used by deconvolution methods, providing custom annotations tailored for specific cell types and tissues. These developments strongly facilitate the study of the immune-cell composition of mouse models and further open new avenues in the investigation of the cellular composition of other tissues and organisms. Availability and implementation: The R package and the documentation are available at https://github.com/omnideconv/immunedeconv.

Beyond binary: bridging neutrophil diversity to new therapeutic approaches in NSCLC.

Neutrophils represent the most abundant myeloid cell subtype in the non-small-cell lung cancer (NSCLC) tumor microenvironment (TME). By anti- or protumor polarization, they impact multiple aspects of tumor biology and affect sensitivity to conventional therapies and immunotherapies. Single-cell RNA sequencing (scRNA-seq) analyses have unraveled an extensive neutrophil heterogeneity, helping our understanding of their pleiotropic role. In this review we summarize recent data and models on tumor-associated neutrophil (TAN) biology, focusing on the diversity that evolves in response to tumor-intrinsic cues. We categorize available transcriptomic profiles from different cancer entities into a defined set of neutrophil subclusters with distinct phenotypic properties, to step beyond the traditional binary N1/2 classification. Finally, we discuss potential ways to exploit these neutrophil states in the setting of anticancer therapy.

Interleukin 36 receptor-inducible matrix metalloproteinase 13 mediates intestinal fibrosis.

Background: Fibrostenotic disease is a common complication in Crohn's disease (CD) patients hallmarked by transmural extracellular matrix (ECM) accumulation in the intestinal wall. The prevention and medical therapy of fibrostenotic CD is an unmet high clinical need. Although targeting IL36R signaling is a promising therapy option, downstream mediators of IL36 during inflammation and fibrosis have been incompletely understood. Candidate molecules include matrix metalloproteinases which mediate ECM turnover and are thereby potential targets for anti-fibrotic treatment. Here, we have focused on understanding the role of MMP13 during intestinal fibrosis. Methods: We performed bulk RNA sequencing of paired colon biopsies taken from non-stenotic and stenotic areas of patients with CD. Corresponding tissue samples from healthy controls and CD patients with stenosis were used for immunofluorescent (IF) staining. MMP13 gene expression was analyzed in cDNA of intestinal biopsies from healthy controls and in subpopulations of patients with CD in the IBDome cohort. In addition, gene regulation on RNA and protein level was studied in colon tissue and primary intestinal fibroblasts from mice upon IL36R activation or blockade. Finally, in vivo studies were performed with MMP13 deficient mice and littermate controls in an experimental model of intestinal fibrosis. Ex vivo tissue analysis included Masson's Trichrome and Sirius Red staining as well as evaluation of immune cells, fibroblasts and collagen VI by IF analysis. Results: Bulk RNA sequencing revealed high upregulation of MMP13 in colon biopsies from stenotic areas, as compared to non-stenotic regions of patients with CD. IF analysis confirmed higher levels of MMP13 in stenotic tissue sections of CD patients and demonstrated αSMA+ and Pdpn+ fibroblasts as a major source. Mechanistic experiments demonstrated that MMP13 expression was regulated by IL36R signaling. Finally, MMP13 deficient mice, as compared to littermate controls, developed less fibrosis in the chronic DSS model and showed reduced numbers of αSMA+ fibroblasts. These findings are consistent with a model suggesting a molecular axis involving IL36R activation in gut resident fibroblasts and MMP13 expression during the pathogenesis of intestinal fibrosis. Conclusion: Targeting IL36R-inducible MMP13 could evolve as a promising approach to interfere with the development and progression of intestinal fibrosis.

A gut bacterial signature in blood and liver tissue characterizes cirrhosis and hepatocellular carcinoma.

BACKGROUND: HCC is the leading cause of cancer in chronic liver disease. A growing body of experimental mouse models supports the notion that gut-resident and liver-resident microbes control hepatic immune responses and, thereby, crucially contribute to liver tumorigenesis. However, a comprehensive characterization of the intestinal microbiome in fueling the transition from chronic liver disease to HCC in humans is currently missing. METHODS: Here, we profiled the fecal, blood, and liver tissue microbiome of patients with HCC by 16S rRNA sequencing and compared profiles to nonmalignant cirrhotic and noncirrhotic NAFLD patients. RESULTS: We report a distinct bacterial profile, defined from 16S rRNA gene sequences, with reduced α-and ß-diversity in the feces of patients with HCC and cirrhosis compared to NAFLD. Patients with HCC and cirrhosis exhibited an increased proportion of fecal bacterial gene signatures in the blood and liver compared to NAFLD. Differential analysis of the relative abundance of bacterial genera identified an increased abundance of Ruminococcaceae and Bacteroidaceae in blood and liver tissue from both HCC and cirrhosis patients compared to NAFLD. Fecal samples from cirrhosis and HCC patients both showed a reduced abundance for several taxa, including short-chain fatty acid-producing genera, such as Blautia and Agathobacter. Using paired 16S rRNA and transcriptome sequencing, we identified a direct association between gut bacterial genus abundance and host transcriptome response within the liver tissue. CONCLUSIONS: Our study indicates perturbations of the intestinal and liver-resident microbiome as a critical determinant of patients with cirrhosis and HCC.

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer.

CD8+ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8+ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8+ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8+ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8+ T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8+ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8+ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8+ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8+ T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8+ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer.

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Tumor-specific T cells support chemokine-driven spatial organization of intratumoral immune microaggregates needed for long survival.

BACKGROUND: The composition of the tumor immune microenvironment (TIME) associated with good prognosis generally also predicts the success of immunotherapy, and both entail the presence of pre-existing tumor-specific T cells. Here, the blueprint of the TIME associated with such an ongoing tumor-specific T-cell response was dissected in a unique prospective oropharyngeal squamous cell carcinoma (OPSCC) cohort, in which tumor-specific tumor-infiltrating T cells were detected (immune responsiveness (IR+)) or not (lack of immune responsiveness (IR-)). METHODS: A comprehensive multimodal, high-dimensional strategy was applied to dissect the TIME of treatment-naive IR+ and IR- OPSCC tissue, including bulk RNA sequencing (NanoString), imaging mass cytometry (Hyperion) for phenotyping and spatial interaction analyses of immune cells, and combined single-cell gene expression profiling and T-cell receptor (TCR) sequencing (single-cell RNA sequencing (scRNAseq)) to characterize the transcriptional states of clonally expanded tumor-infiltrating T cells. RESULTS: IR+ patients had an excellent survival during >10 years follow-up. The tumors of IR+ patients expressed higher levels of genes strongly related to interferon gamma signaling, T-cell activation, TCR signaling, and mononuclear cell differentiation, as well as genes involved in several immune signaling pathways, than IR- patients. The top differently overexpressed genes included CXCL12 and LTB, involved in ectopic lymphoid structure development. Moreover, scRNAseq not only revealed that CD4+ T cells were the main producers of LTB but also identified a subset of clonally expanded CD8+ T cells, dominantly present in IR+ tumors, which secreted the T cell and dendritic cell (DC) attracting chemokine CCL4. Indeed, immune cell infiltration in IR+ tumors is stronger, highly coordinated, and has a distinct spatial phenotypical signature characterized by intratumoral microaggregates of CD8+CD103+ and CD4+ T cells with DCs. In contrast, the IR- TIME comprised spatial interactions between lymphocytes and various immunosuppressive myeloid cell populations. The impact of these chemokines on local immunity and clinical outcome was confirmed in an independent The Cancer Genome Atlas OPSCC cohort. CONCLUSION: The production of lymphoid cell attracting and organizing chemokines by tumor-specific T cells in IR+ tumors constitutes a positive feedback loop to sustain the formation of the DC-T-cell microaggregates and identifies patients with excellent survival after standard therapy.

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,283,972 single cells from 556 samples and 318 patients across 29 datasets, including our dataset capturing cells with low mRNA content. We stratify patients into immune-deserted, B cell, T cell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identify cellular components associated with tumor histology and genotypes. We then focus on the analysis of tissue-resident neutrophils (TRNs) and uncover distinct subpopulations that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. Finally, we show that a TRN-derived gene signature is associated with anti-programmed cell death ligand 1 (PD-L1) treatment failure.

CD161 expression and regulation defines rapidly responding effector CD4+ T cells associated with improved survival in HPV16-associated tumors.

BACKGROUND: Expression of killer cell lectin-like receptor B1 (KLRB1), the gene encoding the cell surface molecule CD161, is associated with favorable prognosis in many cancers. CD161 is expressed by several lymphocyte populations, but its role and regulation on tumor-specific CD4+ T cells is unknown. METHODS: We examined the clinical impact of CD4+CD161+ T cells in human papillomavirus (HPV)16+ oropharyngeal squamous cell carcinoma (OPSCC), analyzed their contribution in a cohort of therapeutically vaccinated patients and used HPV16-specific CD4+CD161+ tumor-infiltrating lymphocytes and T cell clones for in-depth mechanistic studies. RESULTS: Central and effector memory CD4+ T cells express CD161, but only CD4+CD161+ effector memory T cells (Tem) are associated with improved survival in OPSCC. Therapeutic vaccination activates and expands type 1 cytokine-producing CD4+CD161+ effector T cells. The expression of CD161 is dynamic and follows a pattern opposite of the checkpoint molecules PD1 and CD39. CD161 did not function as an immune checkpoint molecule as demonstrated using multiple experimental approaches using antibodies to block CD161 and gene editing to knockout CD161 expression. Single-cell transcriptomics revealed KLRB1 expression in many T cell clusters suggesting differences in their activation. Indeed, CD4+CD161+ effector cells specifically expressed the transcriptional transactivator SOX4, known to enhance T cell receptor (TCR) signaling via CD3ε. Consistent with this observation, CD4+CD161+ cells respond more vigorously to limiting amounts of cognate antigen in presence of interleukin (IL)-12 and IL-18 compared to their CD161- counterparts. The expression of CD161/KLRB1 and SOX4 was downregulated upon TCR stimulation and this effect was boosted by transforming growth factor (TGF)ß1. CONCLUSION: High levels of CD4+CD161+ Tem are associated with improved survival and our data show that CD161 is dynamically regulated by cell intrinsic and extrinsic factors. CD161 expressing CD4+ T cells rapidly respond to suboptimal antigen stimulation suggesting that CD161, similar to SOX4, is involved in the amplification of TCR signals in CD4+ T cells.

Tissue heterogeneity is prevalent in gene expression studies.

Lack of reproducibility in gene expression studies is a serious issue being actively addressed by the biomedical research community. Besides established factors such as batch effects and incorrect sample annotations, we recently reported tissue heterogeneity, a consequence of unintended profiling of cells of other origins than the tissue of interest, as a source of variance. Although tissue heterogeneity exacerbates irreproducibility, its prevalence in gene expression data remains unknown. Here, we systematically analyse 2 667 publicly available gene expression datasets covering 76 576 samples. Using two independent data compendia and a reproducible, open-source software pipeline, we find a prevalence of tissue heterogeneity in gene expression data that affects between 1 and 40% of the samples, depending on the tissue type. We discover both cases of severe heterogeneity, which may be caused by mistakes in annotation or sample handling, and cases of moderate heterogeneity, which are likely caused by tissue infiltration or sample contamination. Our analysis establishes tissue heterogeneity as a widespread phenomenon in publicly available gene expression datasets, which constitutes an important source of variance that should not be ignored. Consequently, we advocate the application of quality-control methods such as BioQC to detect tissue heterogeneity prior to mining or analysing gene expression data.