Related Cell Types Research Articles

Abstract PR012: KRAS mutation-specific effects on the tumor immune microenvironment drive tumor progression in pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with abysmal survival rates. The highly immunosuppressive microenvironment of PDAC poses a major barrier to effective therapy. KRAS mutations are near-ubiquitous in PDAC, and studies indicate clinical outcomes vary depending on the specific KRAS genotype. Given that tumor genotype can shape the immune cell composition of tumors, we examined the immune cell composition of pancreatic tumors with specific KRAS mutations, focusing on KRASG12D mutations (representing 40% of PDAC cases which have the worst prognosis) and KRASG12R mutations (which make up 20% of PDAC cases and have a better prognosis). Orthotopically implanted Kras G12R/+ ;Trp53 KO mouse pancreatic tumors have a distinct immune profile in comparison to Kras G12D/+ ;Trp53 KO tumors, characterized by an influx of intratumoral CD3+ T cells and mature dendritic cells. Selective depletion of CD4+ T cells induces a rapid progression in KRASG12R, but not KRASG12D mouse tumors. RNA sequencing of KRASG12R tumor cells revealed enrichment of Type I interferon (IFN) pathways accompanied by related chemokines, CXCL9 and CXCL10 in comparison to KRASG12D tumors. Analysis of publicly available and real-world human PDAC datasets and enrichment of dendritic cells and other immune related cell types in KRASG12R tumors. Collectively, these data link the KRASG12R mutation to a unique immunogenic role. Citation Format: Andrew Wenger, Tal Baron, Erika Hissong, Rohit Chandiwani, Lukas Dow, Despina Siolas, Katherine Bacchi, Huanhuan Sun, Erika Hissong, Adrian Vega, Whitney Sisso, and Kawther Abdilleh. KRAS mutation-specific effects on the tumor immune microenvironment drive tumor progression in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr PR012.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

BackgroundThe use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data.MethodsNS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells.ResultsBy modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions in simulation studies and on its ability to identify markers with higher On-Target Fraction values for closely related cell types in real data, and outperforms other marker gene selection approaches for cell type classification with significantly higher F-beta scores when applied to datasets from three human organs—brain, kidney, and lung.DiscussionFinally, we discuss potential use cases of the NS-Forest marker genes, including for designing spatial transcriptomics gene panels and semantic representation of cell types in biomedical ontologies, for the broad user community.

Common modes of ERK induction resolve into context-specific signalling via emergent networks and cell-type-specific transcriptional repression.

Fibroblast Growth Factor signalling via ERK exerts diverse roles in development and disease. In mammalian preimplantation embryos and naïve pluripotent stem cells ERK promotes differentiation, whereas in primed pluripotent states closer to somatic differentiation ERK sustains self-renewal. How can the same pathway produce different outcomes in two related cell types? To explore context-dependent ERK signalling we generated cell and mouse lines that allow for tissue- and time-specific ERK activation. Using these tools, we find that specificity in ERK response is mostly mediated by repression of transcriptional targets that occur in tandem with reductions in chromatin accessibility at regulatory regions. Furthermore, immediate early ERK responses are largely shared by different cell types but produce cell-specific programmes as these responses interface with emergent networks in the responding cells. Induction in naïve pluripotency is accompanied by chromatin changes, whereas in later stages it is not, suggesting that chromatin context does not shape signalling response. Altogether, our data suggest that cell-type-specific responses to ERK signalling exploit the same immediate early response, but then sculpt it to specific lineages via repression of distinct cellular programmes.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders.

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generate single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

Network Analysis of Enhancer-Promoter Interactions Highlights Cell-Type-Specific Mechanisms of Transcriptional Regulation Variation.

Gene expression is orchestrated by a complex array of gene regulatory elements that govern transcription in a cell-type-specific manner. Though previously studied, the ability to utilize regulatory elements to identify disrupting variants remains largely elusive. To identify important factors within these regions, we generated enhancer-promoter interaction (EPI) networks and investigated the presence of disease-associated variants that fall within these regions. Our study analyzed six neuronal cell types across neural differentiation, allowing us to examine closely related cell types and across differentiation stages. Our results expand upon previous findings of cell-type specificity of enhancer, promoter, and transcription factor binding sites. Notably, we find that regulatory regions within EPI networks can identify the enrichment of variants associated with neuropsychiatric disorders within specific cell types and network sub-structures. This enrichment within sub-structures can allow for a better understanding of potential mechanisms by which variants may disrupt transcription. Together, our findings suggest that EPIs can be leveraged to better understand cell-type-specific regulatory architecture and used as a selection method for disease-associated variants to be tested in future functional assays. Combined with these future functional characterization assays, EPIs can be used to better identify and characterize regulatory variants' effects on such networks and model their mechanisms of gene regulation disruption across different disorders. Such findings can be applied in practical settings, such as diagnostic tools and drug development.

Combinatorial regulatory states define cell fate diversity during embryogenesis

Cell fate specification occurs along invariant species-specific trajectories that define the animal body plan. This process is controlled by gene regulatory networks that regulate the expression of the limited set of transcription factors encoded in animal genomes. Here we globally assess the spatial expression of ~90% of expressed transcription factors during sea urchin development from embryo to larva to determine the activity of gene regulatory networks and their regulatory states during cell fate specification. We show that >200 embryonically expressed transcription factors together define >70 cell fates that recapitulate the morphological and functional organization of this organism. Most cell fate-specific regulatory states consist of ~15–40 transcription factors with similarity particularly among functionally related cell types regardless of developmental origin. Temporally, regulatory states change continuously during development, indicating that progressive changes in regulatory circuit activity determine cell fate specification. We conclude that the combinatorial expression of transcription factors provides molecular definitions that suffice for the unique specification of cell states in time and space during embryogenesis.

Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

Single-cell proteomics differentiates Arabidopsis root cell types.

Single-cell proteomics (SCP) is an emerging approach to resolve cellular heterogeneity within complex tissues of multi-cellular organisms. Here, we demonstrate the feasibility of SCP on plant samples using the model plant Arabidopsis thaliana. Specifically, we focused on examining isolated single cells from the cortex and endodermis, which are two adjacent root cell types derived from a common stem cell lineage. From 756 root cells, we identified 3763 proteins and 1118 proteins/cell. Ultimately, we focus on 3217 proteins quantified following stringent filtering. Of these, we identified 596 proteins whose expression is enriched in either the cortex or endodermis and are able to differentiate these closely related plant cell types. Collectivity, this study demonstrates that SCP can resolve neighboring cell types with distinct functions, thereby facilitating the identification of biomarkers and candidate proteins to enable functional genomics.

Designing Cell-Type-Specific Promoter Sequences Using Conservative Model-Based Optimization.

Gene therapies have the potential to treat disease by delivering therapeutic genetic cargo to disease-associated cells. One limitation to their widespread use is the lack of short regulatory sequences, or promoters, that differentially induce the expression of delivered genetic cargo in target cells, minimizing side effects in other cell types. Such cell-type-specific promoters are difficult to discover using existing methods, requiring either manual curation or access to large datasets of promoter-driven expression from both targeted and untargeted cells. Model-based optimization (MBO) has emerged as an effective method to design biological sequences in an automated manner, and has recently been used in promoter design methods. However, these methods have only been tested using large training datasets that are expensive to collect, and focus on designing promoters for markedly different cell types, overlooking the complexities associated with designing promoters for closely related cell types that share similar regulatory features. Therefore, we introduce a comprehensive framework for utilizing MBO to design promoters in a data-efficient manner, with an emphasis on discovering promoters for similar cell types. We use conservative objective models (COMs) for MBO and highlight practical considerations such as best practices for improving sequence diversity, getting estimates of model uncertainty, and choosing the optimal set of sequences for experimental validation. Using three relatively similar blood cancer cell lines (Jurkat, K562, and THP1), we show that our approach discovers many novel cell-type-specific promoters after experimentally validating the designed sequences. For K562 cells, in particular, we discover a promoter that has 75.85% higher cell-type-specificity than the best promoter from the initial dataset used to train our models.

The transcriptomic and spatial organization of telencephalic GABAergic neuronal types.

The telencephalon of the mammalian brain comprises multiple regions and circuit pathways that play adaptive and integrative roles in a variety of brain functions. There is a wide array of GABAergic neurons in the telencephalon; they play a multitude of circuit functions, and dysfunction of these neurons has been implicated in diverse brain disorders. In this study, we conducted a systematic and in-depth analysis of the transcriptomic and spatial organization of GABAergic neuronal types in all regions of the mouse telencephalon and their developmental origins. This was accomplished by utilizing 611,423 single-cell transcriptomes from the comprehensive and high-resolution transcriptomic and spatial cell type atlas for the adult whole mouse brain we have generated, supplemented with an additional single-cell RNA-sequencing dataset containing 99,438 high-quality single-cell transcriptomes collected from the pre- and postnatal developing mouse brain. We present a hierarchically organized adult telencephalic GABAergic neuronal cell type taxonomy of 7 classes, 52 subclasses, 284 supertypes, and 1,051 clusters, as well as a corresponding developmental taxonomy of 450 clusters across different ages. Detailed charting efforts reveal extraordinary complexity where relationships among cell types reflect both spatial locations and developmental origins. Transcriptomically and developmentally related cell types can often be found in distant and diverse brain regions indicating that long-distance migration and dispersion is a common characteristic of nearly all classes of telencephalic GABAergic neurons. Additionally, we find various spatial dimensions of both discrete and continuous variations among related cell types that are correlated with gene expression gradients. Lastly, we find that cortical, striatal and some pallidal GABAergic neurons undergo extensive postnatal diversification, whereas septal and most pallidal GABAergic neuronal types emerge simultaneously during the embryonic stage with limited postnatal diversification. Overall, the telencephalic GABAergic cell type taxonomy can serve as a foundational reference for molecular, structural and functional studies of cell types and circuits by the entire community.

Characterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes

BackgroundAlzheimer’s disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Most existing studies have solely focused on molecular perturbations within each cell, but cells live in microenvironments rather than in isolated entities. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing in the human prefrontal cortex to investigate cell-to-cell communication in healthy brains and their perturbations in AD. We uniformly processed the snRNA-seq with strict QCs and labeled canonical cell types consistent with the definitions from the BRAIN Initiative Cell Census Network. From ligand and receptor gene expression, we built a high-confidence cell-to-cell communication network to investigate signaling differences between AD and healthy brains.ResultsSpecifically, we first performed broad communication pattern analyses to highlight that biologically related cell types in normal brains rely on largely overlapping signaling networks and that the AD brain exhibits the irregular inter-mixing of cell types and signaling pathways. Secondly, we performed a more focused cell-type-centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other non-neuronal cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFβ, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and from endothelial to neuronal cells for the WNT pathway. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons.ConclusionsIn summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type-specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, giving the mechanistic intra- and inter-cellular picture of AD.

UniCell: Universal Cell Nucleus Classification via Prompt Learning

The recognition of multi-class cell nuclei can significantly facilitate the process of histopathological diagnosis. Numerous pathological datasets are currently available, but their annotations are inconsistent. Most existing methods require individual training on each dataset to deduce the relevant labels and lack the use of common knowledge across datasets, consequently restricting the quality of recognition. In this paper, we propose a universal cell nucleus classification framework (UniCell), which employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets. Moreover, we develop a Dynamic Prompt Module (DPM) that exploits the properties of multiple datasets to enhance features. The DPM first integrates the embeddings of datasets and semantic categories, and then employs the integrated prompts to refine image representations, efficiently harvesting the shared knowledge among the related cell types and data sources. Experimental results demonstrate that the proposed method effectively achieves the state-of-the-art results on four nucleus detection and classification benchmarks. Code and models are available at https://github.com/lhaof/UniCell

Repressing miR-23a promotes the transdifferentiation of pancreatic α cells to β cells via negatively regulating the expression of SDF-1α.

Pancreatic β-cell failure is a pathological feature in type 1 diabetes. One promising approach involves inducing transdifferentiation of related pancreatic cell types, specifically α cells that produce glucagon. The chemokine stromal cell-derived factor-1 alpha (SDF-1α) is implicated in pancreatic α-to-β like cell transition. Here, the serum level of SDF-1α was lower in T1D with C-peptide loss, the miR-23a was negatively correlated with SDF-1α. We discovered that exosomal miR-23a, secreted from β cells, functionally downregulates the expression of SDF-1α, leading to increased Pax4 expression and decreased Arx expression in vivo. Adenovirus-vectored miR-23a sponge and mimic were constructed to further explored the miR-23a on pancreatic α-to-β like cell transition in vitro, which yielded results consistent with our cell-based assays. Suppression of miR-23a upregulated insulin level and downregulated glucagon level in STZ-induced diabetes mice models, effectively promoting α-to-β like cell transition. Our findings highlight miR-23a as a new therapeutic target for regenerating pancreatic β cells from α cells.

Cancer-on-a-chip model shows that the adenomatous polyposis coli mutation impairs T cell engagement and killing of cancer spheroids

Evaluating the ability of cytotoxic T lymphocytes (CTLs) to eliminate tumor cells is crucial, for instance, to predict the efficiency of cell therapy in personalized medicine. However, the destruction of a tumor by CTLs involves CTL migration in the extra-tumoral environment, accumulation on the tumor, antigen recognition, and cooperation in killing the cancer cells. Therefore, identifying the limiting steps in this complex process requires spatio-temporal measurements of different cellular events over long periods. Here, we use a cancer-on-a-chip platform to evaluate the impact of adenomatous polyposis coli (APC) mutation on CTL migration and cytotoxicity against 3D tumor spheroids. The APC mutated CTLs are found to have a reduced ability to destroy tumor spheroids compared with control cells, even though APC mutants migrate in the extra-tumoral space and accumulate on the spheroids as efficiently as control cells. Once in contact with the tumor however, mutated CTLs display reduced engagement with the cancer cells, as measured by a metric that distinguishes different modes of CTL migration. Realigning the CTL trajectories around localized killing cascades reveals that all CTLs transition to high engagement in the 2 h preceding the cascades, which confirms that the low engagement is the cause of reduced cytotoxicity. Beyond the study of APC mutations, this platform offers a robust way to compare cytotoxic cell efficiency of even closely related cell types, by relying on a multiscale cytometry approach to disentangle complex interactions and to identify the steps that limit the tumor destruction.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models.

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

Systematic identification of genotype-dependent enhancer variants in eosinophilic esophagitis

Eosinophilic esophagitis (EoE) is a rare atopic disorder associated with esophageal dysfunction, including difficulty swallowing, food impaction, and inflammation, that develops in a small subset of people with food allergies. Genome-wide association studies (GWASs) have identified 9 independent EoE risk loci reaching genome-wide significance (p<5×10-8) and 27 additional loci of suggestive significance (5×10-8<p<1×10-5). In the current study, we perform linkage disequilibrium (LD) expansion of these loci to nominate a set of 531 variants that are potentially causal. To systematically interrogate the gene regulatory activity of these variants, we designed a massively parallel reporter assay (MPRA) containing the alleles of each variant within their genomic sequence context cloned into a GFP reporter library. Analysis of reporter gene expression in TE-7, HaCaT, and Jurkat cells revealed cell-type-specific gene regulation. We identify 32 allelic enhancer variants, representing 6 genome-wide significant EoE loci and 7 suggestive EoE loci, that regulate reporter gene expression in a genotype-dependent manner in at least one cellular context. By annotating these variants with expression quantitative trait loci (eQTL) and chromatin looping data in related tissues and cell types, we identify putative target genes affected by genetic variation in individuals with EoE. Transcription factor enrichment analyses reveal possible roles for cell-type-specific regulators, including GATA3. Our approach reduces the large set of EoE-associated variants to a set of 32 with allelic regulatory activity, providing functional insights into the effects of genetic variation in this disease.

Functionally Informed Polygenic Risk Scores for Alzheimer’s Disease

AbstractBackgroundAlzheimer’s Disease (AD) is a heritable, neurodegenerative disease. Most AD cases can be categorized as Late Onset Alzheimer’s Disease (LOAD) cases, which are polygenic. Genome Wide Association Studies (GWAS) of LOAD have identified several associated single nucleotide polymorphisms (SNPs). These variants are in 75 genetic loci, and have implicated a diverse array of genes, cell types, and biological pathways in LOAD. Additionally, many associated variants are not in protein coding regions of the genome. However, it is difficult to determine if all associated variants are functionally relevant and contribute to the etiology or progression of the disease. Here, we present CoRE‐BED, a variant annotation tool that can categorize SNPs into functional classes, such as promoters or enhancers, for a given cell type. These functional variants in each cell type are subsequently used to determine functional variant contributions to LOAD heritability and risk.MethodWe annotated a recently published GWAS of AD (Bellenguez et al 2022) using CoRE‐BED. CoRE‐BED leverages 19 functional assays across 33 distinct cell types from EpiMap to classify variants into functional categories for each supported cell type. Annotated variants were used to estimate cell type specific partitioned heritability using LD Score Regression. Finally, annotated variants were used to build cell type specific Polygenic Risk Scores (PRS). PRS was tested using multiple PRS software and utilized the annotated GWAS as a training dataset and independent cases and controls derived from Vanderbilt University’s biobank, BioVU.ResultCoRE‐BED provided functional classifications for all variants in the GWAS for individual tissue types. Variants that pass genome wide significance (P &lt; 5×10−8) are enriched for functional annotations. Immune related cell types contribute the greatest proportion of partitioned heritability. Standard PRS methods outperform cell type informed PRS measures.ConclusionCoRE‐BED is a robust annotation tool that can determine functional annotations at the SNP level. AD GWAS significant associations overlap with functionally annotated SNPs in multiple cell types. Partitioned heritability indicates that immune related cell types may play an important role in the heritability of AD. However, conventional methods for PRS still provide the most robust means to predict AD risk.

Divergent roles for STAT4 in shaping differentiation of cytotoxic ILC1 and NK cells during gut inflammation

Natural killer (NK) cells and type 1 innate lymphoid cells (ILC1) require signal transducer and activator of transcription 4 (STAT4) to elicit rapid effector responses and protect against pathogens. By combining genetic and transcriptomic approaches, we uncovered divergent roles for STAT4 in regulating effector differentiation of these functionally related cell types. Stat4 deletion in Ncr1-expressing cells led to impaired NK cell terminal differentiation as well as to an unexpected increased generation of cytotoxic ILC1 during intestinal inflammation. Mechanistically, Stat4-deficient ILC1 exhibited upregulation of gene modules regulated by STAT5 in vivo and an aberrant effector differentiation upon in vitro stimulation with IL-2, used as a prototypical STAT5 activator. Moreover, STAT4 expression in NCR+ innate lymphocytes restrained gut inflammation in the dextran sulfate sodium-induced colitis model limiting pathogenic production of IL-13 from adaptive CD4+ T cells in the large intestine. Collectively, our data shed light on shared and distinctive mechanisms of STAT4-regulated transcriptional control in NK cells and ILC1 required for intestinal inflammatory responses.

Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications

BackgroundThe cellular and molecular dynamics of human prepuce are crucial for understanding its biological and physiological functions, as well as the prevention of related genital diseases. However, the cellular compositions and heterogeneity of human prepuce at single-cell resolution are still largely unknown. Here we systematically dissected the prepuce of children and adults based on the single-cell RNA-seq data of 90,770 qualified cells.ResultsWe identified 15 prepuce cell subtypes, including fibroblast, smooth muscle cells, T/natural killer cells, macrophages, vascular endothelial cells, and dendritic cells. The proportions of these cell types varied among different individuals as well as between children and adults. Moreover, we detected cell-type-specific gene regulatory networks (GRNs), which could contribute to the unique functions of related cell types. The GRNs were also highly dynamic between the prepuce cells of children and adults. Our cell–cell communication network analysis among different cell types revealed a set of child-specific (e.g., CD96, EPO, IFN-1, and WNT signaling pathways) and adult-specific (e.g., BMP10, NEGR, ncWNT, and NPR1 signaling pathways) signaling pathways. The variations of GRNs and cellular communications could be closely associated with prepuce development in children and prepuce maintenance in adults.ConclusionsCollectively, we systematically analyzed the cellular variations and molecular changes of the human prepuce at single-cell resolution. Our results gained insights into the heterogeneity of prepuce cells and shed light on the underlying molecular mechanisms of prepuce development and maintenance.

Current perspective on biological properties of plasmacytoid dendritic cells and dysfunction in gut.

Plasmacytoid dendritic cells (pDCs), a subtype of DC, possess unique developmental, morphological, and functional traits that have sparked much debate over the years whether they should be categorized as DCs. The digestive system has the greatest mucosal tissue overall, and the pDC therein is responsible for shaping the adaptive and innate immunity of the gastrointestinal tract, resisting pathogen invasion through generating type I interferons, presenting antigens, and participating in immunological responses. Therefore, its alleged importance in the gut has received a lot of attention in recent years, and a fresh functional overview is still required. Here, we summarize the current understanding of mouse and human pDCs, ranging from their formation and different qualities compared with related cell types to their functional characteristics in intestinal disorders, including colon cancer, infections, autoimmune diseases, and intestinal graft-versus-host disease. The purpose of this review is to convey our insights, demonstrate the limits of existing research, and lay a theoretical foundation for the rational development and use of pDCs in future clinical practice.