Cross-tissue immune cell analysis reveals tissue-specific features in humans

Transformers in single-cell omics: a review and new perspectives

Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.

Diversity of group 1 innate lymphoid cells in human tissues

Group 1 innate lymphoid cells (ILC1s) are cytotoxic and interferon gamma-producing lymphocytes lacking antigen-specific receptors, which include ILC1s and natural killer (NK) cells. In mice, ILC1s differ from NK cells, as they develop independently of the NK-specifying transcription factor EOMES, while requiring the repressor ZFP683 (ZNF683 in humans) for tissue residency. Here we identify highly variable ILC1 subtypes across tissues through investigation of human ILC1 diversity by single-cell RNA sequencing and flow cytometry. The intestinal epithelium contained abundant mature EOMES- ILC1s expressing PRDM1 rather than ZNF683, alongside a few immature TCF7+PRDM1- ILC1s. Other tissues harbored NK cells expressing ZNF683 and EOMES transcripts; however, EOMES protein content was variable. These ZNF683+ NK cells are tissue-imprinted NK cells phenotypically resembling ILC1s. The tissue ILC1-NK spectrum also encompassed conventional NK cells and NK cells distinguished by PTGDS expression. These findings establish a foundation for evaluating phenotypic and functional changes within the NK-ILC1 spectrum in diseases.

Tissue-resident memory T cells in human kidney transplants have alloreactive potential

The extent to which tissue-resident memory T (TRM) cells in transplanted organs possess alloreactivity is uncertain. This study investigates the alloreactive potential of TRM cells in kidney explants from 4 patients who experienced severe acute rejection leading to graft loss. Alloreactive T cell receptor (TCR) clones were identified in pretransplant blood samples through mixed lymphocyte reactions, followed by single-cell RNA and TCR sequencing of the proliferated recipient T cells. Subsequently, these TCR clones were traced in the TRM cells of kidney explants, which were also subjected to single-cell RNA and TCR sequencing. The proportion of recipient-derived TRM cells expressing an alloreactive TCR in the 4 kidney explants varied from 0% to 9%. Notably, these alloreactive TCRs were predominantly found among CD4+ and CD8+ TRM cells with an effector phenotype. Intriguingly, these clones were present not only in recipient-derived TRM cells but also in donor-derived TRM cells, constituting up to 4% of the donor population, suggesting the presence of self-reactive TRM cells. Overall, our study demonstrates that T cells with alloreactive potential present in the peripheral blood prior to transplantation can infiltrate the kidney transplant and adopt a TRM phenotype.

Immunological memory diversity in the human upper airway

The upper airway is an important site of infection, but immune memory in the human upper airway is poorly understood, with implications for COVID-19 and many other human diseases1-4. Here we demonstrate that nasal and nasopharyngeal swabs can be used to obtain insights into these challenging problems, and define distinct immune cell populations, including antigen-specific memory B cells and T cells, in two adjacent anatomical sites in the upper airway. Upper airway immune cell populations seemed stable over time in healthy adults undergoing monthly swabs for more than 1 year, and prominent tissue resident memory T (TRM) cell and B (BRM) cell populations were defined. Unexpectedly, germinal centre cells were identified consistently in many nasopharyngeal swabs. In subjects with SARS-CoV-2 breakthrough infections, local virus-specific BRM cells, plasma cells and germinal centre B cells were identified, with evidence of local priming and an enrichment of IgA+ memory B cells in upper airway compartments compared with blood. Local plasma cell populations were identified with transcriptional profiles of longevity. Local virus-specific memory CD4+ TRM cells and CD8+ TRM cells were identified, with diverse additional virus-specific T cells. Age-dependent upper airway immunological shifts were observed. These findings provide new understanding of immune memory at a principal mucosal barrier tissue in humans.

Large-scale foundation model on single-cell transcriptomics

Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.

Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer

Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.

Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping

The functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic, receptor-ligand interactions and adaptive-like responses to viral infections. In the present study, we generated a single-cell transcriptional reference map of healthy human blood- and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene-regulatory networks defining NK cell differentiation. Transfer learning facilitated incorporation of tumor-infiltrating NK cell transcriptomes (39 datasets, 7 solid tumors, 427 patients) into the reference map to analyze tumor microenvironment (TME)-induced perturbations. Of the six functionally distinct NK cell states identified, a dysfunctional stressed CD56bright state susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant effector CD56dim state were commonly enriched across tumor types, the ratio of which was predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of new NK cell therapies and can be extended through transfer learning to interrogate new datasets from experimental perturbations or disease conditions.

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Background

Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model.

Methods

Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration.

Results

The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed.

Conclusion

CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.

Directly selecting cell-type marker genes for single-cell clustering analyses

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma

Background

Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses.

Methods

Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814).

Results

Functional analysis revealed an enrichment of genes related to the TGF-β signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score.

Conclusion

This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-β pathway and provided insights into LUAD prognosis and immune activities.

Unbiasedly decoding the tumor microenvironment with single-cell multiomics analysis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor prognosis and limited therapeutic options. Research on the tumor microenvironment (TME) of PDAC has propelled the development of immunotherapeutic and targeted therapeutic strategies with a promising future. The emergence of single-cell sequencing and mass spectrometry technologies, coupled with spatial omics, has collectively revealed the heterogeneity of the TME from a multiomics perspective, outlined the development trajectories of cell lineages, and revealed important functions of previously underrated myeloid cells and tumor stroma cells. Concurrently, these findings necessitated more refined annotations of biological functions at the cell cluster or single-cell level. Precise identification of all cell clusters is urgently needed to determine whether they have been investigated adequately and to identify target cell clusters with antitumor potential, design compatible treatment strategies, and determine treatment resistance. Here, we summarize recent research on the PDAC TME at the single-cell multiomics level, with an unbiased focus on the functions and potential classification bases of every cellular component within the TME, and look forward to the prospects of integrating single-cell multiomics data and retrospectively reusing bulk sequencing data, hoping to provide new insights into the PDAC TME.

Spatiotemporal single-cell analysis decodes cellular dynamics underlying different responses to immunotherapy in colorectal cancer

Expanding the efficacy of immune checkpoint blockade (ICB) in colorectal cancer (CRC) presses for a comprehensive understanding of treatment responsiveness. Here, we analyze multiple sequential single-cell samples from 22 patients undergoing PD-1 blockade to map the evolution of local and systemic immunity of CRC patients. In tumors, we identify coordinated cellular programs exhibiting distinct response associations. Specifically, exhausted T (Tex) or tumor-reactive-like CD8+ T (Ttr-like) cells are closely related to treatment efficacy, and Tex cells show correlated proportion changes with multiple other tumor-enriched cell types following PD-1 blockade. In addition, we reveal the less-exhausted phenotype of blood-associated Ttr-like cells in tumors and find that their higher abundance suggests better treatment outcomes. Finally, a higher major histocompatibility complex (MHC) II-related signature in circulating CD8+ T cells at baseline is linked to superior responses. Our study provides insights into the spatiotemporal cellular dynamics following neoadjuvant PD-1 blockade in CRC.

CD163+ Macrophages Induce Endothelial-to-Mesenchymal Transition in Atheroma

BACKGROUND

Cell phenotype switching is increasingly being recognized in atherosclerosis. However, our understanding of the exact stimuli for such cellular transformations and their significance for human atherosclerosis is still evolving. Intraplaque hemorrhage is thought to be a major contributor to plaque progression in part by stimulating the influx of CD163+ macrophages. Here, we explored the hypothesis that CD163+ macrophages cause plaque progression through the induction of proapoptotic endothelial-to-mesenchymal transition (EndMT) within the fibrous cap.

METHODS

Human coronary artery sections from CVPath's autopsy registry were selected for pathological analysis. Athero-prone ApoE-/- and ApoE-/-/CD163-/- mice were used for in vivo studies. Human peripheral blood mononuclear cell-induced macrophages and human aortic endothelial cells were used for in vitro experiments.

RESULTS

In 107 lesions with acute coronary plaque rupture, 55% had pathological evidence of intraplaque hemorrhage in nonculprit vessels/lesions. Thinner fibrous cap, greater CD163+ macrophage accumulation, and a larger number of CD31/FSP-1 (fibroblast specific protein-1) double-positive cells and TUNEL (terminal deoxynucleotidyl transferase-dUTP nick end labeling) positive cells in the fibrous cap were observed in nonculprit intraplaque hemorrhage lesions, as well as in culprit rupture sections versus nonculprit fibroatheroma sections. Human aortic endothelial cells cultured with supernatants from hemoglobin/haptoglobin-exposed macrophages showed that increased mesenchymal marker proteins (transgelin and FSP-1) while endothelial markers (VE-cadherin and CD31) were reduced, suggesting EndMT induction. Activation of NF-κB (nuclear factor kappa β) signaling by proinflammatory cytokines released from CD163+ macrophages directly regulated the expression of Snail, a critical transcription factor during EndMT induction. Western blot analysis for cleaved caspase-3 and microarray analysis of human aortic endothelial cells indicated that apoptosis was stimulated during CD163+ macrophage-induced EndMT. Additionally, CD163 deletion in athero-prone mice suggested that CD163 is required for EndMT and plaque progression. Using single-cell RNA sequencing from human carotid endarterectomy lesions, a population of EndMT was detected, which demonstrated significant upregulation of apoptosis-related genes.

CONCLUSIONS

CD163+ macrophages provoke EndMT, which may promote plaque progression through fibrous cap thinning.

An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases

Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.

Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data

Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.

ezSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell's streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application ( https://immunesinglecell.org/ezsc/ ) or a software package with a shinyApp interface ( https://github.com/JinmiaoChenLab/ezSingleCell2 ) for offline analysis.

Influence of METTL3 knockdown on PDLSC osteogenesis in E. coli LPS-induced inflammation

OBJECTIVE

This study aimed to investigate the effect of METTL3 knockdown on osteogenic differentiation of human periodontal ligament stem cells (PDLSCs) in the weak inflammation microenvironments, as well as the underlying mechanisms.

MATERIALS AND METHODS

PDLSCs were stimulated by lipopolysaccharide from Escherichia coli (E. coli LPS), followed by quantification of METTL3. METTL3 expression was assessed using RT-qPCR and Western blot analysis in periodontitis. METTL3 knockdown PDLSCs were stimulated with or without E. coli LPS. The evaluation included proinflammatory cytokines, osteogenic markers, ALP activity, and mineralized nodules. Bioinformatics analysis and Western blot determined the association between METTL3 and the PI3K/Akt pathway.

RESULTS

METTL3 was overexpressed in periodontitis. METTL3 knockdown in PDLSCs reduced proinflammatory cytokines, osteogenic markers, ALP activity, and mineralized nodules in both environments. Bioinformatics analysis suggested a link between METTL3 and the PI3K/Akt pathway. METTL3 knockdown inhibited PI3K/Akt signaling pathway activation.

CONCLUSION

METTL3 knockdown might inhibit osteogenesis in PDLSCs through the inactivation of PI3K/Akt signaling pathway. Concomitant findings might shed novel light on the roles and potential mechanisms of METTL3 in the LPS-stimulated inflammatory microenvironments of PDLSCs.

Identification of a clinically efficacious CAR T cell subset in diffuse large B cell lymphoma by dynamic multidimensional single-cell profiling

Chimeric antigen receptor (CAR) T cells used for the treatment of B cell malignancies can identify T cell subsets with superior clinical activity. Here, using infusion products of individuals with large B cell lymphoma, we integrated functional profiling using timelapse imaging microscopy in nanowell grids with subcellular profiling and single-cell RNA sequencing to identify a signature of multifunctional CD8+ T cells (CD8-fit T cells). CD8-fit T cells are capable of migration and serial killing and harbor balanced mitochondrial and lysosomal volumes. Using independent datasets, we validate that CD8-fit T cells (1) are present premanufacture and are associated with clinical responses in individuals treated with axicabtagene ciloleucel, (2) longitudinally persist in individuals after treatment with CAR T cells and (3) are tumor migrating cytolytic cells capable of intratumoral expansion in solid tumors. Our results demonstrate the power of multimodal integration of single-cell functional assessments for the discovery and application of CD8-fit T cells as a T cell subset with optimal fitness in cell therapy.

Compartmentalization of the inflammatory response during bacterial sepsis and severe COVID-19

Acute infections cause local and systemic disorders which can lead in the most severe forms to multi-organ failure and eventually to death. The host response to infection encompasses a large spectrum of reactions with a concomitant activation of the so-called inflammatory response aimed at fighting the infectious agent and removing damaged tissues or cells, and the anti-inflammatory response aimed at controlling inflammation and initiating the healing process. Fine-tuning at the local and systemic levels is key to preventing local and remote injury due to immune system activation. Thus, during bacterial sepsis and Coronavirus disease 2019 (COVID-19), concomitant systemic and compartmentalized pro-inflammatory and compensatory anti-inflammatory responses are occurring. Immune cells (e.g., macrophages, neutrophils, natural killer cells, and T-lymphocytes), as well as endothelial cells, differ from one compartment to another and contribute to specific organ responses to sterile and microbial insult. Furthermore, tissue-specific microbiota influences the local and systemic response. A better understanding of the tissue-specific immune status, the organ immunity crosstalk, and the role of specific mediators during sepsis and COVID-19 can foster the development of more accurate biomarkers for better diagnosis and prognosis and help to define appropriate host-targeted treatments and vaccines in the context of precision medicine.

Human SARS-CoV-2 challenge uncovers local and systemic response dynamics

The COVID-19 pandemic is an ongoing global health threat, yet our understanding of the dynamics of early cellular responses to this disease remains limited1. Here in our SARS-CoV-2 human challenge study, we used single-cell multi-omics profiling of nasopharyngeal swabs and blood to temporally resolve abortive, transient and sustained infections in seronegative individuals challenged with pre-Alpha SARS-CoV-2. Our analyses revealed rapid changes in cell-type proportions and dozens of highly dynamic cellular response states in epithelial and immune cells associated with specific time points and infection status. We observed that the interferon response in blood preceded the nasopharyngeal response. Moreover, nasopharyngeal immune infiltration occurred early in samples from individuals with only transient infection and later in samples from individuals with sustained infection. High expression of HLA-DQA2 before inoculation was associated with preventing sustained infection. Ciliated cells showed multiple immune responses and were most permissive for viral replication, whereas nasopharyngeal T cells and macrophages were infected non-productively. We resolved 54 T cell states, including acutely activated T cells that clonally expanded while carrying convergent SARS-CoV-2 motifs. Our new computational pipeline Cell2TCR identifies activated antigen-responding T cells based on a gene expression signature and clusters these into clonotype groups and motifs. Overall, our detailed time series data can serve as a Rosetta stone for epithelial and immune cell responses and reveals early dynamic responses associated with protection against infection.

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

Cell of origin alters myeloid-mediated immunosuppression in lung adenocarcinoma

Solid carcinomas are often highly heterogenous cancers, arising from multiple epithelial cells of origin. Yet, how the cell of origin influences the response of the tumor microenvironment is poorly understood. Lung adenocarcinoma (LUAD) arises in the distal alveolar epithelium which is populated primarily by alveolar epithelial type I (AT1) and type II (AT2) cells. It has been previously reported that Gramd2 + AT1 cells can give rise to a histologically-defined LUAD that is distinct in pathology and transcriptomic identity from that arising from Sftpc + AT2 cells1,2. To determine how cells of origin influence the tumor immune microenvironment (TIME) landscape, we comprehensively characterized transcriptomic, molecular, and cellular states within the TIME of Gramd2 + AT1 and Sftpc + AT2-derived LUAD using KRASG12D oncogenic driver mouse models. Myeloid cells within the Gramd2 + AT1-derived LUAD TIME were increased, specifically, immunoreactive monocytes and tumor associated macrophages (TAMs). In contrast, the Sftpc + AT2 LUAD TIME was enriched for Arginase-1+ myeloid derived suppressor cells (MDSC) and TAMs expressing profiles suggestive of immunosuppressive function. Validation of immune infiltration was performed using flow cytometry, and intercellular interaction analysis between the cells of origin and major myeloid cell populations indicated that cell-type specific markers SFTPD in AT2 cells and CAV1 in AT1 cells mediated unique interactions with myeloid cells of the differential immunosuppressive states within each cell of origin mouse model. Taken together, Gramd2 + AT1-derived LUAD presents with an anti-tumor, immunoreactive TIME, while the TIME of Sftpc + AT2-derived LUAD has hallmarks of immunosuppression. This study suggests that LUAD cell of origin influences the composition and suppression status of the TIME landscape and may hold critical implications for patient response to immunotherapy.

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy (ACT), have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA sequencing (RNA-seq) datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

BACKGROUND

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models.

RESULTS

Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism.

CONCLUSIONS

Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis

Periodontitis affects billions of people worldwide. To address relationships of periodontal niche cell types and microbes in periodontitis, we generated an integrated single-cell RNA sequencing (scRNAseq) atlas of human periodontium (34-sample, 105918-cell), including sulcular and junctional keratinocytes (SK/JKs). SK/JKs displayed altered differentiation states and were enriched for effector cytokines in periodontitis. Single-cell metagenomics revealed 37 bacterial species with cell-specific tropism. Fluorescence in situ hybridization detected intracellular 16 S and mRNA signals of multiple species and correlated with SK/JK proinflammatory phenotypes in situ. Cell-cell communication analysis predicted keratinocyte-specific innate and adaptive immune interactions. Highly multiplexed immunofluorescence (33-antibody) revealed peri-epithelial immune foci, with innate cells often spatially constrained around JKs. Spatial phenotyping revealed immunosuppressed JK-microniches and SK-localized tertiary lymphoid structures in periodontitis. Here, we demonstrate impacts on and predicted interactomics of SK and JK cells in health and periodontitis, which requires further investigation to support precision periodontal interventions in states of chronic inflammation.

Identification and experimental validation of cuproptosis regulatory program in a sepsis immune microenvironment through a combination of single-cell and bulk RNA sequencing

Background

In spite of its high mortality rate and poor prognosis, the pathogenesis of sepsis is still incompletely understood. This study established a cuproptosis-based risk model to diagnose and predict the risk of sepsis. In addition, the cuproptosis-related genes were identified for targeted therapy.

Methods

Single-cell sequencing analyses were used to characterize the cuproptosis activity score (CuAS) and intercellular communications in sepsis. Differential cuproptosis-related genes (CRGs) were identified in conjunction with single-cell and bulk RNA sequencing. LASSO and Cox regression analyses were employed to develop a risk model. Three external cohorts were conducted to assess the model's accuracy. Differences in immune infiltration, immune cell subtypes, pathway enrichment, and the expression of immunomodulators were further evaluated in distinct groups. Finally, various in-vitro experiments, such as flow cytometry, Western blot, and ELISA, were used to explore the role of LST1 in sepsis.

Results

ScRNA-seq analysis demonstrated that CuAS was highly enriched in monocytes and was closely related to the poor prognosis of sepsis patients. Patients with higher CuAS exhibited prominent strength and numbers of cell-cell interactions. A total of five CRGs were identified based on the LASSO and Cox regression analyses, and a CRG-based risk model was established. The lower riskScore cohort exhibited enhanced immune cell infiltration, elevated immune scores, and increased expression of immune modulators, indicating the activation of an antibacterial response. Ultimately, in-vitro experiments demonstrated that LST1, a key gene in the risk model, was enhanced in the macrophage in response to LPS, which was closely related to the decrease of macrophage survival rate, the enhancement of apoptosis and oxidative stress injury, and the imbalance of the M1/M2 phenotype.

Conclusions

This study constructed a cuproptosis-related risk model to accurately predict the prognosis of sepsis. We further characterized the cuproptosis-related gene LST1 to provide a theoretical framework for sepsis therapy.

CAraCAl: CAMML with the integration of chromatin accessibility

BACKGROUND

A vital step in analyzing single-cell data is ascertaining which cell types are present in a dataset, and at what abundance. In many diseases, the proportions of varying cell types can have important implications for health and prognosis. Most approaches for cell type annotation have centered around cell typing for single-cell RNA-sequencing (scRNA-seq) and have had promising success. However, reliable methods are lacking for many other single-cell modalities such as single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), which quantifies the extent to which genes of interest in each cell are epigenetically "open" for expression.

RESULTS

To leverage the informative potential of scATAC-seq data, we developed CAMML with the integration of chromatin accessibility (CAraCAl), a bioinformatic method that performs cell typing on scATAC-seq data. CAraCAl performs cell typing by scoring each cell for its enrichment of cell type-specific gene sets. These gene sets are composed of the most upregulated or downregulated genes present in each cell type according to projected gene activity.

CONCLUSIONS

We found that CAraCAl does not improve performance beyond CAMML when scRNA-seq is present, but if only scATAC-seq is available, CAraCAl performs cell typing relatively successfully. As such, we also discuss best practices for cell typing and the strengths and weaknesses of various cell annotation options.

Leveraging Multi-Tissue, Single-Cell Atlases as Tools to Elucidate Shared Mechanisms of Immune-Mediated Inflammatory Diseases

The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis, and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being "shared" between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour before considering future directions in the field.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Human γδ T cells in diverse tissues exhibit site-specific maturation dynamics across the life span

During ontogeny, γδ T cells emerge from the thymus and directly seed peripheral tissues for in situ immunity. However, their functional role in humans has largely been defined from blood. Here, we analyzed the phenotype, transcriptome, function, and repertoire of human γδ T cells in blood and mucosal and lymphoid tissues from 176 donors across the life span, revealing distinct profiles in children compared with adults. In early life, clonally diverse Vδ1 subsets predominate across blood and tissues, comprising naïve and differentiated effector and tissue repair functions, whereas cytolytic Vδ2 subsets populate blood, spleen, and lungs. With age, Vδ1 and Vδ2 subsets exhibit clonal expansions and elevated cytolytic signatures, which are disseminated across sites. In adults, Vδ2 cells predominate in blood, whereas Vδ1 cells are enriched across tissues and express residency profiles. Thus, antigenic exposures over childhood drive the functional evolution and tissue compartmentalization of γδ T cells, leading to age-dependent roles in immunity.

Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics

Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.

Mitochondrial Oxidative Stress Regulates FOXP3+ T-Cell Activity and CD4-Mediated Inflammation in Older Adults with Frailty

In healthy older adults, the immune system generally preserves its response and contributes to a long, healthy lifespan. However, rapid deterioration in immune regulation can lead to chronic inflammation, termed inflammaging, which accelerates pathological aging and diminishes the quality of life in older adults with frailty. A significant limitation in current aging research is the predominant focus on comparisons between young and older populations, often overlooking the differences between healthy older adults and those experiencing pathological aging. Our study elucidates the intricate immunological dynamics of the CD4/Treg axis in frail older adults compared to comparable age-matched healthy older adults. By utilizing publicly available RNA sequencing and single-cell RNA sequencing (scRNAseq) data from peripheral blood mononuclear cells (PBMCs), we identified a specific Treg cell subset and transcriptional landscape contributing to the dysregulation of CD4+ T-cell responses. We explored the molecular mechanisms underpinning Treg dysfunction, revealing that Tregs from frail older adults exhibit reduced mitochondrial protein levels, impairing mitochondrial oxidative phosphorylation. This impairment is driven by the TNF/NF-kappa B pathway, leading to cumulative inflammation. Further, we gained a deeper understanding of the CD4/Treg axis by predicting the effects of gene perturbations on cellular signaling networks. Collectively, these findings highlight the age-related relationship between mitochondrial dysfunction in the CD4/Treg axis and its role in accelerating aging and frailty in older adults. Targeting Treg dysfunction offers a critical basis for developing tailored therapeutic strategies aimed at improving the quality of life in older adults.

Hepatocytes and the art of killing Plasmodium softly

The Plasmodium parasites that cause malaria undergo asymptomatic development in the parenchymal cells of the liver, the hepatocytes, prior to infecting erythrocytes and causing clinical disease. Traditionally, hepatocytes have been perceived as passive bystanders that allow hepatotropic pathogens such as Plasmodium to develop relatively unchallenged. However, now there is emerging evidence suggesting that hepatocytes can mount robust cell-autonomous immune responses that target Plasmodium, limiting its progression to the blood and reducing the incidence and severity of clinical malaria. Here we discuss our current understanding of hepatocyte cell-intrinsic immune responses that target Plasmodium and how these pathways impact malaria.

Airborne environmentally persistent free radicals (EPFRs) in PM2.5 from combustion sources: Abundance, cytotoxicity and potential exposure risks

As an emerging atmospheric pollutant, airborne environmentally persistent free radicals (EPFRs) are formed during many combustion processes and pose various adverse health effects. In health-oriented air pollution control, it is vital to evaluate the health effects of atmospheric fine particulate matter (PM2.5) from different emission sources. In this study, various types of combustion-derived PM2.5 were collected on filters in a partial-flow dilution tunnel sampling system from three typical emission sources: coal combustion, biomass burning, and automobile exhaust. Substantial concentrations of EPFRs were determined in PM2.5 samples and associated with significant potential exposure risks. Results from in vitro cytotoxicity and oxidative potential assays suggest that EPFRs may cause substantial generation of reactive oxygen species (ROS) upon inhalation exposure to PM2.5 from anthropogenic combustion sources, especially from automobile exhaust. This study provides important evidence for the source- and concentration-dependent health effects of EPFRs in PM2.5 and motivates further assessments to advance public health-oriented PM2.5 emission control.

Aire in Autoimmunity

The role of the autoimmune regulator (Aire) in central immune tolerance and thymic self-representation was first described more than 20 years ago, but fascinating new insights into its biology continue to emerge, particularly in the era of advanced single-cell genomics. We briefly describe the role of human genetics in the discovery of Aire, as well as insights into its function gained from genotype-phenotype correlations and the spectrum of Aire-associated autoimmunity-including insights from patients with Aire mutations with broad and diverse implications for human health. We then highlight emerging trends in Aire biology, focusing on three topic areas. First, we discuss medullary thymic epithelial diversity and the role of Aire in thymic epithelial development. Second, we highlight recent developments regarding the molecular mechanisms of Aire and its binding partners. Finally, we describe the rapidly evolving biology of the identity and function of extrathymic Aire-expressing cells (eTACs), and a novel eTAC subset called Janus cells, as well as their potential roles in immune homeostasis.

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations

CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2's ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.

The development process of 'fit-for-purpose' imaging biomarkers to characterize the tumor microenvironment

Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, reliable biomarkers for the assessment of immune features in patients with cancer are of significant importance. Biomarkers (BM) that enable a comprehensive and repeatable assessment of the tumor microenvironment (TME), the lymphoid system, and the dynamics induced by drug treatment can fill this gap. Medical imaging, notably positron emission tomography (PET) and magnetic resonance imaging (MRI), providing whole-body imaging BMs, might deliver such BMs. However, those imaging BMs must be well characterized as being 'fit for purpose' for the intended use. This review provides an overview of the key steps involved in the development of 'fit-for-purpose' imaging BMs applicable in drug development, with a specific focus on pharmacodynamic biomarkers for assessing the TME and its modulation by immunotherapy. The importance of the qualification of imaging BMs according to their context of use (COU) as defined by the Food and Drug Administration (FDA) and National Institutes of Health Biomarkers, EndpointS, and other Tools (BEST) glossary is highlighted. We elaborate on how an imaging BM qualification for a specific COU can be achieved.

Androgen receptor activity inversely correlates with immune cell infiltration and immunotherapy response across multiple cancer lineages

There is now increasing recognition of the important role of androgen receptor (AR) in modulating immune function. To gain a comprehensive understanding of the effects of AR activity on cancer immunity, we employed a computational approach to profile AR activity in 33 human tumor types using RNA-Seq datasets from The Cancer Genome Atlas. Our pan-cancer analysis revealed that the genes most negatively correlated with AR activity across cancers are involved in active immune system processes. Importantly, we observed a significant negative correlation between AR activity and IFNγ pathway activity at the pan-cancer level. Indeed, using a matched biopsy dataset from subjects with prostate cancer before and after AR-targeted treatment, we verified that inhibiting AR enriches immune cell abundances and is associated with higher IFNγ pathway activity. Furthermore, by analyzing immunotherapy datasets in multiple cancers, our results demonstrate that low AR activity was significantly associated with a favorable response to immunotherapy. Together, our data provide a comprehensive assessment of the relationship between AR signaling and tumor immunity.

Integrated single-cell and bulk RNA-Seq analysis enhances prognostic accuracy of PD-1/PD-L1 immunotherapy response in lung adenocarcinoma through necroptotic anoikis gene signatures

In addition to presenting significant diagnostic and treatment challenges, lung adenocarcinoma (LUAD) is the most common form of lung cancer. Using scRNA-Seq and bulk RNA-Seq data, we identify three genes referred to as HMR, FAM83A, and KRT6A these genes are related to necroptotic anoikis-related gene expression. Initial validation, conducted on the GSE50081 dataset, demonstrated the model's ability to categorize LUAD patients into high-risk and low-risk groups with significant survival differences. This model was further applied to predict responses to PD-1/PD-L1 blockade therapies, utilizing the IMvigor210 and GSE78220 cohorts, and showed strong correlation with patient outcomes, highlighting its potential in personalized immunotherapy. Further, LUAD cell lines were analyzed using quantitative PCR (qPCR) and Western blot analysis to confirm their expression levels, further corroborating the model's relevance in LUAD pathophysiology. The mutation landscape of these genes was also explored, revealing their broad implication in various cancer types through a pan-cancer analysis. The study also delved into molecular subclustering, revealing distinct expression profiles and associations with different survival outcomes, emphasizing the model's utility in precision oncology. Moreover, the diversity of immune cell infiltration, analyzed in relation to the necroptotic anoikis signature, suggested significant implications for immune evasion mechanisms in LUAD. While the findings present a promising stride towards personalized LUAD treatment, especially in immunotherapy, limitations such as the retrospective nature of the datasets and the need for larger sample sizes are acknowledged. Prospective clinical trials and further experimental research are essential to validate these findings and enhance the clinical applicability of our prognostic model.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons, however, the electrophysiological properties endogenous to tumor cells remain obscure. To address this, we employed Patch-sequencing on human glioma specimens and found that one third of patched cells in IDH mutant (IDH mut ) tumors demonstrate properties of both neurons and glia by firing single, short action potentials. To define these hybrid cells (HCs) and discern if they are tumor in origin, we developed a computational tool, Single Cell Rule Association Mining (SCRAM), to annotate each cell individually. SCRAM revealed that HCs represent tumor and non-tumor cells that feature GABAergic neuron and oligodendrocyte precursor cell signatures. These studies are the first to characterize the combined electrophysiological and molecular properties of human glioma cells and describe a new cell type in human glioma with unique electrophysiological and transcriptomic properties that are likely also present in the non-tumor mammalian brain.

Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis

Objective

Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach.

Methods

We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods that have successfully identified relevant cell types in other diseases. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes.

Results

Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA-seq data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. Co-localization analysis revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two under-studied loci, ENTR1 (aka SDCCAG3) and B3GNT2.

Conclusion

Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.

Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions

T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses - for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.

A blueprint for tumor-infiltrating B cells across human cancers

B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell-receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell-driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell-targeting immunotherapy.

Multi-omics reveals the switch role of abnormal methylation in the regulation of decidual macrophages function in recurrent spontaneous abortion

RSA, recurrent spontaneous abortion, often causes serious physical damage and psychological pressure in reproductive women with unclarified pathogenesis. Abnormal function of decidual cells and aberrant DNA methylation have been reported to cause RSA, but their association remains unclear. Here, we integrated transcriptome, DNA methylome, and scRNA-seq to clarify the regulatory relationship between DNA methylation and decidual cells in RSA. We found that DNA methylation mainly influenced the function of decidual macrophages (DMs), of which four hub genes, HLA-A, HLA-F, SQSTM1/P62, and Interferon regulatory factor 7 (IRF7), related to 22 hypomethylated CpG sites, regulated 16 hub pathways to participate in RSA pathogenesis. In particular, using transcription factor analysis, it is suggested that the upregulation of IRF7 transcription was associated with enhanced recruitment of the transcription factor STAT1 by the hypomethylated promoter region of IRF7. As the current research on DNA methylation of macrophages in the uterine microenvironment of RSA is still blank, our systematic picture of abnormal DNA methylation in regulating DM function provides new insights into the role of DNA methylation in RSA occurrence, which may aid in further prevention and treatment of RSA.

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

Background

This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses.

Methods

Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies.

Results

Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model's broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib.

Conclusions

The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

Update on asthma biology

This is an exciting time to be conducting asthma research. The recent development of targeted asthma biologics has validated the power of basic research to discover new molecules amenable to therapeutic intervention. Advances in high-throughput sequencing are providing a wealth of "omics" data about genetic and epigenetic underpinnings of asthma, as well as about new cellular interacting networks and potential endotypes in asthma. Airway epithelial cells have emerged not only as key sensors of the outside environment but also as central drivers of dysregulated mucosal immune responses in asthma. Emerging data suggest that the airway epithelium in asthma remembers prior encounters with environmental exposures, resulting in potentially long-lasting changes in structure and metabolism that render asthmatic individuals susceptible to subsequent exposures. Here we summarize recent insights into asthma biology, focusing on studies using human cells or tissue that were published in the past 2 years. The studies are organized thematically into 6 content areas to draw connections and spur future research (on genetics and epigenetics, prenatal and early-life origins, microbiome, immune and inflammatory pathways, asthma endotypes and biomarkers, and lung structural alterations). We highlight recent studies of airway epithelial dysfunction and response to viral infections and conclude with a framework for considering how bidirectional interactions between alterations in airway structure and mucosal immunity can lead to sustained lung dysfunction in asthma.

Age-specific nasal epithelial responses to SARS-CoV-2 infection

Children infected with SARS-CoV-2 rarely progress to respiratory failure. However, the risk of mortality in infected people over 85 years of age remains high. Here we investigate differences in the cellular landscape and function of paediatric (<12 years), adult (30-50 years) and older adult (>70 years) ex vivo cultured nasal epithelial cells in response to infection with SARS-CoV-2. We show that cell tropism of SARS-CoV-2, and expression of ACE2 and TMPRSS2 in nasal epithelial cell subtypes, differ between age groups. While ciliated cells are viral replication centres across all age groups, a distinct goblet inflammatory subtype emerges in infected paediatric cultures and shows high expression of interferon-stimulated genes and incomplete viral replication. In contrast, older adult cultures infected with SARS-CoV-2 show a proportional increase in basaloid-like cells, which facilitate viral spread and are associated with altered epithelial repair pathways. We confirm age-specific induction of these cell types by integrating data from in vivo COVID-19 studies and validate that our in vitro model recapitulates early epithelial responses to SARS-CoV-2 infection.

Single-cell immune repertoire analysis

Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.

Identification of CNKSR1 as a biomarker for "cold" tumor microenvironment in lung adenocarcinoma: An integrative analysis based on a novel workflow

Background

Therapies targeting PD1/PD-L1 pathway have revolutionized the treatment of lung cancer. However, anti-PD1/PD-L1 therapies have proven beneficial for only a select group of lung adenocarcinoma (LUAD) patients and generally do not work for immuno-cold tumors characterized by a lack of immune cell infiltration. Identifying novel biomarkers is vital to broad therapeutic options for LUAD patients with no response to anti-PD1/PD-L1 immunotherapies.

Methods

Our study has developed a novel strategy to identify a promising biomarker that addresses the limitations of anti-PD1/PD-L1 immunotherapy in treating immunological cold tumors. We exacted LUAD RNA-seq data from the Cancer Genome Atlas database (TCGA). Using several machine learning methods, we identified the candidate biomarker. Based on the expression level of PD-L1 and the identified biomarker, samples were categorized into four groups. We further used ESTIMATE, ssGSEA, and CIBERSORT algorithms to calculate the immune infiltration level of each group. The results were validated in three independent bulk datasets and one scRNA-seq dataset. Immunohistochemistry (IHC) assessments were performed in clinical samples to further evaluate the coexpression of CNKSR1 and PD-L1, and to compare CD8 + T cell infiltration among groups.

Results

After comprehensive analyses, CNKSR1 was identified as a novel promising biomarker for immuno-cold LUAD. CNKSR1 mRNA expression levels exhibited a negative correlation with both PD-L1 mRNA expression and the extent of immune cell infiltration in LUAD. Besides, in contrast to the significant association between the expression of PD-L1 and the majority of other well-established or widely studied immune checkpoint molecules, a mutually exclusive expression pattern is observed between CNKSR1 and these molecules. The aforementioned results were consistent in validation datasets. The prognostic model built based on the CNKSR1 coexpression module also showed robust predictive performance. Additionally, IHC assessments have confirmed that the coexpression of CNKSR1 and PD-L1 is rare in LUAD samples. Notably, LUADs in the high-CNKSR1 group, characterized by high CNKSR1 but low PD- L1 expression, demonstrated reduced infiltration of CD8+ T cells.

Conclusions

In summary, CNKSR1 emerges as a promising biomarker for immune-cold LUADs, and the study into CNKSR1 modulating T-cell infiltration may lead to the identification of compensatory molecules to enhance the effectiveness of current immunotherapy for LUAD.