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DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease. Deconvolution algorithms facilitate studying cell type-specific changes using bulk data from complex tissues. Here, the authors present a deconvolution method that predicts DNA methylation levels in 12 leukocyte subtypes using human microarray data and apply it to various examples.

Since the outset of the COVID-19 pandemic, increasing evidence suggests that the innate immune responses play an important role in the disease development. A dysregulated inflammatory state has been proposed as a key driver of clinical complications in COVID-19, with a potential detrimental role of granulocytes. However, a comprehensive phenotypic description of circulating granulocytes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–infected patients is lacking. In this study, we used high-dimensional flow cytometry for granulocyte immunophenotyping in peripheral blood collected from COVID-19 patients during acute and convalescent phases. Severe COVID-19 was associated with increased levels of both mature and immature neutrophils, and decreased counts of eosinophils and basophils. Distinct immunotypes were evident in COVID-19 patients, with altered expression of several receptors involved in activation, adhesion, and migration of granulocytes (e.g., CD62L, CD11a/b, CD69, CD63, CXCR4). Paired sampling revealed recovery and phenotypic restoration of the granulocytic signature in the convalescent phase. The identified granulocyte immunotypes correlated with distinct sets of soluble inflammatory markers, supporting pathophysiologic relevance. Furthermore, clinical features, including multiorgan dysfunction and respiratory function, could be predicted using combined laboratory measurements and immunophenotyping. This study provides a comprehensive granulocyte characterization in COVID-19 and reveals specific immunotypes with potential predictive value for key clinical features associated with COVID-19.

Acetylsalicylic acid (ASA) can exert proanaphylactic effects, but the extent of this phenomenon and its underlying mechanisms are undefined. Yet, low homeostatic prostaglandin E2 (PGE2) levels have been associated with anaphylaxis. In this study, we investigated whether the proanaphylactic effect of ASA is PGE2 dependent. We assessed the effect of ASA in experimental anaphylaxis models, analyzed a large dataset of patients with anaphylaxis, and performed titrated allergen challenges in ASA-treated allergic individuals. Registry data indicated an increased risk for severe anaphylaxis in patients with ASA comedication. ASA pretreatment aggravated allergen-dependent anaphylaxis in mice, whereas histamine-induced anaphylaxis remained unaffected. Exacerbation was due to reduced PGE2, as its stabilization or the use of prostanoid E receptor (EP) agonists reversed the proanaphylactic effects of ASA. EP2-, EP3-, and EP4 receptor-deficient mice revealed that each receptor individually contributed to ASA susceptibility. In patients with allergy, prior ASA intake increased skin responsiveness to allergen but not to histamine. Conversely, the responses of basophils to ex vivo FcεRI aggregation remained unaltered, indicating that ASA operated by enhancing the stimulability of mast cells in a PGE2-dependent manner. Collectively, our data reveal a central role of the PGE2 network in ASA-aggravated anaphylaxis. EP receptors could be potential targets to prevent or alter the outcome of anaphylaxis.

Myeloid cells are known to suppress antitumour immunity 1 . However, the molecular drivers of immunosuppressive myeloid cell states are not well defined. Here we used single-cell RNA sequencing of human and mouse non-small cell lung cancer (NSCLC) lesions, and found that in both species the type 2 cytokine interleukin-4 (IL-4) was predicted to be the primary driver of the tumour-infiltrating monocyte-derived macrophage phenotype. Using a panel of conditional knockout mice, we found that only deletion of the IL-4 receptor IL-4Rα in early myeloid progenitors in bone marrow reduced tumour burden, whereas deletion of IL-4Rα in downstream mature myeloid cells had no effect. Mechanistically, IL-4 derived from bone marrow basophils and eosinophils acted on granulocyte-monocyte progenitors to transcriptionally programme the development of immunosuppressive tumour-promoting myeloid cells. Consequentially, depletion of basophils profoundly reduced tumour burden and normalized myelopoiesis. We subsequently initiated a clinical trial of the IL-4Rα blocking antibody dupilumab 2 – 5 given in conjunction with PD-1/PD-L1 checkpoint blockade in patients with relapsed or refractory NSCLC who had progressed on PD-1/PD-L1 blockade alone (ClinicalTrials.gov identifier NCT05013450 ). Dupilumab supplementation reduced circulating monocytes, expanded tumour-infiltrating CD8 T cells, and in one out of six patients, drove a near-complete clinical response two months after treatment. Our study defines a central role for IL-4 in controlling immunosuppressive myelopoiesis in cancer, identifies a novel combination therapy for immune checkpoint blockade in humans, and highlights cancer as a systemic malady that requires therapeutic strategies beyond the primary disease site. Single-cell transcriptomics studies on human and mouse non-small cell lung cancer and conditional knockout mouse models show that IL-4 from bone marrow basophils drives the development of granulocyte-monocyte progenitors to myeloid cells that suppress antitumour immunity.

BackgroundObservational studies have described an association between peripheral immune cell counts and inflammatory bowel diseases (IBD), but the exact causal relationship between them remains unclear. Therefore, a bidirectional two-sample Mendelian Randomization (MR) study is conducted to explore this potential causality.MethodsGWAS summary data for peripheral immune cell counts were obtained from the Blood Cell Consortium with 563,085 subjects of European ancestry, and data for IBD, Cronh’s disease (CD), and ulcerative colitis (UC) were obtained from five independent cohorts with 368,819 subjects of European ancestry. Single-variable Mendelian randomization (SVMR) was performed and followed by multivariable Mendelian randomization (MVMR) to assess the causal effects of peripheral immune cell counts on IBD risk (IDDF2024-ABS-0393 Figure 1. The flowchart providing an overview of instrumental variables (IVs) selection process and MR analysis plan).ResultsSVMR estimates showed that genetically predicted higher leukocytes [odds ratio (OR):1.225, 95% confidence interval (CI):1.027-1.452, p=0.027] and neutrophils (OR:1.281, 95% CI:1.044-1.572, p=0.018) increased risk of CD, and higher eosinophils increased risk of UC (OR:1.288, 95% CI:1.067-1.412, p=0.005). In MVMR, the effects of neutrophils (OR:1.653, 95% CI:1.139-2.405, p=0.010) and eosinophils (OR:1.303, 95% CI:1.016-1.668, p=0.040) were still significant. Reverse MR analysis showed higher basophils (OR:1.013, 95% CI:1.001-1.014, p=0.049) and eosinophils (OR:1.012, 95% CI:1.004-1.023, p=0.027), and lower lymphocytes (OR:0.993, 95% CI:0.984-0,997, p=0.015) in IBD patients, with specific increase in neutrophils for CD (OR:1.011, 95% CI:1.005-1.014, p=0.044) and basophils for UC (OR:1.012, 95% CI:1.004-1.023, p=0.013) (IDDF2024-ABS-0393 Figure 2. Forest plot for the causal effect of peripheral immune cell counts on IBD and vice versa).Abstract IDDF2024-ABS-0393 Figure 1The flowchart providing an overview of instrumental variables (IVs) selection process and MR analysis plan.Abstract IDDF2024-ABS-0393 Figure 2Forest plot for the causal effect of peripheral immune cell counts on IBD and vice versa.ConclusionsThese findings imply that increased neutrophil and eosinophil counts are potential causal risk factors for IBD, and IBD could affect the level of basophil, neutrophil, and lymphocyte count in turn. Our results may offer potential insights for the development of biomarkers and targeted treatment strategies for IBD.

Extracellular DNA trap formation is a cellular function of neutrophils, eosinophils, and basophils that facilitates the immobilization and killing of invading microorganisms in the extracellular milieu. To form extracellular traps, granulocytes release a scaffold consisting of mitochondrial DNA in association with granule proteins. As we understand more about the molecular mechanism for the formation of extracellular DNA traps, the in vivo function of this phenomenon under pathological conditions remains an enigma. In this article, we critically review the literature to summarize the evidence for extracellular DNA trap formation under in vivo conditions. Extracellular DNA traps have not only been detected in infectious diseases but also in chronic inflammatory diseases, as well as in cancer. While on the one hand, extracellular DNA traps clearly exhibit an important function in host defense, it appears that they can also contribute to the maintenance of inflammation and metastasis, suggesting that they may represent an interesting drug target for such pathological conditions.

Hematopoietic Stem/Progenitor cells (HSPCs) are endowed with the role of maintaining a diverse pool of blood cells throughout the human life. Despite recent efforts, the nature of the early cell fate decisions remains contentious. Using single-cell RNA-Seq, we show that existing approaches to stratify bone marrow CD34+ cells reveal a hierarchically-structured transcriptional landscape of hematopoietic differentiation. Still, this landscape misses important early fate decisions. We here provide a broader transcriptional profiling of bone marrow lineage negative hematopoietic progenitors that recovers a key missing branchpoint into basophils and expands our understanding of the underlying structure of early adult human haematopoiesis. We also show that this map has strong similarities in topology and gene expression to that found in mouse. Finally, we identify the sialomucin CD164, as a reliable marker for the earliest branches of HSPCs specification and we showed how its use can foster the design of alternative transplantation cell products. Human Hematopoietic stem and progenitor cells (HSPCs) are commonly defined by CD34 expression. Here, the authors map single-cell RNA states both inside and outside the CD34 compartment, uncovering previously unappreciated branchpoints and validating CD164 as an efficient marker for early HSPCs.

Inflammation is an important component of fibrosis but immune processes that orchestrate kidney fibrosis are not well understood. Here we apply single-cell sequencing to a mouse model of kidney fibrosis. We identify a subset of kidney tubule cells with a profibrotic-inflammatory phenotype characterized by the expression of cytokines and chemokines associated with immune cell recruitment. Receptor–ligand interaction analysis and experimental validation indicate that CXCL1 secreted by profibrotic tubules recruits CXCR2+ basophils. In mice, these basophils are an important source of interleukin-6 and recruitment of the TH17 subset of helper T cells. Genetic deletion or antibody-based depletion of basophils results in reduced renal fibrosis. Human kidney single-cell, bulk gene expression and immunostaining validate a function for basophils in patients with kidney fibrosis. Collectively, these studies identify basophils as contributors to the development of renal fibrosis and suggest that targeting these cells might be a useful clinical strategy to manage chronic kidney disease.How fibrosis and inflammation are integrated is not entirely clear. Here the authors show that profibrotic proximal tubular cells in the kidneys recruit basophils and activate them to produce interleukin-6 and drive TH17 cell responses.

Flow cytometry is used extensively to examine immune cells in non-lymphoid tissues. However, a method of flow cytometric analysis that is both comprehensive and widely applicable has not been described. We developed a protocol for the flow cytometric analysis of non-lymphoid tissues, including methods of tissue preparation, a 10-fluorochrome panel for cell staining, and a standardized gating strategy, that allows the simultaneous identification and quantification of all major immune cell types in a variety of normal and inflamed non-lymphoid tissues. We demonstrate that our basic protocol minimizes cell loss, reliably distinguishes macrophages from dendritic cells (DC), and identifies all major granulocytic and mononuclear phagocytic cell types. This protocol is able to accurately quantify 11 distinct immune cell types, including T cells, B cells, NK cells, neutrophils, eosinophils, inflammatory monocytes, resident monocytes, alveolar macrophages, resident/interstitial macrophages, CD11b- DC, and CD11b+ DC, in normal lung, heart, liver, kidney, intestine, skin, eyes, and mammary gland. We also characterized the expression patterns of several commonly used myeloid and macrophage markers. This basic protocol can be expanded to identify additional cell types such as mast cells, basophils, and plasmacytoid DC, or perform detailed phenotyping of specific cell types. In examining models of primary and metastatic mammary tumors, this protocol allowed the identification of several distinct tumor associated macrophage phenotypes, the appearance of which was highly specific to individual tumor cell lines. This protocol provides a valuable tool to examine immune cell repertoires and follow immune responses in a wide variety of tissues and experimental conditions.

The COVID-19 influenza emerged and proved to be fatal, causing millions of deaths worldwide. Vaccines were eventually discovered, effectively preventing the severe symptoms caused by the disease. However, some of the population (elderly and patients with comorbidities) are still vulnerable to severe symptoms such as breathlessness and chest pain. Identifying these patients in advance is imperative to prevent a bad prognosis. Hence, machine learning and deep learning algorithms have been used for early COVID-19 severity prediction using clinical and laboratory markers. The COVID-19 data was collected from two Manipal hospitals after obtaining ethical clearance. Multiple nature-inspired feature selection algorithms are used to choose the most crucial markers. A maximum testing accuracy of 95% was achieved by the classifiers. The predictions obtained by the classifiers have been demystified using five explainable artificial intelligence techniques (XAI). According to XAI, the most important markers are c-reactive protein, basophils, lymphocytes, albumin, D-Dimer and neutrophils. The models could be deployed in various healthcare facilities to predict COVID-19 severity in advance so that appropriate treatments could be provided to mitigate a severe prognosis. The computer aided diagnostic method can also aid the healthcare professionals and ease the burden on already suffering healthcare infrastructure.