Single-cell and spatiotemporal profile of ovulation in the mouse ovary.

Ovulation is a spatiotemporally coordinated process that involves several tightly controlled events, including oocyte meiotic maturation, cumulus expansion, follicle wall rupture and repair, and ovarian stroma remodeling. To date, no studies have detailed the precise window of ovulation at single-cell resolution. Here, we performed parallel single-cell RNA-seq and spatial transcriptomics on paired mouse ovaries across an ovulation time course to map the spatiotemporal profile of ovarian cell types. We show that major ovarian cell types exhibit time-dependent transcriptional states enriched for distinct functions and have specific localization profiles within the ovary. We also identified gene markers for ovulation-dependent cell states and validated these using orthogonal methods. Finally, we performed cell-cell interaction analyses to identify ligand-receptor pairs that may drive ovulation, revealing previously unappreciated interactions. Taken together, our data provides a rich and comprehensive resource of murine ovulation that can be mined for discovery by the scientific community.

B cell activation gene signature in blood and liver of HBeAg+ immune active chronic hepatitis B patients.

BACKGROUND: Immunological studies on chronic hepatitis B virus (HBV) infection have convincingly shown immune dysfunction involving multiple cell types. The focus of the majority of studies has been on the role of T cells and showed an impaired functional T cell response to HBV. B cells have been evaluated more recently, but in contrast to T cells, more pronounced activation of circulating B cells has been reported. To gain more insight into the activation status of B cells, we investigated the activation gene profile of B cells in the blood and liver of chronic HBV patients. METHODS: RNA-seq and flow cytometric analysis was performed on peripheral blood B cells of immune active chronic HBV patients, comparing them with samples from healthy controls. In addition, gene expression profiles of B cells in the liver were analyzed by bulk and single-cell RNA-seq. RESULTS AND CONCLUSIONS: Our data show a distinctive B cell activation gene signature in the blood of immune active chronic HBV patients, characterized by a significant upregulation of immune-related genes, including IRF1, STAT1, STAT3, TAP1, and TAPBP. This peripheral activation profile was also observed in B cells from the liver by single cell RNA-seq showing upregulation of IRF1, CD83 and significantly higher CD69 expression, with naive and memory B cell subsets being the primary carriers of the signature. Our findings suggest that B cell gene profiles reflect responsiveness to HBV infection, these findings are relevant for clinical studies evaluating immunomodulatory treatment strategies for HBV.

B cell heterogeneity in human tuberculosis highlights compartment-specific phenotype and functional roles.

B cells are important in tuberculosis (TB) immunity, but their role in the human lung is understudied. Here, we characterize B cells from lung tissue and matched blood of patients with TB and found they are decreased in the blood and increased in the lungs, consistent with recruitment to infected tissue, where they are located in granuloma associated lymphoid tissue. Flow cytometry and transcriptomics identify multiple B cell populations in the lung, including those associated with tissue resident memory, germinal centers, antibody secretion, proinflammatory atypical B cells, and regulatory B cells, some of which are expanded in TB disease. Additionally, TB lungs contain high levels of Mtb-reactive antibodies, specifically IgM, which promotes Mtb phagocytosis. Overall, these data reveal the presence of functionally diverse B cell subsets in the lungs of patients with TB and suggest several potential localized roles that may represent a target for interventions to promote immunity or mitigate immunopathology.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data.

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.

Intrahepatic plasma cells, but not atypical memory B cells, associate with clinical phases of chronic hepatitis B.

Studies have traditionally focused on the role of T cells in chronic hepatitis B (CHB), but recent evidence supports a role for B cells. The enrichment of so-called atypical memory (AtM) B cells, which show reduced signaling and impaired differentiation, is believed to be a characteristic feature of CHB, potentially contributing to the observed dysfunctional anti-HBsAg B-cell responses. Our study, involving 62 CHB patients across clinical phases, identified AtM B cells expressing IFNLR1 and interferon-stimulated genes. Contrary to previous reports, we found relatively low frequencies of AtM B cells in the liver, comparable to peripheral blood. However, liver plasma cell frequencies were significantly higher, particularly during phases with elevated viral loads and liver enzyme levels. Liver plasma cells exhibited signs of active proliferation, especially in the immune active phase. Our findings suggest a potential role for plasma cells, alongside potential implications and consequences of local proliferation, within the livers of CHB patients. While the significance of AtM B cells remains uncertain, further investigation is warranted to determine their responsiveness to interferons and their role in CHB.

An enhanced IL17 and muted type I interferon nasal epithelial cell state characterizes severe COVID-19 with fungal coinfection.

Recent case reports and epidemiological data suggest that fungal infections represent an underappreciated complication among people with severe COVID-19. However, the frequency of fungal colonization in patients with COVID-19 and associations with specific immune responses in the airways remain incompletely defined. We previously generated a single-cell RNA-sequencing data set characterizing the upper respiratory microenvironment during COVID-19 and mapped the relationship between disease severity and the local behavior of nasal epithelial cells and infiltrating immune cells. Our previous study, in agreement with findings from related human cohorts, demonstrated that a profound deficiency in host immunity, particularly in type I and type III interferon signaling in the upper respiratory tract, is associated with rapid progression to severe disease and worse clinical outcomes. We have now performed further analysis of this cohort and identified a subset of participants with severe COVID-19 and concurrent detection of Candida species-derived transcripts within samples collected from the nasopharynx and trachea. Here, we present the clinical characteristics of these individuals. Using matched single-cell transcriptomic profiles of these individuals' respiratory mucosa, we identify epithelial immune signatures suggestive of IL17 stimulation and anti-fungal immunity. Further, we observe a significant expression of anti-fungal inflammatory cascades in the nasal and tracheal epithelium of all participants who went on to develop severe COVID-19, even among participants without detectable genetic material from fungal pathogens. Together, our data suggest that IL17 stimulation-in part driven by Candida colonization-and blunted interferon signaling represent a common feature of severe COVID-19 infection. IMPORTANCE: In this paper, we present an analysis suggesting that symptomatic and asymptomatic fungal coinfections can impact patient disease progression during COVID-19 hospitalization. By looking into the presence of other pathogens and their effect on the host immune response during COVID-19 hospitalizations, we aim to offer insight into an underestimated scenario, furthering our current knowledge of determinants of severity that could be considered for future diagnostic and intervention strategies.

A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment.

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data.

Clustering is commonly used in single-cell RNA-sequencing (scRNA-seq) pipelines to characterize cellular heterogeneity. However, current methods face two main limitations. First, they require user-specified heuristics which add time and complexity to bioinformatic workflows; second, they rely on post-selective differential expression analyses to identify marker genes driving cluster differences, which has been shown to be subject to inflated false discovery rates. We address these challenges by introducing nonparametric clustering of single-cell populations (NCLUSION): an infinite mixture model that leverages Bayesian sparse priors to identify marker genes while simultaneously performing clustering on single-cell expression data. NCLUSION uses a scalable variational inference algorithm to perform these analyses on datasets with up to millions of cells. By analyzing publicly available scRNA-seq studies, we demonstrate that NCLUSION (i) matches the performance of other state-of-the-art clustering techniques with significantly reduced runtime and (ii) provides statistically robust and biologically relevant transcriptomic signatures for each of the clusters it identifies. Overall, NCLUSION represents a reliable hypothesis-generating tool for understanding patterns of expression variation present in single-cell populations.

Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection.

BACKGROUND: The Lentivirus human immunodeficiency virus (HIV) causes chronic inflammation and AIDS in humans, with variable rates of disease progression between individuals driven by both host and viral factors. Similarly, simian lentiviruses vary in their pathogenicity based on characteristics of both the host species and the virus strain, yet the immune underpinnings that drive differential Lentivirus pathogenicity remain incompletely understood. METHODS: We profile immune responses in a unique model of differential lentiviral pathogenicity where pig-tailed macaques are infected with highly genetically similar variants of SIV that differ in virulence. We apply longitudinal single-cell transcriptomics to this cohort, along with single-cell resolution cell-cell communication techniques, to understand the immune mechanisms underlying lentiviral pathogenicity. RESULTS: Compared to a minimally pathogenic lentiviral variant, infection with a highly pathogenic variant results in a more delayed, broad, and sustained activation of inflammatory pathways, including an extensive global interferon signature. Conversely, individual cells infected with highly pathogenic Lentivirus upregulated fewer interferon-stimulated genes at a lower magnitude, indicating that highly pathogenic Lentivirus has evolved to partially escape from interferon responses. Further, we identify CXCL10 and CXCL16 as important molecular drivers of inflammatory pathways specifically in response to highly pathogenic Lentivirus infection. Immune responses to highly pathogenic Lentivirus infection are characterized by amplifying regulatory circuits of pro-inflammatory cytokines with dense longitudinal connectivity. CONCLUSIONS: Our work presents a model of lentiviral pathogenicity where failures in early viral control mechanisms lead to delayed, sustained, and amplifying pro-inflammatory circuits, which in turn drives disease progression.

Glycan-costumed virus-like particles promote type 1 anti-tumor immunity.

Cancer vaccine development is inhibited by a lack of strategies for directing dendritic cell (DC) induction of effective tumor-specific cellular immunity. Pathogen engagement of DC lectins and toll-like receptors (TLRs) shapes immunity by directing T cell function. Strategies to activate specific DC signaling pathways via targeted receptor engagement are crucial to unlocking type 1 cellular immunity. Here, we engineered a glycan-costumed virus-like particle (VLP) vaccine that delivers programmable peptide antigens to induce tumor-specific cellular immunity in vivo. VLPs encapsulating TLR7 agonists and decorated with a selective mannose-derived ligand for the lectin DC-SIGN induced robust DC activation and type 1 cellular immunity, whereas VLPs lacking this key DC-SIGN ligand failed to promote DC-mediated immunity. Vaccination with glycan-costumed VLPs generated tumor antigen-specific Th1 CD4+ and CD8+ T cells that infiltrated solid tumors, inhibiting tumor growth in a murine melanoma model. Thus, VLPs employing lectin-driven immune reprogramming provide a framework for advancing cancer immunotherapies.

Comparative analysis of cell-cell communication at single-cell resolution.

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

Early cellular and molecular signatures correlate with severity of West Nile virus infection.

Infection with West Nile virus (WNV) drives a wide range of responses, from asymptomatic to flu-like symptoms/fever or severe cases of encephalitis and death. To identify cellular and molecular signatures distinguishing WNV severity, we employed systems profiling of peripheral blood from asymptomatic and severely ill individuals infected with WNV. We interrogated immune responses longitudinally from acute infection through convalescence employing single-cell protein and transcriptional profiling complemented with matched serum proteomics and metabolomics as well as multi-omics analysis. At the acute time point, we detected both elevation of pro-inflammatory markers in innate immune cell types and reduction of regulatory T cell activity in participants with severe infection, whereas asymptomatic donors had higher expression of genes associated with anti-inflammatory CD16+ monocytes. Therefore, we demonstrated the potential of systems immunology using multiple cell-type and cell-state-specific analyses to identify correlates of infection severity and host cellular activity contributing to an effective anti-viral response.

Chronic metabolic stress drives developmental programs and loss of tissue functions in non-transformed liver that mirror tumor states and stratify survival.

Under chronic stress, cells must balance competing demands between cellular survival and tissue function. In metabolic dysfunction-associated steatotic liver disease (MASLD, formerly NAFLD/NASH), hepatocytes cooperate with structural and immune cells to perform crucial metabolic, synthetic, and detoxification functions despite nutrient imbalances. While prior work has emphasized stress-induced drivers of cell death, the dynamic adaptations of surviving cells and their functional repercussions remain unclear. Namely, we do not know which pathways and programs define cellular responses, what regulatory factors mediate (mal)adaptations, and how this aberrant activity connects to tissue-scale dysfunction and long-term disease outcomes. Here, by applying longitudinal single-cell multi -omics to a mouse model of chronic metabolic stress and extending to human cohorts, we show that stress drives survival-linked tradeoffs and metabolic rewiring, manifesting as shifts towards development-associated states in non-transformed hepatocytes with accompanying decreases in their professional functionality. Diet-induced adaptations occur significantly prior to tumorigenesis but parallel tumorigenesis-induced phenotypes and predict worsened human cancer survival. Through the development of a multi -omic computational gene regulatory inference framework and human in vitro and mouse in vivo genetic perturbations, we validate transcriptional (RELB, SOX4) and metabolic (HMGCS2) mediators that co-regulate and couple the balance between developmental state and hepatocyte functional identity programming. Our work defines cellular features of liver adaptation to chronic stress as well as their links to long-term disease outcomes and cancer hallmarks, unifying diverse axes of cellular dysfunction around core causal mechanisms.

Identification of drug candidates targeting monocyte reprogramming in people living with HIV.

Introduction: People living with HIV (PLHIV) are characterized by functional reprogramming of innate immune cells even after long-term antiretroviral therapy (ART). In order to assess technical feasibility of omics technologies for application to larger cohorts, we compared multiple omics data layers. Methods: Bulk and single-cell transcriptomics, flow cytometry, proteomics, chromatin landscape analysis by ATAC-seq as well as ex vivo drug stimulation were performed in a small number of blood samples derived from PLHIV and healthy controls from the 200-HIV cohort study. Results: Single-cell RNA-seq analysis revealed that most immune cells in peripheral blood of PLHIV are altered in their transcriptomes and that a specific functional monocyte state previously described in acute HIV infection is still existing in PLHIV while other monocyte cell states are only occurring acute infection. Further, a reverse transcriptome approach on a rather small number of PLHIV was sufficient to identify drug candidates for reversing the transcriptional phenotype of monocytes in PLHIV. Discussion: These scientific findings and technological advancements for clinical application of single-cell transcriptomics form the basis for the larger 2000-HIV multicenter cohort study on PLHIV, for which a combination of bulk and single-cell transcriptomics will be included as the leading technology to determine disease endotypes in PLHIV and to predict disease trajectories and outcomes.

Tobacco smoke exposure recruits inflammatory airspace monocytes that establish permissive lung niches for Mycobacterium tuberculosis.

Tobacco smoking doubles the risk of active tuberculosis (TB) and accounts for up to 20% of all active TB cases globally. How smoking promotes lung microenvironments permissive to Mycobacterium tuberculosis (Mtb) growth remains incompletely understood. We investigated primary bronchoalveolar lavage cells from current and never smokers by performing single-cell RNA sequencing (scRNA-seq), flow cytometry, and functional assays. We observed the enrichment of immature inflammatory monocytes in the lungs of smokers compared with nonsmokers. These monocytes exhibited phenotypes consistent with recent recruitment from blood, ongoing differentiation, increased activation, and states similar to those with chronic obstructive pulmonary disease. Using integrative scRNA-seq and flow cytometry, we identified CD93 as a marker for a subset of these newly recruited smoking-associated lung monocytes and further provided evidence that the recruitment of monocytes into the lung was mediated by CCR2-binding chemokines, including CCL11. We also show that these cells exhibit elevated inflammatory responses upon exposure to Mtb and accelerated intracellular growth of Mtb compared with mature macrophages. This elevated Mtb growth could be inhibited by anti-inflammatory small molecules, providing a connection between smoking-induced pro-inflammatory states and permissiveness to Mtb growth. Our findings suggest a model in which smoking leads to the recruitment of immature inflammatory monocytes from the periphery to the lung, which results in the accumulation of these Mtb-permissive cells in the airway. This work defines how smoking may lead to increased susceptibility to Mtb and identifies host-directed therapies to reduce the burden of TB among those who smoke.

Programs, Origins, and Niches of Immunomodulatory Myeloid Cells in Gliomas.

Gliomas are incurable malignancies notable for an immunosuppressive microenvironment with abundant myeloid cells whose immunomodulatory properties remain poorly defined. Here, utilizing scRNA-seq data for 183,062 myeloid cells from 85 human tumors, we discover that nearly all glioma-associated myeloid cells express at least one of four immunomodulatory activity programs: Scavenger Immunosuppressive, C1Q Immunosuppressive, CXCR4 Inflammatory, and IL1B Inflammatory. All four programs are present in IDH1 mutant and wild-type gliomas and are expressed in macrophages, monocytes, and microglia whether of blood or resident myeloid cell origins. Integrating our scRNA-seq data with mitochondrial DNA-based lineage tracing, spatial transcriptomics, and organoid explant systems that model peripheral monocyte infiltration, we show that these programs are driven by microenvironmental cues and therapies rather than myeloid cell type, origin, or mutation status. The C1Q Immunosuppressive program is driven by routinely administered dexamethasone. The Scavenger Immunosuppressive program includes ligands with established roles in T-cell suppression, is induced in hypoxic regions, and is associated with immunotherapy resistance. Both immunosuppressive programs are less prevalent in lower-grade gliomas, which are instead enriched for the CXCR4 Inflammatory program. Our study provides a framework to understand immunomodulatory myeloid cells in glioma, and a foundation to develop more effective immunotherapies.

CD8+ lymphocytes are critical for early control of tuberculosis in macaques.

The functional role of CD8+ lymphocytes in tuberculosis remains poorly understood. We depleted innate and/or adaptive CD8+ lymphocytes in macaques and showed that loss of all CD8α+ cells (using anti-CD8α antibody) significantly impaired early control of Mycobacterium tuberculosis (Mtb) infection, leading to increased granulomas, lung inflammation, and bacterial burden. Analysis of barcoded Mtb from infected macaques demonstrated that depletion of all CD8+ lymphocytes allowed increased establishment of Mtb in lungs and dissemination within lungs and to lymph nodes, while depletion of only adaptive CD8+ T cells (with anti-CD8ß antibody) worsened bacterial control in lymph nodes. Flow cytometry and single-cell RNA sequencing revealed polyfunctional cytotoxic CD8+ lymphocytes in control granulomas, while CD8-depleted animals were unexpectedly enriched in CD4 and γδ T cells adopting incomplete cytotoxic signatures. Ligand-receptor analyses identified IL-15 signaling in granulomas as a driver of cytotoxic T cells. These data support that CD8+ lymphocytes are required for early protection against Mtb and suggest polyfunctional cytotoxic responses as a vaccine target.

Single-Cell RNA Sequencing Reveals That Adaptation of Human Aortic Endothelial Cells to Antiproliferative Therapies Is Modulated by Flow-Induced Shear Stress.

BACKGROUND: Endothelial cells (ECs) are capable of quickly responding in a coordinated manner to a wide array of stresses to maintain vascular homeostasis. Loss of EC cellular adaptation may be a potential marker for cardiovascular disease and a predictor of poor response to endovascular pharmacological interventions such as drug-eluting stents. Here, we report single-cell transcriptional profiling of ECs exposed to multiple stimulus classes to evaluate EC adaptation. METHODS: Human aortic ECs were costimulated with both pathophysiological flows mimicking shear stress levels found in the human aorta (laminar and turbulent, ranging from 2.5 to 30 dynes/cm2) and clinically relevant antiproliferative drugs, namely paclitaxel and rapamycin. EC state in response to these stimuli was defined using single-cell RNA sequencing. RESULTS: We identified differentially expressed genes and inferred the TF (transcription factor) landscape modulated by flow shear stress using single-cell RNA sequencing. These flow-sensitive markers differentiated previously identified spatially distinct subpopulations of ECs in the murine aorta. Moreover, distinct transcriptional modules defined flow- and drug-responsive EC adaptation singly and in combination. Flow shear stress was the dominant driver of EC state, altering their response to pharmacological therapies. CONCLUSIONS: We showed that flow shear stress modulates the cellular capacity of ECs to respond to paclitaxel and rapamycin administration, suggesting that while responding to different flow patterns, ECs experience an impairment in their transcriptional adaptation to other stimuli.

Enhanced production of eicosanoids in plasma and activation of DNA damage pathways in PBMCs are correlated with the severity of ancestral COVID-19 infection.

Background: Many questions remain unanswered regarding the implication of lipid metabolites in severe SARS-CoV-2 infections. By re-analyzed sequencing data from the nasopharynx of a previously published cohort, we found that alox genes, involved in eicosanoid synthesis, were up-regulated in high WHO score patients, especially in goblet cells. Herein, we aimed to further understand the roles played by eicosanoids during severe SARS-CoV-2 infection. Methods and findings: We performed a total fatty acid panel on plasma and bulk RNA-seq analysis on peripheral blood mononuclear cells (PBMCs) collected from 10 infected and 10 uninfected patients. Univariate comparison of lipid metabolites revealed that lipid metabolites were increased in SARS-CoV-2 patients including the lipid mediators Arachidonic Acid (AA) and Eicosapentaenoic Acid (EPA). AA, EPA and the fatty acids Docosahexaenoic acid (DHA) and Docosapentaenoic acid (DPA), were positively correlated to WHO disease severity score. Transcriptomic analysis demonstrated that COVID-19 patients can be segregated based on WHO scores. Ontology, KEGG and Reactome analysis identified pathways enriched for genes related to innate immunity, interactions between lymphoid and nonlymphoid cells, interleukin signaling and, cell cycling pathways. Conclusions: Our study offers an association between nasopharynx mucosa eicosanoid genes expression, specific serum inflammatory lipids and, subsequent DNA damage pathways activation in PBMCs to severity of COVID-19 infection.