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Advances in multimodal single cell analysis can empower high-resolution dissection of human vaccination responses. The resulting data capture multiple layers of biological variations, including molecular and cellular states, vaccine formulations, inter- and intra-subject differences, and responses unfolding over time. Transforming such data into biological insight remains a major challenge. Here we present a systematic framework applied to multimodal single cell data obtained before and after influenza vaccination without adjuvants or pandemic H5N1 vaccination with the AS03 adjuvant. Our approach pinpoints responses shared across or unique to specific cell types and identifies adjuvant specific signatures, including pro-survival transcriptional states in B lymphocytes that emerged one day after vaccination. We also reveal that high antibody responders to the unadjuvanted vaccine have a distinct baseline involving a rewired network of cell type specific transcriptional states. Remarkably, the status of certain innate immune cells in this network in high responders of the unadjuvanted vaccine appear “naturally adjuvanted”: they resemble phenotypes induced early in the same cells only by vaccination with AS03. Furthermore, these cell subsets have elevated frequency in the blood at baseline and increased cell-intrinsic phospho-signaling responses after LPS stimulationex vivoin high compared to low responders. Our findings identify how variation in the status of multiple immune cell types at baseline may drive robust differences in innate and adaptive responses to vaccination and thus open new avenues for vaccine development and immune response engineering in humans.

Immune checkpoint inhibitors (ICIs) have changed the cancer treatment landscape, but severe immune-related adverse events (irAEs) can be life-threatening or fatal and may prohibit patients from receiving further ICI treatment. While the clinical features of irAEs are well documented, molecular signatures, predictive biomarkers, and mechanisms of impending irAEs are largely unknown. In addition, the markers and mechanisms of ICI-induced antitumor immunity often overlap with those for irAEs. It is thus critical to uncover signatures associated specifically with irAEs but not with antitumor immunity. To identify circulating immune cell states associated with irAEs, we applied multimodal single cell analysis (CITE-seq) to simultaneously measure the transcriptome and surface proteins from peripheral blood mononuclear cells (PBMCs) collected before and after treatment with an anti-PD-L1 antibody (avelumab) in patients with thymic cancers (thymic epithelial tumors). All patients had an antitumor response, yet a subset developed muscle autoimmunity (myositis), a potentially life-threatening irAE. Mixed-effect modeling disentangled cell type-specific transcriptional states associated with ICI treatment responses from those of irAEs to identify temporally stable pre-treatment immune set points associated with irAEs only. These pre-treatment baseline signatures of irAE developed post-avelumab irAEs reflect correlated transcriptional states of multiple innate and adaptive immune cell populations, including elevation of metabolic genes downstream of mTOR signaling in T-cell subsets. Together these findings suggest putative pre-treatment biomarkers for irAEs following ICI therapy in thymic cancer patients and raise the prospect of therapeutically dampening autoimmunity while sparing antitumor activity in cancer patients treated with ICIs. Together, pre-treatment biomarkers and interventional therapeutics could help mitigate treatment discontinuation and improve clinical outcomes.

Personalized cancer vaccines are a promising approach for inducing T cell immunity to tumor neoantigens. Using a self-assembling nanoparticle vaccine that links neoantigen peptides to a Toll-like receptor 7/8 agonist (SNP-7/8a), we show how the route and dose alter the magnitude and quality of neoantigen-specific CD8+ T cells. Intravenous vaccination (SNP-IV) induced a higher proportion of TCF1+PD-1+CD8+ T cells as compared to subcutaneous immunization (SNP-SC). Single-cell RNA sequencing showed that SNP-IV induced stem-like genes (Tcf7, Slamf6, Xcl1) whereas SNP-SC enriched for effector genes (Gzmb, Klrg1, Cx3cr1). Stem-like cells generated by SNP-IV proliferated and differentiated into effector cells upon checkpoint blockade, leading to superior antitumor response as compared to SNP-SC in a therapeutic model. The duration of antigen presentation by dendritic cells controlled the magnitude and quality of CD8+ T cells. These data demonstrate how to optimize antitumor immunity by modulating vaccine parameters for specific generation of effector or stem-like CD8+ T cells. Seder and colleagues use a self-assembling nanoparticle vaccine and adjuvant to expand stem-like CD8+ T cells and trigger potent antitumor responses.

Responses to vaccination and to diseases vary widely across individuals, which may be partly due to baseline immune variations. Identifying such baseline predictors of immune responses and their biological basis is of broad interest, given their potential importance for cancer immunotherapy, disease outcomes, vaccination and infection responses. Here we uncover baseline blood transcriptional signatures predictive of antibody responses to both influenza and yellow fever vaccinations in healthy subjects. These same signatures evaluated at clinical quiescence are correlated with disease activity in patients with systemic lupus erythematosus with plasmablast-associated flares. CITE-seq profiling of 82 surface proteins and transcriptomes of 53,201 single cells from healthy high and low influenza vaccination responders revealed that our signatures reflect the extent of activation in a plasmacytoid dendritic cell–type I IFN–T/B lymphocyte network. Our findings raise the prospect that modulating such immune baseline states may improve vaccine responsiveness and mitigate undesirable autoimmune disease activity. Simultaneous single-cell protein and transcriptome analysis identifies a baseline immune circuit associated with antibody responses to vaccination in healthy individuals and the severity of disease flares in patients with a subtype of systemic lupus erythematosus.

Disrupted molecular pathways are often robustly associated with disease outcome in cancer1–3. Although biologically informative transcriptional pathways can be revealed by RNA sequencing (RNA-seq) at up to hundreds of folds reduction in conventionally used coverage4–6, it remains unknown how low-depth sequencing datasets perform in the challenging context of developing transcriptional signatures to predict clinical outcomes. Here we assessed the possibility of cancer prognosis with shallow tumor RNA-seq, which would potentially enable cost-effective assessment of much larger numbers of samples for deeper biological and predictive insights. By statistically modeling the relative risk of an adverse outcome for thousands of subjects in The Cancer Genome Atlas7–13, we present evidence that subsampled tumor RNA-seq data with a few hundred thousand reads per sample provide sufficient information for outcome prediction in several types of cancer. Analysis of predictive models revealed robust contributions from pathways known to be associated with outcomes. Our findings indicate that predictive models of outcomes in cancer may be developed with dramatically increases in sample numbers at low cost, thus potentially enabling the development of more realistic predictive models that incorporate diverse variables and their interactions. This strategy could also be used, for example, in longitudinal analysis of multiple regions of a tumor alongside treatment for quantitative modeling and prediction of outcome in personalized oncology. Application of machine-learning models on very shallow RNA sequencing data maintains predictive performance across different tumor types.

Viral infections can have profound and durable functional impacts on the immune system. There is an urgent need to characterize the long-term immune effects of SARS-CoV-2 infection given the persistence of symptoms in some individuals and the continued threat of novel variants. Here we use systems immunology, including longitudinal multimodal single cell analysis (surface proteins, transcriptome, and V(D)J sequences) from 33 previously healthy individuals after recovery from mild, non-hospitalized COVID-19 and 40 age- and sex-matched healthy controls with no history of COVID-19 to comparatively assess the post-infection immune status (mean: 151 days after diagnosis) and subsequent innate and adaptive responses to seasonal influenza vaccination. Identification of both sex-specific and -independent temporally stable changes, including signatures of T-cell activation and repression of innate defense/immune receptor genes (e.g., Toll-like receptors) in monocytes, suggest that mild COVID-19 can establish new post-recovery immunological set-points. COVID-19-recovered males had higher innate, influenza-specific plasmablast, and antibody responses after vaccination compared to healthy males and COVID-19-recovered females, partly attributable to elevated pre-vaccination frequencies of a GPR56 expressing CD8+ T-cell subset in male recoverees that are "poised" to produce higher levels of IFNγ upon inflammatory stimulation. Intriguingly, by day 1 post-vaccination in COVID-19-recovered subjects, the expression of the repressed genes in monocytes increased and moved towards the pre-vaccination baseline of healthy controls, suggesting that the acute inflammation induced by vaccination could partly reset the immune states established by mild COVID-19. Our study reveals sex-dimorphic immune imprints and

BackgroundImmune checkpoint inhibitors (ICIs) have changed the cancer treatment landscape, but immune-related adverse events (irAEs) can affect a wide range of tissues in patients receiving ICIs. Severe irAEs can be life-threatening or fatal and prohibit patients from receiving further ICI treatment. While the clinical features of irAEs are well documented, the pathological mechanisms and predictive biomarkers are largely unknown. In addition, there is a critical need to preserve ICI-induced anti-tumor immunity while controlling for irAEs, which requires deciphering molecular and cellular signatures associated specifically with irAEs beyond those more generally linked to anti-tumor immunity.MethodsTo unbiasedly identify immune cells and states associated with irAEs, we applied CITE-seq to measure transcripts and surface proteins (83 protein markers) from PBMCs collected from patients with thymic epithelial tumors before and after treatment with an anti-PD-L1 antibody (avelumab, NCT01772004, NCT03076554).ResultsSamples from 9 patients were analyzed. No patient had a history of pre-existing paraneoplastic autoimmune disease. Anti-tumor activity was observed in all cases, and 5 patients had clinical and/or biochemical evidence of immune-related muscle inflammation (myositis with or without myocarditis). Multilevel models applied within highly resolved cell clusters revealed transcriptional states associated with ICI response and more uniquely with irAEs. A total of 190,000 cells were included in the analysis after quality control. Most notably, CD45RA+ effector memory CD8 T cells with an mTOR transcriptional signature were highly enriched at baseline and post treatment in patients with irAEs.ConclusionsOur findings suggest the potential therapeutic avenues by using mTOR inhibitors to dampen autoimmune responses while potentially sparing anti-tumor activity, to prevent treatment discontinuation and improve clinical outcomes for cancer patients treated with ICIs.AcknowledgementsThis research was supported in part by the Intramural Research Program of the NCI (the Center for Cancer Research), NIAID and NIAMS, and through a Cooperative Research and Development Agreement between the National Cancer Institute and EMD Serono.Trial RegistrationNCT01772004, NCT03076554Ethics ApprovalThis study is approved by NCI institutional review board.

CD4

SummaryCD4+T cells have a remarkable potential to differentiate into diverse effector lineages following activation. Here, we probed the heterogeneity present among naïve CD4+T cells before encountering their cognate antigen to ask whether their effector potential is modulated by pre-existing transcriptional and epigenetic differences. Using single-cell RNA sequencing, we showed that key drivers of variability are genes involved in T cell receptor (TCR) signaling. Using CD5 expression as a read-out of the strength of tonic TCR interactions with self-peptide MHC, and sorting on the ends of this self-reactivity spectrum, we find that pre-existing transcriptional differences among naïve CD4+T cells impact follicular helper cell (TFH) versus non-TFHeffector lineage choice. Moreover, our data implicate TCR signal strength during thymic development in establishing differences in naïve CD4 T cell chromatin landscapes that ultimately shape their effector potential.

COVID-19 exhibits extensive patient-to-patient heterogeneity. To link immune response variation to disease severity and outcome over time, we longitudinally assessed circulating proteins as well as 188 surface protein markers, transcriptome, and T-cell receptor sequence simultaneously in single peripheral immune cells from COVID-19 patients. Conditional-independence network analysis revealed primary correlates of disease severity, including gene expression signatures of apoptosis in plasmacytoid dendritic cells and attenuated inflammation but increased fatty acid metabolism in CD56dimCD16hi NK cells linked positively to circulating IL-15. CD8+ T cell activation was apparent without signs of exhaustion. While cellular inflammation was depressed in severe patients early after hospitalization, it became elevated by days 17-23 post symptom onset, suggestive of a late wave of inflammatory responses. Furthermore, circulating protein trajectories at this time were divergent between and predictive of recovery-fatal outcomes. Our findings stress the importance of timing in the analysis, clinical monitoring, and therapeutic intervention of COVID-19., A multiparameter metric developed by integrating circulating cytokines, multimodal single cell profiling, cell surface protein and immune cell analyses of COVID-19 patients over time, reveals a late wave of diverging inflammatory and host immune responses that predicts recovery versus fatality.

Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called "dsb" (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at "dsb [ https://cran.r-project.org/package=dsb ]".

Lethality after respiratory infection with influenza A virus (IAV) is associated with potent immune activation and lung tissue damage. In a well-controlled animal model of infection, we sought to determine if one could predict lethality using transcriptional information obtained from whole blood early after influenza virus exposure. We started with publicly available transcriptomic data from the lung, which is the primary site of the infection and pathology, to derive a multigene transcriptional signature of death reflective of innate inflammation associated with tissue damage. We refined this affected tissue signature with data from infected mouse and human blood to develop and validate a machine learning model that can robustly predict survival in mice after IAV challenge using data obtained from as little as 10 μl of blood from early time points post infection. Furthermore, in genetically identical, cohoused mice infected with the same viral bolus, the same model can predict the lethality of individual animals but, intriguingly, only within a specific time window that overlapped with the early effector phase of adaptive immunity. These findings raise the possibility of predicting disease outcome in respiratory virus infections with blood transcriptional data and pave the way for translating such approaches to humans.

The incidence of pulmonary nontuberculous mycobacteria (NTM) disease is increasing, but host responses in respiratory epithelium infected with NTM are not fully understood. In this work, we aimed to identify infection-relevant gene expression signatures of NTM infection of the respiratory epithelium. We infected air-liquid interface (ALI) primary respiratory epithelial cell cultures with Mycobacterium avium subsp. avium (MAC) or Mycobacterium abscessus subsp. abscessus (MAB). We used cells from four different donors to obtain generalizable data. Differentiated respiratory epithelial cells at the ALI were infected with MAC or MAB at a multiplicity of infection of 100:1 or 1,000:1, and RNA sequencing was performed at Days 1 and 3 after infection. In response to infection, we found down-regulation of ciliary genes but upregulation of genes associated with cytokines/chemokines, such as IL-32, and cholesterol biosynthesis. Inflammatory response genes tended to be more upregulated by MAB than by MAC infection. Primary respiratory epithelial cell infection with NTM at the ALI identified ciliary function, cholesterol biosynthesis, and cytokine/chemokine production as major host responses to infection. Some of these pathways may be amenable to therapeutic manipulation.

The mammalian gut is colonized by numerous microorganisms collectively termed the microbiota, which have a mutually beneficial relationship with their host. Normally, the gut microbiota matures during ontogeny to a state of balanced commensalism marked by the absence of adverse inflammation. Subsets of innate lymphoid cells (ILCs) and conventional T cells are considered to have redundant functions in containment and clearance of microbial pathogens, but how these two major lymphoid-cell populations each contribute to shaping the mature commensal microbiome and help to maintain tissue homeostasis has not been determined. Here we identify, using advanced multiplex quantitative imaging methods, an extensive and persistent phosphorylated-STAT3 signature in group 3 ILCs and intestinal epithelial cells that is induced by interleukin (IL)-23 and IL-22 in mice that lack CD4+ T cells. By contrast, in immune-competent mice, phosphorylated-STAT3 activation is induced only transiently by microbial colonization at weaning. This early signature is extinguished as CD4+ T cell immunity develops in response to the expanding commensal burden. Physiologically, the persistent IL-22 production from group 3 ILCs that occurs in the absence of adaptive CD4+ T-cell activity results in impaired host lipid metabolism by decreasing lipid transporter expression in the small bowel. These findings provide new insights into how innate and adaptive lymphocytes operate sequentially and in distinct ways during normal development to establish steady-state commensalism and tissue metabolic homeostasis.

Summary CD4+ T cells have a remarkable potential to differentiate into diverse effector lineages following activation. Here, we probe the heterogeneity present among naive CD4+ T cells before encountering their cognate antigen to ask whether their effector potential is modulated by pre-existing transcriptional and chromatin landscape differences. Single-cell RNA sequencing shows that key drivers of variability are genes involved in T cell receptor (TCR) signaling. Using CD5 expression as a readout of the strength of tonic TCR interactions with self-peptide MHC, and sorting on the ends of this self-reactivity spectrum, we find that pre-existing transcriptional differences among naive CD4+ T cells impact follicular helper T (TFH) cell versus non-TFH effector lineage choice. Moreover, our data implicate TCR signal strength during thymic development in establishing differences in naive CD4+ T cell chromatin landscapes that ultimately shape their effector potential.

Nearly all chronic human infections are associated with alterations in the memory B cell (MBC) compartment, including a large expansion of CD19(hi)T-bet(hi) MBC in the peripheral blood of HIV-infected individuals with chronic viremia. Despite their prevalence, it is unclear how these B cells arise and whether they contribute to the inefficiency of antibody-mediated immunity in chronic infectious diseases. We addressed these questions by characterizing T-bet-expressing B cells in lymph nodes (LN) and identifying a strong T-bet signature among HIV-specific MBC associated with poor immunologic outcome. Confocal microscopy and quantitative imaging revealed that T-bet(hi) B cells in LN of HIV-infected chronically viremic individuals distinctly accumulated outside germinal centers (GC), which are critical for optimal antibody responses. In single-cell analyses, LN T-bet(hi) B cells of HIV-infected individuals were almost exclusively found among CD19(hi) MBC and expressed reduced GC-homing receptors. Furthermore, HIV-specific B cells of infected individuals were enriched among LN CD19(hi)T-bet(hi) MBC and displayed a distinct transcriptome, with features similar to CD19(hi)T-bet(hi) MBC in blood and LN GC B cells (GCBC). LN CD19(hi)T-bet(hi) MBC were also related to GCBC by B cell receptor (BCR)–based phylogenetic linkage but had lower BCR mutation frequencies and reduced HIV-neutralizing capacity, consistent with diminished participation in GC-mediated affinity selection. Thus, in the setting of chronic immune activation associated with HIV viremia, failure of HIV-specific B cells to enter or remain in GC may help explain the rarity of high-affinity protective antibodies.

Despite existing evidence for tuning of innate immunity to different classes of bacteria, the molecular mechanisms used by macrophages to tailor inflammatory responses to specific pathogens remain incompletely defined. By stimulating mouse macrophages with a titration matrix of TLR ligand pairs, we identified distinct stimulus requirements for activating and inhibitory events that evoked diverse cytokine production dynamics. These regulatory events were linked to patterns of inflammatory responses that distinguished between Gram-positive and Gram-negative bacteria, both in vitro and after in vivo lung infection. Stimulation beyond a TLR4 threshold and Gram-negative bacteria-induced responses were characterized by a rapid type I IFN-dependent decline in inflammatory cytokine production, independent of IL-10, whereas inflammatory responses to Gram-positive species were more sustained due to the absence of this IFN-dependent regulation. Thus, disparate triggering of a cytokine negative feedback loop promotes tuning of macrophage responses in a bacteria class-specific manner and provides context-dependent regulation of inflammation dynamics.

Disrupted molecular pathways are often robustly associated with disease outcome in cancer

The innate immune system distinguishes low-level homeostatic microbial stimuli from those of invasive pathogens, yet we lack understanding of how qualitatively similar microbial products yield context-specific macrophage functional responses. Using quantitative approaches, we found that NF-κB and MAPK signaling was activated at different concentrations of a stimulatory TLR4 ligand in both mouse and human macrophages. Above a threshold of ligand, MAPK were activated in a switch-like manner, facilitating production of inflammatory mediators. At ligand concentrations below this threshold, NF-κB signaling occurred, promoting expression of a restricted set of genes and macrophage priming. Among TLR-induced genes, we observed an inverse correlation between MAPK dependence and ligand sensitivity, highlighting the role of this signaling dichotomy in partitioning innate responses downstream of a single receptor. Our study reveals an evolutionarily conserved innate immune response system in which danger discrimination is enforced by distinct thresholds for NF-κB and MAPK activation, which provide sequential barriers to inflammatory mediator production.

Lymphoid tissue inducer (LTi) cells are regarded as a subset of innate lymphoid cells (ILCs). However, these cells are not derived from the ILC common progenitor, which generates other ILC subsets and is defined by the expression of the transcription factor PLZF. Here, we examined transcription factor(s) determining the fate of LTi progenitors versus non-LTi ILC progenitors. Conditional deletion of Gata3 resulted in the loss of PLZF+ non-LTi progenitors but not the LTi progenitors that expressed the transcription factor RORγt. Consistently, PLZF+ non-LTi progenitors expressed high amounts of GATA3, whereas GATA3 expression was low in RORγt+ LTi progenitors. The generation of both progenitors required the transcriptional regulator Id2, which defines the common helper-like innate lymphoid progenitor (ChILP), but not cytokine signaling. Nevertheless, low GATA3 expression was necessary for the generation of functionally mature LTi cells. Thus, differential expression of GATA3 determines the fates and functions of distinct ILC progenitors.

Summary Neutrophils are attracted to and generate dense swarms at sites of cell damage in diverse tissues, often extending the local disruption of organ architecture produced by the initial insult. Whether the inflammatory damage resulting from such neutrophil accumulation is an inescapable consequence of parenchymal cell death has not been explored. Using a combination of dynamic intravital imaging and confocal multiplex microscopy, we report here that tissue-resident macrophages rapidly sense the death of individual cells and extend membrane processes that sequester the damage, a process that prevents initiation of the feedforward chemoattractant signaling cascade that results in neutrophil swarms. Through this "cloaking" mechanism, the resident macrophages prevent neutrophil-mediated inflammatory damage, maintaining tissue homeostasis in the face of local cell injury that occurs on a regular basis in many organs because of mechanical and other stresses. Video Abstract

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational “deconvolution” of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of ‘ON’ versus ‘OFF’ cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies., Author Summary Different cells can make different amounts of biomolecules such as RNA transcripts of genes. New technologies are emerging to measure the transcript level of many genes in single cells. However, accurate quantification of the biological variation from cell to cell can be challenging due to the low transcript level of many genes and the presence of substantial measurement noise. Here we present a flexible, novel computational approach to quantify biological cell-to-cell variation that can use different types of data, namely measurements directly obtained from single cells, and/or those from random pools of k-cells (e.g., k = 10). Assessment of these different inputs using simulated and real data revealed that each data type can offer advantages under different scenarios, but combining both single- and k-cell measurements tend to offer the best of both. Application of our approach to single- and k-cell data obtained from resting and inflammatory macrophages, an important type of immune cells implicated in diverse diseases, revealed interesting changes in cell-to-cell variation in transcript levels upon inflammatory stimulation, thus suggesting that inflammation can shape not only the average expression level of a gene but also the gene’s degree of expression variation among single cells.

Summary Cell-to-cell variation in gene expression and the propagation of such variation (PoV or "noise propagation") from one gene to another in the gene network, as reflected by gene-gene correlation across single cells, are commonly observed in single-cell transcriptomic studies and can shape the phenotypic diversity of cell populations. While gene network "rewiring" is known to accompany cellular adaptation to different environments, how PoV changes between environments and its underlying regulatory mechanisms are less understood. Here, we systematically explored context-dependent PoV among genes in human macrophages, utilizing different cytokines as natural perturbations of multiple molecular parameters that may influence PoV. Our single-cell, epigenomic, computational, and stochastic simulation analyses reveal that environmental adaptation can tune PoV to potentially shape cellular heterogeneity by changing parameters such as the degree of phosphorylation and transcription factor-chromatin interactions. This quantitative tuning of PoV may be a widespread, yet underexplored, property of cellular adaptation to distinct environments.

Macrophages are known to show highly dynamic responses to diverse stimuli, and have been implicated in metabolic syndrome and related diseases. Genome-wide association studies have identified many single-nucleotide polymorphisms (SNPs) associated with these common conditions, largely in non-coding regions; how these SNPs influence disease processes remains largely unknown. We hypothesized that examining RNA expression and chromatin state in macrophages across diverse activation conditions would elucidate the roles of macrophages in mediating the effects of disease-linked SNPs on the development of disease. We measured gene expression via RNAseq and histone 3 lysine 27 acetylation via ChIPseq as an indicator of promoter/enhancer activity in human blood monocyte-derived macrophages stimulated in vitro with a range of inflammatory and disease-relevant stimuli. Over 11,000 genes and 43,000 regulatory elements show condition-dependent activity in this dataset. Functional enrichment in modules of co-expressed transcripts confirmed expected stimuli-specific biology and reveal potentially novel relationships between distinct stimulation states. Correlation of regulatory element activity with module gene expression shows broad inflammatory/anti-inflammatory separation, and metabolic-trait associated SNPs tend to be embedded within regulatory elements whose activity is associated with both non-inflammatory and, surprisingly, some anti-viral macrophage gene modules. Ongoing work includes using these data to infer the transcriptional networks underlying these states. This study was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, NIH.

Inflammation is common in virtually all diseases. Since immune cells travel through the blood to inflamed tissues, peripheral blood reflects inflammation at distant sites. However, blood biomarkers are hard to detect in global gene expression data since blood is not the site of perturbation and statistical corrections for multiple testing reduce sensitivity. We hypothesized that combining transcriptional signatures from inflamed tissue and machine learning would be useful to derive blood biomarkers. We first tested this in the mouse model of influenza infection. We identified a set of early genes in the lungs associated with lethality after infection. We then tested their differential abundance early in the blood of mice infected with a range of influenza doses. We applied elastic net to build a biomarker able to predict disease outcome (4 classes: early lethal, late lethal, non-lethal vs. non-infected; accuracy on training and validation sets: 91% and 79%). We next confirmed our hypothesis in lung cancer and breast cancer in humans. We identified sets of genes associated with tumors but not with tumor-free tissues and tested their differential abundance in the blood of patients compared to healthy controls. Using elastic net, we built two blood biomarkers to classify subjects according to disease status (case vs. control; accuracy on training and validation sets in breast cancer: 92% and 67%; in lung cancer: 89% and 78%). In conclusion, combining inflamed tissue signatures and machine learning, we were able to derive blood biomarkers for three different diseases in two different species. We expect that this novel strategy can be applied in other diseases as well. This work was supported by the Intramural Research Program of NIAID, NIH.

The mammalian gut is colonized by trillions of microorganisms termed the “microbiota”, which have a mutually beneficial relationship with their host. In normal individuals, the gut microbiota matures after birth to a state of balanced commensalism that is marked by the absence of adverse inflammation. Both innate lymphoid cells (ILCs) and antigen-specific conventional T cells contribute to containment and clearance of microbial pathogens. But how these two major lymphoid cell populations help shape the mature commensal (non-pathogenic) microbiome and maintain tissue homeostasis has not been determined. Using advanced multiplex quantitative imaging methods, here we show that in the absence of adaptive lymphocytes, IL-23 induced by specific commensal bacteria such as Segmented Filamentous Bacteria (SFB) persistently activates RORgt+ group 3 innate lymphoid cells (ILC3s) in the ileum to produce IL-22, which induces STAT3 activation in virtually all epithelial cells, contributing to production of molecules such as anti-microbial peptides that protect the tissue from microbial damage. The distinct roles of ILCs in handling gut microbes play out in normal mice during development. The pSTAT3 signature is absent after birth, which is followed by microbial colonization and strong ILC3 activation and an extensive epithelial pSTAT3 signature upon weaning. This innate immune activity is subsequently extinguished as adaptive CD4+ T cell immunity develops in response to the expanding commensal burden. Our findings provide new insights into how innate and adaptive lymphocytes sequentially operate during normal development to establish steady state commensalism.

Toll-like receptors (TLRs) expressed on macrophages are ligated in various combinations by diverse pathogen-associated molecular patterns (PAMPs), but how mixtures of signals through these receptors are integrated to mediate distinct biological responses to different pathogens remains unknown. By stimulating macrophages using a wide matrix of TLR2/TLR4 concentration combinations, we identified conditions resulting in non-linear responses. Transcriptomic and RNAi analyses identified negative regulators induced in a TLR-specific manner that regulated cytokine production dynamics and chemokine milieus across the combinatorial matrix. TLR4 ligand concentration explained much of the variation in production of many chemokines. However, combinations of sub-maximal amounts of either single ligand produced a zone of non-linear responses with sustained production of select inflammatory mediators, due to lack of negative regulation induced only at higher individual ligand concentrations. A similar sustained response was observed for Gram-positive bacteria, as compared to a panel of Gram-negative species, which express high amounts of the TLR4 ligand LPS. The novel TLR4-specific negative regulators we describe thus aid the innate immune system in discriminating Gram-negative from Gram-positive species, supporting qualitatively distinct inflammatory responses to these classes of bacteria. This research was supported by the Intramural Research Program of the NIH, NIAID

The immune system is composed of multiple dynamic molecular and cellular networks, the complexity of which has been revealed by decades of exacting reductionist research. However, understanding of the immune system sufficient to anticipate its response to novel perturbations requires a more integrative view, or systems approach, to immunology. While methods for unbiased high-throughput data acquisition and computational integration of the resulting datasets are still relatively new, they have begun to substantially add to our understanding of immunological phenomena. Such approaches have expanded our view of interconnected signaling and transcriptional networks and have highlighted the function of non-linear processes such as spatial regulation and feedback loops. In addition, advances in single cell measurement technology have demonstrated the potential sources and functions of response heterogeneity in system behavior. The success of the studies reviewed here often depended upon integration of one or more systems biology approaches with more traditional methods. We hope these examples will inspire a broader range of immunologists to probe questions in a quantitative and integrated manner, to advance the common efforts to understand the immune “system”.

B cell clonal expansion is a hallmark of host-defense and vaccination responses. Given the vast immunoglobulin repertoire, individuals may expand B cells carrying largely distinct immunoglobulin genes following antigenic challenge. Using immunoglobulin-repertoire sequencing to dynamically track responses to influenza vaccination, Jackson et al. find evidence of convergent immunoglobulin responses across individuals.