Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age.

The immune system comprises multiple cell lineages and heterogeneous subsets found in blood and tissues throughout the body. While human immune responses differ between sites and over age, the underlying sources of variation remain unclear as most studies are limited to peripheral blood. Here, we took a systems approach to comprehensively profile RNA and surface protein expression of over 1.25 million immune cells isolated from blood, lymphoid organs, and mucosal tissues of 24 organ donors aged 20-75 years. We applied a multimodal classifier to annotate the major immune cell lineages (T cells, B cells, innate lymphoid cells, and myeloid cells) and their corresponding subsets across the body, leveraging probabilistic modeling to define bases for immune variations across donors, tissue, and age. We identified dominant tissue-specific effects on immune cell composition and function across lineages for lymphoid sites, intestines, and blood-rich tissues. Age-associated effects were intrinsic to both lineage and site as manifested by macrophages in mucosal sites, B cells in lymphoid organs, and T and NK cells in blood-rich sites. Our results reveal tissue-specific signatures of immune homeostasis throughout the body and across different ages. This information provides a basis for defining the transcriptional underpinnings of immune variation and potential associations with disease-associated immune pathologies across the human lifespan.

Sodium perturbs mitochondrial respiration and induces dysfunctional Tregs.

FOXP3+ regulatory T cells (Tregs) are central for peripheral tolerance, and their deregulation is associated with autoimmunity. Dysfunctional autoimmune Tregs display pro-inflammatory features and altered mitochondrial metabolism, but contributing factors remain elusive. High salt (HS) has been identified to alter immune function and to promote autoimmunity. By investigating longitudinal transcriptional changes of human Tregs, we identified that HS induces metabolic reprogramming, recapitulating features of autoimmune Tregs. Mechanistically, extracellular HS raises intracellular Na+, perturbing mitochondrial respiration by interfering with the electron transport chain (ETC). Metabolic disturbance by a temporary HS encounter or complex III blockade rapidly induces a pro-inflammatory signature and FOXP3 downregulation, leading to long-term dysfunction in vitro and in vivo. The HS-induced effect could be reversed by inhibition of mitochondrial Na+/Ca2+ exchanger (NCLX). Our results indicate that salt could contribute to metabolic reprogramming and that short-term HS encounter perturb metabolic fitness and long-term function of human Tregs with important implications for autoimmunity.

Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation.

Despite its high prevalence, the cellular and molecular mechanisms of chronic obstructive pulmonary disease (COPD) are far from being understood. Here, we determine disease-related changes in cellular and molecular compositions within the alveolar space and peripheral blood of a cohort of COPD patients and controls. Myeloid cells were the largest cellular compartment in the alveolar space with invading monocytes and proliferating macrophages elevated in COPD. Modeling cell-to-cell communication, signaling pathway usage, and transcription factor binding predicts TGF-ß1 to be a major upstream regulator of transcriptional changes in alveolar macrophages of COPD patients. Functionally, macrophages in COPD showed reduced antigen presentation capacity, accumulation of cholesteryl ester, reduced cellular chemotaxis, and mitochondrial dysfunction, reminiscent of impaired immune activation.

DestVI identifies continuums of cell types in spatial transcriptomics data.

Most spatial transcriptomics technologies are limited by their resolution, with spot sizes larger than that of a single cell. Although joint analysis with single-cell RNA sequencing can alleviate this problem, current methods are limited to assessing discrete cell types, revealing the proportion of cell types inside each spot. To identify continuous variation of the transcriptome within cells of the same type, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI). Using simulations, we demonstrate that DestVI outperforms existing methods for estimating gene expression for every cell type inside every spot. Applied to a study of infected lymph nodes and of a mouse tumor model, DestVI provides high-resolution, accurate spatial characterization of the cellular organization of these tissues and identifies cell-type-specific changes in gene expression between different tissue regions or between conditions. DestVI is available as part of the open-source software package scvi-tools ( https://scvi-tools.org ).

Systems-based approaches to study immunometabolism.

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

Metabolic modeling of single Th17 cells reveals regulators of autoimmunity.

Metabolism is a major regulator of immune cell function, but it remains difficult to study the metabolic status of individual cells. Here, we present Compass, an algorithm to characterize cellular metabolic states based on single-cell RNA sequencing and flux balance analysis. We applied Compass to associate metabolic states with T helper 17 (Th17) functional variability (pathogenic potential) and recovered a metabolic switch between glycolysis and fatty acid oxidation, akin to known Th17/regulatory T cell (Treg) differences, which we validated by metabolic assays. Compass also predicted that Th17 pathogenicity was associated with arginine and downstream polyamine metabolism. Indeed, polyamine-related enzyme expression was enhanced in pathogenic Th17 and suppressed in Treg cells. Chemical and genetic perturbation of polyamine metabolism inhibited Th17 cytokines, promoted Foxp3 expression, and remodeled the transcriptome and epigenome of Th17 cells toward a Treg-like state. In vivo perturbations of the polyamine pathway altered the phenotype of encephalitogenic T cells and attenuated tissue inflammation in CNS autoimmunity.

Oleic acid restores suppressive defects in tissue-resident FOXP3 Tregs from patients with multiple sclerosis.

FOXP3+ Tregs rely on fatty acid ß-oxidation-driven (FAO-driven) oxidative phosphorylation (OXPHOS) for differentiation and function. Recent data demonstrate a role for Tregs in the maintenance of tissue homeostasis, with tissue-resident Tregs possessing tissue-specific transcriptomes. However, specific signals that establish tissue-resident Treg programs remain largely unknown. Tregs metabolically rely on FAO, and considering the lipid-rich environments of tissues, we hypothesized that environmental lipids drive Treg homeostasis. First, using human adipose tissue to model tissue residency, we identified oleic acid as the most prevalent free fatty acid. Mechanistically, oleic acid amplified Treg FAO-driven OXPHOS metabolism, creating a positive feedback mechanism that increased the expression of FOXP3 and phosphorylation of STAT5, which enhanced Treg-suppressive function. Comparing the transcriptomic program induced by oleic acid with proinflammatory arachidonic acid, we found that Tregs sorted from peripheral blood and adipose tissue of healthy donors transcriptomically resembled the Tregs treated in vitro with oleic acid, whereas Tregs from patients with multiple sclerosis (MS) more closely resembled an arachidonic acid transcriptomic profile. Finally, we found that oleic acid concentrations were reduced in patients with MS and that exposure of MS Tregs to oleic acid restored defects in their suppressive function. These data demonstrate the importance of fatty acids in regulating tissue inflammatory signals.

T Follicular Regulatory Cell-Derived Fibrinogen-like Protein 2 Regulates Production of Autoantibodies and Induction of Systemic Autoimmunity.

T follicular regulatory (TFR) cells limit Ab responses, but the underlying mechanisms remain largely unknown. In this study, we identify Fgl2 as a soluble TFR cell effector molecule through single-cell gene expression profiling. Highly expressed by TFR cells, Fgl2 directly binds to B cells, especially light-zone germinal center B cells, as well as to T follicular helper (TFH) cells, and directly regulates B cells and TFH in a context-dependent and type 2 Ab isotype-specific manner. In TFH cells, Fgl2 induces the expression of Prdm1 and a panel of checkpoint molecules, including PD1, TIM3, LAG3, and TIGIT, resulting in TFH cell dysfunction. Mice deficient in Fgl2 had dysregulated Ab responses at steady-state and upon immunization. In addition, loss of Fgl2 results in expansion of autoreactive B cells upon immunization. Consistent with this observation, aged Fgl2-/- mice spontaneously developed autoimmunity associated with elevated autoantibodies. Thus, Fgl2 is a TFR cell effector molecule that regulates humoral immunity and limits systemic autoimmunity.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Injury Activates Transient Olfactory Stem Cell States with Diverse Lineage Capacities.

Tissue homeostasis and regeneration are mediated by programs of adult stem cell renewal and differentiation. However, the mechanisms that regulate stem cell fates under such widely varying conditions are not fully understood. Using single-cell techniques, we assessed the transcriptional changes associated with stem cell self-renewal and differentiation and followed the maturation of stem cell-derived clones using sparse lineage tracing in the regenerating mouse olfactory epithelium. Following injury, quiescent olfactory stem cells rapidly shift to activated, transient states unique to regeneration and tailored to meet the demands of injury-induced repair, including barrier formation and proliferation. Multiple cell fates, including renewed stem cells and committed differentiating progenitors, are specified during this early window of activation. We further show that Sox2 is essential for cells to transition from the activated to neuronal progenitor states. Our study highlights strategies for stem cell-mediated regeneration that may be conserved in other adult stem cell niches.

Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution.

A detailed understanding of the paths that stem cells traverse to generate mature progeny is vital for elucidating the mechanisms governing cell fate decisions and tissue homeostasis. Adult stem cells maintain and regenerate multiple mature cell lineages in the olfactory epithelium. Here we integrate single-cell RNA sequencing and robust statistical analyses with in vivo lineage tracing to define a detailed map of the postnatal olfactory epithelium, revealing cell fate potentials and branchpoints in olfactory stem cell lineage trajectories. Olfactory stem cells produce support cells via direct fate conversion in the absence of cell division, and their multipotency at the population level reflects collective unipotent cell fate decisions by single stem cells. We further demonstrate that Wnt signaling regulates stem cell fate by promoting neuronal fate choices. This integrated approach reveals the mechanisms guiding olfactory lineage trajectories and provides a model for deconstructing similar hierarchies in other stem cell niches.

New Role for Interleukin-13 Receptor α1 in Myocardial Homeostasis and Heart Failure.

BACKGROUND: The immune system plays a pivotal role in myocardial homeostasis and response to injury. Interleukins-4 and -13 are anti-inflammatory type-2 cytokines, signaling via the common interleukin-13 receptor α1 chain and the type-2 interleukin-4 receptor. The role of interleukin-13 receptor α1 in the heart is unknown. METHODS AND RESULTS: We analyzed myocardial samples from human donors (n=136) and patients with end-stage heart failure (n=177). We found that the interleukin-13 receptor α1 is present in the myocardium and, together with the complementary type-2 interleukin-4 receptor chain Il4ra, is significantly downregulated in the hearts of patients with heart failure. Next, we showed that Il13ra1-deficient mice develop severe myocardial dysfunction and dyssynchrony compared to wild-type mice (left ventricular ejection fraction 29.7±9.9 versus 45.0±8.0; P=0.004, left ventricular end-diastolic diameter 4.2±0.2 versus 3.92±0.3; P=0.03). A bioinformatic analysis of mouse hearts indicated that interleukin-13 receptor α1 regulates critical pathways in the heart other than the immune system, such as extracellular matrix (normalized enrichment score=1.90; false discovery rate q=0.005) and glucose metabolism (normalized enrichment score=-2.36; false discovery rate q=0). Deficiency of Il13ra1 was associated with reduced collagen deposition under normal and pressure-overload conditions. CONCLUSIONS: The results of our studies in humans and mice indicate, for the first time, a role of interleukin-13 receptor α1 in myocardial homeostasis and heart failure and suggests a new therapeutic target to treat heart disease.

Revealing the vectors of cellular identity with single-cell genomics.

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction.

Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients' survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism.

The Role of Temporal Trends in Growing Networks.

The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network's tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment.

Functional Alignment of Metabolic Networks.

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Glutamine synthetase activity fuels nucleotide biosynthesis and supports growth of glutamine-restricted glioblastoma.

L-Glutamine (Gln) functions physiologically to balance the carbon and nitrogen requirements of tissues. It has been proposed that in cancer cells undergoing aerobic glycolysis, accelerated anabolism is sustained by Gln-derived carbons, which replenish the tricarboxylic acid (TCA) cycle (anaplerosis). However, it is shown here that in glioblastoma (GBM) cells, almost half of the Gln-derived glutamate (Glu) is secreted and does not enter the TCA cycle, and that inhibiting glutaminolysis does not affect cell proliferation. Moreover, Gln-starved cells are not rescued by TCA cycle replenishment. Instead, the conversion of Glu to Gln by glutamine synthetase (GS; cataplerosis) confers Gln prototrophy, and fuels de novo purine biosynthesis. In both orthotopic GBM models and in patients, (13)C-glucose tracing showed that GS produces Gln from TCA-cycle-derived carbons. Finally, the Gln required for the growth of GBM tumours is contributed only marginally by the circulation, and is mainly either autonomously synthesized by GS-positive glioma cells, or supplied by astrocytes.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia.

High-throughput omics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet-induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner­treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease's omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

Network-level architecture and the evolutionary potential of underground metabolism.

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.