Explore the vast range of titles available.

Conceptual scientific and medical advances have led to a recent realization that there may be no single, one-size-fits-all diet and that differential human responses to dietary inputs may rather be driven by unique and quantifiable host and microbiome features. Integration of these person-specific host and microbiome readouts into actionable modules may complement traditional food measurement approaches in devising diets that are of benefit to the individual. Although many host-derived factors are hardwired and difficult to modulate, the microbiome may be more readily reshaped by environmental factors such as dietary exposures and is increasingly recognized to potentially impact human physiology by participating in digestion, the absorption of nutrients, shaping of the mucosal immune response and the synthesis or modulation of a plethora of potentially bioactive compounds. Thus, diet-induced microbiota alterations may be harnessed in order to induce changes in host physiology, including disease development and progression. However, major limitations in ‘big-data’ processing and analysis still limit our interpretive and translational capabilities concerning these person-specific host, microbiome and diet interactions. In this Review, we describe the latest advances in understanding diet–microbiota interactions, the individuality of gut microbiota composition and how this knowledge could be harnessed for personalized nutrition strategies to improve human health.

Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of cardiometabolic syndrome, which often also includes obesity, diabetes, and dyslipidemia. It is rapidly becoming the most prevalent liver disease worldwide. A sizable minority of NAFLD patients develop nonalcoholic steatohepatitis (NASH), which is characterized by inflammatory changes that can lead to progressive liver damage, cirrhosis, and hepatocellular carcinoma. Recent studies have shown that in addition to genetic predisposition and diet, the gut microbiota affects hepatic carbohydrate and lipid metabolism as well as influences the balance between pro‐inflammatory and anti‐inflammatory effectors in the liver, thereby impacting NAFLD and its progression to NASH. In this review, we will explore the impact of gut microbiota and microbiota‐derived compounds on the development and progression of NAFLD and NASH, and the unexplored factors related to potential microbiome contributions to this common liver disease. Graphical Abstract In this review, Kolodziejczyk, Elinav and colleagues explore the impact that the gut microbiota may have on the development and progression of nonalcoholic liver diseases (i.e. NAFLD and NASH), and discuss the unexplored factors related to potential microbiome contributions to this common disorder.

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.

Acute liver failure (ALF) is a fulminant complication of multiple etiologies, characterized by rapid hepatic destruction, multi-organ failure and mortality. ALF treatment is mainly limited to supportive care and liver transplantation. Here we utilize the acetaminophen (APAP) and thioacetamide (TAA) ALF models in characterizing 56,527 single-cell transcriptomes to define the mouse ALF cellular atlas. We demonstrate that unique, previously uncharacterized stellate cell, endothelial cell, Kupffer cell, monocyte and neutrophil subsets, and their intricate intercellular crosstalk, drive ALF. We unravel a common MYC-dependent transcriptional program orchestrating stellate, endothelial and Kupffer cell activation during ALF, which is regulated by the gut microbiome through Toll-like receptor (TLR) signaling. Pharmacological inhibition of MYC, upstream TLR signaling checkpoints or microbiome depletion suppress this cell-specific, MYC-dependent program, thereby attenuating ALF. In humans, we demonstrate upregulated hepatic MYC expression in ALF transplant recipients compared to healthy donors. Collectively we demonstrate that detailed cellular/genetic decoding may enable pathway-specific ALF therapeutic intervention. A single-cell map of transcriptomic changes during acute liver failure unveils new insights into pathogenesis and potential therapeutic targets.

Cigarette smoking constitutes a leading global cause of morbidity and preventable death 1 , and most active smokers report a desire or recent attempt to quit 2 . Smoking-cessation-induced weight gain (SCWG; 4.5 kg reported to be gained on average per 6–12 months, >10 kg year –1 in 13% of those who stopped smoking 3 ) constitutes a major obstacle to smoking abstinence 4 , even under stable 5 , 6 or restricted 7 caloric intake. Here we use a mouse model to demonstrate that smoking and cessation induce a dysbiotic state that is driven by an intestinal influx of cigarette-smoke-related metabolites. Microbiome depletion induced by treatment with antibiotics prevents SCWG. Conversely, fecal microbiome transplantation from mice previously exposed to cigarette smoke into germ-free mice naive to smoke exposure induces excessive weight gain across diets and mouse strains. Metabolically, microbiome-induced SCWG involves a concerted host and microbiome shunting of dietary choline to dimethylglycine driving increased gut energy harvest, coupled with the depletion of a cross-regulated weight-lowering metabolite, N -acetylglycine, and possibly by the effects of other differentially abundant cigarette-smoke-related metabolites. Dimethylglycine and N -acetylglycine may also modulate weight and associated adipose-tissue immunity under non-smoking conditions. Preliminary observations in a small cross-sectional human cohort support these findings, which calls for larger human trials to establish the relevance of this mechanism in active smokers. Collectively, we uncover a microbiome-dependent orchestration of SCWG that may be exploitable to improve smoking-cessation success and to correct metabolic perturbations even in non-smoking settings. A study of mice exposed to cigarette smoke suggests that smoking-cessation-induced weight gain is associated with a dysbiotic state that is driven by smoking-related metabolites.

Background Hematopoietic stem cells (HSCs) are a rare cell type with the ability of long-term self-renewal and multipotency to reconstitute all blood lineages. HSCs are typically purified from the bone marrow using cell surface markers. Recent studies have identified significant cellular heterogeneities in the HSC compartment with subsets of HSCs displaying lineage bias. We previously discovered that the transcription factor Bcl11a has critical functions in the lymphoid development of the HSC compartment. Results In this report, we employ single-cell transcriptomic analysis to dissect the molecular heterogeneities in HSCs. We profile the transcriptomes of 180 highly purified HSCs ( Bcl11a +/+ and Bcl11a −/− ). Detailed analysis of the RNA-seq data identifies cell cycle activity as the major source of transcriptomic variation in the HSC compartment, which allows reconstruction of HSC cell cycle progression in silico. Single-cell RNA-seq profiling of Bcl11a −/− HSCs reveals abnormal proliferative phenotypes. Analysis of lineage gene expression suggests that the Bcl11a −/− HSCs are constituted of two distinct myeloerythroid-restricted subpopulations. Remarkably, similar myeloid-restricted cells could also be detected in the wild-type HSC compartment, suggesting selective elimination of lymphoid-competent HSCs after Bcl11a deletion. These defects are experimentally validated in serial transplantation experiments where Bcl11a −/− HSCs are myeloerythroid-restricted and defective in self-renewal. Conclusions Our study demonstrates the power of single-cell transcriptomics in dissecting cellular process and lineage heterogeneities in stem cell compartments, and further reveals the molecular and cellular defects in the Bcl11a -deficient HSC compartment.

Polycomb repressive complexes (PRCs) are important histone modifiers, which silence gene expression; yet, there exists a subset of PRC-bound genes actively transcribed by RNA polymerase II (RNAPII). It is likely that the role of Polycomb repressive complex is to dampen expression of these PRC-active genes. However, it is unclear how this flipping between chromatin states alters the kinetics of transcription. Here, we integrate histone modifications and RNAPII states derived from bulk ChIP-seq data with single-cell RNA-sequencing data. We find that Polycomb repressive complex-active genes have greater cell-to-cell variation in expression than active genes, and these results are validated by knockout experiments. We also show that PRC-active genes are clustered on chromosomes in both two and three dimensions, and interactions with active enhancers promote a stabilization of gene expression noise. These findings provide new insights into how chromatin regulation modulates stochastic gene expression and transcriptional bursting, with implications for regulation of pluripotency and development. Polycomb repressive complexes modify histones but it is unclear how changes in chromatin states alter kinetics of transcription. Here, the authors use single-cell RNAseq and ChIPseq to find that actively transcribed genes with Polycomb marks have greater cell-to-cell variation in expression.

Key Points A narrow definition of dysbiosis is as a stable microbial community state that functionally contributes to the aetiology, diagnosis or treatment of a disease. Dysbiosis is often driven by infection and inflammation, diet and xenobiotics, host genetics or the host's environment. Innate and adaptive immunity control the colonization niche of the intestinal microbiota through mechanisms including the production of antimicrobial peptides and IgA antibodies. A dysbiotic microbiota may actively influence its colonization niche by altering the functions of innate and adaptive intestinal immunity. Dysbiosis has been associated with many immune-related human diseases, but in many cases it remains to be established whether dysbiosis is a cause or consequence of the disease. Personalized nutrition and metabolite-based 'postbiotic' therapy may present ways in which to harness the increasing knowledge about dysbiosis in disease for the design of new therapies. An increasing number of multifactorial diseases have been linked to intestinal dysbiosis — that is, changes in the composition and function of the gut microbiome. Here, the authors explore the causes and consequences of dysbiosis, and discuss implications for the aetiology and treatment of many common immune-mediated diseases. Throughout the past century, we have seen the emergence of a large number of multifactorial diseases, including inflammatory, autoimmune, metabolic, neoplastic and neurodegenerative diseases, many of which have been recently associated with intestinal dysbiosis — that is, compositional and functional alterations of the gut microbiome. In linking the pathogenesis of common diseases to dysbiosis, the microbiome field is challenged to decipher the mechanisms involved in the de novo generation and the persistence of dysbiotic microbiome configurations, and to differentiate causal host–microbiome associations from secondary microbial changes that accompany disease course. In this Review, we categorize dysbiosis in conceptual terms and provide an overview of immunological associations; the causes and consequences of bacterial dysbiosis, and their involvement in the molecular aetiology of common diseases; and implications for the rational design of new therapeutic approaches. A molecular- level understanding of the origins of dysbiosis, its endogenous and environmental regulatory processes, and its downstream effects may enable us to develop microbiome-targeting therapies for a multitude of common immune-mediated diseases.

A statistical method that uses spike-ins to model the dependence of technical noise on transcript abundance in single-cell RNA-seq experiments allows identification of genes wherein observed variability in read counts can be reliably interpreted as a signal of biological variability as opposed to the effect of technical noise. Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus .

Single-cell RNA-sequencing (scRNA-seq) facilitates identification of new cell types and gene regulatory networks as well as dissection of the kinetics of gene expression and patterns of allele-specific expression. However, to facilitate such analyses, separating biological variability from the high level of technical noise that affects scRNA-seq protocols is vital. Here we describe and validate a generative statistical model that accurately quantifies technical noise with the help of external RNA spike-ins. Applying our approach to investigate stochastic allele-specific expression in individual cells, we demonstrate that a large fraction of stochastic allele-specific expression can be explained by technical noise, especially for lowly and moderately expressed genes: we predict that only 17.8% of stochastic allele-specific expression patterns are attributable to biological noise with the remainder due to technical noise. Single-cell RNA-sequencing (scRNA-seq) can be applied to dissect the kinetics of gene expression and patterns of allele-specific expression. Here, Kim et al. report a generative statistical model that can separate biological variability from technical noise by quantifying technical noise using external RNA spike-ins.