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The drivers of immune evasion are not entirely clear, limiting the success of cancer immunotherapies. Here we applied single-cell spatial and perturbational transcriptomics to delineate immune evasion in high-grade serous tubo-ovarian cancer. To this end, we first mapped the spatial organization of high-grade serous tubo-ovarian cancer by profiling more than 2.5 million cells in situ in 130 tumors from 94 patients. This revealed a malignant cell state that reflects tumor genetics and is predictive of T cell and natural killer cell infiltration levels and response to immune checkpoint blockade. We then performed Perturb-seq screens and identified genetic perturbations-including knockout of PTPN1 and ACTR8-that trigger this malignant cell state. Finally, we show that these perturbations, as well as a PTPN1/PTPN2 inhibitor, sensitize ovarian cancer cells to T cell and natural killer cell cytotoxicity, as predicted. This study thus identifies ways to study and target immune evasion by linking genetic variation, cell-state regulators and spatial biology.

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood1-3. Here, the Molecular Transducers of Physical Activity Consortium4 profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository ( https://motrpac-data.org/ ).

PurposeMiscarriage, often resulting from a variety of genetic factors, is a common pregnancy outcome. Preconception genetic carrier screening (PGCS) identifies at-risk partners for newborn genetic disorders; however, PGCS panels currently lack miscarriage-related genes. In this study, we evaluated the potential impact of both known and candidate genes on prenatal lethality and the effectiveness of PGCS in diverse populations.MethodsWe analyzed 125,748 human exome sequences and mouse and human gene function databases. Our goals were to identify genes crucial for human fetal survival (lethal genes), to find variants not present in a homozygous state in healthy humans, and to estimate carrier rates of known and candidate lethal genes in various populations and ethnic groups.ResultsThis study identified 138 genes in which heterozygous lethal variants are present in the general population with a frequency of 0.5% or greater. Screening for these 138 genes could identify 4.6% (in the Finnish population) to 39.8% (in the East Asian population) of couples at risk of miscarriage. This explains the cause of pregnancy loss in approximately 1.1-10% of cases affected by biallelic lethal variants.ConclusionThis study has identified a set of genes and variants potentially associated with lethality across different ethnic backgrounds. The variation of these genes across ethnic groups underscores the need for a comprehensive, pan-ethnic PGCS panel that includes genes related to miscarriage.

High fructose consumption is associated with an increased risk of cardiometabolic disease, and fructose feeding dose-dependently induces markers reflective of poor metabolic health. However, unlike glucose, surprisingly little is known about person-to-person differences in postprandial plasma fructose patterns. Herein, we performed post hoc analyses of two published studies to address this question. In the first cohort, 16 participants' all-day plasma fructose concentration patterns (08:00-23:30) were determined (8 women and 8 men) while consuming mixed meals (breakfast, lunch, and dinner) with a fructose-sweetened beverage at each meal (30% of calories). Individually plotted results demonstrate remarkably disparate fructose patterns with respect to peak concentration and timing. A secondary study confirmed substantial interindividual variability in plasma fructose patterns over 240 min in 16 adults consuming Ensure®, a commercially available mixed macronutrient drink containing a low dose of fructose. The health ramifications of interindividual variations in postprandial fructose metabolism and the underlying physiological mechanisms driving differences in post-meal blood patterns remain to be explored. Future research is warranted to determine if interindividual variability in fructose digestion, metabolism, and postprandial blood concentration patterns is associated with cardiometabolic health phenotypes and disease risk.

Despite decades of stress research, there still exist substantial gaps in our understanding of how social, environmental, and biological factors interact and combine with developmental stressor exposures, cognitive appraisals of stressors, and psychosocial coping processes to shape individuals stress reactivity, health, and disease risk. Relatively new biological profiling approaches, called multi-omics, are helping address these issues by enabling researchers to quantify thousands of molecules from a single blood or tissue sample, thus providing a panoramic snapshot of the molecular processes occurring in an organism from a systems perspective. In this review, we summarize two types of research designs for which multi-omics approaches are best suited, and describe how these approaches can help advance our understanding of stress processes and the development, prevention, and treatment of stress-related pathologies. We first discuss incorporating multi-omics approaches into theory-rich, intensive longitudinal study designs to characterize, in high-resolution, the transition to stress-related multisystem dysfunction and disease throughout development. Next, we discuss how multi-omics approaches should be incorporated into intervention research to better understand the transition from stress-related dysfunction back to health, which can help inform novel precision medicine approaches to managing stress and fostering biopsychosocial resilience. Throughout, we provide concrete recommendations for types of studies that will help advance stress research, and translate multi-omics data into better health and health care.

Biallelic mutations in the gene that encodes the enzyme N-glycanase 1 (NGLY1) cause a rare disease with multi-symptomatic features including developmental delay, intellectual disability, neuropathy, and seizures. NGLY1s activity in human neural cells is currently not well understood. To understand how NGLY1 gene loss leads to the specific phenotypes of NGLY1 deficiency, we employed direct conversion of NGLY1 patient-derived induced pluripotent stem cells (iPSCs) to functional cortical neurons. Transcriptomic, proteomic, and functional studies of iPSC-derived neurons lacking NGLY1 function revealed several major cellular processes that were altered, including protein aggregate-clearing functionality, mitochondrial homeostasis, and synaptic dysfunctions. These phenotypes were rescued by introduction of a functional NGLY1 gene and were observed in iPSC-derived mature neurons but not astrocytes. Finally, laser capture microscopy followed by mass spectrometry provided detailed characterization of the composition of protein aggregates specific to NGLY1-deficient neurons. Future studies will harness this knowledge for therapeutic development.

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

The field of psychoneuroimmunology (PNI) has grown substantially in both relevance and prominence over the past 40 years. Notwithstanding its impressive trajectory, a majority of PNI studies are still based on a relatively small number of analytes. To advance this work, we suggest that PNI, and health research in general, can benefit greatly from adopting a multi-omics approach, which involves integrating data across multiple biological levels (e.g., the genome, proteome, transcriptome, metabolome, lipidome, and microbiome/metagenome) to more comprehensively profile biological functions and relate these profiles to clinical and behavioral outcomes. To assist investigators in this endeavor, we provide an overview of multi-omics research, highlight recent landmark multi-omics studies investigating human health and disease risk, and discuss how multi-omics can be applied to better elucidate links between psychological, nervous system, and immune system activity. In doing so, we describe how to design high-quality multi-omics studies, decide which biological samples (e.g., blood, stool, urine, saliva, solid tissue) are most relevant, incorporate behavioral and wearable sensing data into multi-omics research, and understand key data quality, integration, analysis, and interpretation issues. PNI researchers are addressing some of the most interesting and important questions at the intersection of psychology, neuroscience, and immunology. Applying a multi-omics approach to this work will greatly expand the horizon of what is possible in PNI and has the potential to revolutionize our understanding of mind-body medicine.

The clinical trajectory of COVID-19 may be influenced by previous responses to heterologous viruses. We examined the relationship of Abs against different viruses to clinical trajectory groups from the National Institutes of Health IMPACC (Immunophenotyping Assessment in a COVID-19 Cohort) study of hospitalized COVID-19 patients. Whereas initial Ab titers to SARS-CoV-2 tended to be higher with increasing severity (excluding fatal disease), those to seasonal coronaviruses trended in the opposite direction. Initial Ab titers to influenza and parainfluenza viruses also tended to be lower with increasing severity. However, no significant relationship was observed for Abs to other viruses, including measles, CMV, EBV, and respiratory syncytial virus. We hypothesize that some individuals may produce lower or less durable Ab responses to respiratory viruses generally (reflected in lower baseline titers in our study), and that this may carry over into poorer outcomes for COVID-19 (despite high initial SARS-CoV-2 titers). We further looked at longitudinal changes in Ab responses to heterologous viruses, but found little change during the course of acute COVID-19 infection. We saw significant trends with age for Ab levels to many of these viruses, but no difference in longitudinal SARS-CoV-2 titers for those with high versus low seasonal coronavirus titers. We detected no difference in longitudinal SARS-CoV-2 titers for CMV seropositive versus seronegative patients, although there was an overrepresentation of CMV seropositives among the IMPACC cohort, compared with expected frequencies in the United States population. Our results both reinforce findings from other studies and suggest (to our knowledge) new relationships between the response to SARS-CoV-2 and Abs to heterologous viruses.

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

Permafrost underlies approximately one quarter of Northern Hemisphere terrestrial surfaces and contains 25-50% of the global soil carbon (C) pool. Permafrost soils and the C stocks within are vulnerable to ongoing and future projected climate warming. The biogeography of microbial communities inhabiting permafrost has not been examined beyond a small number of sites focused on local-scale variation. Permafrost is different from other soils. Perennially frozen conditions in permafrost dictate that microbial communities do not turn over quickly, thus possibly providing strong linkages to past environments. Thus, the factors structuring the composition and function of microbial communities may differ from patterns observed in other terrestrial environments. Here, we analyzed 133 permafrost metagenomes from North America, Europe, and Asia. Permafrost biodiversity and taxonomic distribution varied in relation to pH, latitude and soil depth. The distribution of genes differed by latitude, soil depth, age, and pH. Genes that were the most highly variable across all sites were associated with energy metabolism and C-assimilation. Specifically, methanogenesis, fermentation, nitrate reduction, and replenishment of citric acid cycle intermediates. This suggests that adaptations to energy acquisition and substrate availability are among some of the strongest selective pressures shaping permafrost microbial communities. The spatial variation in metabolic potential has primed communities for specific biogeochemical processes as soils thaw due to climate change, which could cause regional- to global- scale variation in C and nitrogen processing and greenhouse gas emissions.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

To understand what determines the success of short- and long-term weight loss, we conduct a secondary analysis of dietary, metabolic, and molecular data collected from 609 participants before, during, and after a 1-year weight-loss intervention with either a healthy low-carbohydrate (HLC) or a healthy low-fat (HLF) diet. Through systematic analysis of multidomain datasets, we find that dietary adherence and diet quality, not just caloric restriction, are important for short-term weight loss in both diets. Interestingly, we observe minimal dietary differences between those who succeeded in long-term weight loss and those who did not. Instead, proteomic and gut microbiota signatures significantly differ between these two groups at baseline. Moreover, the baseline respiratory quotient may suggest a specific diet for better weight-loss outcomes. Overall, the identification of these dietary, molecular, and metabolic factors, common or unique to the HLC and HLF diets, provides a roadmap for developing individualized weight-loss strategies.

Despite a strong genetic component, only a few genes have been identified in congenital heart diseases (CHDs). We introduced systems analyses to uncover the hidden organization on biological networks of mutations in CHDs and leveraged network analysis to integrate the protein interactome, patient exomes, and single-cell transcriptomes of the developing heart. We identified a CHD network regulating heart development and observed that a sub-network also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHDs and neurodevelopmental conditions. At a small scale, we experimentally verified uncharacterized cardiac functions of several proteins. At a global scale, our study revealed developmental dynamics of the network and observed its association with the hypoplastic left heart syndrome (HLHS), which was further supported by the dysregulation of the network in HLHS endothelial cells. Overall, our work identified previously uncharacterized CHD factors and provided a generalizable framework applicable to studying many other complex diseases. A record of this paper's Transparent Peer Review process is included in the supplemental information.

As our closest living relatives, non-human primates uniquely enable explorations of human health, disease, development, and evolution. Considerable effort has thus been devoted to generating induced pluripotent stem cells (iPSCs) from multiple non-human primate species. Here, we establish improved culture methods for chimpanzee (Pan troglodytes) and pig-tailed macaque (Macaca nemestrina) iPSCs. Such iPSCs spontaneously differentiate in conventional culture conditions, but can be readily propagated by inhibiting endogenous WNT signaling. As a unique functional test of these iPSCs, we injected them into the pre-implantation embryos of another non-human species, rhesus macaques (Macaca mulatta). Ectopic expression of gene BCL2 enhances the survival and proliferation of chimpanzee and pig-tailed macaque iPSCs within the pre-implantation embryo, although the identity and long-term contribution of the transplanted cells warrants further investigation. In summary, we disclose transcriptomic and proteomic data, cell lines, and cell culture resources that may be broadly enabling for non-human primate iPSCs research.

AbstractCongenital heart diseases (CHD) are a class of birth defects affecting ∼1% of live births. These conditions are hallmarked by extreme genetic heterogeneity, and therefore, despite a strong genetic component, only a very handful of at-risk loci in CHD have been identified. We herein introduced systems analyses to uncover the hidden organization on biological networks of genomic mutations in CHD, and leveraged network analysis techniques to integrate the human interactome, large-scale patient exomes, the fetal heart spatial transcriptomes, and single-cell transcriptomes of clinical samples. We identified a highly connected network in CHD where most of the member proteins had previously uncharacterized functions in regulating fetal heart development. While genes on the network displayed strong enrichment for heart-specific functions, a sub-group, active specifically at early developmental stages, also regulates fetal brain development, thereby providing mechanistic insights into the clinical comorbidities between CHD and neurodevelopmental conditions. At a small scale, we experimentally verified previously uncharacterized cardiac functions of several novel proteins employing cellular assays and gene editing techniques. At a global scale, our study revealed developmental dynamics of the identified CHD network and observed the strongest enrichment for pathogenic mutations in the network specific to hypoplastic left heart syndrome (HLHS). Our single-cell transcriptome analysis further identified pervasive dysregulation of the network in cardiac endothelial cells and the conduction system in the HLHS left ventricle. Taken together, our systems analyses identified novel factors in CHD, revealed key molecular mechanisms in HLHS, and provides a generalizable framework readily applicable to studying many other complex diseases.

Exercise confers protection against obesity, type 2 diabetes and other cardiometabolic diseases1-5. However, the molecular and cellular mechanisms that mediate the metabolic benefits of physical activity remain unclear6. Here we show that exercise stimulates the production of N-lactoyl-phenylalanine (Lac-Phe), a blood-borne signalling metabolite that suppresses feeding and obesity. The biosynthesis of Lac-Phe from lactate and phenylalanine occurs in CNDP2+ cells, including macrophages, monocytes and other immune and epithelial cells localized to diverse organs. In diet-induced obese mice, pharmacological-mediated increases in Lac-Phe reduces food intake without affecting movement or energy expenditure. Chronic administration of Lac-Phe decreases adiposity and body weight and improves glucose homeostasis. Conversely, genetic ablation of Lac-Phe biosynthesis in mice increases food intake and obesity following exercise training. Last, large activity-inducible increases in circulating Lac-Phe are also observed in humans and racehorses, establishing this metabolite as a molecular effector associated with physical activity across multiple activity modalities and mammalian species. These data define a conserved exercise-inducible metabolite that controls food intake and influences systemic energy balance.

Psychiatry stands to benefit from brief non-pharmacological treatments that effectively reduce depressive symptoms. To address this need, we conducted a single-blind randomized clinical trial assessing how a 6-day immersive psychosocial training program, followed by 10-min daily psychosocial exercises for 30 days, improves depressive symptoms. Forty-five adults were block-randomized by depression score to two arms: (a) the immersive psychosocial training program and 10-min daily exercise group (36 days total; total n = 23; depressed at baseline n = 14); or (b) a gratitude journaling control group (36 days total; total n = 22; depressed at baseline n = 13). The self-report PHQ-9 was used to assess depression levels in both groups at three time points: baseline, study week one, and study week six. Depression severity improved over time, with a significantly greater reduction in the psychosocial training program group (-82.7%) vs. the control group (-23%), p = 0.02 for baseline vs. week six. The effect size for this reduction in depression symptoms was large for the intervention group (d = -1.3; 95% CI, -2.07, -0.45; p

RNA polymerase III (Pol III) includes two alternate isoforms, defined by mutually exclusive incorporation of subunit POLR3G (RPC7α) or POLR3GL (RPC7β), in mammals. The contributions of POLR3G and POLR3GL to transcription potential has remained poorly defined. Here, we discover that loss of subunit POLR3G is accompanied by a restricted repertoire of genes transcribed by Pol III. Particularly sensitive is snaR-A, a small noncoding RNA implicated in cancer proliferation and metastasis. Analysis of Pol III isoform biases and downstream chromatin features identifies loss of POLR3G and snaR-A during differentiation, and conversely, re-establishment of POLR3G gene expression and SNAR-A gene features in cancer contexts. Our results support a model in which Pol III identity functions as an important transcriptional regulatory mechanism. Upregulation of POLR3G, which is driven by MYC, identifies a subgroup of patients with unfavorable survival outcomes in specific cancers, further implicating the POLR3G-enhanced transcription repertoire as a potential disease factor.

In this Article, the authors Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) were mistakenly omitted from the ENCODE Project Consortium author list. The original Article has been corrected online.

In the version of this article initially published, two members of the ENCODE Project Consortium were missing from the author list. Rizi Ai (Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA) and Shantao Li (Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA) are now included in the author list. These errors have been corrected in the online version of the article.

Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.