Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns

Single-cell RNA-seq with spike-in cells enables accurate quantification of cell-specific drug effects in pancreatic islets

BACKGROUND

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool to dissect cell-specific effects of drug treatment in complex tissues. This application requires high levels of precision, robustness, and quantitative accuracy-beyond those achievable with existing methods for mainly qualitative single-cell analysis. Here, we establish the use of standardized reference cells as spike-in controls for accurate and robust dissection of single-cell drug responses.

RESULTS

We find that contamination by cell-free RNA can constitute up to 20% of reads in human primary tissue samples, and we show that the ensuing biases can be removed effectively using a novel bioinformatics algorithm. Applying our method to both human and mouse pancreatic islets treated ex vivo, we obtain an accurate and quantitative assessment of cell-specific drug effects on the transcriptome. We observe that FOXO inhibition induces dedifferentiation of both alpha and beta cells, while artemether treatment upregulates insulin and other beta cell marker genes in a subset of alpha cells. In beta cells, dedifferentiation and insulin repression upon artemether treatment occurs predominantly in mouse but not in human samples.

CONCLUSIONS

This new method for quantitative, error-correcting, scRNA-seq data normalization using spike-in reference cells helps clarify complex cell-specific effects of pharmacological perturbations with single-cell resolution and high quantitative accuracy.

Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes

Impaired function of pancreatic islet cells is a major cause of metabolic dysregulation and disease in humans. Despite this, it remains challenging to directly link physiological dysfunction in islet cells to precise changes in gene expression. Here we show that single-cell RNA sequencing combined with electrophysiological measurements of exocytosis and channel activity (patch-seq) can be used to link endocrine physiology and transcriptomes at the single-cell level. We collected 1,369 patch-seq cells from the pancreata of 34 human donors with and without diabetes. An analysis of function and gene expression networks identified a gene set associated with functional heterogeneity in β cells that can be used to predict electrophysiology. We also report transcriptional programs underlying dysfunction in type 2 diabetes and extend this approach to cryopreserved cells from donors with type 1 diabetes, generating a valuable resource for understanding islet cell heterogeneity in health and disease.

A single-cell transcriptomic landscape of primate arterial aging

Our understanding of how aging affects the cellular and molecular components of the vasculature and contributes to cardiovascular diseases is still limited. Here we report a single-cell transcriptomic survey of aortas and coronary arteries in young and old cynomolgus monkeys. Our data define the molecular signatures of specialized arteries and identify eight markers discriminating aortic and coronary vasculatures. Gene network analyses characterize transcriptional landmarks that regulate vascular senility and position FOXO3A, a longevity-associated transcription factor, as a master regulator gene that is downregulated in six subtypes of monkey vascular cells during aging. Targeted inactivation of FOXO3A in human vascular endothelial cells recapitulates the major phenotypic defects observed in aged monkey arteries, verifying FOXO3A loss as a key driver for arterial endothelial aging. Our study provides a critical resource for understanding the principles underlying primate arterial aging and contributes important clues to future treatment of age-associated vascular disorders.

Arterial degeneration, closely associated with cardiovascular diseases, is driven by aging-related vascular cell-specific transcriptomics changes. This study provides a single-cell transcriptomic atlas for senile aortic and coronary arteries and underscores FOXO3A-based the transcriptional network in vasoprotection during aging.

Noise-driven cellular heterogeneity in circadian periodicity

Our findings have revealed a previously unrecognized link between circadian oscillations and intercellular variation and provide experimental evidence that stochastic transcriptional noise contributes significantly to cell-autonomous circadian periodicity. Interestingly, in separate studies, aging and cancer have been associated with increased transcriptional noise and less robust circadian rhythms. Here, we establish a direct association between transcriptional noise and circadian period. These findings may provide additional directions for researchers in the aging and cancer fields. Furthermore, circadian period may also be used as an indicator of variance in heterogeneity research and drug screening for noise control.

Nongenetic cellular heterogeneity is associated with aging and disease. However, the origins of cell-to-cell variability are complex and the individual contributions of different factors to total phenotypic variance are still unclear. Here, we took advantage of clear phenotypic heterogeneity of circadian oscillations in clonal cell populations to investigate the underlying mechanisms of cell-to-cell variability. Using a fully automated tracking and analysis pipeline, we examined circadian period length in thousands of single cells and hundreds of clonal cell lines and found that longer circadian period is associated with increased intercellular heterogeneity. Based on our experimental results, we then estimated the contributions of heritable and nonheritable factors to this variation in circadian period length using a variance partitioning model. We found that nonheritable noise predominantly drives intercellular circadian period variation in clonal cell lines, thereby revealing a previously unrecognized link between circadian oscillations and intercellular heterogeneity. Moreover, administration of a noise-enhancing drug reversibly increased both period length and variance. These findings suggest that circadian period may be used as an indicator of cellular noise and drug screening for noise control.

Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche

The existence of progenitors within pancreatic ducts has been studied for decades, but the hypothesis that they may help regenerate the adult endocrine compartment (chiefly insulin-producing β-cells) remains contentious. Here, we examine the single-cell transcriptome of the human ductal tree. Our data confirm the paradigm-shifting notion that specific lineages, long thought to be cast in stone, are in fact in a state of flux between differentiation stages. In addition to pro-ductal and pro-acinar transcriptomic gradients, our analysis suggests the existence of a third (ducto-endocrine) differentiation axis. Such prediction was experimentally validated by transplanting sorted progenitor-like cells, which revealed their tri-lineage differentiation potential. Our findings further indicate that progenitors might be activated in situ for therapeutic purposes.

We have described multipotent progenitor-like cells within the major pancreatic ducts (MPDs) of the human pancreas. They express PDX1, its surrogate surface marker P2RY1, and the bone morphogenetic protein (BMP) receptor 1A (BMPR1A)/activin-like kinase 3 (ALK3), but not carbonic anhydrase II (CAII). Here we report the single-cell RNA sequencing (scRNA-seq) of ALK3^bright+-sorted ductal cells, a fraction that harbors BMP-responsive progenitor-like cells. Our analysis unveiled the existence of multiple subpopulations along two major axes, one that encompasses a gradient of ductal cell differentiation stages, and another featuring cells with transitional phenotypes toward acinar tissue. A third potential ducto-endocrine axis is revealed upon integration of the ALK3^bright+ dataset with a single-cell whole-pancreas transcriptome. When transplanted into immunodeficient mice, P2RY1⁺/ALK3^bright+ populations (enriched in PDX1⁺/ALK3⁺/CAII⁻ cells) differentiate into all pancreatic lineages, including functional β-cells. This process is accelerated when hosts are treated systemically with an ALK3 agonist. We found PDX1⁺/ALK3⁺/CAII⁻ progenitor-like cells in the MPDs of types 1 and 2 diabetes donors, regardless of the duration of the disease. Our findings open the door to the pharmacological activation of progenitor cells in situ.

Understanding generation and regeneration of pancreatic β cells from a single-cell perspective

Understanding the mechanisms that underlie the generation and regeneration of β cells is crucial for developing treatments for diabetes. However, traditional research methods, which are based on populations of cells, have limitations for defining the precise processes of β-cell differentiation and trans-differentiation, and the associated regulatory mechanisms. The recent development of single-cell technologies has enabled re-examination of these processes at a single-cell resolution to uncover intermediate cell states, cellular heterogeneity and molecular trajectories of cell fate specification. Here, we review recent advances in understanding β-cell generation and regeneration, in vivo and in vitro, from single-cell technologies, which could provide insights for optimization of diabetes therapy strategies.

Single-cell omics in ageing: a young and growing field

Organismal ageing results from interlinked molecular changes in multiple organs over time. The study of ageing at the molecular level is complicated by varying decay characteristics and kinetics-both between and within organs-driven by intrinsic and extracellular factors. Emerging single-cell omics methods allow for molecular and spatial profiling of cells, and probing of regulatory states and cell-fate determination, thus providing promising tools for unravelling the heterogeneous process of ageing and making it amenable to intervention. These new strategies are enabled by advances in genomic, epigenomic and transcriptomic technologies. Combined with methods for proteome and metabolome analysis, single-cell techniques provide multidimensional, integrated data with unprecedented detail and throughput. Here, we provide an overview of the current state, and perspectives on the future, of this emerging field. We discuss how single-cell approaches can advance understanding of mechanisms underlying organismal ageing and aid in the development of interventions for ageing and ageing-associated diseases.

Temporal changes in the gene expression heterogeneity during brain development and aging

Cells in largely non-mitotic tissues such as the brain are prone to stochastic (epi-)genetic alterations that may cause increased variability between cells and individuals over time. Although increased inter-individual heterogeneity in gene expression was previously reported, whether this process starts during development or if it is restricted to the aging period has not yet been studied. The regulatory dynamics and functional significance of putative aging-related heterogeneity are also unknown. Here we address these by a meta-analysis of 19 transcriptome datasets from three independent studies, covering diverse human brain regions. We observed a significant increase in inter-individual heterogeneity during aging (20 + years) compared to postnatal development (0 to 20 years). Increased heterogeneity during aging was consistent among different brain regions at the gene level and associated with lifespan regulation and neuronal functions. Overall, our results show that increased expression heterogeneity is a characteristic of aging human brain, and may influence aging-related changes in brain functions.

Caloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging

Aging causes a functional decline in tissues throughout the body that may be delayed by caloric restriction (CR). However, the cellular profiles and signatures of aging, as well as those ameliorated by CR, remain unclear. Here, we built comprehensive single-cell and single-nucleus transcriptomic atlases across various rat tissues undergoing aging and CR. CR attenuated aging-related changes in cell type composition, gene expression, and core transcriptional regulatory networks. Immune cells were increased during aging, and CR favorably reversed the aging-disturbed immune ecosystem. Computational prediction revealed that the abnormal cell-cell communication patterns observed during aging, including the excessive proinflammatory ligand-receptor interplay, were reversed by CR. Our work provides multi-tissue single-cell transcriptional landscapes associated with aging and CR in a mammal, enhances our understanding of the robustness of CR as a geroprotective intervention, and uncovers how metabolic intervention can act upon the immune system to modify the process of aging.

Multifaceted secretion of htNSC-derived hypothalamic islets induces survival and antidiabetic effect via peripheral implantation in mice

We report that mouse hypothalamic stem/progenitor cells produce multiple pancreatic, gastrointestinal and hypothalamic peptides in addition to exosomes. Through cell sorting and selection according to insulin promoter activity, we generated a subpopulation(s) of these cells which formed 3D spherical structure with combined features of hypothalamic neurospheres and pancreatic islets. Through testing streptozotocin-induced pancreatic islet disruption and fatal diabetes, we found that peripheral implantation of these spheres in mice led to remarkable improvements in general health and survival in addition to a moderate antidiabetic effect, and notably these pro-survival versus metabolic effects were dissociable to a significant extent. Mechanistically, secretion of exosomes by these spheres was essential for enhancing survival while production of insulin was important for the antidiabetic effect. In summary, hypothalamic neural stem/progenitor cells comprise subpopulations with multifaceted secretion, and their derived hypothalamic islets can be implanted peripherally to enhance general health and survival together with an antidiabetic benefit.

The ageing epigenome and its rejuvenation

Ageing is characterized by the functional decline of tissues and organs and the increased risk of ageing-associated disorders. Several 'rejuvenating' interventions have been proposed to delay ageing and the onset of age-associated decline and disease to extend healthspan and lifespan. These interventions include metabolic manipulation, partial reprogramming, heterochronic parabiosis, pharmaceutical administration and senescent cell ablation. As the ageing process is associated with altered epigenetic mechanisms of gene regulation, such as DNA methylation, histone modification and chromatin remodelling, and non-coding RNAs, the manipulation of these mechanisms is central to the effectiveness of age-delaying interventions. This Review discusses the epigenetic changes that occur during ageing and the rapidly increasing knowledge of how these epigenetic mechanisms have an effect on healthspan and lifespan extension, and outlines questions to guide future research on interventions to rejuvenate the epigenome and delay ageing processes.

scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the classification of thousands of cells through transcriptome profiling, wherein accurate cell type identification is critical for mechanistic studies. In most current analysis protocols, cell type-based cluster annotation is manually performed and heavily relies on prior knowledge, resulting in poor replicability of cell type annotation. This study aimed to introduce a single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity (scCATCH, https://github.com/ZJUFanLab/scCATCH). Using three benchmark datasets, the feasibility of evidence-based scoring and tissue-specific cellular annotation strategies were demonstrated by high concordance among cell types, and scCATCH outperformed Seurat, a popular method for marker genes identification, and cell-based annotation methods. Furthermore, scCATCH accurately annotated 67%–100% (average, 83%) clusters in six published scRNA-seq datasets originating from various tissues. The present results show that scCATCH accurately revealed cell identities with high reproducibility, thus potentially providing insights into mechanisms underlying disease pathogenesis and progression.

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Construction of a comprehensive tissue-specific reference database of cell markers

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Paired comparisons to identify potential marker genes for clusters to ensure accuracy

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Evidence-based scoring and annotation for clustered cells from scRNA-seq data

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Accurate and replicable annotation on cell types of clusters without prior knowledge

Single-Cell Transcriptomic Atlas of Primate Ovarian Aging

Molecular mechanisms of ovarian aging and female age-related fertility decline remain unclear. We surveyed the single-cell transcriptomic landscape of ovaries from young and aged non-human primates (NHPs) and identified seven ovarian cell types with distinct gene-expression signatures, including oocyte and six types of ovarian somatic cells. In-depth dissection of gene-expression dynamics of oocytes revealed four subtypes at sequential and stepwise developmental stages. Further analysis of cell-type-specific aging-associated transcriptional changes uncovered the disturbance of antioxidant signaling specific to early-stage oocytes and granulosa cells, indicative of oxidative damage as a crucial factor in ovarian functional decline with age. Additionally, inactivated antioxidative pathways, increased reactive oxygen species, and apoptosis were observed in granulosa cells from aged women. This study provides a comprehensive understanding of the cell-type-specific mechanisms underlying primate ovarian aging at single-cell resolution, revealing new diagnostic biomarkers and potential therapeutic targets for age-related human ovarian disorders.

SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection

To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

Adaptive Landscape Shaped by Core Endogenous Network Coordinates Complex Early Progenitor Fate Commitments in Embryonic Pancreas

The classical development hierarchy of pancreatic cell fate commitments describes that multipotent progenitors (MPs) first bifurcate into tip cells and trunk cells, and then these cells give rise to acinar cells and endocrine/ductal cells separately. However, lineage tracings reveal that pancreatic progenitors are highly heterogeneous in tip and trunk domains in embryonic pancreas. The progenitor fate commitments from multipotency to unipotency during early pancreas development is insufficiently characterized. In pursuing a mechanistic understanding of the complexity in progenitor fate commitments, we construct a core endogenous network for pancreatic lineage decisions based on genetic regulations and quantified its intrinsic dynamic properties using dynamic modeling. The dynamics reveal a developmental landscape with high complexity that has not been clarified. Not only well-characterized pancreatic cells are reproduced, but also previously unrecognized progenitors-tip progenitor (TiP), trunk progenitor (TrP), later endocrine progenitor (LEP), and acinar progenitors (AciP/AciP2) are predicted. Further analyses show that TrP and LEP mediate endocrine lineage maturation, while TiP, AciP, AciP2 and TrP mediate acinar and ductal lineage maturation. The predicted cell fate commitments are validated by analyzing single-cell RNA sequencing (scRNA-seq) data. Significantly, this is the first time that a redefined hierarchy with detailed early pancreatic progenitor fate commitment is obtained.

Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function

Owing to the prevalence and high mortality rates of cardiac diseases, a more detailed characterization of the human heart is necessary; however, this has been largely impeded by the cellular diversity of cardiac tissue and limited access to samples. Here, we show transcriptome profiling of 21,422 single cells-including cardiomyocytes (CMs) and non-CMs (NCMs)-from normal, failed and partially recovered (left ventricular assist device treatment) adult human hearts. Comparative analysis of atrial and ventricular cells revealed pronounced inter- and intracompartmental CM heterogeneity as well as compartment-specific utilization of NCM cell types as major cell-communication hubs. Systematic analysis of cellular compositions and cell-cell interaction networks showed that CM contractility and metabolism are the most prominent aspects that are correlated with changes in heart function. We also uncovered active engagement of NCMs in regulating the behaviour of CMs, exemplified by ACKR1⁺-endothelial cells, injection of which preserved cardiac function after injury. Beyond serving as a rich resource, our study provides insights into cell-type-targeted intervention of heart diseases.

Single-cell analysis reveals different age-related somatic mutation profiles between stem and differentiated cells in human liver

Accumulating somatic mutations have been implicated in age-related cellular degeneration and death. Because of their random nature and low abundance, somatic mutations are difficult to detect except in single cells or clonal cell lineages. Here, we show that in single hepatocytes from human liver, an organ exposed to high levels of genotoxic stress, somatic mutation frequencies are high and increase substantially with age. Considerably lower mutation frequencies were observed in liver stem cells (LSCs) and organoids derived from them. Mutational spectra in hepatocytes showed signatures of oxidative stress that were different in old age and in LSCs. A considerable number of mutations were found in functional parts of the liver genome, suggesting that somatic mutagenesis could causally contribute to the age-related functional decline and increased incidence of disease of human liver. These results underscore the importance of stem cells in maintaining genome sequence integrity in aging somatic tissues.

The somatic mutation landscape of the human body

BACKGROUND

Somatic mutations in healthy tissues contribute to aging, neurodegeneration, and cancer initiation, yet they remain largely uncharacterized.

RESULTS

To gain a better understanding of the genome-wide distribution and functional impact of somatic mutations, we leverage the genomic information contained in the transcriptome to uniformly call somatic mutations from over 7500 tissue samples, representing 36 distinct tissues. This catalog, containing over 280,000 mutations, reveals a wide diversity of tissue-specific mutation profiles associated with gene expression levels and chromatin states. For example, lung samples with low expression of the mismatch-repair gene MLH1 show a mutation signature of deficient mismatch repair. In addition, we find pervasive negative selection acting on missense and nonsense mutations, except for mutations previously observed in cancer samples, which are under positive selection and are highly enriched in many healthy tissues.

CONCLUSIONS

These findings reveal fundamental patterns of tissue-specific somatic evolution and shed light on aging and the earliest stages of tumorigenesis.

Senescent cell turnover slows with age providing an explanation for the Gompertz law

A causal factor in mammalian aging is the accumulation of senescent cells (SnCs). SnCs cause chronic inflammation, and removing SnCs decelerates aging in mice. Despite their importance, turnover rates of SnCs are unknown, and their connection to aging dynamics is unclear. Here we use longitudinal SnC measurements and induction experiments to show that SnCs turn over rapidly in young mice, with a half-life of days, but slow their own removal rate to a half-life of weeks in old mice. This leads to a critical-slowing-down that generates persistent SnC fluctuations. We further demonstrate that a mathematical model, in which death occurs when fluctuating SnCs cross a threshold, quantitatively recapitulates the Gompertz law of mortality in mice and humans. The model can go beyond SnCs to explain the effects of lifespan-modulating interventions in Drosophila and C. elegans, including scaling of survival-curves and rapid effects of dietary shifts on mortality.

One of the underlying causes of aging is the accumulation of senescent cells, but their turnover rates and dynamics during ageing are unknown. Here the authors measure and model senescent cell production and removal and explore implications for mortality.

Demethylation and derepression of genomic retroelements in the skeletal muscles of aged mice

Changes in DNA methylation influence the aging process and contribute to aging phenotypes, but few studies have been conducted on DNA methylation changes in conjunction with skeletal muscle aging. We explored the DNA methylation changes in a variety of retroelement families throughout aging (at 2, 20, and 28 months of age) in murine skeletal muscles by methyl‐binding domain sequencing (MBD‐seq). The two following contrasting patterns were observed among the members of each repeat family in superaged mice: (a) hypermethylation in weakly methylated retroelement copies and (b) hypomethylation in copies with relatively stronger methylation levels, representing a pattern of “regression toward the mean” within a single retroelement family. Interestingly, these patterns depended on the sizes of the copies. While the majority of the elements showed a slight increase in methylation, the larger copies (>5 kb) displayed evident demethylation. All these changes were not observed in T cells. RNA sequencing revealed a global derepression of retroelements during the late phase of aging (between 20 and 28 months of age), which temporally coincided with retroelement demethylation. Following this methylation drift trend of “regression toward the mean,” aging tended to progressively lose the preexisting methylation differences and local patterns in the genomic regions that had been elaborately established during the early period of development.

Studying immune to non-immune cell cross-talk using single-cell technologies

Single-cell RNA-sequencing has uncovered immune heterogeneity, including novel cell types, states and lineages that have expanded our understanding of the immune system as a whole. More recently, studies involving both immune and non-immune cells have demonstrated the importance of immune microenvironment in development, homeostasis and disease. This review focuses on the single-cell studies mapping cell-cell interactions for variety of tissues in development, health and disease. In addition, we address the need to generate a comprehensive interaction map to answer fundamental questions in immunology as well as experimental and computational strategies required for this purpose.

Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging

Aging is a pleiotropic process affecting many aspects of mammalian physiology. Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear. Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types. We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type. Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating. These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.

Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

BACKGROUND

Systematic interrogation of single-nucleotide variants (SNVs) is one of the most promising approaches to delineate the cellular heterogeneity and phylogenetic relationships at the single-cell level. While SNV detection from abundant single-cell RNA sequencing (scRNA-seq) data is applicable and cost-effective in identifying expressed variants, inferring sub-clones, and deciphering genotype-phenotype linkages, there is a lack of computational methods specifically developed for SNV calling in scRNA-seq. Although variant callers for bulk RNA-seq have been sporadically used in scRNA-seq, the performances of different tools have not been assessed.

RESULTS

Here, we perform a systematic comparison of seven tools including SAMtools, the GATK pipeline, CTAT, FreeBayes, MuTect2, Strelka2, and VarScan2, using both simulation and scRNA-seq datasets, and identify multiple elements influencing their performance. While the specificities are generally high, with sensitivities exceeding 90% for most tools when calling homozygous SNVs in high-confident coding regions with sufficient read depths, such sensitivities dramatically decrease when calling SNVs with low read depths, low variant allele frequencies, or in specific genomic contexts. SAMtools shows the highest sensitivity in most cases especially with low supporting reads, despite the relatively low specificity in introns or high-identity regions. Strelka2 shows consistently good performance when sufficient supporting reads are provided, while FreeBayes shows good performance in the cases of high variant allele frequencies.

CONCLUSIONS

We recommend SAMtools, Strelka2, FreeBayes, or CTAT, depending on the specific conditions of usage. Our study provides the first benchmarking to evaluate the performances of different SNV detection tools for scRNA-seq data.

Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians

Exceptionally long-lived people such as supercentenarians tend to spend their entire lives in good health, implying that their immune system remains active to protect against infections and tumors. However, their immunological condition has been largely unexplored. We profiled thousands of circulating immune cells from supercentenarians at single-cell resolution and identified CD4 T cells that have cytotoxic features. This characteristic is very unique to supercentenarians, because generally CD4 T cells have helper, but not cytotoxic, functions under physiological conditions. We further profiled their T cell receptors and revealed that the cytotoxic CD4 T cells were accumulated through clonal expansion. The conversion of helper CD4 T cells to a cytotoxic variety might be an adaptation to the late stage of aging.

Supercentenarians, people who have reached 110 y of age, are a great model of healthy aging. Their characteristics of delayed onset of age-related diseases and compression of morbidity imply that their immune system remains functional. Here we performed single-cell transcriptome analysis of 61,202 peripheral blood mononuclear cells (PBMCs), derived from 7 supercentenarians and 5 younger controls. We identified a marked increase of cytotoxic CD4 T cells (CD4 cytotoxic T lymphocytes [CTLs]) as a signature of supercentenarians. Furthermore, single-cell T cell receptor sequencing of 2 supercentenarians revealed that CD4 CTLs had accumulated through massive clonal expansion, with the most frequent clonotypes accounting for 15 to 35% of the entire CD4 T cell population. The CD4 CTLs exhibited substantial heterogeneity in their degree of cytotoxicity as well as a nearly identical transcriptome to that of CD8 CTLs. This indicates that CD4 CTLs utilize the transcriptional program of the CD8 lineage while retaining CD4 expression. Indeed, CD4 CTLs extracted from supercentenarians produced IFN-γ and TNF-α upon ex vivo stimulation. Our study reveals that supercentenarians have unique characteristics in their circulating lymphocytes, which may represent an essential adaptation to achieve exceptional longevity by sustaining immune responses to infections and diseases.

Somatic mutations - Evolution within the individual

With the rapid advancement of sequencing technologies over the last two decades, it is becoming feasible to detect rare variants from somatic tissue samples. Studying such somatic mutations can provide deep insights into various senescence-related diseases, including cancer, inflammation, and sporadic psychiatric disorders. While it is still a difficult task to identify true somatic mutations, relentless efforts to combine experimental and computational methods have made it possible to obtain reliable data. Furthermore, state-of-the-art machine learning approaches have drastically improved the efficiency and sensitivity of these methods. Meanwhile, we can regard somatic mutations as a counterpart of germline mutations, and it is possible to apply well-formulated mathematical frameworks developed for population genetics and molecular evolution to analyze this 'somatic evolution'. For example, retrospective cell lineage tracing is a promising technique to elucidate the mechanism of pre-diseases using single-cell RNA-sequencing (scRNA-seq) data.

Protective effects of Clec11a in islets against lipotoxicity via modulation of proliferation and lipid metabolism in mice

The lipotoxicity is considered as one of the risk for diabetes. Here we report C-type lectin domain family 11, member A (Clec11a) as a new regulator in islet playing a protective role in lipotoxicity induced dysfunction. Islet transcriptome sequencing was performed using the high-fat diet induced obesity (DIO) mice model. We found a significant decrease of Clec11a expression in islets of DIO mice compared to normal control mice, which was further confirmed by real-time PCR. Immunostaining demonstrated the localization of the Clec11a protein in mouse islets. Administration of recombinant human Clec11a (rClec11a) protein promoted the proliferation of islet cells and rescued the inhibition of fatty acid on cell proliferation, which involved the activation of Erk signaling pathway. We also found that the rClec11a altered the expression of genes involved in lipid metabolism.

RadiationGeneSigDB: a database of oxic and hypoxic radiation response gene signatures and their utility in pre-clinical research

OBJECTIVE

Radiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. In this era of personalized radiation medicine, high-throughput data now provide the means to investigate novel biomarkers of radiation response. Large-scale efforts have identified several radiation response signatures, which poses two challenges, namely, their analytical validity and redundancy of gene signatures.

METHODS

To address these fundamental radiogenomics questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database by gaining an understanding of hypoxia-associated miRNA by applying a penalized multivariate model; by comparing breast cancer oxic signatures in cell line data vs patient data; and by comparing the similarity of head and neck cancer hypoxia signatures at the pathway level in clinical tumour data.

RESULTS

We obtained a set of miRNA highly associated both positively and negatively to the hypoxia gene signatures, across pan-cancer. In addition, we identified moderate correlations between breast cancer oxic signatures in patient data, and significant differences across molecular subtypes. Moreover, we also found that different set of pathways to be enriched using the head and neck hypoxia signatures, although, they are found to be concordant when applied on the patient data.

CONCLUSION

This valuable, curated repertoire of published gene expression signatures provides motivating case studies for how to search for similarities in radiation response for tumours arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel biological hypotheses about the functions of non-coding RNA.

ADVANCES IN KNOWLEDGE

We envision that RadiationSigDB database will help accelerate preclinical radiotherapeutic discovery pipelines in terms of analytical validity of novel biomarkers of radiation response and the need for ensemble approaches to clinical genomic biomarkers.

Ageing affects DNA methylation drift and transcriptional cell-to-cell variability in mouse muscle stem cells

Age-related tissue alterations have been associated with a decline in stem cell number and function. Although increased cell-to-cell variability in transcription or epigenetic marks has been proposed to be a major hallmark of ageing, little is known about the molecular diversity of stem cells during ageing. Here we present a single cell multi-omics study of mouse muscle stem cells, combining single-cell transcriptome and DNA methylome profiling. Aged cells show a global increase of uncoordinated transcriptional heterogeneity biased towards genes regulating cell-niche interactions. We find context-dependent alterations of DNA methylation in aged stem cells. Importantly, promoters with increased methylation heterogeneity are associated with increased transcriptional heterogeneity of the genes they drive. These results indicate that epigenetic drift, by accumulation of stochastic DNA methylation changes in promoters, is associated with the degradation of coherent transcriptional networks during stem cell ageing. Furthermore, our observations also shed light on the mechanisms underlying the DNA methylation clock.

Age-related tissue alterations have been associated with a decline in stem cell number and function. Here the authors report a single cell multi-omics study of mouse muscle stem cells, combining single cell transcriptome and DNA methylome profiling and find that aged cells have a global increase of uncoordinated transcriptional heterogeneity biased towards genes regulating cell-niche interactions.

Single-cell transcriptomic profiling of the aging mouse brain

The mammalian brain is complex, with multiple cell types performing a variety of diverse functions, but exactly how each cell type is affected in aging remains largely unknown. Here we performed a single-cell transcriptomic analysis of young and old mouse brains. We provide comprehensive datasets of aging-related genes, pathways and ligand-receptor interactions in nearly all brain cell types. Our analysis identified gene signatures that vary in a coordinated manner across cell types and gene sets that are regulated in a cell-type specific manner, even at times in opposite directions. These data reveal that aging, rather than inducing a universal program, drives a distinct transcriptional course in each cell population, and they highlight key molecular processes, including ribosome biogenesis, underlying brain aging. Overall, these large-scale datasets (accessible online at https://portals.broadinstitute.org/single_cell/study/aging-mouse-brain ) provide a resource for the neuroscience community that will facilitate additional discoveries directed towards understanding and modifying the aging process.

Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors

Human embryonic stem cells (hESCs) are a potential unlimited source of insulin-producing β cells for diabetes treatment. A greater understanding of how β cells form during embryonic development will improve current hESC differentiation protocols. All pancreatic endocrine cells, including β cells, are derived from Neurog3-expressing endocrine progenitors. This study characterizes the single-cell transcriptomes of 6,905 mouse embryonic day (E) 15.5 and 6,626 E18.5 pancreatic cells isolated from Neurog3-Cre; Rosa26^mT/mG embryos, allowing for enrichment of endocrine progenitors (yellow; tdTomato + EGFP) and endocrine cells (green; EGFP). Using a NEUROG3-2A-eGFP CyT49 hESC reporter line (N5-5), 4,462 hESC-derived GFP+ cells were sequenced. Differential expression analysis revealed enrichment of markers that are consistent with progenitor, endocrine, or previously undescribed cell-state populations. This study characterizes the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors and serves as a resource (https://lynnlab.shinyapps.io/embryonic_pancreas) for improving the formation of functional β-like cells from hESCs.

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Single-cell transcriptome of embryonic mouse pancreas and hESC-derived cells

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Identification of novel cell types during mouse pancreas development

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Pseudotime analysis reveals developmental trajectories of endocrine cell lineage

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hESC-derived endocrine cells resemble immature β cells

Self-assembling manifolds in single-cell RNA sequencing data

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

Challenges in measuring and understanding biological noise

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.

Cellular and epigenetic drivers of stem cell ageing

Adult tissue stem cells have a pivotal role in tissue maintenance and regeneration throughout the lifespan of multicellular organisms. Loss of tissue homeostasis during post-reproductive lifespan is caused, at least in part, by a decline in stem cell function and is associated with an increased incidence of diseases. Hallmarks of ageing include the accumulation of molecular damage, failure of quality control systems, metabolic changes and alterations in epigenome stability. In this Review, we discuss recent evidence in support of a novel concept whereby cell-intrinsic damage that accumulates during ageing and cell-extrinsic changes in ageing stem cell niches and the blood result in modifications of the stem cell epigenome. These cumulative epigenetic alterations in stem cells might be the cause of the deregulation of developmental pathways seen during ageing. In turn, they could confer a selective advantage to mutant and epigenetically drifted stem cells with altered self-renewal and functions, which contribute to the development of ageing-associated organ dysfunction and disease.

Targeting individual cells by barcode in pooled sequence libraries

Transcriptional profiling of thousands of single cells in parallel by RNA-seq is now routine. However, due to reliance on pooled library preparation, targeting analysis to particular cells of interest is difficult. Here, we present a multiplexed PCR method for targeted sequencing of select cells from pooled single-cell sequence libraries. We demonstrated this molecular enrichment method on multiple cell types within pooled single-cell RNA-seq libraries produced from primary human blood cells. We show how molecular enrichment can be combined with FACS to efficiently target ultra-rare cell types, such as the recently identified AXL⁺SIGLEC6⁺ dendritic cell (AS DC) subset, in order to reduce the required sequencing effort to profile single cells by 100-fold. Our results demonstrate that DNA barcodes identifying cells within pooled sequencing libraries can be used as targets to enrich for specific molecules of interest, for example reads from a set of target cells.

CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. We develop CellBIC (single Cell BImodal Clustering) to cluster scRNA-seq data based on modality in the gene expression distribution. Compared with classical bottom-up approaches that rely on a distance metric, CellBIC performs hierarchical clustering in a top-down manner. CellBIC outperformed the bottom-up hierarchical clustering approach and other recently developed clustering algorithms while maintaining the hierarchical structure of cells. Importantly, CellBIC identifies type 2 diabetes and age specific β cell signatures characterized by SIX3 and CDH2, respectively.

Genetic mapping of cell type specificity for complex traits

Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers.

Tissue- and cell type-specific information helps to interpret findings from genome-wide association studies. Here, the authors leverage multiple single cell expression datasets to infer cell type specificity of traits.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Label-free Mass Cytometry for Unveiling Cellular Metabolic Heterogeneity

Comprehensive analysis of single-cell metabolites is critical since differences in cellular chemical compositions give rise to specialized biological functions. Herein, we propose a label-free mass cytometry by coupling flow cytometry to ESI-MS (named CyESI-MS) for high-coverage and high-throughput detection of cellular metabolites. Cells in suspension were isolated, online extracted by sheath fluid, and lysed during gas-assisted electrospray, followed by real-time MS analysis. Hundreds of metabolites, including nucleotides, amino acids, peptides, carbohydrates, fatty acyls, glycerolipids, glycerophospholipids, and sphingolipids, were detected and identified from one single cell. Discrimination of four types of cancer cell lines and even three subtypes of breast cancer cells was readily achieved using their distinct metabolic profiles. Furthermore, we screened out 102 characteristic ions from 615 detected peak signals for distinguishing breast cancer cell subtypes and identified 40 characteristic molecules which exhibited significant differences among these subtypes and would be potential metabolic markers for clinical diagnosis. CyESI-MS is expected to be a new-generation mass cytometry for studying cell heterogeneity on the metabolic level.

Navigating the Depths and Avoiding the Shallows of Pancreatic Islet Cell Transcriptomes

Islet gene expression has been widely studied to better understand the transcriptional features that define a healthy β-cell. Transcriptomes of FACS-purified α-, β-, and δ-cells using bulk RNA-sequencing have facilitated our understanding of the complex network of cross talk between islet cells and its effects on β-cell function. However, these approaches were by design not intended to resolve heterogeneity between individual cells. Several recent studies used single-cell RNA sequencing (scRNA-Seq) to report considerable heterogeneity within mouse and human β-cells. In this Perspective, we assess how this newfound ability to assess gene expression at single-cell resolution has enhanced our understanding of β-cell heterogeneity. We conduct a comprehensive assessment of several single human β-cell transcriptome data sets and ask if the heterogeneity reported by these studies showed overlap and concurred with previously known examples of β-cell heterogeneity. We also illustrate the impact of the inevitable limitations of working at or below the limit of detection of gene expression at single cell resolution and their consequences for the quality of single-islet cell transcriptome data. Finally, we offer some guidance on when to opt for scRNA-Seq and when bulk sequencing approaches may be better suited.

Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts

Cancer-associated fibroblasts (CAF) are major players in the progression and drug resistance of pancreatic ductal adenocarcinoma (PDAC). CAFs constitute a diverse cell population consisting of several recently described subtypes, although the extent of CAF heterogeneity has remained undefined. Here we use single-cell RNA sequencing to thoroughly characterize the neoplastic and tumor microenvironment content of human and mouse PDAC tumors. We corroborate the presence of myofibroblastic CAFs and inflammatory CAFs and define their unique gene signatures in vivo. Moreover, we describe a new population of CAFs that express MHC class II and CD74, but do not express classic costimulatory molecules. We term this cell population "antigen-presenting CAFs" and find that they activate CD4⁺ T cells in an antigen-specific fashion in a model system, confirming their putative immune-modulatory capacity. Our cross-species analysis paves the way for investigating distinct functions of CAF subtypes in PDAC immunity and progression. SIGNIFICANCE: Appreciating the full spectrum of fibroblast heterogeneity in pancreatic ductal adenocarcinoma is crucial to developing therapies that specifically target tumor-promoting CAFs. This work identifies MHC class II-expressing CAFs with a capacity to present antigens to CD4⁺ T cells, and potentially to modulate the immune response in pancreatic tumors.See related commentary by Belle and DeNardo, p. 1001.This article is highlighted in the In This Issue feature, p. 983.

Integrative analysis of Dupuytren's disease identifies novel risk locus and reveals a shared genetic etiology with BMI

Dupuytren's disease is a common inherited tissue‐specific fibrotic disorder, characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand. Although genome‐wide association study (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytren's disease by integrating the most recent, largest GWAS (3,871 cases and 4,686 controls) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome‐wide association study. We identify 43 tissue‐specific gene associations with Dupuytren's risk, including one in a novel risk region. We also estimate the genome‐wide genetic correlation between Dupuytren's disease and 45 complex traits and find significant genetic correlations between Dupuytren's disease and body mass index (BMI), type II diabetes, triglycerides, and high‐density lipoprotein (HDL), suggesting a shared genetic etiology between these traits. We further examine local genetic correlation to identify 8 and 3 novel regions significantly correlated with BMI and HDL respectively. Our results are consistent with previous epidemiological findings showing that lower BMI increases risk for Dupuytren's disease. These 12 novel risk regions provide new insight into the biological mechanisms of Dupuytren's disease and serve as a starting point for functional validation.

Charting cellular identity during human in vitro β-cell differentiation

In vitro differentiation of human stem cells can produce pancreatic β-cells; the loss of this insulin-secreting cell type underlies type 1 diabetes. Here, as a step towards understanding this differentiation process, we report the transcriptional profiling of more than 100,000 human cells undergoing in vitro β-cell differentiation, and describe the cells that emerged. We resolve populations that correspond to β-cells, α-like poly-hormonal cells, non-endocrine cells that resemble pancreatic exocrine cells and a previously unreported population that resembles enterochromaffin cells. We show that endocrine cells maintain their identity in culture in the absence of exogenous growth factors, and that changes in gene expression associated with in vivo β-cell maturation are recapitulated in vitro. We implement a scalable re-aggregation technique to deplete non-endocrine cells and identify CD49a (also known as ITGA1) as a surface marker of the β-cell population, which allows magnetic sorting to a purity of 80%. Finally, we use a high-resolution sequencing time course to characterize gene-expression dynamics during the induction of human pancreatic endocrine cells, from which we develop a lineage model of in vitro β-cell differentiation. This study provides a perspective on human stem-cell differentiation, and will guide future endeavours that focus on the differentiation of pancreatic islet cells, and their applications in regenerative medicine.

Targeted Elimination of Senescent Beta Cells Prevents Type 1 Diabetes

Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by hyperglycemia due to progressive loss of pancreatic beta cells. Immune-mediated beta cell destruction drives the disease, but whether beta cells actively participate in the pathogenesis remains unclear. Here, we show that during the natural history of T1D in humans and the non-obese diabetic (NOD) mouse model, a subset of beta cells acquires a senescence-associated secretory phenotype (SASP). Senescent beta cells upregulated pro-survival mediator Bcl-2, and treatment of NOD mice with Bcl-2 inhibitors selectively eliminated these cells without altering the abundance of the immune cell types involved in the disease. Significantly, elimination of senescent beta cells halted immune-mediated beta cell destruction and was sufficient to prevent diabetes. Our findings demonstrate that beta cell senescence is a significant component of the pathogenesis of T1D and indicate that clearance of senescent beta cells could be a new therapeutic approach for T1D.

Multi-level remodeling of transcriptional landscapes in aging and longevity

In multi-cellular organisms, the control of gene expression is key not only for development, but also for adult cellular homeostasis, and gene expression has been observed to be deregulated with aging. In this review, we discuss the current knowledge on the transcriptional alterations that have been described to occur with age in metazoans. First, we discuss age-related transcriptional changes in protein-coding genes, the expected functional impact of such changes, and how known pro-longevity interventions impact these changes. Second, we discuss the changes and impact of emerging aspects of transcription in aging, including age-related changes in splicing, lncRNAs and circRNAs. Third, we discuss the changes and potential impact of transcription of transposable elements with aging. Fourth, we highlight small ncRNAs and their potential impact on the regulation of aging phenotypes. Understanding the aging transcriptome will be key to identify important regulatory targets, and ultimately slow-down or reverse aging and extend healthy lifespan in humans.

Insights Into Local Orbital Immunity: Evidence for the Involvement of the Th17 Cell Pathway in Thyroid-Associated Ophthalmopathy

CONTEXT

Unique features of local immunity in thyroid-associated ophthalmopathy (TAO) may affect disease progression.

OBJECTIVE

To investigate the association between the orbital immune microenvironment and TAO development.

DESIGN/SETTING/PARTICIPANTS

TAO and control orbital connective tissues were collected.

MAIN OUTCOME MEASURES

Single-cell sequencing examined orbital lymphocytic infiltrates. Multicolor flow cytometry explored the phenotypes of different cell subsets and in vitro models for cell functional studies. Coculture experiment and western blotting assay were used to determine underlying mechanism of the enhanced T helper 17 (Th17) cell pathway.

RESULTS

The TAO orbital microenvironment was composed of natural killer cells, dendritic cells, macrophages, T cells, plasma cells, and CD34+ orbital fibroblasts, but few B cells. Increases in CD3+CD8- IL-17A-producing and RAR-related orphan receptor (ROR)γt-expressing T cells and in CD3+CD8- IL-13-producing and GATA3-expressing T cells suggested Th17 and Th2 cell responses in TAO orbits. Increased interferon-γ (IFN-γ)-producing and RORγt+Tbet+ T cells indicated a Th1-like phenotype of orbital-infiltrating Th17 cells. Higher IL-23R and IL-1R expression and lower IL-21R expression were also observed on Th17 cells in TAO orbits. Multivariate analyses revealed that the Th17 pathway [IL-17A (P = 0.001), IFN-γ (P = 0.009), RORγt (P = 0.003), IL-23R (P = 0.033), IL-21R (P = 0.019)], and Th2 pathway [IL-13 (P = 0.015), GATA3 (P = 0.012)] were associated with TAO. IL-17A, IL-23R, and IL-1R correlated with clinical activity score and visual acuity. CD34+ orbital fibroblasts exhibited distinct cell surface marker expression and promoted IL-23R and IL-1R expression on T cells to facilitate the Th17-cell phenotype through prostaglandin E2-EP2/EP4-cAMP signaling.

CONCLUSION

Our study addresses the importance of retroorbital immunity and suggests possible means of disrupting TAO pathogenesis.

Single-cell whole-genome sequencing reveals the functional landscape of somatic mutations in B lymphocytes across the human lifespan

Accumulation of mutations in somatic cells has been implicated as a cause of aging since the 1950s. However, attempts to establish a causal relationship between somatic mutations and aging have been constrained by the lack of methods to directly identify mutational events in primary human tissues. Here we provide genome-wide mutation frequencies and spectra of human B lymphocytes from healthy individuals across the entire human lifespan using a highly accurate single-cell whole-genome sequencing method. We found that the number of somatic mutations increases from <500 per cell in newborns to >3,000 per cell in centenarians. We discovered mutational hotspot regions, some of which, as expected, were located at Ig genes associated with somatic hypermutation (SHM). B cell-specific mutation signatures associated with development, aging, or SHM were found. The SHM signature strongly correlated with the signature found in human B cell tumors, indicating that potential cancer-causing events are already present even in B cells of healthy individuals. We also identified multiple mutations in sequence features relevant to cellular function (i.e., transcribed genes and gene regulatory regions). Such mutations increased significantly during aging, but only at approximately one-half the rate of the genome average, indicating selection against mutations that impact B cell function. This full characterization of the landscape of somatic mutations in human B lymphocytes indicates that spontaneous somatic mutations accumulating with age can be deleterious and may contribute to both the increased risk for leukemia and the functional decline of B lymphocytes in the elderly.

Ageing and sources of transcriptional heterogeneity

Cellular heterogeneity is an important contributor to biological function and is employed by cells, tissues and organisms to adapt, compensate, respond, defend and/or regulate specific processes. Research over the last decades has revealed that transcriptional noise is a major driver for cell-to-cell variability. In this review we will discuss sources of transcriptional variability, in particular bursting of gene expression and how it could contribute to cellular states and fate decisions. We will highlight recent developments in single cell sequencing technologies that make it possible to address cellular heterogeneity in unprecedented detail. Finally, we will review recent literature, in which these new technologies are harnessed to address pressing questions in the field of ageing research, such as transcriptional noise and cellular heterogeneity in the course of ageing.