Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity

Immunological biomarkers of aging

The immune system has long been recognized for its critical role in the elimination of pathogens and the development of autoimmune diseases, but recent evidence demonstrates that it also contributes to noncommunicable diseases associated with biological aging processes, such as cancer, cardiovascular disease, neurodegeneration, and frailty. This review examines immunological biomarkers of aging, focusing on how the immune system evolves with age and its impact on health and disease. It discusses the historical development of immunological assessments, technological advancements, and the creation of novel biomarkers and models to study immune aging. We also explore the clinical implications of immune aging, such as increased susceptibility to infectious diseases, poor vaccine responses, and a higher incidence of noncommunicable diseases. In summary, we provide a comprehensive overview of current research, highlight the clinical relevance of immune aging, and identify gaps in knowledge that require further investigation.

Dynamic changes in gene expression through aging in Drosophila melanogaster heads

Work in many systems has shown large-scale changes in gene expression during aging. However, many studies employ just 2 arbitrarily chosen timepoints to measure expression and can only observe an increase or a decrease in expression between "young" and "old" animals, failing to capture any dynamic, nonlinear changes that occur throughout the aging process. We used RNA sequencing to measure expression in male head tissue at 15 timepoints through the lifespan of an inbred Drosophila melanogaster strain. We detected >6,000 significant, age-related genes, nearly all of which have been seen in previous Drosophila aging expression studies and that include several known to harbor lifespan-altering mutations. We grouped our gene set into 28 clusters via their temporal expression change, observing a diversity of trajectories; some clusters show a linear change over time, while others show more complex, nonlinear patterns. Notably, reanalysis of our dataset comparing the earliest and latest timepoints-mimicking a 2-timepoint design-revealed fewer differentially expressed genes (around 4,500). Additionally, those genes exhibiting complex expression trajectories in our multitimepoint analysis were most impacted in this reanalysis; their identification, and the inferred change in gene expression with age, was often dependent on the timepoints chosen. Informed by our trajectory-based clusters, we executed a series of gene enrichment analyses, identifying enriched functions/pathways in all clusters, including the commonly seen increase in stress- and immune-related gene expression with age. Finally, we developed a pair of accessible Shiny apps to enable exploration of our differential expression and gene enrichment results.

Phenotypic upregulation of hexocylceramides and ether-linked phosphocholines as markers of human extreme longevity

Centenarians and their relatives possess a notable survival advantage, with higher longevity and reduced susceptibility to major age-related diseases. To date, characteristic omics profiles of centenarians have been described, demonstrating that these individuals with exceptional longevity regulate their metabolism to adapt and incorporate more resilient biomolecules into their cells. Among these adaptations, the lipidomic profile stands out. However, it has not yet been determined whether this lipidomic profile is specific to centenarians or is the consequence of extreme longevity genetics and is also present in centenarians' offspring. This distinction is crucial for defining potential therapeutic targets that could help delay the aging process and associated pathologies. We applied mass-spectrometry-based techniques to quantify 569 lipid species in plasma samples from 39 centenarians, 63 centenarians' offspring, and 69 noncentenarians' offspring without familial connections. Based on this profile, we calculated different indexes to characterize the functional and structural properties of plasma lipidome. Our findings demonstrate that extreme longevity genetics (centenarians and centenarians' offspring) determines a specific lipidomic signature characterized by (i) an enrichment of hexosylceramides, (ii) a decrease of specific species of ceramides and sulfatides, (iii) a global increase of ether-PC and ether-LPC, and (iv) changes in the fluidity and diversity of specific lipid classes. We point out the conversion of ceramides to hexosylceramides and the maintenance of the levels of the ether-linked PC as a phenotypic trait to guarantee extreme longevity. We propose that this molecular signature is the result of an intrinsic adaptive program that preserves protective mechanisms and cellular identity.

A computational framework for detecting inter-tissue gene-expression coordination changes with aging

Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.

Age-invariant genes: multi-tissue identification and characterization of murine reference genes

Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they can serve as reference genes in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes, and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. Age-invariant genes have shorter transcripts and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, even though hallmarks of aging typically involve change, select genes associated with these hallmarks resist age-related change. Finally, our analysis provides a list of murine tissues where classical reference genes are appropriate for application in aging studies. However, no classical reference gene is appropriate across all aging tissues. Instead, we provide novel tissue-specific and pan-tissue reference genes for assays utilizing gene normalization (RT-qPCR) that can be applied to mice across the lifespan.

Dynamic Changes in Gene Expression Through Aging in Drosophila melanogaster Heads

Work in many systems has shown large-scale changes in gene expression during aging. However, many studies employ just two, arbitrarily-chosen timepoints at which to measure expression, and can only observe an increase or a decrease in expression between "young" and "old" animals, failing to capture any dynamic, non-linear changes that occur throughout the aging process. We used RNA sequencing to measure expression in male head tissue at 15 timepoints through the lifespan of an inbred Drosophila melanogaster strain. We detected >6,000 significant, age-related genes, nearly all of which have been seen in previous fly aging expression studies, and which include several known to harbor lifespan-altering mutations. We grouped our gene set into 28 clusters via their temporal expression change, observing a diversity of trajectories; some clusters show a linear change over time, while others show more complex, non-linear patterns. Notably, re-analysis of our dataset comparing the earliest and latest timepoints - mimicking a two-timepoint design - revealed fewer differentially-expressed genes (around 4,500). Additionally, those genes exhibiting complex expression trajectories in our multi-timepoint analysis were most impacted in this re-analysis; Their identification, and the inferred change in gene expression with age, was often dependent on the timepoints chosen. Informed by our trajectory-based clusters, we executed a series of gene enrichment analyses, identifying enriched functions/pathways in all clusters, including the commonly seen increase in stress- and immune-related gene expression with age. Finally, we developed a pair of accessible shiny apps to enable exploration of our differential expression and gene enrichment results.

Loss of immune cell identity with age inferred from large atlases of single cell transcriptomes

By analyzing two large atlases of almost 4 million cells, we show that immune-senescence involves a gradual loss of cellular identity, reflecting increased cellular heterogeneity, for effector, and cytotoxic immune cells. The effects are largely similar in both males and females and were robustly reproduced in two atlases, one assembled from 35 diverse studies including 678 adults, the other the OneK1K study of 982 adults. Since the mean transcriptional differences among cell-types remain constant across age deciles, there is little evidence for the alternative mechanism of convergence of cell-type identity. Key pathways promoting activation and stemness are down-regulated in aged T cells, while CD8 TEM and CD4 CTLs exhibited elevated inflammatory, and cytotoxicity in older individuals. Elevated inflammatory signaling pathways, such as MAPK and TNF-alpha signaling via NF-kB, also occur across all aged immune cells, particularly amongst effector immune cells. This finding of lost transcriptional identity with age carries several implications, spanning from a fundamental biological understanding of aging mechanisms to clinical perspectives on the efficacy of immunomodulation in elderly people.

A cellular identity crisis? Plasticity changes during aging and rejuvenation

Cellular plasticity in adult multicellular organisms is a protective mechanism that allows certain tissues to regenerate in response to injury. Considering that aging involves exposure to repeated injuries over a lifetime, it is conceivable that cell identity itself is more malleable-and potentially erroneous-with age. In this review, we summarize and critically discuss the available evidence that cells undergo age-related shifts in identity, with an emphasis on those that contribute to age-associated pathologies, including neurodegeneration and cancer. Specifically, we focus on reported instances of programs associated with dedifferentiation, biased differentiation, acquisition of features from alternative lineages, and entry into a preneoplastic state. As some of the most promising approaches to rejuvenate cells reportedly also elicit transient changes to cell identity, we further discuss whether cell state change and rejuvenation can be uncoupled to yield more tractable therapeutic strategies.

A comprehensive map of the aging blood methylome in humans

BACKGROUND

During aging, the human methylome undergoes both differential and variable shifts, accompanied by increased entropy. The distinction between variably methylated positions (VMPs) and differentially methylated positions (DMPs), their contribution to epigenetic age, and the role of cell type heterogeneity remain unclear.

RESULTS

We conduct a comprehensive analysis of > 32,000 human blood methylomes from 56 datasets (age range = 6-101 years). We find a significant proportion of the blood methylome that is differentially methylated with age (48% DMPs; FDR < 0.005) and variably methylated with age (37% VMPs; FDR < 0.005), with considerable overlap between the two groups (59% of DMPs are VMPs). Bivalent and Polycomb regions become increasingly methylated and divergent between individuals, while quiescent regions lose methylation more uniformly. Both chronological and biological clocks, but not pace-of-aging clocks, show a strong enrichment for CpGs undergoing both mean and variance changes during aging. The accumulation of DMPs shifting towards a methylation fraction of 50% drives the increase in entropy, smoothening the epigenetic landscape. However, approximately a quarter of DMPs exhibit anti-entropic effects, opposing this direction of change. While changes in cell type composition minimally affect DMPs, VMPs and entropy measurements are moderately sensitive to such alterations.

CONCLUSION

This study represents the largest investigation to date of genome-wide DNA methylation changes and aging in a single tissue, providing valuable insights into primary molecular changes relevant to chronological and biological aging.

Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level.

A multi-organ map of the human immune system across age, sex and ethnicity

Understanding tissue biology's heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

Age-Invariant Genes: Multi-Tissue Identification and Characterization of Murine Reference Genes

Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they serve as reference genes (often called housekeeping genes) in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan. Here, we build upon a common pipeline for identifying reference genes in RNA-seq datasets to identify age-invariant genes across seventeen C57BL/6 mouse tissues (brain, lung, bone marrow, muscle, white blood cells, heart, small intestine, kidney, liver, pancreas, skin, brown, gonadal, marrow, and subcutaneous adipose tissue) spanning 1 to 21+ months of age. We identify 9 pan-tissue age-invariant genes and many tissue-specific age-invariant genes. These genes are stable across the lifespan and are validated in independent bulk RNA-seq datasets and RT-qPCR. We find age-invariant genes have shorter transcripts on average and are enriched for CpG islands. Interestingly, pathway enrichment analysis for age-invariant genes identifies an overrepresentation of molecular functions associated with some, but not all, hallmarks of aging. Thus, though hallmarks of aging typically involve changes in cell maintenance mechanisms, select genes associated with these hallmarks resist fluctuations in expression with age. Finally, our analysis concludes no classical reference gene is appropriate for aging studies in all tissues. Instead, we provide tissue-specific and pan-tissue genes for assays utilizing reference gene normalization (i.e., RT-qPCR) that can be applied to animals across the lifespan.

Comprehensive transcriptome analysis reveals altered mRNA splicing and post-transcriptional changes in the aged mouse brain

A comprehensive understanding of molecular changes during brain aging is essential to mitigate cognitive decline and delay neurodegenerative diseases. The interpretation of mRNA alterations during brain aging is influenced by the health and age of the animal cohorts studied. Here, we carefully consider these factors and provide an in-depth investigation of mRNA splicing and dynamics in the aging mouse brain, combining short- and long-read sequencing technologies with extensive bioinformatic analyses. Our findings encompass a spectrum of age-related changes, including differences in isoform usage, decreased mRNA dynamics and a module showing increased expression of neuronal genes. Notably, our results indicate a reduced abundance of mRNA isoforms leading to nonsense-mediated RNA decay and suggest a regulatory role for RNA-binding proteins, indicating that their regulation may be altered leading to the reshaping of the aged brain transcriptome. Collectively, our study highlights the importance of studying mRNA splicing events during brain aging.

Time is ticking faster for long genes in aging

Recent studies of aging organisms have identified a systematic phenomenon, characterized by a negative correlation between gene length and their expression in various cell types, species, and diseases. We term this phenomenon gene-length-dependent transcription decline (GLTD) and suggest that it may represent a bottleneck in the transcription machinery and thereby significantly contribute to aging as an etiological factor. We review potential links between GLTD and key aging processes such as DNA damage and explore their potential in identifying disease modification targets. Notably, in Alzheimer's disease, GLTD spotlights extremely long synaptic genes at chromosomal fragile sites (CFSs) and their vulnerability to postmitotic DNA damage. We suggest that GLTD is an integral element of biological aging.

Deciphering the origin of developmental stability: The role of intracellular expression variability in evolutionary conservation

Progress in evolutionary developmental biology (evo-devo) has deepened our understanding of how intrinsic properties of embryogenesis, along with natural selection and population genetics, shape phenotypic diversity. A focal point of recent empirical and theoretical research is the idea that highly developmentally stable phenotypes are more conserved in evolution. Previously, we demonstrated that in Japanese medaka (Oryzias latipes), embryonic stages and genes with high stability, estimated through whole-embryo RNA-seq, are highly conserved in subsequent generations. However, the precise origin of the stability of gene expression levels evaluated at the whole-embryo level remained unclear. Such stability could be attributed to two distinct sources: stable intracellular expression levels or spatially stable expression patterns. Here we demonstrate that stability observed in whole-embryo RNA-seq can be attributed to stability at the cellular level (low variability in gene expression at the cellular levels). We quantified the intercellular variations in expression levels and spatial gene expression patterns for seven key genes involved in patterning dorsoventral and rostrocaudal regions during early development in medaka. We evaluated intracellular variability by counting transcripts and found its significant correlation with variation observed in whole-embryo RNA-seq data. Conversely, variation in spatial gene expression patterns, assessed through intraindividual left-right asymmetry, showed no correlation. Given the previously reported correlation between stability and conservation of expression levels throughout embryogenesis, our findings suggest a potential general trend: the stability or instability of developmental systems-and the consequent evolutionary diversity-may be primarily anchored in intrinsic fundamental elements such as the variability of intracellular states.

Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer

INTRODUCTION

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx.

RESULTS

We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns.

CONCLUSION

We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

Comprehensive single cell aging atlas of mammary tissues reveals shared epigenomic and transcriptomic signatures of aging and cancer

Aging is the greatest risk factor for breast cancer; however, how age-related cellular and molecular events impact cancer initiation is unknown. We investigate how aging rewires transcriptomic and epigenomic programs of mouse mammary glands at single cell resolution, yielding a comprehensive resource for aging and cancer biology. Aged epithelial cells exhibit epigenetic and transcriptional changes in metabolic, pro-inflammatory, or cancer-associated genes. Aged stromal cells downregulate fibroblast marker genes and upregulate markers of senescence and cancer-associated fibroblasts. Among immune cells, distinct T cell subsets (Gzmk+, memory CD4+, γδ) and M2-like macrophages expand with age. Spatial transcriptomics reveal co-localization of aged immune and epithelial cells in situ. Lastly, transcriptional signatures of aging mammary cells are found in human breast tumors, suggesting mechanistic links between aging and cancer. Together, these data uncover that epithelial, immune, and stromal cells shift in proportions and cell identity, potentially impacting cell plasticity, aged microenvironment, and neoplasia risk.

Serum Response Factor Reduces Gene Expression Noise and Confers Cell State Stability

The role of serum response factor (Srf), a central mediator of actin dynamics and mechanical signaling, in cell identity regulation is debated to be either a stabilizer or a destabilizer. We investigated the role of Srf in cell fate stability using mouse pluripotent stem cells. Despite the fact that serum-containing cultures yield heterogeneous gene expression, deletion of Srf in mouse pluripotent stem cells leads to further exacerbated cell state heterogeneity. The exaggerated heterogeneity is detectible not only as increased lineage priming but also as the developmentally earlier 2C-like cell state. Thus, pluripotent cells explore more variety of cellular states in both directions of development surrounding naïve pluripotency, a behavior that is constrained by Srf. These results support that Srf functions as a cell state stabilizer, providing rationale for its functional modulation in cell fate intervention and engineering.

Identifying novel age-modulating compounds and quantifying cellular aging using novel computational framework for evaluating transcriptional age

The differentiation of human pluripotent stem cells (hPSCs) provides access to most cell types and tissues. However, hPSC-derived lineages capture a fetal-stage of development and methods to accelerate progression to an aged identity are limited. Understanding the factors driving cellular age and rejuvenation is also essential for efforts aimed at extending human life and health span. A prerequisite for such studies is the development of methods to score cellular age and simple readouts to assess the relative impact of various age modifying strategies. Here we established a transcriptional score (RNAge) in young versus old primary fibroblasts, frontal cortex and substantia nigra tissue. We validated the score in independent RNA-seq datasets and demonstrated a strong cell and tissue specificity. In fibroblasts we observed a reset of RNAge during iPSC reprogramming while direct reprogramming of aged fibroblasts to induced neurons (iN) resulted in the maintenance of both a neuronal and a fibroblast aging signature. Increased RNAge in hPSC-derived neurons was confirmed for several age-inducing strategies such as SATB1 loss, progerin expression or chemical induction of senescence (SLO). Using RNAge as a probe set, we next performed an in-silico screen using the LINCS L1000 dataset. We identified and validated several novel age-inducing and rejuvenating compounds, and we observed that RNAage captures age-related changes associated with distinct cellular hallmarks of age. Our study presents a simple tool to score age manipulations and identifies compounds that greatly expand the toolset of age-modifying strategies in hPSC derived lineages.

Transcriptomic reprogramming for neuronal age reversal

Aging is a progressive multifaceted functional decline of a biological system. Chronic age-related conditions such as neurodegenerative diseases are leading causes of death worldwide, and they are becoming a pressing problem for our society. To address this global challenge, there is a need for novel, safe, and effective rejuvenation therapies aimed at reversing age-related phenotypes and improving human health. With gene expression being a key determinant of cell identity and function, and in light of recent studies reporting rejuvenation effects through genetic perturbations, we propose an age reversal strategy focused on reprogramming the cell transcriptome to a youthful state. To this end, we suggest using transcriptomic data from primary human cells to predict rejuvenation targets and develop high-throughput aging assays, which can be used in large perturbation screens. We propose neural cells as particularly relevant targets for rejuvenation due to substantial impact of neurodegeneration on human frailty. Of all cell types in the brain, we argue that glutamatergic neurons, neuronal stem cells, and oligodendrocytes represent the most impactful and tractable targets. Lastly, we provide experimental designs for anti-aging reprogramming screens that will likely enable the development of neuronal age reversal therapies, which hold promise for dramatically improving human health.

Convergent alteration of the mesenchymal stem cell heterogeneity in adipose tissue during aging

Adipose-derived stem cells (ASCs) from distinct age groups possess different characteristics; however, the age-associated changes in ASCs heterogenicity remain largely unknown. In this study, several publicly available single-cell RNA sequencing (RNA-seq) data cohorts of inguinal adipose tissues, including young (2 weeks), adult (8 weeks), and old (18 months) C57BL/6 mice, were analyzed. Transcriptomic clustering of integrated single-cell RNA-seq data from different age groups revealed the existence of five ASCs subtypes. Interestingly, ASCs showed a loss of heterogeneity with aging, and ASCs subtype 4 (ASC-4) was the dominant subpopulation accounting for more than 98% of aged ASCs converging to the terminal differentiation state. The multidirectional differentiation potentials of different ASCs subtypes were largely distinct while the adipogenic ability of ASC-4 increased with age persistently. Regulon analysis of ASC subtypes further identified Cebpb as the ASC-4-specific transcription factor, which was known as one of the major adipogenic regulators. Analysis of ligand-receptor pairs between ASCs and other cell types in adipose tissue identified age-associated upregulation of inflammatory responses-associated factors including CCL2 and CCL7. Treatment with 100 ng/mL CCL2 in vitro could significantly promote the adipogenesis of ASCs through enhanced phosphorylation of AKT and decreased expression of β-catenin. In addition, supplementation of 100 ng/mL CCL7 could significantly increase the expression of inflammatory genes and ASC-4-specific transcriptional factors in 2-week-old ASCs, potentially acting as a driver of ASCs convergence. Our findings help to delineate the complex biological processes of ASCs aging and shed light on better regenerative and therapeutic applications of ASCs.

Single-cell landscape analysis reveals systematic senescence in mammalian Down syndrome

BACKGROUND

Down syndrome (DS), which is characterized by various malfunctions, is the most common chromosomal disorder. As the DS population continues to grow and most of those with DS live beyond puberty, early-onset health problems have become apparent. However, the cellular landscape and molecular alterations have not been thoroughly studied.

METHODS

This study utilized single-cell resolution techniques to examine DS in humans and mice, spanning seven distinct organs. A total of 71 934 mouse and 98 207 human cells were analyzed to uncover the molecular alterations occurring in different cell types and organs related to DS, specifically starting from the fetal stage. Additionally, SA-β-Gal staining, western blot, and histological study were employed to verify the alterations.

RESULTS

In this study, we firstly established the transcriptomic profile of the mammalian DS, deciphering the cellular map and molecular mechanism. Our analysis indicated that DS cells across various types and organs experienced senescence stresses from as early as the fetal stage. This was marked by elevated SA-β-Gal activity, overexpression of cell cycle inhibitors, augmented inflammatory responses, and a loss of cellular identity. Furthermore, we found evidence of mitochondrial disturbance, an increase in ribosomal protein transcription, and heightened apoptosis in fetal DS cells. This investigation also unearthed a regulatory network driven by an HSA21 gene, which leads to genome-wide expression changes.

CONCLUSION

The findings from this study offer significant insights into the molecular alterations that occur in DS, shedding light on the pathological processes underlying this disorder. These results can potentially guide future research and treatment development for DS.

Aging Fly Cell Atlas identifies exhaustive aging features at cellular resolution

Aging is characterized by a decline in tissue function, but the underlying changes at cellular resolution across the organism remain unclear. Here, we present the Aging Fly Cell Atlas, a single-nucleus transcriptomic map of the whole aging Drosophila. We characterized 163 distinct cell types and performed an in-depth analysis of changes in tissue cell composition, gene expression, and cell identities. We further developed aging clock models to predict fly age and show that ribosomal gene expression is a conserved predictive factor for age. Combining all aging features, we find distinctive cell type-specific aging patterns. This atlas provides a valuable resource for studying fundamental principles of aging in complex organisms.

Quantile Regression for biomarkers in the UK Biobank

Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. An alternative and easy to apply approach is quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest by modeling conditional quantiles within a regression framework. Quantile regression can be applied efficiently at biobank scale using standard statistical packages in much the same way as linear regression, while having some unique advantages such as identifying variants with heterogeneous effects across different quantiles, including non-additive effects and variants involved in gene-environment interactions; accommodating a wide range of phenotype distributions with invariance to trait transformation; and overall providing more detailed information about the underlying genotype-phenotype associations. Here, we demonstrate the value of quantile regression in the context of GWAS by applying it to 39 quantitative traits in the UK Biobank (n > 300 , 000 individuals). Across these 39 traits we identify 7,297 significant loci, including 259 loci only detected by quantile regression. We show that quantile regression can help uncover replicable but unmodelled gene-environment interactions, and can provide additional key insights into poorly understood genotype-phenotype correlations for clinically relevant biomarkers at minimal additional cost.

The Road Less Traveled: Uncovering the Convergence Toward Specific Pleiotropic Phenotypes in Aging

Aging is a complex process influenced by a wide range of environmental and molecular factors. Despite this complexity, individuals tend to age in highly similar ways, leading to the question of what drives this convergence. Recent research, including my own discoveries, suggests that the length of transcript molecules plays a crucial role in age-dependent changes to the transcriptome. Drawing inspiration from the road trip analogy of cellular transcription, I propose that a non-linear scaling law drives convergence towards specific pleiotropic phenotypes in biological aging. This scaling law is based on the notion that molecular changes observed during aging may reflect unspecific damage to cellular physiology. By validating this hypothesis, I can improve our understanding of biological aging and identify new candidate compounds for anti-aging interventions, as well as re-identify one known intervention. This work has actionable implications for improving human health and extending lifespans.

Pan-cancer transcriptomic analysis identified six classes of immunosenescence genes revealed molecular links between aging, immune system and cancer

Aging is a complex process that significantly impacts the immune system. The aging-related decline of the immune system, termed immunosenescence, can lead to disease development, including cancer. The perturbation of immunosenescence genes may characterize the associations between cancer and aging. However, the systematical characterization of immunosenescence genes in pan-cancer remains largely unexplored. In this study, we comprehensively investigated the expression of immunosenescence genes and their roles in 26 types of cancer. We developed an integrated computational pipeline to identify and characterize immunosenescence genes in cancer based on the expression profiles of immune genes and clinical information of patients. We identified 2218 immunosenescence genes that were significantly dysregulated in a wide variety of cancers. These immunosenescence genes were divided into six categories based on their relationships with aging. Besides, we assessed the importance of immunosenescence genes in clinical prognosis and identified 1327 genes serving as prognostic markers in cancers. BTN3A1, BTN3A2, CTSD, CYTIP, HIF1AN, and RASGRP1 were associated with ICB immunotherapy response and served as prognostic factors after ICB immunotherapy in melanoma. Collectively, our results furthered the understanding of the relationship between immunosenescence and cancer and provided insights into immunotherapy for patients.

Multiregion transcriptomic profiling of the primate brain reveals signatures of aging and the social environment

Aging is accompanied by a host of social and biological changes that correlate with behavior, cognitive health and susceptibility to neurodegenerative disease. To understand trajectories of brain aging in a primate, we generated a multiregion bulk (N = 527 samples) and single-nucleus (N = 24 samples) brain transcriptional dataset encompassing 15 brain regions and both sexes in a unique population of free-ranging, behaviorally phenotyped rhesus macaques. We demonstrate that age-related changes in the level and variance of gene expression occur in genes associated with neural functions and neurological diseases, including Alzheimer's disease. Further, we show that higher social status in females is associated with younger relative transcriptional ages, providing a link between the social environment and aging in the brain. Our findings lend insight into biological mechanisms underlying brain aging in a nonhuman primate model of human behavior, cognition and health.

Aging is associated with a systemic length-associated transcriptome imbalance

Aging is among the most important risk factors for morbidity and mortality. To contribute toward a molecular understanding of aging, we analyzed age-resolved transcriptomic data from multiple studies. Here, we show that transcript length alone explains most transcriptional changes observed with aging in mice and humans. We present three lines of evidence supporting the biological importance of the uncovered transcriptome imbalance. First, in vertebrates the length association primarily displays a lower relative abundance of long transcripts in aging. Second, eight antiaging interventions of the Interventions Testing Program of the National Institute on Aging can counter this length association. Third, we find that in humans and mice the genes with the longest transcripts enrich for genes reported to extend lifespan, whereas those with the shortest transcripts enrich for genes reported to shorten lifespan. Our study opens fundamental questions on aging and the organization of transcriptomes.

Molecular Modelling Hurdle in the Next-Generation Sequencing Era

There are challenges in the genetic diagnosis of rare diseases, and pursuing an optimal strategy to identify the cause of the disease is one of the main objectives of any clinical genomics unit. A range of techniques are currently used to characterize the genomic variability within the human genome to detect causative variants of specific disorders. With the introduction of next-generation sequencing (NGS) in the clinical setting, geneticists can study single-nucleotide variants (SNVs) throughout the entire exome/genome. In turn, the number of variants to be evaluated per patient has increased significantly, and more information has to be processed and analyzed to determine a proper diagnosis. Roughly 50% of patients with a Mendelian genetic disorder are diagnosed using NGS, but a fair number of patients still suffer a diagnostic odyssey. Due to the inherent diversity of the human population, as more exomes or genomes are sequenced, variants of uncertain significance (VUSs) will increase exponentially. Thus, assigning relevance to a VUS (non-synonymous as well as synonymous) in an undiagnosed patient becomes crucial to assess the proper diagnosis. Multiple algorithms have been used to predict how a specific mutation might affect the protein's function, but they are far from accurate enough to be conclusive. In this work, we highlight the difficulties of genomic variability determined by NGS that have arisen in diagnosing rare genetic diseases, and how molecular modelling has to be a key component to elucidate the relevance of a specific mutation in the protein's loss of function or malfunction. We suggest that the creation of a multi-omics data model should improve the classification of pathogenicity for a significant amount of the detected genomic variability. Moreover, we argue how it should be incorporated systematically in the process of variant evaluation to be useful in the clinical setting and the diagnostic pipeline.

Lack of evidence for increased transcriptional noise in aged tissues

Aging is often associated with a loss of cell type identity that results in an increase in transcriptional noise in aged tissues. If this phenomenon reflects a fundamental property of aging remains an open question. Transcriptional changes at the cellular level are best detected by single-cell RNA sequencing (scRNAseq). However, the diverse computational methods used for the quantification of age-related loss of cellular identity have prevented reaching meaningful conclusions by direct comparison of existing scRNAseq datasets. To address these issues we created Decibel, a Python toolkit that implements side-to-side four commonly used methods for the quantification of age-related transcriptional noise in scRNAseq data. Additionally, we developed Scallop, a novel computational method for the quantification of membership of single cells to their assigned cell type cluster. Cells with a greater Scallop membership score are transcriptionally more stable. Application of these computational tools to seven aging datasets showed large variability between tissues and datasets, suggesting that increased transcriptional noise is not a universal hallmark of aging. To understand the source of apparent loss of cell type identity associated with aging, we analyzed cell type-specific changes in transcriptional noise and the changes in cell type composition of the mammalian lung. No robust pattern of cell type-specific transcriptional noise alteration was found across aging lung datasets. In contrast, age-associated changes in cell type composition of the lung were consistently found, particularly of immune cells. These results suggest that claims of increased transcriptional noise of aged tissues should be reformulated.