NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health

Developing transcriptomic signatures as a biomarker of cellular senescence

Cellular senescence is a cell fate driven by different types of stress, where damaged cells exit from the cell cycle and, in many cases, develop an inflammatory senescence-associated secretory phenotype (SASP). Senescence has often been linked to driving aging and the onset of multiple diseases conferred by the harmful SASP, which disrupts tissue homeostasis and impairs the regular function of many tissues. This phenomenon was first observed in vitro when fibroblasts halted replication after approximately 50 population doublings. In addition to replication-induced senescence, factors such as DNA damage and oncogene activation can induce cellular senescence both in culture and in vivo. Despite their contribution to aging and disease, identifying senescent cells in vivo has been challenging due to their heterogeneity. Although senescent cells can express the cell cycle inhibitors p16Ink4a and/or p21Cip1 and exhibit SA-ß-gal activity and evidence of a DNA damage response, there is no universal biomarker for these cells, regardless of inducer or cell type. Recent studies have analyzed the transcriptomic characteristics of these cells, leading to the identification of signature gene sets like CellAge, SeneQuest, and SenMayo. Advancements in single-cell and spatial RNA sequencing now allow for analyzing senescent cell heterogeneity within the same tissue and the development of machine learning algorithms, e.g., SenPred, SenSig, and SenCID, to discover cellular senescence using RNA sequencing data. Such insights not only deepen our understanding of the genetic pathways driving cellular senescence, but also promote the development of its quantifiable biomarkers. This review summarizes the current knowledge of transcriptomic signatures of cellular senescence and their potential as in vivo biomarkers.

Spatial top-down proteomics for the functional characterization of human kidney

Background

The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging.

Results

Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions.

Conclusions

We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.

Race and Ethnicity, Socioeconomic Factors, and Epigenetic Age Acceleration in Survivors of Childhood Cancer

Importance

Current research in epigenetic age acceleration (EAA) is limited to non-Hispanic White individuals. It is imperative to improve inclusivity by considering racial and ethnic minorities in EAA research.

Objective

To compare non-Hispanic Black with non-Hispanic White survivors of childhood cancer by examining the associations of EAA with cancer treatment exposures, potential racial and ethnic disparity in EAA, and mediating roles of social determinants of health (SDOH).

Design, Setting, and Participants

In this cross-sectional study, participants were from the St Jude Lifetime Cohort, which was initiated in 2007 with ongoing follow-up. Eligible participants included non-Hispanic Black and non-Hispanic White survivors of childhood cancer treated at St Jude Children's Research Hospital between 1962 and 2012 who had DNA methylation data. Data analysis was conducted from February 2023 to May 2024.

Exposure

Three treatment exposures for childhood cancer (chest radiotherapy, alkylating agents, and epipodophyllotoxin).

Main Outcomes and Measures

DNA methylation was generated from peripheral blood mononuclear cell-derived DNA. EAA was calculated as residuals from regressing Levine or Horvath epigenetic age on chronological age. SDOH included educational attainment, annual personal income, and the socioeconomic area deprivation index (ADI). General linear models evaluated cross-sectional associations of EAA with race and ethnicity (non-Hispanic Black and non-Hispanic White) and/or SDOH, adjusting for sex, body mass index, smoking, and cancer treatments. Adjusted least square means (ALSM) of EAA were calculated for group comparisons. Mediation analysis treated SDOH as mediators with average causal mediation effect (ACME) calculated for the association of EAA with race and ethnicity.

Results

Among a total of 1706 survivors including 230 non-Hispanic Black survivors (median [IQR] age at diagnosis, 9.5 [4.3-14.3] years; 103 male [44.8%] and 127 female [55.2%]) and 1476 non-Hispanic White survivors (median [IQR] age at diagnosis, 9.3 [3.9-14.6] years; 766 male [51.9%] and 710 female [48.1%]), EAA was significantly greater among non-Hispanic Black survivors (ALSM = 1.41; 95% CI, 0.66 to 2.16) than non-Hispanic White survivors (ALSM = 0.47; 95% CI, 0.12 to 0.81). Among non-Hispanic Black survivors, EAA was significantly increased among those exposed to chest radiotherapy (ALSM = 2.82; 95% CI, 1.37 to 4.26) vs those unexposed (ALSM = 0.46; 95% CI, -0.60 to 1.51), among those exposed to alkylating agents (ALSM = 2.33; 95% CI, 1.21 to 3.45) vs those unexposed (ALSM = 0.95; 95% CI, -0.38 to 2.27), and among those exposed to epipodophyllotoxins (ALSM = 2.83; 95% CI, 1.27 to 4.40) vs those unexposed (ALSM = 0.44; 95% CI, -0.52 to 1.40). The association of EAA with epipodophyllotoxins differed by race and ethnicity (β for non-Hispanic Black survivors, 2.39 years; 95% CI, 0.74 to 4.04 years; β for non-Hispanic White survivors, 0.68; 95% CI, 0.05 to 1.31 years) and the difference was significant (1.77 years; 95% CI, 0.01 to 3.53 years; P for interaction = .049). Racial and ethnic disparities in EAA were mediated by educational attainment (

Conclusions and Relevance

In this cross-sectional study of childhood cancer survivors, race and ethnicity moderated the association of EAA with epipodophyllotoxin exposure and racial and ethnic differences in EAA were partially mediated by educational attainment and ADI, indicating differential treatment toxic effects by race and ethnicity. These findings suggest that improving social support systems may mitigate socioeconomic disadvantages associated with even greater accelerated aging and reduce health disparities among childhood cancer survivors.

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Key Words Collapse

MESH Headings

• Humans
• Female
• Male
• Cross-Sectional Studies
• Cancer Survivors/statistics & numerical data
• Child
• Socioeconomic Factors
• Epigenesis, Genetic
• Neoplasms/genetics
• Neoplasms/ethnology
• Adolescent
• White People/statistics & numerical data
• White People/genetics
• Black or African American/statistics & numerical data
• Black or African American/genetics
• DNA Methylation
• Adult
• Ethnicity/statistics & numerical data
• Social Determinants of Health/statistics & numerical data

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Grants

• P30 CA021765 NCI NIH HHS
• U01 CA195547 NCI NIH HHS
• U24 CA055727 NCI NIH HHS

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Affiliation(s)

Cheng Chen The Fourth Affiliated Hospital of Soochow University, SuZhou, Jiangsu, China State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Noel-Marie Plonski Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Qian Dong Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Nan Song College of Pharmacy, Chungbuk National University, Cheongju, Korea
Xijun Zhang Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Hemang M. Parikh Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa
Emily R. Finch Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
John Easton Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee
Heather L. Mulder Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee
Emily Walker Hartwell Center, St Jude Children’s Research Hospital, Memphis, Tennessee
Geoffrey Neale Hartwell Center, St Jude Children’s Research Hospital, Memphis, Tennessee
Yue Pan Department of Biostatistics, St Jude Children’s Research Hospital, Memphis, Tennessee
Qian Li Department of Biostatistics, St Jude Children’s Research Hospital, Memphis, Tennessee
Jinghui Zhang Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee
Kevin Krull Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee Department of Psychology, St Jude Children’s Research Hospital, Memphis, Tennessee
Leslie L. Robison Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Gregory T. Armstrong Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Yutaka Yasui Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Kirsten K. Ness Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Melissa M. Hudson Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee Department of Oncology, St Jude Children’s Research Hospital, Memphis, Tennessee
Hui Wang State Key Laboratory of Systems Medicine for Cancer, Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
I-Chan Huang Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee
Zhaoming Wang Department of Epidemiology and Cancer Control, St Jude Children’s Research Hospital, Memphis, Tennessee Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, Tennessee

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Effect of Cellular Senescence in Disease Progression and Transplantation: Immune Cells and Solid Organs

Aging of the world population significantly impacts healthcare globally and specifically, the field of transplantation. Together with end-organ dysfunction and prolonged immunosuppression, age increases the frequency of comorbid chronic diseases in transplant candidates and recipients, contributing to inferior outcomes. Although the frequency of death increases with age, limited use of organs from older deceased donors reflects the concerns about organ durability and inadequate function. Cellular senescence (CS) is a hallmark of aging, which occurs in response to a myriad of cellular stressors, leading to activation of signaling cascades that stably arrest cell cycle progression to prevent tumorigenesis. In aging and chronic conditions, senescent cells accumulate as the immune system's ability to clear them wanes, which is causally implicated in the progression of chronic diseases, immune dysfunction, organ damage, decreased regenerative capacity, and aging itself. The intimate interplay between senescent cells, their proinflammatory secretome, and immune cells results in a positive feedback loop, propagating chronic sterile inflammation and the spread of CS. Hence, senescent cells in organs from older donors trigger the recipient's alloimmune response, resulting in the increased risk of graft loss. Eliminating senescent cells or attenuating their inflammatory phenotype is a novel, potential therapeutic target to improve transplant outcomes and expand utilization of organs from older donors. This review focuses on the current knowledge about the impact of CS on circulating immune cells in the context of organ damage and disease progression, discusses the impact of CS on abdominal solid organs that are commonly transplanted, and reviews emerging therapies that target CS.

Publication, funding, and experimental data in support of Human Reference Atlas construction and usage

Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.

SenNet recommendations for detecting senescent cells in different tissues

Once considered a tissue culture-specific phenomenon, cellular senescence has now been linked to various biological processes with both beneficial and detrimental roles in humans, rodents and other species. Much of our understanding of senescent cell biology still originates from tissue culture studies, where each cell in the culture is driven to an irreversible cell cycle arrest. By contrast, in tissues, these cells are relatively rare and difficult to characterize, and it is now established that fully differentiated, postmitotic cells can also acquire a senescence phenotype. The SenNet Biomarkers Working Group was formed to provide recommendations for the use of cellular senescence markers to identify and characterize senescent cells in tissues. Here, we provide recommendations for detecting senescent cells in different tissues based on a comprehensive analysis of existing literature reporting senescence markers in 14 tissues in mice and humans. We discuss some of the recent advances in detecting and characterizing cellular senescence, including molecular senescence signatures and morphological features, and the use of circulating markers. We aim for this work to be a valuable resource for both seasoned investigators in senescence-related studies and newcomers to the field.

Spatial transcriptomics in health and disease

The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

Cellular senescence: Neither irreversible nor reversible

Cellular senescence is a critical stress response program implicated in embryonic development, wound healing, aging, and immunity, and it backs up apoptosis as an ultimate cell-cycle exit mechanism. In analogy to replicative exhaustion of telomere-eroded cells, premature types of senescence-referring to oncogene-, therapy-, or virus-induced senescence-are widely considered irreversible growth arrest states as well. We discuss here that entry into full-featured senescence is not necessarily a permanent endpoint, but dependent on essential maintenance components, potentially transient. Unlike a binary state switch, we view senescence with its extensive epigenomic reorganization, profound cytomorphological remodeling, and distinctive metabolic rewiring rather as a journey toward a full-featured arrest condition of variable strength and depth. Senescence-underlying maintenance-essential molecular mechanisms may allow cell-cycle reentry if not continuously provided. Importantly, senescent cells that resumed proliferation fundamentally differ from those that never entered senescence, and hence would not reflect a reversion but a dynamic progression to a post-senescent state that comes with distinct functional and clinically relevant ramifications.

Telomeres, cellular senescence, and aging: past and future

Over half a century has passed since Alexey Olovnikov's groundbreaking proposal of the end-replication problem in 1971, laying the foundation for our understanding of telomeres and their pivotal role in cellular senescence. This review paper delves into the intricate and multifaceted relationship between cellular senescence, the influence of telomeres in this process, and the far-reaching consequences of telomeres in the context of aging and age-related diseases. Additionally, the paper investigates the various factors that can influence telomere shortening beyond the confines of the end-replication problem and how telomeres can exert their impact on aging, even in the absence of significant shortening. Ultimately, this paper stands as a tribute to the pioneering work of Olovnikov, whose seminal contributions established the solid foundation upon which our ongoing explorations of telomeres and the aging process are based.

Proteomic Analysis of the Senescence-Associated Secretory Phenotype: GDF-15, IGFBP-2, and Cystatin-C Are Associated With Multiple Aging Traits

Cellular senescence, a hallmark of aging, results in a senescence-associated secretory phenotype (SASP) with an increased production of proinflammatory cytokines, growth factors, and proteases. Evidence from nonhuman models demonstrates that SASP contributes to tissue dysfunction and pathological effects of aging. However, there are relatively few human studies on the relationship between SASP and aging-related health outcomes. Proteins from the SASP Atlas were measured in plasma using aptamer-based proteomics (SomaLogic). Regression models were used to identify SASP protein associations with aging-related traits representing multiple aspects of physiology in 1 201 participants from 2 human cohort studies (BLSA/GESTALT and InCHIANTI). Traits examined were fasting glucose, C-reactive protein, interleukin-6, alkaline phosphatase, blood urea nitrogen, albumin, red blood cell distribution width, waist circumference, systolic and diastolic blood pressure, gait speed, and grip strength. Study results were combined with a fixed-effect inverse-variance weighted meta-analysis. In the meta-analysis, 28 of 77 SASP proteins were significantly associated with age. Of the 28 age-associated SASP proteins, 18 were significantly associated with 1 or more clinical traits, and 7 SASP proteins were significantly associated with 3 or more traits. Growth/differentiation factor 15, Insulin-like growth factor-binding protein 2, and Cystatin-C showed significant associations with inflammatory markers and measures of physical function (grip strength or gait speed). These results support the relevance of SASP proteins to human aging, identify specific traits that are potentially affected by SASP, and prioritize specific SASP proteins for their utility as biomarkers of human aging.

Lessons from inducible pluripotent stem cell models on neuronal senescence in aging and neurodegeneration

Age remains the central risk factor for many neurodegenerative diseases including Parkinson's disease, Alzheimer's disease and amyotrophic lateral sclerosis. Although the mechanisms of aging are complex, the age-related accumulation of senescent cells in neurodegeneration is well documented and their clearance can alleviate disease-related features in preclinical models. Senescence-like characteristics are observed in both neuronal and glial lineages, but their relative contribution to aging and neurodegeneration remains unclear. Human pluripotent stem cell-derived neurons provide an experimental model system to induce neuronal senescence. However, the extensive heterogeneity in the profile of senescent neurons and the methods to assess senescence remain major challenges. Here, we review the evidence of cellular senescence in neuronal aging and disease, discuss human pluripotent stem cell-based model systems used to investigate neuronal senescence and propose a panel of cellular and molecular hallmarks to characterize senescent neurons. Understanding the role of neuronal senescence may yield novel therapeutic opportunities in neurodegenerative disease.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Senescent adipocytes and type 2 diabetes - current knowledge and perspective concepts

Among civilization diseases, the number of individuals suffering from type 2 diabetes (T2DM) is expected to increase to more than a billion in less than 20 years, which is associated with, e.g., populational aging, poor diet, sedentary lifestyle, genetic predispositions, and immunological factors. T2DM affects many organs and is characterized by insulin resistance, high glucose levels, and adipocyte dysfunction, which are related to senescence. Although this type of cellular aging has beneficial biological functions, it can also act unfavorable since senescent adipocytes resist apoptosis, enhance cytokine secretion, downregulate cell identity genes, and acquire the senescence-associated secretory phenotype that renders a more oxidative environment. Opposing T2DM is possible via a wide variety of senotherapies, including senolytics and senomorphics; nevertheless, further research is advised to expand therapeutic possibilities and benefits. Consequences that ought to be deeply researched include secretory phenotype, chronic inflammation, increasing insulin resistance, as well as impairment of adipogenesis and functioning of adipocyte cells. Herein, despite reviewing T2DM and fat tissue senescence, we summarized the latest adipocyte-related anti-diabetes solutions and suggested further research directions.

scResolve: Recovering single cell expression profiles from multi-cellular spatial transcriptomics

Many popular spatial transcriptomics techniques lack single-cell resolution. Instead, these methods measure the collective gene expression for each location from a mixture of cells, potentially containing multiple cell types. Here, we developed scResolve, a method for recovering single-cell expression profiles from spatial transcriptomics measurements at multi-cellular resolution. scResolve accurately restores expression profiles of individual cells at their locations, which is unattainable from cell type deconvolution. Applications of scResolve on human breast cancer data and human lung disease data demonstrate that scResolve enables cell type-specific differential gene expression analysis between different tissue contexts and accurate identification of rare cell populations. The spatially resolved cellular-level expression profiles obtained through scResolve facilitate more flexible and precise spatial analysis that complements raw multi-cellular level analysis.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

A tissue ubiquitous gene set for cellular senescence

An accurate gene set for cellular senescence is crucial for identifying and studying senescent cells in single-cell RNA-seq datasets. We integrated nine existing senescence gene sets and identified a core senescence gene set comprising four genes: CDKN1A, CDKN2A, IL6, and CDKN2B. We found that these genes are ubiquitously associated with cellular senescence across human and mouse tissues. Using this gene set, we identified cell types enriched with senescent cells and cell-cell communication targets and pathways associated with cellular senescence in human and mouse single-cell datasets.

Deep identifiable modeling of single-cell atlases enables zero-shot query of cellular states

With the emerging single-cell RNA-seq datasets at atlas levels, the potential of a universal model built on existing atlas that can extrapolate to new data remains unclear. A fundamental yet challenging problem for such a model is to identify the underlying biological and batch variations in a zero-shot manner, which is crucial for characterizing scRNA-seq datasets with new biological states. In this work, we present scShift, a mechanistic model that learns batch and biological patterns from atlas-level scRNA-seq data as well as perturbation scRNA-seq data. scShift models genes as functions of latent biological processes, with sparse shifts induced by batch effects and biological perturbations, leveraging recent advances of causal representation learning. Through benchmarking in holdout real datasets, we show scShift reveals unified cell type representations as well as underlying biological variations for query data in zero-shot manners, outperforming widely-used atlas integration, batch correction, and perturbation modeling approaches. scShift enables mapping of gene expression profiles to perturbation labels, and predicts meaningful targets for exhausted T cells as well as a list of diseases in the CellxGene blood atlas.

Senescence and Inflammation: Summary of a Gerontological Society of America and National Institute on Aging-Sponsored Symposium

The National Institute on Aging sponsored a symposium at the Gerontological Society of America (GSA) annual meeting in Indianapolis, Indiana, to discuss recent discoveries related to senescent and inflammatory mechanisms in aging and disease. Consistent with the 2022 Biological Sciences GSA program led by Dr. Rozalyn Anderson, the symposium featured early-stage investigators and a leader in the field of geroscience research. Cell senescence and immune interactions coordinate homeostatic and protective programming throughout the life span. Dysfunctional communication in this exchange eventuates in inflammation-related compositional changes in aged tissues, including propagation of the senescence-associated secretory phenotype and accumulation of senescent and exhausted immune cells. Presentations in this symposium explored senescent and immune-related dysfunction in aging from diverse viewpoints and featured emerging cellular and molecular methods. A central takeaway from the event was that the use of new models and approaches, including single-cell -omics, novel mouse models, and 3D culture systems, is revealing dynamic properties and interactions of senescent and immune cell fates. This knowledge is critical for devising new therapeutic approaches with important translational relevance.

Pancreatic β-cell senescence in diabetes: mechanisms, markers and therapies

Cellular senescence is a response to a wide variety of stressors, including DNA damage, oncogene activation and physiologic aging, and pathologically accelerated senescence contributes to human disease, including diabetes mellitus. Indeed, recent work in this field has demonstrated a role for pancreatic β-cell senescence in the pathogenesis of Type 1 Diabetes, Type 2 Diabetes and monogenic diabetes. Small molecule or genetic targeting of senescent β-cells has shown promise as a novel therapeutic approach for preventing and treating diabetes. Despite these advances, major questions remain around the molecular mechanisms driving senescence in the β-cell, identification of molecular markers that distinguish senescent from non-senescent β-cell subpopulations, and translation of proof-of-concept therapies into novel treatments for diabetes in humans. Here, we summarize the current state of the field of β-cell senescence, highlighting insights from mouse models as well as studies on human islets and β-cells. We identify markers that have been used to detect β-cell senescence to unify future research efforts in this field. We discuss emerging concepts of the natural history of senescence in β-cells, heterogeneity of senescent β-cells subpopulations, role of sex differences in senescent responses, and the consequences of senescence on integrated islet function and microenvironment. As a young and developing field, there remain many open research questions which need to be addressed to move senescence-targeted approaches towards clinical investigation.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduced MarsGT: Multi-omics Analysis for Rare population inference using Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperformed existing tools in identifying rare cells across 400 simulated and four real human datasets. In mouse retina data, it revealed unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detected an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identified a rare MAIT-like population impacted by a high IFN-I response and revealed the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Impact of Geroscience on Therapeutic Strategies for Older Adults With Cardiovascular Disease: JACC Scientific Statement

Geroscience posits that cardiovascular disease (CVD) and other chronic diseases result from progressive erosion of the effectiveness of homeostatic mechanisms that oppose age-related accumulation of molecular damage. This hypothetical common root to chronic diseases explains why patients with CVD are often affected by multimorbidity and frailty and why older age negatively affects CVD prognosis and treatment response. Gerotherapeutics enhance resilience mechanisms that counter age-related molecular damage to prevent chronic diseases, frailty, and disability, thereby extending healthspan. Here, we describe the main resilience mechanisms of mammalian aging, with a focus on how they can affect CVD pathophysiology. We next present novel gerotherapeutic approaches, some of which are already used in management of CVD, and explore their potential to transform care and management of CVD. The geroscience paradigm is gaining traction broadly in medical specialties, with potential to mitigate premature aging, reduce health care disparities, and improve population healthspan.

Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)

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

Spatial mapping of cellular senescence: emerging challenges and opportunities

Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.

Regulation of Cellular Senescence in Type 2 Diabetes Mellitus: From Mechanisms to Clinical Applications

Cellular senescence is accelerated by hyperglycemia through multiple pathways. Therefore, senescence is an important cellular mechanism to consider in the pathophysiology of type 2 diabetes mellitus (T2DM) and an additional therapeutic target. The use of drugs that remove senescent cells has led to improvements in blood glucose levels and diabetic complications in animal studies. Although the removal of senescent cells is a promising approach for the treatment of T2DM, two main challenges limit its clinical application: the molecular basis of cellular senescence in each organ is yet to be understood, and the specific effect of removing senescent cells in each organ has to be determined. This review aims to discuss future applications of targeting senescence as a therapeutic option in T2DM and elucidate the characteristics of cellular senescence and senescence-associated secretory phenotype in the tissues important for regulating glucose levels: pancreas, liver, adipocytes, and skeletal muscle.

Increased cell senescence in human metabolic disorders

Cell senescence (CS) is at the nexus between aging and associated chronic disorders, and aging increases the burden of CS in all major metabolic tissues. However, CS is also increased in adult obesity, type 2 diabetes (T2D), and nonalcoholic fatty liver disease independent of aging. Senescent tissues are characterized by dysfunctional cells and increased inflammation, and both progenitor cells and mature, fully differentiated and nonproliferating cells are afflicted. Recent studies have shown that hyperinsulinemia and associated insulin resistance (IR) promote CS in both human adipose and liver cells. Similarly, increased CS promotes cellular IR, showing their interdependence. Furthermore, the increased adipose CS in T2D is independent of age, BMI, and degree of hyperinsulinemia, suggesting premature aging. These results suggest that senomorphic/senolytic therapy may become important for treating these common metabolic disorders.

Senolytic and senomorphic interventions to defy senescence-associated mitochondrial dysfunction

The accumulation of senescent cells in the aging individual is associated with an increase in the occurrence of age-associated pathologies that contribute to poor health, frailty, and mortality. The number and type of senescent cells is viewed as a contributor to the body's senescence burden. Cellular models of senescence are based on induction of senescence in cultured cells in the laboratory. One type of senescence is triggered by mitochondrial dysfunction. There are several indications that mitochondria defects contribute to body aging. Senotherapeutics, targeting senescent cells, have been shown to induce their lysis by means of senolytics, or repress expression of their secretome, by means of senomorphics, senostatics or gerosuppressors. An outline of the mechanism of action of various senotherapeutics targeting mitochondria and senescence-associated mitochondria dysfunction will be here addressed. The combination of geroprotective interventions together with senotherapeutics will help to strengthen mitochondrial energy metabolism, biogenesis and turnover, and lengthen the mitochondria healthspan, minimizing one of several molecular pathways contributing to the aging phenotype.

Cellular Senescence, Inflammation, and Cancer in the Gastrointestinal Tract

Due to modern medical advancements, greater proportions of the population will continue to age with longer life spans. Increased life span, however, does not always correlate with improved health span, and may result in an increase in aging-related diseases and disorders. These diseases are often attributed to cellular senescence, in which cells become disengaged from the cell cycle and inert to cell death. These cells are characterized by a proinflammatory secretome. The proinflammatory senescence-associated secretory phenotype, although part of a natural function intended to prevent further DNA damage, creates a microenvironment suited to tumor progression. This microenvironment is most evident in the gastrointestinal tract (GI), where a combination of bacterial infections, senescent cells, and inflammatory proteins can lead to oncogenesis. Thus, it is important to find potential senescence biomarkers as targets of novel therapies for GI diseases and disorders including cancers. However, finding therapeutic targets in the GI microenvironment to reduce the risk of GI tumor onset may also be of value. This review summarizes the effects of cellular senescence on GI aging, inflammation, and cancers, and aims to improve our understanding of these processes with a goal of enhancing future therapy.

Single-cell transcriptomic analysis of skeletal muscle regeneration across mouse lifespan identifies altered stem cell states associated with senescence

Skeletal muscle regeneration is driven by the interaction of myogenic and non-myogenic cells. In aging, regeneration is impaired due to dysfunctions of myogenic and non-myogenic cells, but this is not understood comprehensively. We collected an integrated atlas of 273,923 single-cell transcriptomes from muscles of young, old, and geriatric mice (~5, 20, 26 months-old) at six time-points following myotoxin injury. We identified eight cell types, including T and NK cells and macrophage subtypes, that displayed accelerated or delayed response dynamics between ages. Through pseudotime analysis, we observed myogenic cell states and trajectories specific to old and geriatric ages. To explain these age differences, we assessed cellular senescence by scoring experimentally derived and curated gene-lists. This pointed to an elevation of senescent-like subsets specifically within the self-renewing muscle stem cells in aged muscles. This resource provides a holistic portrait of the altered cellular states underlying skeletal muscle regenerative decline across mouse lifespan.

Breast cancer risk based on a deep learning predictor of senescent cells in normal tissue

Background

The ability to predict future risk of cancer development in non-malignant biopsies is poor. Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumor-promoting microenvironmental mechanism that secretes pro-inflammatory paracrine factors. With most work done in non-human models and the heterogenous nature of senescence the precise role of senescent cells in the development of cancer in humans is not well understood. Further, more than one million non-malignant breast biopsies are taken every year that could be a major source of risk-stratification for women.

Methods

We applied single cell deep learning senescence predictors based on nuclear morphology to histological images of 4,411 H&E-stained breast biopsies from healthy female donors. Senescence was predicted in the epithelial, stromal, and adipocyte compartments using predictor models trained on cells induced to senescence by ionizing radiation (IR), replicative exhaustion (RS), or antimycin A, Atv/R and doxorubicin (AAD) exposures. To benchmark our senescence-based prediction results we generated 5-year Gail scores, the current clinical gold standard for breast cancer risk prediction.

Findings

We found significant differences in adipocyte-specific IR and AAD senescence prediction for the 86 out of 4,411 healthy women who developed breast cancer an average 4.8 years after study entry. Risk models demonstrated that individuals in the upper median of scores for the adipocyte IR model had a higher risk (OR=1.71 [1.10-2.68], p=0.019), while the adipocyte AAD model revealed a reduced risk (OR=0.57 [0.36-0.88], p=0.013). Individuals with both adipocyte risk factors had an OR of 3.32 ([1.68-7.03], p<0.001). Alone, 5-year Gail scores yielded an OR of 2.70 ([1.22-6.54], p=0.019). When combining Gail scores with our adipocyte AAD risk model, we found that individuals with both of these risk predictors had an OR of 4.70 ([2.29-10.90], p<0.001).

Interpretation

Assessment of senescence with deep learning allows considerable prediction of future cancer risk from non-malignant breast biopsies, something that was previously impossible to do. Furthermore, our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols.

Funding

This study was funded by the Novo Nordisk Foundation (#NNF17OC0027812), and by the National Institutes of Health (NIH) Common Fund SenNet program (U54AG075932).

Conundrums of choice of 'normal' kidney tissue for single cell studies

PURPOSE OF REVIEW

Defining molecular changes in key kidney cell types across lifespan and in disease states is essential to understand the pathogenetic basis of disease progression and targeted therapies. Various single cell approaches are being applied to define disease associated molecular signatures. Key considerations include the choice of reference tissue or 'normal' for comparison to diseased human specimens and a benchmark reference atlas. We provide an overview of select single cell technologies, key considerations for experimental design, quality control, choices and challenges associated with assay type and source for reference tissue.

RECENT FINDINGS

Several initiatives including Kidney Precision Medicine Project, Human Biomolecular Molecular Atlas Project, Genitourinary Disease Molecular Anatomy Project, ReBuilding a Kidney consortium, Human Cell Atlas and Chan Zuckerburg Initiative are generating single cell atlases of 'normal' or disease kidney. Different sources of kidney tissue are used as reference. Signatures of injury, resident pathology and procurement associated biological and technical artifacts have been identified in human kidney reference tissue.

SUMMARY

Committing to a particular reference or 'normal' tissue has significant implications in interpretation of data from disease samples or in ageing. Voluntarily donated kidney tissue from healthy individuals is generally unfeasible. Having reference datasets for different types of 'normal' tissue can aid in mitigating the confounds of choice of reference tissue and sampling biases.