Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells. 

Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.

Purpose: Features of cellular senescence have been described in diabetic retinal vasculature. The aim of this study was to investigate how the high glucose microenvironment impacts on the senescence program of retinal endothelial cells. Methods: Human retinal microvascular endothelial cells were cultured under control and high glucose conditions of 5 mM and 25 mM D-glucose, respectively. Isomeric l-glucose was used as the osmotic control. Cells were counted using CASY technology until they reached their Hayflick limit. Senescence-associated β-Galactosidase was used to identify senescent cells. Endothelial cell functionality was evaluated by the clonogenic, 3D tube formation, and barrier formation assays. Cell metabolism was characterized using the Seahorse Bioanalyzer. Gene expression analysis was performed by bulk RNA sequencing. Retinal tissues from db/db and db/+ mice were evaluated for the presence of senescent cells. Publicly available scRNA-sequencing data for retinas from Akimba and control mice was used for gene set enrichment analysis. Results: Long term exposure to 25 mM D-Glucose accelerated the establishment of cellular senescence in human retinal endothelial cells when compared to 5 mM D-glucose and osmotic controls. This was shown from 4 weeks, by a significant slower growth, higher percentages of cells positive for senescence-associated β-galactosidase, an increase in cell size, and lower expression of pRb and HMGB2. These senescence features were associated with decreased clonogenic capacity, diminished tubulogenicity, and impaired barrier function. Long term high glucose-cultured cells exhibited diminished glycolysis, with lower protein expression of GLUT1, GLUT3, and PFKFB3. Transcriptomic analysis, after 4 weeks of culture, identified downregulation of ALDOC, PFKL, and TPI1, in cells cultured with 25 mM D-glucose when compared to controls. The retina from db/db mice showed a significant increase in acellular capillaries associated with a significant decrease in vascular density in the intermediate and deep retinal plexuses, when compared to db/+ mice. Senescent endothelial cells within the db/db retinal vasculature were identified by senescence-associated β-galactosidase staining. Analysis of single cell transcriptomics data for the Akimba mouse retina highlighted an enrichment of senescence and senescence-associated secretory phenotype gene signatures when compared to control mice. Conclusion: A diabetic-like microenvironment of 25 mM D-glucose was sufficient to accelerate the establishment of cellular senescence in human retinal microvascular endothelial cells.

/pdf/long-term-high-glucose-exposure-induces-premature-senescence-2xxsr74r.pdf

Long term high glucose exposure induces premature senescence in retinal endothelial cells

Mammalian aging is characterized by the progressive loss of tissue function and increased risk for disease. Accumulation of senescent cells in aging tissues partly contributes to this decline, and targeted depletion of senescent cells in vivo ameliorates many age-related phenotypes. The fundamental molecular mechanisms responsible for the decline of cellular health and fitness during senescence and aging are largely unknown. In this study, we investigated whether chromatin-mediated loss of transcriptional fidelity, known to contribute to fitness and survival in yeast and worms, also occurs during human cellular senescence and mouse aging. Our findings reveal aberrant transcription initiation inside genes during senescence and aging that co-occurs with changes in the chromatin landscape. Interventions that alter these spurious transcripts have profound consequences on cellular health, primarily affecting intracellular signal transduction pathways. We propose that age-related spurious transcription promotes a noisy transcriptome and degradation of coherent transcriptional networks. 

Spurious intragenic transcription is a feature of mammalian cellular senescence and tissue aging

https://journals.lww.com/nrronline/Fulltext/2023/05000/Molecular_hallmarks_of_long_non_coding_RNAs_in.5.aspx

Molecular hallmarks of long non-coding RNAs in aging and its significant effect on aging-associated diseases

Cellular senescence affects many physiological and pathological processes and is characterized by durable cell cycle arrest, an inflammatory secretory phenotype and metabolic reprogramming. Here, by using dynamic transcriptome and metabolome profiling in human fibroblasts with different subtypes of senescence, we show that a homoeostatic switch that results in glycerol-3-phosphate (G3P) and phosphoethanolamine (pEtN) accumulation links lipid metabolism to the senescence gene expression programme. Mechanistically, p53-dependent glycerol kinase activation and post-translational inactivation of phosphate cytidylyltransferase 2, ethanolamine regulate this metabolic switch, which promotes triglyceride accumulation in lipid droplets and induces the senescence gene expression programme. Conversely, G3P phosphatase and ethanolamine-phosphate phospho-lyase-based scavenging of G3P and pEtN acts in a senomorphic way by reducing G3P and pEtN accumulation. Collectively, our study ties G3P and pEtN accumulation to controlling lipid droplet biogenesis and phospholipid flux in senescent cells, providing a potential therapeutic avenue for targeting senescence and related pathophysiology. 

A homoeostatic switch causing glycerol-3-phosphate and phosphoethanolamine accumulation triggers senescence by rewiring lipid metabolism

The process wherein dividing cells exhaust proliferative capacity and enter into replicative senescence has become a prominent model for cellular aging in vitro. Despite decades of study, this cellular state is not fully understood in culture and even much less so during aging. Here, we revisit Leonard Hayflick’s original observation of replicative senescence in WI-38 human lung fibroblasts equipped with a battery of modern techniques including RNA-seq, single-cell RNA-seq, proteomics, metabolomics, and ATAC-seq. We find evidence that the transition to a senescent state manifests early, increases gradually, and corresponds to a concomitant global increase in DNA accessibility in nucleolar and lamin associated domains. Furthermore, we demonstrate that senescent WI-38 cells acquire a striking resemblance to myofibroblasts in a process similar to the epithelial to mesenchymal transition (EMT) that is regulated by t YAP1/TEAD1 and TGF-β2. Lastly, we show that verteporfin inhibition of YAP1/TEAD1 activity in aged WI-38 cells robustly attenuates this gene expression program.

Novel insights from a multiomics dissection of the Hayflick limit

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Michelle Chan

Author Tools

Chat about Author

Papers

Novel insights from a multiomics dissection of the Hayflick limit