A new research paper was published in Aging (Aging-US) Volume 15, Issue 8, entitled, “Single-cell transcriptomic analysis uncovers diverse and dynamic senescent cell populations.”

Senescence is a state of enduring growth arrest triggered by sublethal cell damage. Given that senescent cells actively secrete proinflammatory and matrix-remodeling proteins, their accumulation in tissues of older persons has been linked to many diseases of aging. Despite intense interest in identifying robust markers of senescence, the highly heterogeneous and dynamic nature of the senescent phenotype has made this task difficult.

In this new study, researchers Noah Wechter, Martina Rossi, Carlos Anerillas, Dimitrios Tsitsipatis, Yulan Piao, Jinshui Fan, Jennifer L. Martindale, Supriyo De, Krystyna Mazan-Mamczarz, and Myriam Gorospe from the National Institute on Aging set out to comprehensively analyze the senescent transcriptome of human diploid fibroblasts at the individual-cell scale by performing single-cell RNA-sequencing analysis through two approaches. 

“Here, we used single-cell RNA sequencing (scRNA-seq) analysis to document both the diverse transcriptomes of human senescent fibroblasts at an individual-cell scale, and the changes in the transcriptome over time during etoposide-triggered senescence.”

First, the researchers characterized the different cell states in cultures undergoing senescence triggered by different stresses, and found distinct cell subpopulations that expressed mRNAs encoding proteins with roles in growth arrest, survival and the secretory phenotype. Second, they characterized the dynamic changes in the transcriptomes of cells as they developed etoposide-induced senescence; by tracking cell transitions across this process, the researchers found two different senescence programs that developed divergently, one in which cells expressed traditional senescence markers such as p16 (CDKN2A) mRNA, and another in which cells expressed long noncoding RNAs and splicing was dysregulated. Finally, they obtained evidence that the proliferation status at the time of senescence initiation affected the path of senescence, as determined based on the expressed RNAs. 

“We propose that a deeper understanding of the transcriptomes during the progression of different senescent cell phenotypes will help develop more effective interventions directed at this detrimental cell population.”

DOI - https://doi.org/10.18632/aging.204666

Corresponding authors - Krystyna Mazan-Mamczarz - krystyna.mazan-mamczarz@nih.gov, and Myriam Gorospe - myriam-gorospe@nih.gov

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Keywords - aging, senescence, single-cell analysis, transcriptome

About Aging-US

Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

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Aging (Aging-US)

Single-Cell Transcriptomic Analysis Uncovers Diverse and Dynamic Senescent Cell Populations

BUFFALO, NY- March 15, 2024 – A new #research paper was #published on the #cover of Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 16, Issue 5, entitled, “Genome-wide transcriptome profiling and development of age prediction models in the human brain.”

Aging-related transcriptome changes in various regions of the healthy human brain have been explored in previous works, however, a study to develop prediction models for age based on the expression levels of specific panels of transcripts is lacking. Moreover, studies that have assessed sexually dimorphic gene activities in the aging brain have reported discrepant results, suggesting that additional studies would be advantageous. The prefrontal cortex (PFC) region was previously shown to have a particularly large number of significant transcriptome alterations during healthy aging in a study that compared different regions in the human brain. 

In this new study, researchers Joseph A. Zarrella and Amy Tsurumi from the Harvard T.H. Chan School of Public Health, Massachusetts General Hospital, Harvard Medical School, and Shriner's Hospitals for Children-Boston aimed to profile PFC transcriptome changes during healthy human aging overall and comparing potential differences between female and male samples, as well as developing chronological age prediction models by various methods.

“We harmonized neuropathologically normal PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years, and found a large number of differentially regulated transcripts in the old and elderly, compared to young samples overall, and compared female and male-specific expression alterations.” 

The team assessed the genes that were associated with age by employing ontology, pathway, and network analyses. Furthermore, they applied various established (least absolute shrinkage and selection operator (Lasso) and Elastic Net (EN)) and recent (eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)) machine learning algorithms to develop accurate prediction models for chronological age and validated them. Studies to further validate these models in other large populations and molecular studies to elucidate the potential mechanisms by which the transcripts identified may be related to aging phenotypes would be advantageous.

“Our results support the notions that specific gene expression changes in the PFC are highly correlated with age, that some transcripts show female and male-specific differences, and that machine learning algorithms are useful tools for developing prediction models for age based on transcriptome information.”

DOI - https://doi.org/10.18632/aging.205609

Corresponding author - Amy Tsurumi - atsurumi@mgh.harvard.edu

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Age Prediction Models in the Human Brain: Genome-wide Transcriptome Profiling and Development

The human brain is a complex organ, and its aging process is influenced by a plethora of factors, both genetic and environmental. Aging-related changes in the brain can lead to cognitive decline and susceptibility to neurodegenerative diseases. Therefore, understanding the molecular mechanisms underlying these changes is crucial for developing therapeutic strategies to delay or prevent age-related cognitive decline.

Over the past few years, a myriad of scientific studies have been conducted to understand the intricate relationship between our genes and the aging process. In a new study, researchers Joseph A. Zarrella and Amy Tsurumi from Harvard T.H. Chan School of Public Health, Massachusetts General Hospital, Harvard Medical School, and Shriner’s Hospitals for Children-Boston explored the concept of genome brain age prediction, a groundbreaking area of study that employs advanced bioinformatics tools to analyze changes in gene expression associated with aging. On February 28, 2024, their research paper was published and chosen as the cover paper for Aging’s Volume 16, Issue 5, entitled, “Genome-wide transcriptome profiling and development of age prediction models in the human brain.”

“[…] we aimed to profile transcriptome changes in the aging PFC [prefrontal cortex] overall and compare females and males, and develop prediction models for age.”

Full blog - https://aging-us.org/2024/03/predicting-brain-age-with-machine-learning-and-transcriptome-profiling/

Paper DOI - https://doi.org/10.18632/aging.205609

Corresponding author - Amy Tsurumi - atsurumi@mgh.harvard.edu

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Keywords - aging, machine learning, prediction model, biomarker, transcriptome

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Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

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Predicting Brain Age With Machine Learning and Transcriptome Profiling

BUFFALO, NY — April 14, 2025 — A new #research paper was #published in Aging (Aging-US) Volume 17, Issue 3, on March 4, 2025, titled “Deciphering age-related transcriptomic changes in the mouse retinal pigment epithelium.”

The study, led by first authors Sushil K. Dubey and Rashmi Dubey with corresponding author Mark E. Kleinman from East Tennessee State University, reveals that aging causes inflammation, oxidative stress, and gene disruption in the retinal pigment epithelium (RPE), a vital layer of cells in the eye. These changes may explain why older adults are more vulnerable to age-related eye diseases. The researchers also developed a human cell model to study retinal aging and test future therapies.

The RPE plays a key role in maintaining retinal health. It recycles light-sensitive molecules, supports the visual cycle, and protects the retina from damage. When this layer becomes damaged, vision problems such as age-related macular degeneration can develop.

In this study, researchers compared gene activity in RPE cells from young and aged mice. They found that aging increased the activity of genes involved in immune system responses, inflammation, and oxidative stress, three known triggers of tissue damage. At the same time, genes related to vision and light detection became less active, weakening the RPE’s ability to support healthy vision.

To reinforce these findings, the research team also aged human RPE cells in the lab. Over time, these cells showed the same patterns: inflammation increased, while genes tied to visual function decreased. This human cell model offers a practical way to explore how RPE degeneration happens over time and how it might be slowed down or reversed.

The research also identified “hub genes,” which are central players of the gene networks involved in RPE aging. These are connected to immune signaling, oxidative damage, and changes in the eye’s structural support. Many of these genes are already known to be involved in age-related retinal degeneration, so they may become important targets for future treatments aimed at protecting vision in older adults.

“GO annotation of downregulated genes included processes related to visual perception, sensory perception of light stimulus, detection of light stimulus, detection of visible light, detection of external stimulus, detection of abiotic stimulus, phototransduction, cellular response to interferon-beta, response to interferon-beta, and response to light stimulus.”

By mapping how the RPE changes with age at the molecular level, this study provides a clearer understanding of why aging leads to eye disease. It also introduces a reliable laboratory model that researchers can use to test new therapies. Altogether, the work is a key step toward developing treatments to slow or prevent vision loss tied to retinal aging.

Read the full paper: DOI: https://doi.org/10.18632/aging.206219

Corresponding author: Mark E. Kleinman- kleinman@etsu.edu 

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Keywords: aging, transcriptome, retinal pigment epithelium, oxidative stress, inflammation, chronological aging

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Aging Triggers Inflammation and Stress in Retinal Cells, Raising Risk of Vision Loss

Aging-US is dedicated to advancing our understanding of the biological mechanisms that drive aging and the development of age-related diseases. Our mission is to serve as a platform for high-quality research that uncovers the cellular, molecular, and systemic processes underlying aging, and translates these insights into strategies to extend healthspan and delay the onset of chronic disease.

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New tracks tagged #transcriptome