A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

Almanzar, Nicole; Antony, Jane; Baghel, Ankit S.; Bakerman, Isaac; Bansal, Ishita; Barres, Ben A.; Beachy, Philip A.; Berdnik, Daniela; Bilen, Biter; Brownfield, Douglas; Cain, Corey J.; Chan, Charles; Chen, Michelle B.; Clarke, Michael F.; Conley, Stephanie D.; Darmanis, Spyros; Demers, Aaron; Demir, Kubilay; Morrée, Antoine de; Divita, Taylor E.; Bois, Haley du; Ebadi, Hamid; Espinoza, Francisco; Fish, Matt; Gan, Qiang; George, Benson M.; Gillich, Astrid; Gómez-Sjöberg, Rafael; Green, Foad; Genetiano, Geraldine; Gu, Xueying; Gulati, Gunsagar S.; Hahn, Oliver; Haney, Michael S.; Hang, Yan; Harris, Lincoln; He, Mu; Hosseinzadeh, Shayan; Huang, Albin; Huang, Kerwyn Casey; Iram, Tal; Isobe, Taichi; Ives, Feather; Jones, Robert C.; Kao, Kevin S.; Karkanias, Jim; Karnam, Guruswamy; Keller, Andreas; Kershner, Aaron M.; Khoury, Nathalie; Kim, Seung K.; Kiss, Bernhard; Kong, William; Krasnow, Mark A.; Kumar, Maya; Kuo, Christin S.; Lam, Jonathan Y.; Lee, Davis P.; Lee, Song Eun; Lehallier, Benoit; Leventhal, Olivia; Li, Guang; Li, Qingyun; Liu, Ling; Lo, Annie; Lu, Wan-Jin; Lugo-Fagundo, Maria; Manjunath, Anoop; May, Andrew P.; Maynard, Ashley; McGeever, Aaron; McKay, Marina; McNerney, M. Windy; Merrill, Bryan D.; Metzger, Ross J.; Mignardi, Marco; Min, Dullei; Nabhan, Ahmad; Neff, Norma; Ng, Katharine M.; Nguyen, Patricia K.; Noh, Joseph J.; Nusse, Roel; Pálovics, Róbert; Patkar, Rasika; Peng, Weng Chuan; Penland, Lolita; Pisco, Angela Oliveira; Pollard, Katherine S.; Puccinelli, Robert; Qi, Zhengjian; Quake, Stephen R.; Rando, Thomas A.; Rulifson, Eric; Schaum, Nicholas; Segal, Joe M.; Sikandar, Shaheen S.; Sinha, Rahul; Sit, Rene; Sonnenburg, Justin L.; Staehli, Daniel; Szade, Krzysztof; Tan, Michelle; Tan, Weilun; Tato, Cristina M.; Tellez, Krissie; Dulgeroff, Laughing Bear Torrez; Travaglini, Kyle J.; Tropini, Carolina; Tsui, Margaret; Waldburger, Lucas; Wang, Bruce M.; Weele, Linda J. van; Weinberg, Kenneth I.; Weissman, Irving L.; Wosczyna, Michael N.; Wu, Sean M.; Wyss‐Coray, Tony; Xiang, Jun; Xue, Soso; Yamauchi, Kevin A.; Yang, Andrew; Yerra, Lakshmi; Youngyunpipatkul, Justin; Yu, Brian; Zanini, Fabio; Zardeneta, Macy E.; Zee, Alexander; Zhao, Chunyu; Zhang, Fan; Zhang, Hui; Zhang, Martin Jinye; Zhou, Lu; Zou, James

doi:10.1038/s41586-020-2496-1

Cited by 857 publications

(558 citation statements)

References 59 publications

Supporting

Mentioning

480

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, this approach could reveal universal senolytic molecular targets as well as facilitating the identification of novel senolytics. Importantly, such single cell analyses can, in fact, be performed in animal models, having been conducted in aging mice to identify senescenceassociated genomic profiles that can be used to predict the effect of senolytics in vivo [169]. The ultimate goal of this approach would be to identify unique molecular signatures that would allow for the prediction as to whether a newly identified senolytic will display favorable effects in clearing senescent tumor cells in vitro and in vivo, especially if coupled with the multi-model strategy proposed above.…”

Section: Single Cell-omicsmentioning

confidence: 99%

Senolytics for Cancer Therapy: Is All that Glitters Really Gold?

Carpenter

Saleh

Gewirtz

2021

Cancers

View full text Add to dashboard Cite

Senolytics represent a group of mechanistically diverse drugs that can eliminate senescent cells, both in tumors and in several aging-related pathologies. Consequently, senolytic use has been proposed as a potential adjuvant approach to improve the response to senescence-inducing conventional and targeted cancer therapies. Despite the unequivocal promise of senolytics, issues of universality, selectivity, resistance, and toxicity remain to be further clarified. In this review, we attempt to summarize and analyze the current preclinical literature involving the use of senolytics in senescent tumor cell models, and to propose tenable solutions and future directions to improve the understanding and use of this novel class of drugs.

show abstract

Section: Single Cell-omicsmentioning

confidence: 99%

Senolytics for Cancer Therapy: Is All that Glitters Really Gold?

Carpenter

Saleh

Gewirtz

2021

Cancers

View full text Add to dashboard Cite

show abstract

“…They have beneficial roles in development and wound healing. However, with age their abundance increases in multiple tissues [4][5][6][7], raising the concentration of pro-inflammatory SASP factors.…”

Section: Introductionmentioning

confidence: 99%

Senescent cell accumulation mechanisms inferred from parabiosis

Karin

2020

GeroScience

View full text Add to dashboard Cite

Senescent cells are growth-arrested cells that cause inflammation and play a causal role in aging. They accumulate with age, and preventing this accumulation delays age-related diseases. However, the mechanism for senescent cell accumulation is not fully understood. Accumulation can result from increasing production or decreasing removal of senescent cells with age, or both. To distinguish between these possibilities, we analyze data from parabiosis, the surgical conjoining of two mice so that they share circulation. Parabiosis between a young and old mouse, called heterochronic parabiosis, reduces senescent cell levels in the old mouse, while raising senescent cell levels in the young mouse. We show that parabiosis data can reject mechanisms for senescent cell accumulation in which only production rises with age or only removal decreases with age; both must vary with age. Since removal drops with age, senescent cell half-life rises with age. This matches a recent model for senescent cell accumulation developed from independent data on senescent cell dynamics, called the SR model, in which production rises linearly with age and senescent cells inhibit their own removal. The SR model further explains the timescales and mechanism of rejuvenation in parabiosis, based on transfer of spare removal capacity from the young mouse to the old. The present quantitative understanding can help design optimal treatments that remove senescent cells, by matching the time between treatments to the time it takes senescent cells to re-accumulate.

show abstract

“…Previous molecular profiling studies of the aging kidney have been limited to microarray based gene expression assays in young versus old kidneys of mice and rats [4][5]. More recently, single cell transcriptional profiling of aging C57BL/6NJ mice has been reported for multiple tissues, including kidney [6]. When aging studies are carried out in single inbred strains, they may display specific and idiosyncratic patterns of aging and thus lack generalizability beyond the particular strain studied [7].…”

Section: Introductionmentioning

confidence: 99%

Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney

Takemon

Chick

Gyuricza

et al. 2020

Preprint

View full text Add to dashboard Cite

The kidney is an excellent model for studying organ aging. Kidney function shows steady decline with age and is easy to assay using urine or blood samples. However, little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand the molecular changes that occur with age, we measured mRNA and protein levels in 188 genetically diverse mice at ages 6, 12, and 18 months. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.

show abstract

A single-cell transcriptomic atlas characterizes ageing tissues in the mouse

Cited by 857 publications

References 59 publications

Senolytics for Cancer Therapy: Is All that Glitters Really Gold?

Senolytics for Cancer Therapy: Is All that Glitters Really Gold?

Senescent cell accumulation mechanisms inferred from parabiosis

Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney

Contact Info

Product

Resources

About