Sequencing of small RNAs of human blood in different 4 age cohorts.
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ABSTRACT:
PROVIDER: PRJNA436121 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Comparison of temporal small RNA expression levels in blood between different human age cohorts. RNA-seq data comprise 4 age groups: 24-29 years, 45-50 years, 60-65 years and 75-80 years. Jena Centre for Systems Biology of Ageing - JenAge (www.jenage.de)
2021-10-19 | GSE111174 | GEO
Project description:Comparison of temporal small RNA expression levels in skin between different human age cohorts. RNA-seq data comprise 4 age groups: 24-29 years, 45-50 years, 60-65years and 75-80 years. Jena Centre for Systems Biology of Ageing - JenAge (www.jenage.de)
2021-10-19 | GSE111281 | GEO
Project description:Sequencing of small RNAs of human skin in different 4 age cohorts.
| PRJNA436396 | ENA
Project description:Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
2023-07-03 | PXD028281 | Pride
Project description:Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
2023-07-03 | PXD028295 | Pride
Project description:It has been observed that immune cell deterioration occurs in the elderly, as well as a chronic low-grade inflammation called inflammaging. These cellular changes must be driven by numerous changes in gene expression and in fact, both protein-coding and non-coding RNA expression alterations have been observed in peripheral blood mononuclear cells from elder people. In the present work we have studied the expression of small non-coding RNA (microRNA and small nucleolar RNA -snoRNA-) from healthy individuals from 24 to 79 years old. We have observed that the expression of 69 non-coding RNAs (56 microRNAs and 13 snoRNAs) changes progressively with chronological age. According to our results, the age range from 47 to 54 is critical given that is the period when the expression trend (increasing or decreasing) of age-related small non-coding RNAs is more pronounced. Furthermore, age-related miRNAs regulate genes that are involved in immune, cell cycle and cancer-related processes, which had already been associated to human aging. Therefore, human aging could be studied as a result of progressive molecular changes, and different age ranges should be analysed to cover the whole aging process.
2016-11-30 | GSE89042 | GEO
Project description:Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.
2023-06-30 | GSE182598 | GEO
Project description:Comparison of gene expression profiles from Mus musculus blood of different age groups. The RNA-seq data comprise 5 groups at ages: 2, 9, 15, 24 and 30 months. Jena Centre for Systems Biology of Ageing - JenAge (www.jenage.de)
2021-10-19 | GSE111164 | GEO
Project description:Sequencing of Mus musculus blood of five different age groups (smallRNA)
| PRJNA436131 | ENA
Project description:Sequencing of Mus musculus blood of five different age groups 2
| PRJNA436096 | ENA