Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Predicting age from the transcriptome of human dermal fibroblasts

ABSTRACT: Predicting age from the transcriptome of human dermal fibroblasts

PROVIDER: PRJNA454681 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR7093810.fastq.gz	Fastqsanger.gz
	SRR7093820.fastq.gz	Fastqsanger.gz
	SRR7093830.fastq.gz	Fastqsanger.gz
	SRR7093840.fastq.gz	Fastqsanger.gz
	SRR7093850.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 143

Similar Datasets

Predicting age from the transcriptome of human dermal fibroblasts

Project description:Predicting age from the transcriptome of human dermal fibroblasts

| PRJNA454681 | ENA

Predicting age from the transcriptome of human dermal fibroblasts

Project description:There is a marked heterogeneity in human lifespan and health outcomes for people of the same chronological age. Thus, one fundamental challenge is to identify molecular and cellular biomarkers of aging that could predict lifespan and be useful in evaluating lifestyle changes and therapeutic strategies in the pursuit of healthy aging. Here, we developed a computational method to predict biological age from gene expression data in skin fibroblast cells using an ensemble of machine learning classifiers. We generated an extensive RNA-seq dataset of fibroblast cell lines derived from 133 healthy individuals whose ages range from 1 to 94 years, and 10 patients with Hutchinson-Gilford Progeria Syndrome (HGPS), a premature aging disease. On this dataset, our method predicted chronological age with a median error of 4 years, outperforming algorithms proposed by prior studies that predicted age from DNA methylation [4–8] and gene expression data [6,9] for fibroblasts. Importantly, our method consistently predicted higher ages for Progeria patients compared to age-matched controls, suggesting that our algorithm can identify accelerated aging in humans. These results show that the transcriptome of skin fibroblasts retains important age-related signatures. Our computational tool may also be applicable to predicting age from other genome-wide datasets.

2018-11-16 | GSE113957 | GEO

Predicting age from the transcriptome of human dermal fibroblasts.

Project description:Biomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases. We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome. We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age. The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.

| S-EPMC6300908 | biostudies-literature

Disrupted mitochondrial function in aged human dermal fibroblasts

Project description:Skin undergoes changes in structure and function with age, including a shift in metabolic programs. This is particularly evident relative to mitochondrial function in skin cells. Dermal fibroblasts have been known to accumulate damage caused by both external and internal stressors, leading to altered bioenergetics that limits efficient synthesis of ATP and NAD+. To better understand the changes in mitochondrial function with age, metabolic profiles and transcriptomic profiles of primary dermal fibroblasts from varying aged donors were analysed.

2020-10-26 | GSE131938 | GEO

FGF2 effects on the transcriptome of the human dermal fibroblasts

Project description:The current study was to determine the effects of Fibroblast Growth Factor 2 (FGF2) on the transcriptome of adult human dermal fibroblasts. Transcriptional profiles of adult human dermal fibroblasts grown in culture medium (with FGF2 or not) were compared. Comparison of the transcriptomes will allow to identify significantly differentially expressed genes exposure to FGF2, which in turn will allow for identification of the pathways affected by these factors in the human adult fibroblasts. Methods: Human dermal fibroblasts were FGF2 (10ng/ML) treatment for 48h hours. Then, the RNA was extracted for library construction Results: FGF2-responsive genes were significantly involved in ECM-receptor interaction, PI3K-Akt signaling and Hippo pathway.

2020-12-22 | GSE157071 | GEO

The transcriptome of murine dermal fibroblasts

Project description:The transcriptome of murine dermal fibroblasts

| PRJNA435488 | ENA

Single-cell analysis of human dermal fibroblasts isolated from a single male donor over 35 years

Project description:Background: Skin homeostasis is mediated by dermal fibroblasts and is affected by aging. Although age-related heterogeneity in fibroblasts has been reported, the effects of donor and species on this heterogeneity are unclear. Methods: To analyze age-related transcriptomic changes in human dermal fibroblasts, single-cell RNA sequencing was performed on dermal fibroblasts (ASF-4 cells) collected from the inner forearm of a volunteer over three decades. Results: Four main cell subpopulations changed with donor age and showed proliferative, homeostasis, fibrotic, and senescence functional annotations. The downregulation of the expression of genes encoding key extracellular matrix production and mechanotransduction components decreased with donor age. Interestingly, dermal fibroblasts have two putative differentiation pathways: one that involves the acquisition of senescent properties and the acquisition of fibrotic properties without the suppression of proliferation. Aging induced fibroblast differentiation in a manner involving the acquisition of senescent properties. Conclusion:Reconciling the various aspects of fibroblast heterogeneity may provide insight into the mechanisms underlying human skin aging and associated phenomena, including wrinkles, sagging, delayed wound healing, and suppressed scar formation.

2023-09-21 | GSE203034 | GEO

Low oxygen and FGF2 effects on the transcriptome of the adult human dermal fibroblasts

Project description:Adult human dermal fibroblasts reside in vivo under low oxygen tension. Thus, low oxygen culture conditions represent a physiological state for adult human dermal fibroblasts. We have also previously shown that low oxygen and addition of basic fibroblast growth factor (FGF2) lead to prolonged life-span of adult human dermal fibroblasts. Therefore, we set to determine effects of low oxygen and FGF2 on the gene expression signature of adult human dermal fibroblasts. This global analysis will allow identification of genes affected and pathways regulated by low oxygen and FGF2.

2016-12-21 | GSE60580 | GEO

ChIP-seq analysis of primary human dermal fibroblasts overexpressing ANKRD1 in human dermal fibroblasts (HDFs)

Project description:ChIP-seq analysis of primary human dermal fibroblasts overexpressing ANKRD1 in human dermal fibroblasts (HDFs)

| PRJNA902739 | ENA

A global transcriptome analysis of human dermal fibroblasts upon overexpression of microRNA-132

Project description:MiR-132 is one of the most upregulated miRNAs in human skin wounds at the inflammatory phase of healing; however its biological role in dermal fibroblasts during wound repair has not been studied. To study the genes regulated by miR-132, we transfected miR-132 mimics (pre-miR-132) into primary human dermal fibroblasts to overexpress miR-132. We performed a global transcriptome analysis of fibroblasts upon overexpression of miR-132 using Affymetrix arrays.

2016-11-01 | GSE87436 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data