Due to the increasing longevity and declining fertility, the phenomenon of population aging is the 21st century's dominant demographic feature. As elder shares rise throughout the world, which is unprecedented in human history, and the pace of population aging is much faster than in the past, in 2020, the number of people aged 60 and over outnumbered that of children under the age of 5 years. At the biological level, aging represents the accumulation of a wide variety of damage to molecules, cells, and tissues over a lifetime, leading to various physical and mental complications and a growing risk of malfunction and disease. For example, age is the primary important risk factor for most neurodegenerative diseases with increased glial cell number, including Alzheimer's disease and Parkinson's disease. Accordingly, cancer incidence and mortality, progressive neuronal death, and neurological dysfunction increase with age. Therefore, prevention and control of health problems of older people necessitate comprehensive public health.
To understand how molecular alterations differ among people of different ages. According to current studies, the effects of age on processes such as epigenetics, mitochondria (e.g. oxidative stress, energy metabolism), autophagy, autophagy-lysosomal pathway, proteostasis, glycogenolysis, inflammation, DNA damage/repair may differentially influence diverse cell types, cell subtypes underlie brain aging and neurodegenerative disorders. Take microglia as an example, previous evidence suggests a link between the primed profile of the aged microglia and the vulnerability of the old brain to inflammation-related secondary injury. Microglia can be eliminated via pharmacological inhibition of the colony-stimulating factor 1 receptor (CSF1R). Withdrawal of CSF1R inhibition stimulates microglial repopulation (gliogenesis), which resulted in a reversal of age-associated changes in neuronal gene expression (e.g. actin cytoskeleton remodeling, synaptogenesis). Hence, a comprehensive bioinformatics analysis of the human genomic, transcriptomic, proteomic, metabolic, and epigenetic modifications related to patients’ age across normal aging and diseases is required. Recent developments in single-cell (include multi-omics) sequencing and spatial transcriptomics further reveal the daunting molecular diversity of cells and cell type-specific aging markers. It would enable us to figure out the relationship or interaction between stem cell and other cell types and move the therapeutic window to earlier stages of aging-associated diseases.
In this Research Topic, we welcome investigators to contribute from Original Research to Review Articles on methods and clinical applications of time-course multi-omic data analysis in age-associated diseases. Potential topics include but are not limited to the following:
• Methods for time-course data analysis including using novel mathematic models;
• Methods development in studying neurodegenerative diseases;
• Casual gene identification and biomarker discovery using multi-omic data for aging-associated diseases;
• Drug development and repositioning for aging-associated diseases using bioinformatics, machine-learning or network methods;
• Research on clarifying the pathogenesis (e.g. epigenetics, mitochondria, proteostasis, glycogenolysis, DNA damage/repair, myelin, and inflammation, ...) of age-associated diseases based on experiments such as molecules, cells, or model organisms;
• Single-cell (include multi-omics) and spatial RNA sequencing in aging and age-associated diseases;
• Stem cell types (e.g. gliogenesis related) and other novel cell subtypes (e.g senescence related) in aging and age-associated diseases.
Due to the increasing longevity and declining fertility, the phenomenon of population aging is the 21st century's dominant demographic feature. As elder shares rise throughout the world, which is unprecedented in human history, and the pace of population aging is much faster than in the past, in 2020, the number of people aged 60 and over outnumbered that of children under the age of 5 years. At the biological level, aging represents the accumulation of a wide variety of damage to molecules, cells, and tissues over a lifetime, leading to various physical and mental complications and a growing risk of malfunction and disease. For example, age is the primary important risk factor for most neurodegenerative diseases with increased glial cell number, including Alzheimer's disease and Parkinson's disease. Accordingly, cancer incidence and mortality, progressive neuronal death, and neurological dysfunction increase with age. Therefore, prevention and control of health problems of older people necessitate comprehensive public health.
To understand how molecular alterations differ among people of different ages. According to current studies, the effects of age on processes such as epigenetics, mitochondria (e.g. oxidative stress, energy metabolism), autophagy, autophagy-lysosomal pathway, proteostasis, glycogenolysis, inflammation, DNA damage/repair may differentially influence diverse cell types, cell subtypes underlie brain aging and neurodegenerative disorders. Take microglia as an example, previous evidence suggests a link between the primed profile of the aged microglia and the vulnerability of the old brain to inflammation-related secondary injury. Microglia can be eliminated via pharmacological inhibition of the colony-stimulating factor 1 receptor (CSF1R). Withdrawal of CSF1R inhibition stimulates microglial repopulation (gliogenesis), which resulted in a reversal of age-associated changes in neuronal gene expression (e.g. actin cytoskeleton remodeling, synaptogenesis). Hence, a comprehensive bioinformatics analysis of the human genomic, transcriptomic, proteomic, metabolic, and epigenetic modifications related to patients’ age across normal aging and diseases is required. Recent developments in single-cell (include multi-omics) sequencing and spatial transcriptomics further reveal the daunting molecular diversity of cells and cell type-specific aging markers. It would enable us to figure out the relationship or interaction between stem cell and other cell types and move the therapeutic window to earlier stages of aging-associated diseases.
In this Research Topic, we welcome investigators to contribute from Original Research to Review Articles on methods and clinical applications of time-course multi-omic data analysis in age-associated diseases. Potential topics include but are not limited to the following:
• Methods for time-course data analysis including using novel mathematic models;
• Methods development in studying neurodegenerative diseases;
• Casual gene identification and biomarker discovery using multi-omic data for aging-associated diseases;
• Drug development and repositioning for aging-associated diseases using bioinformatics, machine-learning or network methods;
• Research on clarifying the pathogenesis (e.g. epigenetics, mitochondria, proteostasis, glycogenolysis, DNA damage/repair, myelin, and inflammation, ...) of age-associated diseases based on experiments such as molecules, cells, or model organisms;
• Single-cell (include multi-omics) and spatial RNA sequencing in aging and age-associated diseases;
• Stem cell types (e.g. gliogenesis related) and other novel cell subtypes (e.g senescence related) in aging and age-associated diseases.
