Similar Datasets
Project description:A healthy adult human body regenerates around 330 billion cells per day, equivalent to about 1% of the total cell number in the entire organism. Cells from different types and tissues regenerate at various speeds, ranging from the fast-renewal of red blood cells, immune cells, and gut epithelial cells, to the rare genesis of adult neurons and glial cells in the brain. With age, the regenerative properties of most tissues gradually decline due to a combination of age-dependent changes in tissue-specific stem cells and environmental cues. However, a consensus study of cell-type-specific turnover dynamics and how such cell-genesis processes change with aging is lacking. Recent advances in single-cell genomics have created unprecedented opportunities to explore the cell-type-specific molecular states across diverse mammalian systems. In particular, single-cell combinatorial indexing sequencing (sci-seq) has emerged as a powerful strategy to label the nucleic acid contents of individual cells in a scalable manner. However, current single-cell techniques only deliver a static snapshot of each isolated cell with temporal information lost, falling short of quantitatively capturing the dynamics such as proliferation and differentiation events. To address this gap, we previously introduced TrackerSci (PMID: 37774676), a single-cell genomic method that combines newborn cell labeling and combinatorial indexing to characterize the transcriptome and chromatin landscape of proliferating progenitor cells in vivo. In the present study, we applied TrackerSci to profile chromatin accessibility in over 3 million newborn cells across 21 mouse tissues, examining three different ages and both sexes. This dataset provides a comprehensive view of cell temporal dynamics throughout the body, enabling us to quantify cell-type-specific proliferation and differentiation changes during aging and uncover molecular programs associated with aging-related population shifts.
2026-05-31 | GSE331488 | GEO
Project description:A healthy adult human body regenerates around 330 billion cells per day, equivalent to about 1% of the total cell number in the entire organism. Cells from different types and tissues regenerate at various speeds, ranging from the fast-renewal of red blood cells, immune cells, and gut epithelial cells, to the rare genesis of adult neurons and glial cells in the brain. With age, the regenerative properties of most tissues gradually decline due to a combination of age-dependent changes in tissue-specific stem cells and environmental cues. However, a consensus study of cell-type-specific turnover dynamics and how such cell-genesis processes change with aging is lacking. Recent advances in single-cell genomics have created unprecedented opportunities to explore the cell-type-specific molecular states across diverse mammalian systems. In particular, single-cell combinatorial indexing sequencing (sci-seq) has emerged as a powerful strategy to label the nucleic acid contents of individual cells in a scalable manner. However, current single-cell techniques only deliver a static snapshot of each isolated cell with temporal information lost, falling short of quantitatively capturing the dynamics such as proliferation and differentiation events. To address this gap, we previously introduced TrackerSci (PMID: 37774676), a single-cell genomic method that combines newborn cell labeling and combinatorial indexing to characterize the transcriptome and chromatin landscape of proliferating progenitor cells in vivo. In the present study, we applied TrackerSci to profile chromatin accessibility in over 3 million newborn cells across 21 mouse tissues, examining three different ages and both sexes. This dataset provides a comprehensive view of cell temporal dynamics throughout the body, enabling us to quantify cell-type-specific proliferation and differentiation changes during aging and uncover molecular programs associated with aging-related population shifts.
2026-05-31 | GSE331489 | GEO
Project description:A global view of cell-type-specific temporal dynamics in mammalian aging
| PRJNA1473008 | ENA
Project description: Not available
| S-EPMC10942312 | biostudies-literature
Project description: Not available
| S-EPMC11910726 | biostudies-literature
Project description:Prior studies applied single-cell approaches to explore gene expression changes in aged brains; however, they were limited by relatively shallow sampling of brain cell populations, and thus failed to characterize the detailed molecular signatures and dynamics of rare cell types associated with aging and diseases. Here, we set out to investigate the age-dependent transcriptional and chromatin dynamics across diverse brain cell types. With EasySci, an extensively improved single-cell combinatorial indexing strategy, we profiled ~1.5 million single-cell transcriptomes with full gene body coverage and ~400,000 single-cell chromatin accessibility profiles across mouse brains spanning different ages, genotypes, and both sexes. With a novel computational framework designed for characterizing cellular subtypes based on the expression of both genes and exons, we identified > 300 cell subtypes and deciphered the underlying molecular programs and spatial locations of rare cell types (e.g., pinealocytes, tanycytes). Leveraging the data, we generated a global readout of age-dependent cell population dynamics with great cellular subtype resolution, providing insights into expanded cell types (e.g., rare astrocytes and vascular leptomeningeal cells in olfactory bulb, reactive microglia and oligodendrocytes) and depleted cell types (e.g., neuronal progenitors, neuroblasts, committed oligodendrocyte precursors) in development and ageing. Furthermore, we explored cell-type-specific responses to genetic perturbations associated with Alzheimer’s disease and discovered rare cell types depleted (e.g., mt-Cytb+, mt-Rnr2+ choroid plexus epithelial cells) or enriched (e.g., Col25a1+ Ndrg1+ interbrain and midbrain neurons) in both AD models. Key findings are consistent between males and females, validated across the transcriptome, chromatin accessibility, and spatial analysis. In summary, these data comprise a rich resource for exploring cell-type-specific population dynamics and the underlying molecular mechanisms in the mammalian aging process.
2023-05-26 | GSE212606 | GEO
Project description:To elucidate the aging-associated cellular population dynamics throughout the body, here we present PanSci, a single-cell transcriptome atlas profiling over 20 million cells from 623 mouse tissue samples, encompassing a range of organs across different developmental stages, genders, and genotypes. This comprehensive dataset allowed us to identify more than 3,000 unique cellular states and catalog over 200 distinct aging-associated cell populations experiencing significant depletion or expansion. Our panoramic analysis uncovered four distinct, temporally structured aging-related cell population dynamic waves that are organ- and lineage-specific. Moreover, we investigated aging-associated alterations in immune cell populations, revealing both widespread shifts and organ-specific changes. We further explored the regulatory roles of the immune system on aging and pinpointed specific age-related cell population expansions that are lymphocyte-dependent. The breadth and depth of our 'cell-omics' methodology not only enhance our comprehension of cellular aging but also lay the groundwork for exploring the complex regulatory networks among varied cell types in the context of aging and aging-associated diseases.
2024-03-05 | GSE247719 | GEO
Project description: Not available
| S-EPMC5054211 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2017-09-20 | GSE87109 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-06-01 | E-GEOD-40498 | biostudies-arrayexpress