StemSC: a cross-dataset human stemness index for single-cell samples
Hailong ZhengJiajing XieKai SongJing YangHuiting XiaoJiashuai ZhangKeru LiRongqiang YuanYuting ZhaoYunyan GuWenyuan Zhao
9
Citation
30
Reference
10
Related Paper
Citation Trend
Abstract:
Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can't leverage the existing experimentally validated stem cells to recognize the stem-like cells.Keywords:
Leverage (statistics)
Abstract Age is well-known to be a significant factor in both disease pathology and response to treatment, yet the molecular changes that occur with age in humans remain ill-defined. Here, using transcriptome profiling of healthy human male skin, we demonstrate that there is a period of significantly elevated, transcriptome-wide expression changes occurring predominantly in middle age. Both pre and post this period, the transcriptome appears to undergo much smaller, linear changes with increasing age. Functional analysis of the transient changes in middle age suggest a period of heightened metabolic activity and cellular damage associated with NF-kappa-B and TNF signaling pathways. Through meta-analysis we also show the presence of global, tissue independent linear transcriptome changes with age which appear to be regulated by NF-kappa-B. These results suggest that aging in human skin is associated with a critical mid-life period with widespread transcriptome changes, both preceded and proceeded by a relatively steady rate of linear change in the transcriptome. The data provides insight into molecular changes associated with normal aging and will help to better understand the increasingly important pathological changes associated with aging.
Senescence
Cite
Citations (60)
Transcriptome analysis is a powerful tool to characterize changes in leukocyte gene expression patterns and reveal very early molecular abnormalities in tissue. Herein, we report on characterization of the very earliest abnormalities in the transcriptome of leukocytes from young "prepathologic" NOD and NON female mice.
Cite
Citations (6)
Abstract The human genome is thought to contain 100 000 genes of which a subset of approximately 15 000 to 20 000 genes is expressed in an individual cell. The set of genes expressed and the stoichiometry of the resulting messenger RNAs, together called a transcriptome, determine the phenotype of a cell, tissue, and whole organism. It is generally accepted that a transcriptome is largely determined by an interplay of hereditary and environmental factors. For example, in the CNS, a challenge from the environment, e.g. a learning or a traumatic experience may lead to an alteration of the transcriptome of target neurons. Thus, transcriptome analysis and subsequent transcriptome comparisons may reveal novel insights in the molecular mechanisms underlying complex processes such learning and memory formation.
Cite
Citations (0)
The transcriptome represents the whole complement of RNA transcripts in cells or tissues and reflects the expressed genes at various life stages, tissue types, physiological states, and environmental conditions. Transcriptomics study concerning medicinal plants has become the most active area in medicinal plant genome research. Transcriptome analysis provides a comprehensive understanding of gene expression and its regulation. The study of its transcriptome has great significance in solving the questions of genetic evolution, genetic breeding, ecology and so on. Here we report the application status of transcriptomics in medicinal plants based on emergence, development and methodology of transcriptomics.
Cite
Citations (10)
Cite
Citations (2)
Abstract Since age related perturbations in gene expression profiles have been described and transcriptomic changes in specific biological pathways have been implicated in the aging process, we performed whole transcriptome sequencing on 4000 HRS participants using RNA obtained from Paxgene tubes collected during the 2016 interview. We will describe design and implementation of innovative quality control procedures to minimize technical variability in transcriptomic measurements and monitor analytical variation in large population studies such as HRS. We will also report the distribution of transcriptomic profiles according to various demographic characteristics (age, sex, racial/ethnic and socioeconomic differences) and describe the prevalence of previously reported aging related transcriptomic signatures in HRS. We will describe the associations between transcriptomic profiles and other measures of biological aging in HRS and report how changes in cell composition can affect transcriptomic profiles observed in population studies such as HRS.
RNA-Seq
Cite
Citations (0)
It has been shown that the best coverage of the HepG2 cell line transcriptome encoded by genes of a single chromosome, chromosome 18, is achieved by a combination of two sequencing platforms, Illumina RNA-Seq and Oxford Nanopore Technologies (ONT), using cut-off levels of FPKM > 0 and TPM > 0, respectively. In this study, we investigated the extent to which the combination of these transcriptomic analysis methods makes it possible to achieve a high coverage of the transcriptome encoded by the genes of other human chromosomes. A comparative analysis of transcriptome coverage for various types of biological material was carried out, and the HepG2 cell line transcriptome was compared with the transcriptome of liver tissue cells. In addition, the contribution of variability in the coverage of expressed genes in human transcriptomes to the creation of a draft human transcriptome was evaluated. For human liver tissues, ONT makes an extremely insignificant contribution to the overall coverage of the transcriptome. Thus, to ensure maximum coverage of the liver tissue transcriptome, it is sufficient to apply only one technology: Illumina RNA-Seq (FPKM > 0).
RNA-Seq
Illumina dye sequencing
Cite
Citations (2)
Organs and tissues age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse tissues and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types, the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.
Proteostasis
Metabolome
Cell type
Cite
Citations (3)
Brassica napus is one of the most important oilseed crops in the world. However, there is currently no enough stem transcriptome information and comparative transcriptome analysis of different tissues, which impedes further functional genomics research on B. napus. In this study, the stem transcriptome of B. napus was characterized by RNA-seq technology. Approximately 13.4 Gb high-quality clean reads with an average length of 100 bp were generated and used for comparative transcriptome analysis with the existing transcriptome sequencing data of roots, leaves, flower buds and immature embryos of B. napus. All the transcripts were annotated against GO and KEGG databases. The common genes in five tissues, differentially expressed genes (DEGs) of the common genes between stems and other tissues, and tissue-specific genes were detected, and the main biochemical activities and pathways implying the common genes, DEGs and tissue-specific genes were investigated. Accordingly, the common transcription factors (TFs) in the five tissues and tissue-specific TFs were identified, and a TFs-based regulation network between TFs and the target genes involved in "Phenylpropanoid biosynthesis" pathway were constructed to show several important TFs and key nodes in the regulation process. Collectively, this study not only provided an available stem transcriptome resource in B. napus, but also revealed a valuable comparative transcriptome information of five tissues of B. napus for future investigation on specific processes, functions and pathways.
KEGG
RNA-Seq
Cite
Citations (16)
Abstract Organs age at different rates within a single individual. Such asynchrony in aging has been widely observed at multiple levels, from functional hallmarks, such as anatomical structures and physiological processes, to molecular endophenotypes, such as the transcriptome and metabolome. However, we lack a conceptual framework to understand why some components age faster than others. Just as demographic models explain why aging evolves, here we test the hypothesis that demographic differences among cell types, determined by cell-specific differences in turnover rate, can explain why the transcriptome shows signs of aging in some cell types but not others. Through analysis of mouse single-cell transcriptome data across diverse organs and ages, we find that cellular age explains a large proportion of the variation in the age-related increase in transcriptome variance. We further show that long-lived cells are characterized by relatively high expression of genes associated with proteostasis, and that the transcriptome of long-lived cells shows greater evolutionary constraint than short-lived cells. In contrast, in short-lived cell types the transcriptome is enriched for genes associated with DNA repair. Based on these observations, we develop a novel heuristic model that explains how and why aging rates differ among cell types.
Proteostasis
Metabolome
Cell type
Cite
Citations (0)