ArticleBMC genomics2012
Cell population-specific expression analysis of human cerebellum.
Article in BMC genomics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
32 citing papers in PubMed, 54 citations in OpenAlex.
- Insights from cerebellar transcriptomic analysis into the pathogenesis of ataxia.JAMA neurology · 2014Trial
- From whole bodies to single cells: A guide to transcriptomic approaches for ecology and evolutionary biology.Molecular ecology · 2025Review
- Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.BMC genomics · 2024Article
- Identifying tumor type and cell type-specific gene expression alterations in pediatric central nervous system tumors.Nature communications · 2024Article
- imply: improving cell-type deconvolution accuracy using personalized reference profiles.Genome medicine · 2024Article
- Cerebellum and Aging: Update and Challenges.Aging and disease · 2024Review
- Article
- Tumor type and cell type-specific gene expression alterations in diverse pediatric central nervous system tumors identified using single nuclei RNA-seq.Research square · 2023Article
- Niche deconvolution of the glioblastoma proteome reveals a distinct infiltrative phenotype within the proneural transcriptomic subgroup.Scientific data · 2022Article
- GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA.Foundations of data science (Springfield, Mo.) · 2022Article
- Transcriptomic analysis of isolated and pooled human postmortem cerebellar Purkinje cells in autism spectrum disorders.Frontiers in genetics · 2022Article
- A Novel SCA3 Knock-in Mouse Model Mimics the Human SCA3 Disease Phenotype Including Neuropathological, Behavioral, and Transcriptional Abnormalities Especially in Oligodendrocytes.Molecular neurobiology · 2022Article
- Cell type-specific potential pathogenic genes and functional pathways in Alzheimer's Disease.BMC neurology · 2021Article
- Tutorial: Guidelines for Single-Cell RT-qPCR.Cells · 2021Review
- Data-driven detection of subtype-specific differentially expressed genes.Scientific reports · 2021Article
- Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites.BMC biology · 2020Article
- Deciphering cellular transcriptional alterations in Alzheimer's disease brains.Molecular neurodegeneration · 2020Article
- Article
- Unraveling Targetable Systemic and Cell-Type-Specific Molecular Phenotypes of Alzheimer's and Parkinson's Brains With Digital Cytometry.Frontiers in neuroscience · 2020Article
- Informing disease modelling with brain-relevant functional genomic annotations.Brain : a journal of neurology · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundInterpreting gene expression profiles obtained from heterogeneous samples can be difficult because bulk gene expression measures are not resolved to individual cell populations. We have recently devised Population-Specific Expression Analysis (PSEA), a statistical method that identifies individual cell types expressing genes of interest and achieves quantitative estimates of cell type-specific expression levels. This procedure makes use of marker gene expression and circumvents the need for additional experimental information like tissue composition.
resultsTo systematically assess the performance of statistical deconvolution, we applied PSEA to gene expression profiles from cerebellum tissue samples and compared with parallel, experimental separation methods. Owing to the particular histological organization of the cerebellum, we could obtain cellular expression data from in situ hybridization and laser-capture microdissection experiments and successfully validated computational predictions made with PSEA. Upon statistical deconvolution of whole tissue samples, we identified a set of transcripts showing age-related expression changes in the astrocyte population.
conclusionsPSEA can predict cell-type specific expression levels from tissues homogenates on a genome-wide scale. It thus represents a computational alternative to experimental separation methods and allowed us to identify age-related expression changes in the astrocytes of the cerebellum. These molecular changes might underlie important physiological modifications previously observed in the aging brain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.