ArticleScientific reports2020
Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Profiling lamina specific pyramidal neurons using postmortem human formalin fixed paraffin embedded frontal cortex tissue in combination with digital spatial profiling.Journal of neuroscience methods · 2025Article
- Gaining insights into Alzheimer's disease by predicting chromatin spatial organization.Bioinformatics advances · 2025Article
- Advancements in Single-Cell RNA Sequencing Research for Neurological Diseases.Molecular neurobiology · 2024Review
- Microglia-targeted inhibition of miR-17 via mannose-coated lipid nanoparticles improves pathology and behavior in a mouse model of Alzheimer's disease.Brain, behavior, and immunity · 2024Article
- Single-cell analysis reveals the spatial-temporal expression of genes associated with esophageal malformations.Scientific reports · 2024Article
- Comprehensive analysis of genetic associations and single-cell expression profiles reveals potential links between migraine and multiple diseases: a phenome-wide association study.Frontiers in neurology · 2024Article
- Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.Genome medicine · 2022Article
- Neuropathology of Alzheimer's Disease.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- Nuclear dynamics and stress responses in Alzheimer's disease.Molecular neurodegeneration · 2021Review
- Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions.International journal of molecular sciences · 2019Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer's brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.
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