ArticleScientific reports2016
Analysis of spatial-temporal gene expression patterns reveals dynamics and regionalization in developing mouse brain.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 28 citations in OpenAlex.
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- Dynamic Spatial-temporal Expression Ratio of X Chromosome to Autosomes but Stable Dosage Compensation in Mammals.Genomics, proteomics & bioinformatics · 2023Article
- Evolution of genetic mechanisms regulating cortical neurogenesis.Developmental neurobiology · 2022Review
- Emerging Roles of RNA-Binding Proteins in Neurodevelopment.Journal of developmental biology · 2022Review
- Transcriptome analysis provides genome annotation and expression profiles in the central nervous system of Lymnaea stagnalis at different ages.BMC genomics · 2021Article
- Down-Regulation ofFrontiers in neurology · 2021Article
- Differential Spatiotemporal Expression of Type I and Type II Cadherins Associated With the Segmentation of the Central Nervous System and Formation of Brain Nuclei in the Developing Mouse.Frontiers in molecular neuroscience · 2021Article
- Magnetically guided virus stamping for the targeted infection of single cells or groups of cells.Nature protocols · 2019Article
- Differential effects on neurodevelopment of FTO variants in obesity and bipolar disorder suggested by in silico prediction of functional impact: An analysis in Mexican population.Brain and behavior · 2019Article
- Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis.Cell research · 2018Article
- Region-specific RNA mOpen biology · 2017Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Allen Brain Atlas (ABA) provides a valuable resource of spatial/temporal gene expressions in mammalian brains. Despite rich information extracted from this database, current analyses suffer from several limitations. First, most studies are either gene-centric or region-centric, thus are inadequate to capture the superposition of multiple spatial-temporal patterns. Second, standard tools of expression analysis such as matrix factorization can capture those patterns but do not explicitly incorporate spatial dependency. To overcome those limitations, we proposed a computational method to detect recurrent patterns in the spatial-temporal gene expression data of developing mouse brains. We demonstrated that regional distinction in brain development could be revealed by localized gene expression patterns. The patterns expressed in the forebrain, medullary and pontomedullary, and basal ganglia are enriched with genes involved in forebrain development, locomotory behavior, and dopamine metabolism respectively. In addition, the timing of global gene expression patterns reflects the general trends of molecular events in mouse brain development. Furthermore, we validated functional implications of the inferred patterns by showing genes sharing similar spatial-temporal expression patterns with Lhx2 exhibited differential expression in the embryonic forebrains of Lhx2 mutant mice. These analysis outcomes confirm the utility of recurrent expression patterns in studying brain development.
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