BrainImageR: spatiotemporal gene set analysis referencing the human brain
Open accessPublished version of a preprint
Abstract
Motivation: Neuronal analyses such as transcriptomics, epigenetics and genome-wide association studies must be assessed in the context of the human brain to generate biologically meaningful inferences. It is often difficult to access primary human brain tissue; therefore, approximations are made using alternative sources such as peripheral tissues or in vitro-derived neurons. Gene sets from these studies are then assessed for their association with the post-mortem human brain. However, most analyses of post-mortem datasets are achieved by building new computational tools each time in-house, which can cause discrepancies from study to study. The field is in need of a user-friendly tool to examine spatiotemporal expression with respect to the postmortem brain. Such a tool will be of use in the molecular interrogation of neurological and psychiatric disorders, with direct advantages for the disease-modeling and human genetics communities. Results: We have developed brainImageR, an R package that calculates both the spatial and temporal association of a dataset with post-mortem human brain. BrainImageR identifies anatomical regions enriched for candidate gene set expression. It further predicts the developmental time point of the sample, a task that has become increasingly important in the field of in vitro neuronal modeling. These functionalities of brainImageR enable a quick and efficient characterization of a given dataset across normal human brain development. Availability and implementation: BrainImageR is released under the Creative Commons CC BY-SA 4.0 license and can be accessed directly at brainimager.salk.edu or the R code can be downloaded through github at https://github.com/saralinker/brainImageR.
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Microglial gene expression was enriched in multiple brain regions, some of which are implicated in Alzheimer's disease (AD), such as the parahippocampal gyrus, temporal pole, fusiform gyrus, and amygdalohippocampal transition zone.
“Interestingly, microglial gene expression was enriched in multiple brain regions, some of which are implicated in Alzheimer's disease (AD), such as the parahippocampal gyrus, temporal pole, fusiform gyrus, and amygdalohippocampal transition zone.”
From The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease (2019) · cites this paper as [14]
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- Authors
- Linker, S. B., Hsu, J. Y., Pfaff, A., Amatya, D., Ko, S.-M., Voter, S., Wong, Q., & Gage, F. H.
- Affiliations
- Salk Institute for Biological Studies (US)
- Corresponding author
- Linker, Sara B
- Gage, Fred H
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-07-13
- Volume
- 35
- Issue
- 2
- Pages
- 343-345
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC6657270
- OpenAlex
- W2949573613
- Cited by
- 7
- References
- 14
- Citations by year
- 2024: 1 · 2022: 1 · 2020: 2 · 2019: 3
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-sa)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Lynn and Edward Streim
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- postmortem brain, spatiotemporal gene expression, gene set enrichment analysis, brain development, in vitro neuronal model, peripheral tissues, transcriptomics, epigenetics, genome-wide association study, R package, neuropsychiatric disorders
- MeSH terms
- Brain, Humans, Neurons, Software, Computational Biology, Epigenesis, Genetic, Genome-Wide Association Study, Transcriptome
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