Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain.
Axonal projection density
Bayesian machine learning
Computational framework
Connectomics
Dictionary learning and sparse coding
Gene expression
In situ hybridization
Linked ICA
Matrix factorisation
Mouse brain mesoconnectome
Spatial transcriptomics
Tract-tracing
Volumetric brain representation
Journal
Neuroinformatics
ISSN: 1559-0089
Titre abrégé: Neuroinformatics
Pays: United States
ID NLM: 101142069
Informations de publication
Date de publication:
10 2021
10 2021
Historique:
accepted:
06
01
2021
pubmed:
12
3
2021
medline:
24
11
2021
entrez:
11
3
2021
Statut:
ppublish
Résumé
Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.
Identifiants
pubmed: 33704701
doi: 10.1007/s12021-021-09511-0
pii: 10.1007/s12021-021-09511-0
pmc: PMC8566442
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
649-667Informations de copyright
© 2021. The Author(s).
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