The neocortical infrastructure for language involves region-specific patterns of laminar gene expression.
Maggie M K WongZhiqiang ShaLukas LütjeXiang-Zhen KongSabrina van HeukelumWilma D J van de BergLaura E JonkmanSimon E FisherClyde FrancksPublished in: Proceedings of the National Academy of Sciences of the United States of America (2024)
The language network of the human brain has core components in the inferior frontal cortex and superior/middle temporal cortex, with left-hemisphere dominance in most people. Functional specialization and interconnectivity of these neocortical regions is likely to be reflected in their molecular and cellular profiles. Excitatory connections between cortical regions arise and innervate according to layer-specific patterns. Here, we generated a gene expression dataset from human postmortem cortical tissue samples from core language network regions, using spatial transcriptomics to discriminate gene expression across cortical layers. Integration of these data with existing single-cell expression data identified 56 genes that showed differences in laminar expression profiles between the frontal and temporal language cortex together with upregulation in layer II/III and/or layer V/VI excitatory neurons. Based on data from large-scale genome-wide screening in the population, DNA variants within these 56 genes showed set-level associations with interindividual variation in structural connectivity between the left-hemisphere frontal and temporal language cortex, and with the brain-related disorders dyslexia and schizophrenia which often involve affected language. These findings identify region-specific patterns of laminar gene expression as a feature of the brain's language network.
Keyphrases
- gene expression
- functional connectivity
- resting state
- dna methylation
- genome wide
- autism spectrum disorder
- single cell
- electronic health record
- poor prognosis
- big data
- working memory
- white matter
- machine learning
- copy number
- endothelial cells
- spinal cord
- rna seq
- deep learning
- single molecule
- long non coding rna
- spinal cord injury
- multiple sclerosis
- high throughput
- bipolar disorder
- cerebral ischemia
- subarachnoid hemorrhage
- circulating tumor
- blood brain barrier
- binding protein