Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types.
Samuel Sungil KimBuu TruongKarthik JagadeeshKushal K DeyAmber Z ShenSoumya RaychaudhuriManolis KellisAlkes L PricePublished in: Nature communications (2024)
Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.
Keyphrases
- single cell
- rna seq
- genome wide
- high throughput
- gene expression
- major depressive disorder
- high resolution
- white matter
- bipolar disorder
- stem cells
- multiple sclerosis
- attention deficit hyperactivity disorder
- spinal cord injury
- bone marrow
- mesenchymal stem cells
- oxidative stress
- electronic health record
- induced apoptosis
- autism spectrum disorder
- machine learning
- dna damage
- young adults
- big data
- brain injury
- signaling pathway
- cell cycle arrest
- transcription factor
- deep learning