Repetitive head impacts (RHI) and traumatic brain injuries are risk factors for the neurodegenerative diseases chronic traumatic encephalopathy (CTE) and amyotrophic lateral sclerosis (ALS). ALS and CTE are distinct disorders, yet in some instances, share pathology, affect similar brain regions, and occur together. The pathways involved and biomarkers for diagnosis of both diseases are largely unknown. MicroRNAs (miRNAs) involved in gene regulation may be altered in neurodegeneration and be useful as stable biomarkers. Thus, we set out to determine associations between miRNA levels and disease state within the prefrontal cortex in a group of brain donors with CTE, ALS, CTE + ALS and controls. Of 47 miRNAs previously implicated in neurological disease and tested here, 28 (60%) were significantly different between pathology groups. Of these, 21 (75%) were upregulated in both ALS and CTE, including miRNAs involved in inflammatory, apoptotic, and cell growth/differentiation pathways. The most significant change occurred in miR-10b, which was significantly increased in ALS, but not CTE or CTE + ALS. Overall, we found patterns of miRNA expression that are common and unique to CTE and ALS and that suggest shared and distinct mechanisms of pathogenesis.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet (DiseaseNeurogenomics.github.io/dreamlet) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4M single nuclei from postmortem brains of 150 Alzheimer’s disease cases and 149 controls.