KCNJ2 inhibition mitigates mechanical injury in a human brain organoid model of traumatic brain injury.

Traumatic brain injury (TBI) strongly correlates with neurodegenerative disease. However, it remains unclear which neurodegenerative mechanisms are intrinsic to the brain and which strategies most potently mitigate these processes. We developed a high-intensity ultrasound platform to inflict mechanical injury to induced pluripotent stem cell (iPSC)-derived cortical organoids. Mechanically injured organoids elicit classic hallmarks of TBI, including neuronal death, tau phosphorylation, and TDP-43 nuclear egress. We found that deep-layer neurons were particularly vulnerable to injury and that TDP-43 proteinopathy promotes cell death. Injured organoids derived from C9ORF72 amyotrophic lateral sclerosis/frontotemporal dementia (ALS/FTD) patients displayed exacerbated TDP-43 dysfunction. Using genome-wide CRISPR interference screening, we identified a mechanosensory channel, KCNJ2, whose inhibition potently mitigated neurodegenerative processes in vitro and in vivo, including in C9ORF72 ALS/FTD organoids. Thus, targeting KCNJ2 may reduce acute neuronal death after brain injury, and we present a scalable, genetically flexible cerebral organoid model that may enable the identification of additional modifiers of mechanical stress.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases.

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.