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.

SYF2 suppression mitigates neurodegeneration in models of diverse forms of ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by many diverse genetic etiologies. Although therapeutics that specifically target causal mutations may rescue individual types of ALS, such approaches cannot treat most patients since they have unknown genetic etiology. Thus, there is a critical need for therapeutic strategies that rescue multiple forms of ALS. Here, we combine phenotypic chemical screening on a diverse cohort of ALS patient-derived neurons with bioinformatic analysis of large chemical and genetic perturbational datasets to identify broadly effective genetic targets for ALS. We show that suppressing the gene-encoding, spliceosome-associated factor SYF2 alleviates TDP-43 aggregation and mislocalization, improves TDP-43 activity, and rescues C9ORF72 and causes sporadic ALS neuron survival. Moreover, Syf2 suppression ameliorates neurodegeneration, neuromuscular junction loss, and motor dysfunction in TDP-43 mice. Thus, suppression of spliceosome-associated factors such as SYF2 may be a broadly effective therapeutic approach for ALS.

Focal enhancement of the skeleton to exercise correlates with responsivity of bone marrow mesenchymal stem cells rather than peak external forces.

Force magnitudes have been suggested to drive the structural response of bone to exercise. As importantly, the degree to which any given bone can adapt to functional challenges may be enabled, or constrained, by regional variation in the capacity of marrow progenitors to differentiate into bone-forming cells. Here, we investigate the relationship between bone adaptation and mesenchymal stem cell (MSC) responsivity in growing mice subject to exercise. First, using a force plate, we show that peak external forces generated by forelimbs during quadrupedal locomotion are significantly higher than hindlimb forces. Second, by subjecting mice to treadmill running and then measuring bone structure with µCT, we show that skeletal effects of exercise are site-specific but not defined by load magnitudes. Specifically, in the forelimb, where external forces generated by running were highest, exercise failed to augment diaphyseal structure in either the humerus or radius, nor did it affect humeral trabecular structure. In contrast, in the ulna, femur and tibia, exercise led to significant enhancements of diaphyseal bone areas and moments of area. Trabecular structure was also enhanced by running in the femur and tibia. Finally, using flow cytometry, we show that marrow-derived MSCs in the femur are more responsive to exercise-induced loads than humeral cells, such that running significantly lowered MSC populations only in the femur. Together, these data suggest that the ability of the progenitor population to differentiate toward osteoblastogenesis may correlate better with bone structural adaptation than peak external forces caused by exercise.