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Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types. Using a deep generative model, CellBender models and denoises droplet-based single-cell data and improves multiple downstream analyses.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding 1 , 2 and computer vision 3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets. A context-aware, attention-based deep learning model pretrained on single-cell transcriptomes enables predictions in settings with limited data in network biology and could accelerate discovery of key network regulators and candidate therapeutic targets.

Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases ( r g  = 0.75, P  = 6.84 × 10 −62 ). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases. Genome-wide cross-trait analysis shows a strong genetic correlation between asthma and allergic diseases. Shared susceptibility loci are enriched for genes involved in immune and inflammatory responses and genes expressed in epithelial tissues.

Background Adaptations are often made to evidence-based practices (EBPs) by systems, organizations, and/or service providers in the implementation process. The degree to which core elements of an EBP can be maintained while allowing for local adaptation is unclear. In addition, adaptations may also be needed at the system, policy, or organizational levels to facilitate EBP implementation and sustainment. This paper describes a study of the feasibility and acceptability of an implementation approach, the Dynamic Adaptation Process (DAP), designed to allow for EBP adaptation and system and organizational adaptations in a planned and considered, rather than ad hoc , way. The DAP involves identifying core elements and adaptable characteristics of an EBP, then supporting implementation with specific training on allowable adaptations to the model, fidelity monitoring and support, and identifying the need for and solutions to system and organizational adaptations. In addition, this study addresses a secondary concern, that of improving EBP model fidelity assessment and feedback in real-world settings. Methods This project examines the feasibility, acceptability, and utility of the DAP; tests the degree to which fidelity can be maintained using the DAP compared to implementation as usual (IAU); and examines the feasibility of using automated phone or internet-enabled, computer-based technology to assess intervention fidelity and client satisfaction. The study design incorporates mixed methods in order to describe processes and factors associated with variations in both how the DAP itself is implemented and how the DAP impacts fidelity, drift, and adaptation. The DAP model is to be examined by assigning six regions in California (USA) to either the DAP (n = 3) or IAU (n = 3) to implement an EBP to prevent child neglect. Discussion The DAP represents a data-informed, collaborative, multiple stakeholder approach to maintain intervention fidelity during the implementation of EBPs in the field by providing support for intervention, system, and organizational adaptation and intervention fidelity to meet local needs. This study is designed to address the real-world implications of EBP implementation in public sector service systems and is relevant for national, state, and local service systems and organizations.

Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function 1 , 2 and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts 3 , 4 , but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL , which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32–1.54, P = 3.3 × 10 −20 ). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images. Genome-wide association analyses identify variants associated with thoracic aortic diameter. A polygenic score for ascending aortic diameter was associated with a diagnosis of thoracic aortic aneurysm in independent samples.

Dilated cardiomyopathy (DCM) is an important cause of heart failure and the leading indication for heart transplantation. Many rare genetic variants have been associated with DCM, but common variant studies of the disease have yielded few associated loci. As structural changes in the heart are a defining feature of DCM, we report a genome-wide association study of cardiac magnetic resonance imaging (MRI)-derived left ventricular measurements in 36,041 UK Biobank participants, with replication in 2184 participants from the Multi-Ethnic Study of Atherosclerosis. We identify 45 previously unreported loci associated with cardiac structure and function, many near well-established genes for Mendelian cardiomyopathies. A polygenic score of MRI-derived left ventricular end systolic volume strongly associates with incident DCM in the general population. Even among carriers of TTN truncating mutations, this polygenic score influences the size and function of the human heart. These results further implicate common genetic polymorphisms in the pathogenesis of DCM. Structural changes to the left ventricle are characteristic of dilated cardiomyopathy (DCM), a disease for which many rare genetic variants are known. Here, Pirruccello et al. report GWAS of seven cardiac MRI measurements in the left ventricle and describe shared loci and polygenic association with DCM.

Marine Group I (MGI) Thaumarchaeota are one of the most abundant and cosmopolitan chemoautotrophs within the global dark ocean. To date, no representatives of this archaeal group retrieved from the dark ocean have been successfully cultured. We used single cell genomics to investigate the genomic and metabolic diversity of thaumarchaea within the mesopelagic of the subtropical North Pacific and South Atlantic Ocean. Phylogenetic and metagenomic recruitment analysis revealed that MGI single amplified genomes (SAGs) are genetically and biogeographically distinct from existing thaumarchaea cultures obtained from surface waters. Confirming prior studies, we found genes encoding proteins for aerobic ammonia oxidation and the hydrolysis of urea, which may be used for energy production, as well as genes involved in 3-hydroxypropionate/4-hydroxybutyrate and oxidative tricarboxylic acid pathways. A large proportion of protein sequences identified in MGI SAGs were absent in the marine cultures Cenarchaeum symbiosum and Nitrosopumilus maritimus, thus expanding the predicted protein space for this archaeal group. Identifiable genes located on genomic islands with low metagenome recruitment capacity were enriched in cellular defense functions, likely in response to viral infections or grazing. We show that MGI Thaumarchaeota in the dark ocean may have more flexibility in potential energy sources and adaptations to biotic interactions than the existing, surface-ocean cultures.

Sekar Kathiresan and colleagues perform a genome-wide association test for coronary artery disease (CAD) using data from the UK Biobank. They identify 15 new loci and perform phenome-wide association scanning, implicating insulin resistance pathways and transendothelial migration of leukocytes in CAD. UK Biobank is among the world's largest repositories for phenotypic and genotypic information in individuals of European ancestry 1 . We performed a genome-wide association study in UK Biobank testing ∼9 million DNA sequence variants for association with coronary artery disease (4,831 cases and 115,455 controls) and carried out meta-analysis with previously published results. We identified 15 new loci, bringing the total number of loci associated with coronary artery disease to 95 at the time of analysis. Phenome-wide association scanning showed that CCDC92 likely affects coronary artery disease through insulin resistance pathways, whereas experimental analysis suggests that ARHGEF26 influences the transendothelial migration of leukocytes.

Families experiencing child maltreatment or risk factors for child maltreatment often receive referrals to interventions focused on changing parenting practices. Compliance with specific parenting programs can be challenging as many of the stressors that place families at-risk may also interfere with program participation. Because families may receive limited benefit from programs they do not fully receive, it is critical to understand the relationship between parenting stress and barriers to program completion. We used structural equation modeling to examine the relationship among parenting stress, perceived barriers to program participation, and program completion in two datasets involving low-income parents. Data were collected at two time points from a sample of parents involved with child welfare services and a sample of parents considered at-risk of future involvement (total study n = 803). Direct paths from parenting stress at time 1 to barriers to participation and parenting stress at time 2, and from parenting stress at time 2 to program completion were significant. Interestingly, increased barriers to participation were related to increased parenting stress at time 2, and greater parenting stress was related to increased program completion. Results suggest that with increasing levels of parenting stress, parents have an increased likelihood of completing the program. Assessing and addressing the influence of perceived barriers and parenting stress on program participation may decrease the likelihood of treatment attrition.