Correction: Seasonal Effects on Gene Expression.

[This corrects the article DOI: 10.1371/journal.pone.

scPrediXcan integrates advances in deep learning and single-cell data into a powerful cell-type-specific transcriptome-wide association study framework.

Transcriptome-wide association studies (TWAS) help identify disease causing genes, but often fail to pinpoint disease mechanisms at the cellular level because of the limited sample sizes and sparsity of cell-type-specific expression data. Here we propose scPrediXcan which integrates state-of-the-art deep learning approaches that predict epigenetic features from DNA sequences with the canonical TWAS framework. Our prediction approach, ctPred, predicts cell-type-specific expression with high accuracy and captures complex gene regulatory grammar that linear models overlook.

The generation of stable microvessels in ischemia is mediated by endothelial cell derived TRAIL.

Reversal of ischemia is mediated by neo-angiogenesis requiring endothelial cell (EC) and pericyte interactions to form stable microvascular networks. We describe an unrecognized role for tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) in potentiating neo-angiogenesis and vessel stabilization. We show that the endothelium is a major source of TRAIL in the healthy circulation compromised in peripheral artery disease (PAD).

Identifying genetic variants that influence the abundance of cell states in single-cell data.

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants.

Efficient and accurate mixed model association tool for single-cell eQTL analysis.

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets.

Exercise rejuvenates microglia and reverses T cell accumulation in the aged female mouse brain.

Slowing and/or reversing brain ageing may alleviate cognitive impairments. Previous studies have found that exercise may mitigate cognitive decline, but the mechanisms underlying this remain largely unclear. Here we provide unbiased analyses of single-cell RNA sequencing data, showing the impacts of exercise and ageing on specific cell types in the mouse hippocampus.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases.

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible.

Deep Learning-Based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell Morphology.

Purpose: Genome-wide association studies have recently uncovered many loci associated with variation in intraocular pressure (IOP). Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs) and thus, IOP regulation.

Design: Experimental study.

A Pilot Study Using Machine Learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery.

Objective: To evaluate whether a machine learning algorithm (i.e. the "NightSignal" algorithm) can be used for the detection of postoperative complications prior to symptom onset after cardiothoracic surgery.

Deciphering cancer cell state plasticity with single-cell genomics and artificial intelligence.

Cancer stem cell plasticity refers to the ability of tumour cells to dynamically switch between states-for example, from cancer stem cells to non-cancer stem cell states. Governed by regulatory processes, cells transition through a continuum, with this transition space often referred to as a cell state landscape. Plasticity in cancer cell states leads to divergent biological behaviours, with certain cell states, or state transitions, responsible for tumour progression and therapeutic response.

Molecular profiling of human substantia nigra identifies diverse neuron types associated with vulnerability in Parkinson's disease.

Parkinson's disease (PD) is characterized pathologically by the loss of dopaminergic (DA) neurons in the substantia nigra (SN). Whether cell types beyond DA neurons in the SN show vulnerability in PD remains unclear. Through transcriptomic profiling of 315,867 high-quality single nuclei in the SN from individuals with and without PD, we identified cell clusters representing various neuron types, glia, endothelial cells, pericytes, fibroblasts, and T cells and investigated cell type-dependent alterations in gene expression in PD.

Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution.

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues.

The potential clinical impact of cell type-specific genetic regulation: Crohn's disease.

Complex diseases are heterogenous due to variation in their genetic and environmental underpinnings, leading to varied treatment responses. Genome-wide association studies (GWAS) integrated with single-cell expression quantitative trait loci analyses (eQTL) can pinpoint cell-type specific candidate disease-relevant genes and pathways. This knowledge can be applied to patient stratification and novel therapeutic target identification.

Novel human pluripotent stem cell-derived hypothalamus organoids demonstrate cellular diversity.

The hypothalamus is a region of the brain that plays an important role in regulating body functions and behaviors. There is a growing interest in human pluripotent stem cells (hPSCs) for modeling diseases that affect the hypothalamus. Here, we established an hPSC-derived hypothalamus organoid differentiation protocol to model the cellular diversity of this brain region.

A village in a dish model system for population-scale hiPSC studies.

The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and different conditions. Human induced pluripotent stem cells are uniquely suited to study these context-dependent effects but cell lines from hundreds or thousands of individuals are required. Village cultures, where multiple induced pluripotent stem lines are cultured and differentiated in a single dish, provide an elegant solution for scaling induced pluripotent stem experiments to the necessary sample sizes required for population-scale studies.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population.

Transitioning single-cell genomics into the clinic.

The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity.

Single-cell genomics meets human genetics.

Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease.

Mapping the dynamic genetic regulatory architecture of genes at single-cell resolution.

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation, and cancer. While coding variation in genes has been extensively documented, regulatory genetic variation modulating expression levels has not been comprehensively investigated. Here, we mapped expression quantitative trait loci (eQTLs) for classical genes across 1,073 individuals and 1,131,414 single cells from three tissues, using personalized reference genomes to mitigate technical confounding.

Role of International Oil Companies in the Net-Zero Emission Energy Transition.

Scientific and engineering capabilities in hydrocarbon supply chains developed over decades in international oil and gas companies (IOCs) uniquely position these companies to drive rapid scale-up and transition to a net-zero emission economy. Flexible large-scale production of energy carriers such as hydrogen, ammonia, methanol, and other synthetic fuels produced with low- or zero-emission renewable power, nuclear energy, or hydrogen derived from natural gas with carbon capture and storage will enable long-distance transport and permanent storage options for clean energy. Use of energy carriers can overcome the inherent constraints of a fully electrified energy system by providing the energy and power densities, as well as transport and storage capacity, required to achieve energy supply and security in a net-zero emission economy, and over time allow optimization to the lowest cost for a consumer anywhere on the globe.