Functional interpretation of single cell similarity maps.

We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration

Lineage dynamics of murine pancreatic development at single-cell resolution.

Organogenesis requires the complex interactions of multiple cell lineages that coordinate their expansion, differentiation, and maturation over time. Here, we profile the cell types within the epithelial and mesenchymal compartments of the murine pancreas across developmental time using a combination of single-cell RNA sequencing, immunofluorescence, in situ hybridization, and genetic lineage tracing. We identify previously underappreciated cellular heterogeneity of the developing mesenchyme and reconstruct potential lineage relationships among the pancreatic mesothelium and mesenchymal cell types. Within the epithelium, we find a previously undescribed endocrine progenitor population, as well as an analogous population in both human fetal tissue and human embryonic stem cells differentiating toward a pancreatic beta cell fate. Further, we identify candidate transcriptional regulators along the differentiation trajectory of this population toward the alpha or beta cell lineages. This work establishes a roadmap of pancreatic development and demonstrates the broad utility of this approach for understanding lineage dynamics in developing organs

Implementing and Applying Multiplexed Single Cell RNA-sequencing to Reveal Context-specific Effects in Systemic Lupus Erythematosus

Droplet single-cell RNA-sequencing (dscRNA-seq) has enabled rapid, massively parallel profiling of transcriptomes. However, assessing differential expression across multiple individuals has been hampered by inefficient sample processing and technical batch effects. Here we describe a computational tool, demuxlet, that harnesses natural genetic variation to determine the sample identity of each cell and detect droplets containing two cells. These capabilities enable multiplexed dscRNA-seq experiments in which cells from unrelated individuals are pooled and captured at higher throughput than in standard workflows. Using simulated data, we show that 50 SNPs per cell are sufficient to assign 97% of singlets and identify 92% of doublets in pools of up to 64 individuals. Given genotyping data for each of 8 pooled samples, demuxlet correctly recovers the sample identity of >99% of singlets and identifies doublets at rates consistent with previous estimates. We also apply demuxlet to assess cell type-specific changes in gene expression in 8 pooled lupus patient samples treated with IFN- and perform eQTL analysis on 23 pooled samples.Systemic lupus erythematosus (SLE) is an autoimmune disease defined by a broad range of symptoms that disproportionately affects women. Our knowledge of which immune cells mediate the etiology and pathogenesis of the disease remains incomplete. Identifying pathogenic cells using bulk gene expression analysis is confounded by the functional overlap and frequency variation of immune cell types. Here, we used multiplexed single-cell RNA-seq (scRNA-seq) to profile ~1 million peripheral blood mononuclear cells from 134 SLE cases and 58 healthy controls. Cases were marked by a reduction of naive CD4+ T cells, clonal restriction of effector memory CD8+ T cells, and elevated expression of interferon-stimulated genes in classical monocytes. An additional 15 cases experiencing active disease flares displayed increased expansion of effector memory CD8+ T cells and the presence of macrophages not seen in managed disease. Although cell-type-specific expression contributed most to inter-individual expression variability across all cells, cell composition accounted for more variability in genes differentially expressed in cases. We integrated dense genotyping data to map thousands of genetic variants, including SLE-associations, whose effects on expression are modified by cell type or interferon activation. Population-scale scRNA-seq analysis reveals changes in cell composition and state associated with SLE, and when integrated with genetic data, ascribes function to disease-associated and disease-modified variants

Quantifying the Reproducibility of Cell-Perturbation Experiments

Experiments adhering to the same protocol can nonetheless lead to different conclusions, for instance, due to batch effects or lab effects. A statistical test applied to measurements from one experiment may yield a vanishingly small $p$-value, yet applying the same test to measurements from a replicate experiment may yield a large $p$-value. Recent work has highlighted this lack of reproducibility in cell-perturbation experiments. We introduce the Reproducible Sign Rate (RSR), a new reproducibility metric for settings in which each hypothesis test has two alternatives (e.g., upregulation and downregulation of gene expression). The RSR identifies the proportion of discoveries that are expected to reproduce in a future replicate. We provide conditions under which the RSR can be estimated accurately -- even when as few as two experimental replicates are available. We also provide conditions under which high RSR implies a low Type S error rate. We demonstrate the uses of RSR with experiments based on several high-throughput technologies, including L1000, Sci-Plex, and CRISPR.Comment: Submitted to AoA

Genetic determinants of co-accessible chromatin regions in activated T cells across humans.

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, consistent with the three-dimensional chromatin organization measured by in situ Hi-C in T cells. Fifteen percent of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (local-ATAC-QTLs). Local-ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression

Green synthesized CaO decorated ternary CaO/g-C3N4/PVA nanocomposite modified glassy carbon electrode for enhanced electrochemical detection of caffeic acid

A highly selective, sensitive caffeic acid (CA) detection based on calcium oxide nanoparticles (CaO NPs) derived from extract of Moringa oleifera leaves decorated graphitic carbon nitride covalently grafted poly vinyl alcohol (CaO/g-C3N4/PVA) nanocomposite modified glassy carbon electrode (GCE) was studied. A facile sonochemical method was adapted to synthesis nanomaterials and characterized by HR-TEM (High resolution transmission electron microscopy), FT-IR (Fourier transform infrared spectroscopy), XRD (X-ray diffraction), FE-SEM (Field emission scanning electron microscopy), EDX (Energy dispersive X-ray analysis), Mapping and BET (Brunauer-Emmett-Teller) analysis, and electrochemical techniques. The nanocomposite modified GCE exhibited an excellent catalytic performance to the oxidation of CA under optimized conditions owing to better electron transfer efficiency, conductivity and high surface area of the electrode material. The present electrochemical sensor showed high selectivity towards the determination of 10 µM CA in the presence of 100-fold higher concentrations of interferents. The modified CA sensor exhibited a wide sensing linear range from 0.01 µM to 70 µM and the detection limit (LOD) was found to be 0.0024 µM (S/N = 3) in 0.1 M phosphate buffer saline (PBS) as a supporting electrolyte at pH 7.0. The fabricated CA sensor provides an excellent stability, reproducibility and selectivity for the determination of CA. The modified CA sensor was applied to real blood plasma samples and obtained good recovery (97.6-100.1%) results. © The Author(s) 2024

Genetic and environmental perturbations lead to regulatory decoherence

Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among N MR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease

Author Correction: Anti-GD2 CAR-NKT cells in relapsed or refractory neuroblastoma: updated phase 1 trial interim results

Correction to "Anti-GD2 CAR-NKT cells in relapsed or refractory neuroblastoma: updated phase 1 trial interim results

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For schoolaged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit