Genome-wide association studies (GWASs) of eye disorders have identified hundreds of genetic variants associated with ocular disease. However, the vast majority of these variants are noncoding, ...making it challenging to interpret their function. Here we present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina with more than 50,000 cells, which we used to analyze single-nucleotide polymorphisms (SNPs) implicated by GWASs of age-related macular degeneration, glaucoma, diabetic retinopathy, myopia, and type 2 macular telangiectasia. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding SNPs with causal roles in eye disease, assess SNP impact on transcription factor binding, and define their known and novel target genes. Our efforts nominate pathogenic SNP-target gene interactions for multiple vision disorders and provide a potentially powerful resource for interpreting noncoding variation in the eye.
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•Single-cell RNA and chromatin profiling of human retina characterizes 13 cell types•H3K27ac HiChIP of human retina identifies enhancer-promoter connections•Deep learning predicts effects of single base changes on chromatin accessibility•Integrative approach prioritizes noncoding risk variants in complex eye diseases
Wang et al. present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina, which they use to analyze risk variants from genome-wide association studies of five eye diseases. They integrate this atlas with chromatin conformation data, expression quantitative trait loci, and deep learning models to nominate gene and cellular targets in the retina for hundreds of noncoding variants implicated in vision disorders.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Significant evidence suggests that plasma-rich in growth factors (PRGF) favor the repair of chronic wounds, enabling a rapid return to functionality. However, components of PRGF and their effects on ...persistent ulcers and epithelial tissues are not well characterized. The goals of this research were to analyze the biological properties of platelet-derived factors, to examine their effectiveness on healing of venous ulcers, and to establish a correlation with clinical and sociodemographic data.
For the preparation of PRGF, the centrifugation technique was used, obtaining a 100 % autologous and biocompatible blood sample that was treated with sodium citrate and calcium chloride. The patients were attended weekly at the outpatient clinic for nursing consultation and wound dressing changes, with PRGF application every 15 days. The treatment protocols are described, and follow-up results are reported.
Initially, the patients' ulcers ranged in sizes from 4 to 84 cm2. After 12 weeks of treatment, there was a significant mean reduction of 46.2 % in ulcer area. At baseline, epithelial tissue was absent in all venous ulcers, but its presence grew significantly by the treatment period. However, the reduction of the area of the ulcers did not show significant correlation with the concentrations of the patient's growth factors.
Using the established protocol for PRGF isolating, it was possible to obtain a product with the presence of the six growth factors related to tissue regeneration and observed a positive response on wound healing following treatment of venous ulcers, with capacity to accelerate re-epithelialization and restore the skin functional integrity.
•Weekly application of PRGF may support the repair of chronic wounds.•Epithelial tissue was absent but grew significantly with treatment of venous ulcers.•The use of plasma rich in growth factors provide positive wound healing response.•Treatment accelerates re-epithelialization and restore the skin functional integrity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Taxonomic composition of the gut microbiota at the time of neutrophil engraftment is associated with the development of acute gastrointestinal graft-versus-host disease (GI GVHD) in patients ...undergoing allogeneic hematopoietic stem cell transplantation. However, less is known about the relationship between the gut microbiota and development of steroid-refractory GI GVHD immediately before the onset of disease. Markers of steroid-refractory GI GVHD are needed to identify patients who may benefit from the early initiation of non-corticosteroid-based GVHD treatment. Our aim was to identify differences in taxonomic composition in stool samples from patients without GVHD, with steroid-responsive GVHD and with steroid-refractory GI GVHD to identify predictive microbiome biomarkers of steroid-refractory GI GVHD. We conducted a retrospective case-control, single institution study, performing shotgun metagenomic sequencing on stool samples from patients with (n = 36) and without GVHD (n = 34) matched for time since transplantation. We compared the taxonomic composition of the gut microbiome in those with steroid-sensitive GI GVHD (n = 17) and steroid-refractory GI GVHD (n = 19) to each other and to those without GVHD. We also performed associations between steroid-refractory GI GVHD, gut taxonomic composition, and fecal calprotectin, a marker of GI GVHD to develop composite fecal markers of steroid-refractory GVHD before the onset of GI disease. We found that fecal samples within 30 days of GVHD onset from patients with and without GVHD or with and without steroid-refractory GI GVHD did not differ significantly in Shannon diversity (alpha-diversity) or in overall taxonomic composition (beta-diversity). Although those patients without GVHD had higher relative abundance of Clostridium spp., those with and without steroid-refractory GI GVHD did not significantly differ in taxonomic composition between one another. In our study, fecal calprotectin before disease onset was significantly higher in patients with GVHD compared to those without GVHD and higher in patients with steroid-refractory GI GVHD compared to steroid-sensitive GI GVHD. No taxa were significantly associated with higher levels of calprotectin.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging ...for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.
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•single-cell POwer Simulation Tool (scPOST) is a fast and flexible simulation framework•scPOST allows users to compare study designs to assess how they affect detection power•Increasing sample size improves power more than increasing cells per sample
To estimate a study design's power to detect differential abundance, we require a framework that simulates many multi-sample single-cell datasets. However, current simulation methods are challenging for large-scale power analyses because they are computationally resource intensive and do not support easy simulation of multi-sample datasets. Current methods also lack modeling of important inter-sample variation, such as the variation in the frequency of cell states between samples that is observed in single-cell data. Thus, we developed single-cell POwer Simulation Tool (scPOST) to address these limitations and help investigators quickly simulate multi-sample single-cell datasets. Users may explore a range of effect sizes and study design choices (such as increasing the number of samples or cells per sample) to determine their effect on power, and thus choose the optimal study design for their planned experiments.
Millard et al. present single-cell POwer Simulation Tool (scPOST), a tool that allows investigators to simulate single-cell datasets in order to perform power analyses for study design optimization, as well as compare and assess algorithm performance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, ...which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes like clinical phenotypes. Current ...statistical approaches typically map cells to clusters then assess differences in cluster abundance. We present covarying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space—termed neighborhoods—that covary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these covarying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis, and identifies a novel T-cell population associated with progression to active tuberculosis.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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