Acute Steroid-Refractory Gastrointestinal Graft-Versus-Host Disease Is Not Associated With Significant Differences in Gut Taxonomic Composition Compared to Steroid-Sensitive Gastrointestinal Graft-Versus-Host Disease Immediately Before Onset of Disease.

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.

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. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.

Deconstruction of rheumatoid arthritis synovium defines inflammatory subtypes.

Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction1. There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity1,2. Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments.

Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease.

The human leukocyte antigen (HLA) locus is associated with more complex diseases than any other locus in the human genome. In many diseases, HLA explains more heritability than all other known loci combined. In silico HLA imputation methods enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and in human immunodeficiency virus infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine mapping. Here, we present a comprehensive tutorial to impute HLA alleles from genotype data. We provide detailed guidance on performing standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids either locally or using the web-based Michigan Imputation Server, which hosts a multi-ancestry HLA imputation reference panel. We also offer best practice recommendations to conduct association tests to define the alleles, amino acids, and haplotypes that affect human traits. Along with the pipeline, we provide a step-by-step online guide with scripts and available software ( https://github.com/immunogenomics/HLA_analyses_tutorial ). This tutorial will be broadly applicable to large-scale genotyping data and will contribute to defining the role of HLA in human diseases across global populations.

Methods and Insights from Single-Cell Expression Quantitative Trait Loci.

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.

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, using personalized reference genomes to mitigate technical confounding. We identified cell-type-specific cis-eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B, and T cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses.

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases.

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.

Cross-tissue, single-cell stromal atlas identifies shared pathological fibroblast phenotypes in four chronic inflammatory diseases.

BACKGROUND: Pro-inflammatory fibroblasts are critical for pathogenesis in rheumatoid arthritis, inflammatory bowel disease, interstitial lung disease, and Sjögren's syndrome and represent a novel therapeutic target for chronic inflammatory disease. However, the heterogeneity of fibroblast phenotypes, exacerbated by the lack of a common cross-tissue taxonomy, has limited our understanding of which pathways are shared by multiple diseases. METHODS: We profiled fibroblasts derived from inflamed and non-inflamed synovium, intestine, lungs, and salivary glands from affected individuals with single-cell RNA sequencing. We integrated all fibroblasts into a multi-tissue atlas to characterize shared and tissue-specific phenotypes. FINDINGS: Two shared clusters, CXCL10+CCL19+ immune-interacting and SPARC+COL3A1+ vascular-interacting fibroblasts, were expanded in all inflamed tissues and mapped to dermal analogs in a public atopic dermatitis atlas. We confirmed these human pro-inflammatory fibroblasts in animal models of lung, joint, and intestinal inflammation. CONCLUSIONS: This work represents a thorough investigation into fibroblasts across organ systems, individual donors, and disease states that reveals shared pathogenic activation states across four chronic inflammatory diseases. FUNDING: Grant from F. Hoffmann-La Roche (Roche) AG.

Repertoire analyses reveal T cell antigen receptor sequence features that influence T cell fate.

T cells acquire a regulatory phenotype when their T cell antigen receptors (TCRs) experience an intermediate- to high-affinity interaction with a self-peptide presented via the major histocompatibility complex (MHC). Using TCRß sequences from flow-sorted human cells, we identified TCR features that promote regulatory T cell (Treg) fate. From these results, we developed a scoring system to quantify TCR-intrinsic regulatory potential (TiRP). When applied to the tumor microenvironment, TiRP scoring helped to explain why only some T cell clones maintained the conventional T cell (Tconv) phenotype through expansion. To elucidate drivers of these predictive TCR features, we then examined the two elements of the Treg TCR ligand separately: the self-peptide and the human MHC class II molecule. These analyses revealed that hydrophobicity in the third complementarity-determining region (CDR3ß) of the TCR promotes reactivity to self-peptides, while TCR variable gene (TRBV gene) usage shapes the TCR's general propensity for human MHC class II-restricted activation.

Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.

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, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying 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 co-vary 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 co-varying 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.

Efficient and precise single-cell reference atlas mapping with Symphony.

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

Maximizing statistical power to detect differentially abundant cell states with scPOST.

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.

Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale.

BACKGROUND: Populations of closely related microbial strains can be simultaneously present in bacterial communities such as the human gut microbiome. We recently developed a de novo genome assembly approach that uses read cloud sequencing to provide more complete microbial genome drafts, enabling precise differentiation and tracking of strain-level dynamics across metagenomic samples. In this case study, we present a proof-of-concept using read cloud sequencing to describe bacterial strain diversity in the gut microbiome of one hematopoietic cell transplantation patient over a 2-month time course and highlight temporal strain variation of gut microbes during therapy. The treatment was accompanied by diet changes and administration of multiple immunosuppressants and antimicrobials. METHODS: We conducted short-read and read cloud metagenomic sequencing of DNA extracted from four longitudinal stool samples collected during the course of treatment of one hematopoietic cell transplantation (HCT) patient. After applying read cloud metagenomic assembly to discover strain-level sequence variants in these complex microbiome samples, we performed metatranscriptomic analysis to investigate differential expression of antibiotic resistance genes. Finally, we validated predictions from the genomic and metatranscriptomic findings through in vitro antibiotic susceptibility testing and whole genome sequencing of isolates derived from the patient stool samples. RESULTS: During the 56-day longitudinal time course that was studied, the patient's microbiome was profoundly disrupted and eventually dominated by Bacteroides caccae. Comparative analysis of B. caccae genomes obtained using read cloud sequencing together with metagenomic RNA sequencing allowed us to identify differences in substrain populations over time. Based on this, we predicted that particular mobile element integrations likely resulted in increased antibiotic resistance, which we further supported using in vitro antibiotic susceptibility testing. CONCLUSIONS: We find read cloud assembly to be useful in identifying key structural genomic strain variants within a metagenomic sample. These strains have fluctuating relative abundance over relatively short time periods in human microbiomes. We also find specific structural genomic variations that are associated with increased antibiotic resistance over the course of clinical treatment.

Intestinal microbiota domination under extreme selective pressures characterized by metagenomic read cloud sequencing and assembly.

BACKGROUND: Low diversity of the gut microbiome, often progressing to the point of intestinal domination by a single species, has been linked to poor outcomes in patients undergoing hematopoietic cell transplantation (HCT). Our ability to understand how certain organisms attain intestinal domination over others has been restricted in part by current metagenomic sequencing technologies that are typically unable to reconstruct complete genomes for individual organisms present within a sequenced microbial community. We recently developed a metagenomic read cloud sequencing and assembly approach that generates improved draft genomes for individual organisms compared to conventional short-read sequencing and assembly methods. Herein, we applied metagenomic read cloud sequencing to four stool samples collected longitudinally from an HCT patient preceding treatment and over the course of heavy antibiotic exposure. RESULTS: Characterization of microbiome composition by taxonomic classification of reads reveals that that upon antibiotic exposure, the subject's gut microbiome experienced a marked decrease in diversity and became dominated by Escherichia coli. While diversity is restored at the final time point, this occurs without recovery of the original species and strain-level composition. Draft genomes for individual organisms within each sample were generated using both read cloud and conventional assembly. Read clouds were found to improve the completeness and contiguity of genome assemblies compared to conventional assembly. Moreover, read clouds enabled the placement of antibiotic resistance genes present in multiple copies both within a single draft genome and across multiple organisms. The occurrence of resistance genes associates with the timing of antibiotics administered to the patient, and comparative genomic analysis of the various intestinal E. coli strains across time points as well as the bloodstream isolate showed that the subject's E. coli bloodstream infection likely originated from the intestine. The E. coli genome from the initial pre-transplant stool sample harbors 46 known antimicrobial resistance genes, while all other species from the pre-transplant sample each contain at most 5 genes, consistent with a model of heavy antibiotic exposure resulting in selective outgrowth of the highly antibiotic-resistant E. coli. CONCLUSION: This study demonstrates the application and utility of metagenomic read cloud sequencing and assembly to study the underlying strain-level genomic factors influencing gut microbiome dynamics under extreme selective pressures in the clinical context of HCT.

Whole blood RNA sequencing reveals a unique transcriptomic profile in patients with ARDS following hematopoietic stem cell transplantation.

BACKGROUND: The acute respiratory distress syndrome (ARDS) is characterized by the acute onset of hypoxemia and bilateral lung infiltrates in response to an inciting event, and is associated with high morbidity and mortality. Patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT) are at increased risk for ARDS. We hypothesized that HSCT patients with ARDS would have a unique transcriptomic profile identifiable in peripheral blood compared to those that did not undergo HSCT. METHODS: We isolated RNA from banked peripheral blood samples from a biorepository of critically ill ICU patients. RNA-Seq was performed on 11 patients with ARDS (5 that had undergone HSCT and 6 that had not) and 12 patients with sepsis without ARDS (5 that that had undergone HCST and 7 that had not). RESULTS: We identified 687 differentially expressed genes between ARDS and ARDS-HSCT (adjusted p-value < 0.01), including IFI44L, OAS3, LY6E, and SPATS2L that had increased expression in ARDS vs. ARDS-HSCT; these genes were not differentially expressed in sepsis vs sepsis-HSCT. Gene ontology enrichment analysis revealed that many differentially expressed genes were related to response to type I interferon. CONCLUSIONS: Our findings reveal significant differences in whole blood transcriptomic profiles of patients with non-HSCT ARDS compared to ARDS-HSCT patients and point toward different immune responses underlying ARDS and ARDS-HSCT that contribute to lung injury.

Morphology Effect of Vertical Graphene on the High Performance of Supercapacitor Electrode.

Graphene and its composites are widely investigated as supercapacitor electrodes due to their large specific surface area. However, the severe aggregation and disordered alignment of graphene sheets hamper the maximum utilization of its surface area. Here we report an optimized structure for supercapacitor electrode, i.e., the vertical graphene sheets, which have a vertical structure and open architecture for ion transport pathway. The effect of morphology and orientation of vertical graphene on the performance of supercapacitor is examined using a combination of model calculation and experimental study. Both results consistently demonstrate that the vertical graphene electrode has a much superior performance than that of lateral graphene electrode. Typically, the areal capacitances of a vertical graphene electrode reach 8.4 mF/cm(2) at scan rate of 100 mV/s; this is about 38% higher than that of a lateral graphene electrode and about 6 times higher than that of graphite paper. To further improve its performance, a MnO2 nanoflake layer is coated on the surface of graphene to provide a high pseudocapacitive contribution to the overall areal capacitance which increases to 500 mF/cm(2) at scan rate of 5 mV/s. The reasons for these significant improvements are studied in detail and are attributed to the fast ion diffusion and enhanced charge storage capacity. The microscopic manipulation of graphene electrode configuration could greatly improve its specific capacitance, and furthermore, boost the energy density of supercapacitor. Our results demonstrate that the vertical graphene electrode is more efficient and practical for the high performance energy storage device with high power and energy densities.