Visualizing scRNA-Seq data at population scale with GloScope.

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

Improving replicability in single-cell RNA-Seq cell type discovery with Dune.

BACKGROUND: Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable. RESULTS: Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor: https://www.bioconductor.org/packages/release/bioc/html/Dune.html . CONCLUSIONS: Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.

A single-cell atlas of IL-23 inhibition in cutaneous psoriasis distinguishes clinical response.

Psoriasis vulgaris and other chronic inflammatory diseases improve markedly with therapeutic blockade of interleukin-23 (IL-23) signaling, but the genetic mechanisms underlying clinical responses remain poorly understood. Using single-cell transcriptomics, we profiled immune cells isolated from lesional psoriatic skin before and during IL-23 blockade. In clinically responsive patients, a psoriatic transcriptional signature in skin-resident memory T cells was strongly attenuated. In contrast, poorly responsive patients were distinguished by persistent activation of IL-17-producing T (T17) cells, a mechanism distinct from alternative cytokine signaling or resistance isolated to epidermal keratinocytes. Even in IL-23 blockade-responsive patients, we detected a recurring set of recalcitrant, disease-specific transcriptional abnormalities. This irreversible immunological state may necessitate ongoing IL-23 inhibition. Spatial transcriptomic analyses also suggested that successful IL-23 blockade requires dampening of >90% of IL-17-induced response in lymphocyte-adjacent keratinocytes, an unexpectedly high threshold. Collectively, our data establish a patient-level paradigm for dissecting responses to immunomodulatory treatments.

A Latent Activated Olfactory Stem Cell State Revealed by Single Cell Transcriptomic and Epigenomic Profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration after injury-induced cell death. In this study, we used single cell sequencing to identify major regulatory players in determining olfactory epithelial stem cell fate after acute injury. We combined gene expression and accessible chromatin profiles of individual lineage traced olfactory stem cells to predict transcription factor activity specific to different lineages and stages of recovery. We further identified a discrete stem cell state that appears poised for activation, characterized by accessible chromatin around wound response and lineage specific genes prior to their later expression in response to injury. Together these results provide evidence that a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Visualizing scRNA-Seq Data at Population Scale with GloScope.

Increasingly scRNA-Seq studies explore the heterogeneity of cell populations across different samples and its effect on an organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call its GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples. These examples demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

Successional adaptive strategies revealed by correlating arbuscular mycorrhizal fungal abundance with host plant gene expression.

The shifts in adaptive strategies revealed by ecological succession and the mechanisms that facilitate these shifts are fundamental to ecology. These adaptive strategies could be particularly important in communities of arbuscular mycorrhizal fungi (AMF) mutualistic with sorghum, where strong AMF succession replaces initially ruderal species with competitive ones and where the strongest plant response to drought is to manage these AMF. Although most studies of agriculturally important fungi focus on parasites, the mutualistic symbionts, AMF, constitute a research system of human-associated fungi whose relative simplicity and synchrony are conducive to experimental ecology. First, we hypothesize that, when irrigation is stopped to mimic drought, competitive AMF species should be replaced by AMF species tolerant to drought stress. We then, for the first time, correlate AMF abundance and host plant transcription to test two novel hypotheses about the mechanisms behind the shift from ruderal to competitive AMF. Surprisingly, despite imposing drought stress, we found no stress-tolerant AMF, probably due to our agricultural system having been irrigated for nearly six decades. Remarkably, we found strong and differential correlation between the successional shift from ruderal to competitive AMF and sorghum genes whose products (i) produce and release strigolactone signals, (ii) perceive mycorrhizal-lipochitinoligosaccharide (Myc-LCO) signals, (iii) provide plant lipid and sugar to AMF, and (iv) import minerals and water provided by AMF. These novel insights frame new hypotheses about AMF adaptive evolution and suggest a rationale for selecting AMF to reduce inputs and maximize yields in commercial agriculture.

Defining Patient-Level Molecular Heterogeneity in Psoriasis Vulgaris Based on Single-Cell Transcriptomics.

Identifying genetic variation underlying human diseases establishes targets for therapeutic development and helps tailor treatments to individual patients. Large-scale transcriptomic profiling has extended the study of such molecular heterogeneity between patients to somatic tissues. However, the lower resolution of bulk RNA profiling, especially in a complex, composite tissue such as the skin, has limited its success. Here we demonstrate approaches to interrogate patient-level molecular variance in a chronic skin inflammatory disease, psoriasis vulgaris, leveraging single-cell RNA-sequencing of CD45+ cells isolated from active lesions. Highly psoriasis-specific transcriptional abnormalities display greater than average inter-individual variance, nominating them as potential sources of clinical heterogeneity. We find that one of these chemokines, CXCL13, demonstrates significant correlation with severity of lesions within our patient series. Our analyses also establish that genes elevated in psoriatic skin-resident memory T cells are enriched for programs orchestrating chromatin and CDC42-dependent cytoskeleton remodeling, specific components of which are distinctly correlated with and against Th17 identity on a single-cell level. Collectively, these analyses describe systematic means to dissect cell type- and patient-level differences in cutaneous psoriasis using high-resolution transcriptional profiles of human inflammatory disease.

A single-cell transcriptional gradient in human cutaneous memory T cells restricts Th17/Tc17 identity.

The homeostatic mechanisms that fail to restrain chronic tissue inflammation in diseases, such as psoriasis vulgaris, remain incompletely understood. We profiled transcriptomes and epitopes of single psoriatic and normal skin-resident T cells, revealing a gradated transcriptional program of coordinately regulated inflammation-suppressive genes. This program, which is sharply suppressed in lesional skin, strikingly restricts Th17/Tc17 cytokine and other inflammatory mediators on the single-cell level. CRISPR-based deactivation of two core components of this inflammation-suppressive program, ZFP36L2 and ZFP36, replicates the interleukin-17A (IL-17A), granulocyte macrophage-colony-stimulating factor (GM-CSF), and interferon gamma (IFNγ) elevation in psoriatic memory T cells deficient in these transcripts, functionally validating their influence. Combinatoric expression analysis indicates the suppression of specific inflammatory mediators by individual program members. Finally, we find that therapeutic IL-23 blockade reduces Th17/Tc17 cell frequency in lesional skin but fails to normalize this inflammatory-suppressive program, suggesting how treated lesions may be primed for recurrence after withdrawal of treatment.

Co-occurrence networks reveal more complexity than community composition in resistance and resilience of microbial communities.

Plant response to drought stress involves fungi and bacteria that live on and in plants and in the rhizosphere, yet the stability of these myco- and micro-biomes remains poorly understood. We investigate the resistance and resilience of fungi and bacteria to drought in an agricultural system using both community composition and microbial associations. Here we show that tests of the fundamental hypotheses that fungi, as compared to bacteria, are (i) more resistant to drought stress but (ii) less resilient when rewetting relieves the stress, found robust support at the level of community composition. Results were more complex using all-correlations and co-occurrence networks. In general, drought disrupts microbial networks based on significant positive correlations among bacteria, among fungi, and between bacteria and fungi. Surprisingly, co-occurrence networks among functional guilds of rhizosphere fungi and leaf bacteria were strengthened by drought, and the same was seen for networks involving arbuscular mycorrhizal fungi in the rhizosphere. We also found support for the stress gradient hypothesis because drought increased the relative frequency of positive correlations.

Classification of human chronic inflammatory skin disease based on single-cell immune profiling.

Inflammatory conditions represent the largest class of chronic skin disease, but the molecular dysregulation underlying many individual cases remains unclear. Single-cell RNA sequencing (scRNA-seq) has increased precision in dissecting the complex mixture of immune and stromal cell perturbations in inflammatory skin disease states. We single-cell-profiled CD45+ immune cell transcriptomes from skin samples of 31 patients (7 atopic dermatitis, 8 psoriasis vulgaris, 2 lichen planus (LP), 1 bullous pemphigoid (BP), 6 clinical/histopathologically indeterminate rashes, and 7 healthy controls). Our data revealed active proliferative expansion of the Treg and Trm components and universal T cell exhaustion in human rashes, with a relative attenuation of antigen-presenting cells. Skin-resident memory T cells showed the greatest transcriptional dysregulation in both atopic dermatitis and psoriasis, whereas atopic dermatitis also demonstrated recurrent abnormalities in ILC and CD8+ cytotoxic lymphocytes. Transcript signatures differentiating these rash types included genes previously implicated in T helper cell (TH2)/TH17 diatheses, segregated in unbiased functional networks, and accurately identified disease class in untrained validation data sets. These gene signatures were able to classify clinicopathologically ambiguous rashes with diagnoses consistent with therapeutic response. Thus, we have defined major classes of human inflammatory skin disease at the molecular level and described a quantitative method to classify indeterminate instances of pathologic inflammation. To make this approach accessible to the scientific community, we created a proof-of-principle web interface (RashX), where scientists and clinicians can visualize their patient-level rash scRNA-seq-derived data in the context of our TH2/TH17 transcriptional framework.

Cell Wall Compositions of Sorghum bicolor Leaves and Roots Remain Relatively Constant Under Drought Conditions.

Renewable fuels are needed to replace fossil fuels in the immediate future. Lignocellulosic bioenergy crops provide a renewable alternative that sequesters atmospheric carbon. To prevent displacement of food crops, it would be advantageous to grow biofuel crops on marginal lands. These lands will likely face more frequent and extreme drought conditions than conventional agricultural land, so it is crucial to see how proposed bioenergy crops fare under these conditions and how that may affect lignocellulosic biomass composition and saccharification properties. We found that while drought impacts the plant cell wall of Sorghum bicolor differently according to tissue and timing of drought induction, drought-induced cell wall compositional modifications are relatively minor and produce no negative effect on biomass conversion. This contrasts with the cell wall-related transcriptome, which had a varied range of highly variable genes (HVGs) within four cell wall-related GO categories, depending on the tissues surveyed and time of drought induction. Further, many HVGs had expression changes in which putative impacts were not seen in the physical cell wall or which were in opposition to their putative impacts. Interestingly, most pre-flowering drought-induced cell wall changes occurred in the leaf, with matrix and lignin compositional changes that did not persist after recovery from drought. Most measurable physical post-flowering cell wall changes occurred in the root, affecting mainly polysaccharide composition and cross-linking. This study couples transcriptomics to cell wall chemical analyses of a C4 grass experiencing progressive and differing drought stresses in the field. As such, we can analyze the cell wall-specific response to agriculturally relevant drought stresses on the transcriptomic level and see whether those changes translate to compositional or biomass conversion differences. Our results bolster the conclusion that drought stress does not substantially affect the cell wall composition of specific aerial and subterranean biomass nor impede enzymatic hydrolysis of leaf biomass, a positive result for biorefinery processes. Coupled with previously reported results on the root microbiome and rhizosphere and whole transcriptome analyses of this study, we can formulate and test hypotheses on individual gene candidates' function in mediating drought stress in the grass cell wall, as demonstrated in sorghum.

Cobolt: integrative analysis of multimodal single-cell sequencing data.

A growing number of single-cell sequencing platforms enable joint profiling of multiple omics from the same cells. We present Cobolt, a novel method that not only allows for analyzing the data from joint-modality platforms, but provides a coherent framework for the integration of multiple datasets measured on different modalities. We demonstrate its performance on multi-modality data of gene expression and chromatin accessibility and illustrate the integration abilities of Cobolt by jointly analyzing this multi-modality data with single-cell RNA-seq and ATAC-seq datasets.

Genome-resolved metagenomics reveals role of iron metabolism in drought-induced rhizosphere microbiome dynamics.

Recent studies have demonstrated that drought leads to dramatic, highly conserved shifts in the root microbiome. At present, the molecular mechanisms underlying these responses remain largely uncharacterized. Here we employ genome-resolved metagenomics and comparative genomics to demonstrate that carbohydrate and secondary metabolite transport functionalities are overrepresented within drought-enriched taxa. These data also reveal that bacterial iron transport and metabolism functionality is highly correlated with drought enrichment. Using time-series root RNA-Seq data, we demonstrate that iron homeostasis within the root is impacted by drought stress, and that loss of a plant phytosiderophore iron transporter impacts microbial community composition, leading to significant increases in the drought-enriched lineage, Actinobacteria. Finally, we show that exogenous application of iron disrupts the drought-induced enrichment of Actinobacteria, as well as their improvement in host phenotype during drought stress. Collectively, our findings implicate iron metabolism in the root microbiome's response to drought and may inform efforts to improve plant drought tolerance to increase food security.

mbkmeans: Fast clustering for single cell data using mini-batch k-means.

Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k-means and other popular single-cell clustering methods. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia.

Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

MethCP: Differentially Methylated Region Detection with Change Point Models.

Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying differentially methylated regions (DMRs) between different conditions. Many methods have been developed, which focus primarily on the setting of two-group comparison. We develop a DMR detecting method MethCP for WGBS data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. For simple two-group comparison, we show that our method outperforms developed methods in accurately detecting the complete DMR on a simulated data set and an Arabidopsis data set. Moreover, we show that MethCP is capable of detecting wide regions with small effect sizes, which can be common in some settings, but existing techniques are poor in detecting such DMRs. We also demonstrate the use of MethCP for time-course data on another data set after methylation throughout seed germination in Arabidopsis.

Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics.

Community assembly of crop-associated fungi is thought to be strongly influenced by deterministic selection exerted by the plant host, rather than stochastic processes. Here we use a simple, sorghum system with abundant sampling to show that stochastic forces (drift or stochastic dispersal) act on fungal community assembly in leaves and roots early in host development and when sorghum is drought stressed, conditions when mycobiomes are small. Unexpectedly, we find no signal for stochasticity when drought stress is relieved, likely due to renewed selection by the host. In our experimental system, the host compartment exerts the strongest effects on mycobiome assembly, followed by the timing of plant development and lastly by plant genotype. Using a dissimilarity-overlap approach, we find a universality in the forces of community assembly of the mycobiomes of the different sorghum compartments and in functional guilds of fungi.

Transcriptomic analysis of field-droughted sorghum from seedling to maturity reveals biotic and metabolic responses.

Drought is the most important environmental stress limiting crop yields. The C4 cereal sorghum [Sorghum bicolor (L.) Moench] is a critical food, forage, and emerging bioenergy crop that is notably drought-tolerant. We conducted a large-scale field experiment, imposing preflowering and postflowering drought stress on 2 genotypes of sorghum across a tightly resolved time series, from plant emergence to postanthesis, resulting in a dataset of nearly 400 transcriptomes. We observed a fast and global transcriptomic response in leaf and root tissues with clear temporal patterns, including modulation of well-known drought pathways. We also identified genotypic differences in core photosynthesis and reactive oxygen species scavenging pathways, highlighting possible mechanisms of drought tolerance and of the delayed senescence, characteristic of the stay-green phenotype. Finally, we discovered a large-scale depletion in the expression of genes critical to arbuscular mycorrhizal (AM) symbiosis, with a corresponding drop in AM fungal mass in the plants' roots.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

Strong succession in arbuscular mycorrhizal fungal communities.

The ecology of fungi lags behind that of plants and animals because most fungi are microscopic and hidden in their substrates. Here, we address the basic ecological process of fungal succession in nature using the microscopic, arbuscular mycorrhizal fungi (AMF) that form essential mutualisms with 70-90% of plants. We find a signal for temporal change in AMF community similarity that is 40-fold stronger than seen in the most recent studies, likely due to weekly samplings of roots, rhizosphere and soil throughout the 17 weeks from seedling to fruit maturity and the use of the fungal DNA barcode to recognize species in a simple, agricultural environment. We demonstrate the patterns of nestedness and turnover and the microbial equivalents of the processes of immigration and extinction, that is, appearance and disappearance. We also provide the first evidence that AMF species co-exist rather than simply co-occur by demonstrating negative, density-dependent population growth for multiple species. Our study shows the advantages of using fungi to test basic ecological hypotheses (e.g., nestedness v. turnover, immigration v. extinction, and coexistence theory) over periods as short as one season.