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Supplementary Figures S1-S2 and Supplementary Tables S2-S4.

Protein isoforms produced by alternative splicing (AS) of many genes have been implicated in several aspects of cancer genesis and progression. These observations motivated a genome-wide assessment of AS in breast cancer. We accomplished this by measuring exon level expression in 31 breast cancer and nonmalignant immortalized cell lines representing luminal, basal, and claudin-low breast cancer subtypes using Affymetrix Human Junction Arrays. We analyzed these data using a computational pipeline specifically designed to detect AS with a low false-positive rate. This identified 181 splice events representing 156 genes as candidates for AS. Reverse transcription-PCR validation of a subset of predicted AS events confirmed 90%. Approximately half of the AS events were associated with basal, luminal, or claudin-low breast cancer subtypes. Exons involved in claudin-low subtype–specific AS were significantly associated with the presence of evolutionarily conserved binding motifs for the tissue-specific Fox2 splicing factor. Small interfering RNA knockdown of Fox2 confirmed the involvement of this splicing factor in subtype-specific AS. The subtype-specific AS detected in this study likely reflects the splicing pattern in the breast cancer progenitor cells in which the tumor arose and suggests the utility of assays for Fox-mediated AS in cancer subtype definition and early detection. These data also suggest the possibility of reducing the toxicity of protein-targeted breast cancer treatments by targeting protein isoforms that are not present in limiting normal tissues. Mol Cancer Res; 8(7); 961–74. ©2010 AACR.

Supplementary Table S1.

Supplementary Figure 1 from Temporal Dissection of Tumorigenesis in Primary Cancers

Supplementary Figure 2 from Temporal Dissection of Tumorigenesis in Primary Cancers

Supplementary Figure Legends 1-4, Methods from Temporal Dissection of Tumorigenesis in Primary Cancers

Supplementary Table 2 from Temporal Dissection of Tumorigenesis in Primary Cancers

Supplementary Figure 3 from Temporal Dissection of Tumorigenesis in Primary Cancers

Supplementary Figure 4 from Temporal Dissection of Tumorigenesis in Primary Cancers

Supplementary Table 1 from Temporal Dissection of Tumorigenesis in Primary Cancers

Please see version 3 for the explanation.

In our manuscript, we utilized scRNA-seq libraries we generated (Classification of human chronic inflammatory skin disease based on single-cell immune profiling (science.org)), samples from the Reynolds et al dataset (Developmental cell programs are co-opted in inflammatory skin disease (science.org)), and samples from the Bangert et al dataset (Persistence of mature dendritic cells, TH2A, and Tc2 cells characterize clinically resolved atopic dermatitis under IL-4Rα blockade (science.org)). An integrated object encompassing samples from these three datasets (Three_together.rds) was generated utilizing the following steps (see the code atYale73/scRNA-seq-for-diverse-human-rashes (github.com)). 1. We first performed QC, normalized, and integrated our generated scRNA-seq data to make an initialSeurat object. (See "Initial low resolution integration-FigureS1A.Rmd"). A Seurat object containing this manuscript data is provided below (Manuscript_raw.rds). 2. The Reynolds et al dataset is fromZenodo DOI:10.5281/zenodo.4249674. Their object was transferred into a Seurat object and then log-transformed and scaled by RStudio. Then immune cell clusters were subsetted based on the "final_clustering" in the object. 3. The Bangert et al dataset was obtained via the GEO repository (identifiers GSE153760 and GSE158432) and a Seurat object was created by performing QC, normalization, and integration. (See code:https://github.com/Yale73/scRNA-seq-for-diverse-human-rashes/blob/main/Made%20the%20final%2041-cluster%20object/SI_Banget_integration.R) 4. Before integrating the three above objects, we labeled the 10X Genomics scRNA-seq kit type/chemistry, the 10X Genomics CellRanger version, as well as the data source, to utilize in batch correction. 5. The three objects were then integrated with harmony batch correction for the individual samples, kit chemistry, the CellRanger version, as well as the data source (https://github.com/Yale73/scRNA-seq-for-diverse-human-rashes/blob/main/Made%20the%20final%2041-cluster%20object/Mix%203%20datasets.R). 6. After integration, we removed contaminant non-immune cells or dying cells to generate the final object (Three_together.rds), as well assubsetted versions of this final object for each of the original three datasets (Manuscript_Object.rds,S_Reynolds.rds, andSI_Bangert.rds)

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 Tregand 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.

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., Fungal community assembly on crop plants is thought to be driven by deterministic selection exerted by the host. Here Gao et al. use a sorghum system to show that stochastic forces act on fungal community assembly in leaves and roots early in host development and when sorghum is drought stressed.

Significance Understanding the molecular response of plants to drought is critical to efforts to improve agricultural yields under increasingly frequent droughts. We grew 2 cultivars of the naturally drought-tolerant food crop sorghum in the field under drought stress. We sequenced the mRNA from weekly samples of these plants, resulting in a molecular profile of drought response over the growing season. We find molecular differences in the 2 cultivars that help explain their differing tolerances to drought and evidence of a disruption in the plant’s symbiosis with arbuscular mycorrhizal fungi. Our findings are of practical importance for agricultural breeding programs, while the resulting data are a resource for the plant and microbial communities for studying the dynamics of drought response., 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.

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.

Arbuscular mycorrhizal fungi (AMF), the mutualistic symbionts with most crops, constitute a research system of human-associated fungi whose relative simplicity and synchrony are conducive to experimental ecology. However, little is known about the shifts in adaptive strategies of sorghum associated AMFs where strong AMF succession replaces initially ruderal species with competitive ones and where the strongest plant response to drought is to manage these AMF. 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. 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 into host gene expression and succession of AMF show adaptive strategies evolved by AMF and their hosts and provide a rationale for selecting AMF to reduce inputs and maximize yield in commercial agriculture. Future research opportunities include testing the specifics and generality of our hypotheses by employing genetically modified host plants, and exploring additional genes underlying the adaptive strategies in natural succession.

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., Advances in omics provide a tool to understand mechanisms for plant–microbial interactions under stress. Here the authors apply genome-resolved metagenomics to investigate sorghum and its microbiome responses to drought, identifying an unexpected role of iron metabolism.

Single-cell measurements of different cellular features or modalities from cells from the same system allow for a comprehensive understanding of a biological process. While the most common single-cell sequencing technologies require separate input cells for different modalities, there are a growing number of platforms that allow for measuring several modalities on a single cell. We present a novel method, Cobolt, for analyzing such multi-modality single-cell sequencing datasets. Cobolt jointly models the multiple modalities via a novel application of Multimodal Variational Autoencoder (MVAE) to a hierarchical generative model. We first demonstrate its performance on data from the multi-modality platform SNARE-seq, consisting of measurements of gene expression and chromatin accessibility on the same cells. We then illustrate the ability of Cobolt to integrate multi-modality platforms with single-modality platforms by jointly analyzing a SNARE-seq dataset, a single-cell gene expression dataset, and a single-cell chromatin accessibility dataset. We compared Cobolt with current options for analyzing such datasets and show that Cobolt provides robust and flexible results for integration of single-cell data on multiple modalities.

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., Author summary We developed the mbkmeans package (https://bioconductor.org/packages/mbkmeans) in Bioconductor, 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.

Interactions between plants and their root-associated microbiome are important for determining host fitness during periods of stress. During drought, monoderm bacteria are more abundant in sorghum roots than in those of watered controls. Additionally, a reversion from monoderm to diderm dominance occurs in drought-stressed roots one week after rewatering. However, the mechanisms driving this rapid microbiome composition shift is currently unknown. To understand if changes in host metabolism are correlated with this shift, we employed 16S amplicon sequencing and metabolomics of root, rhizosphere, and soil at the peak of a preflowering drought and 24 hours after rewatering. The microbiomes of droughted roots, rhizospheres, and soils differed from watered controls, and shifts in bacterial composition were observed in root and rhizosphere 24 hours after rewatering, highlighting the rapid response of microbes to the cessation of drought. Next, we performed metabolomic profiling to identify putative drivers of this process. During drought, we observed a high abundance of abiotic stress response factors, including antioxidants, osmolytes, amino acids, and plant hormones. After rewatering, large shifts in metabolite abundances were observed in rhizosphere, whereas shifts in root and soil were subtle. In addition, pipecolic acid, a well-characterized systemic acquired resistance signalling compound, was enriched in roots and rhizosphere during drought. We found that exogenous application of pipecolic acid suppresses root growth via a systemic acquired resistance-independent mechanism. Collectively, these data provide a comprehensive characterization of metabolite shifts across three compartments during drought, and elucidate a potential role of pipecolic acid in the sorghum drought response.IMPORTANCEPlant-associated microbial communities shift in composition and contribute to host fitness during drought. In particular, Actinobacteria are enriched in plant roots and rhizosphere during drought. However, the mechanisms plants use to drive this shift are poorly understood. Here we apply a combination of bacterial and metabolite profiling in root, rhizosphere, and soil during drought and drought-recovery to investigate potential contributions of host metabolism towards shifts in bacterial composition. Our results demonstrate that drought alters metabolic profiles and that the response to rewatering differs between compartments; we identify drought-responsive metabolites that are highly correlated with Actinobacteria abundance. Furthermore, our study reports for the first time that pipecolic acid is a drought-enriched metabolite in sorghum roots. We demonstrate that exogenous application of pipecolic acid is able to provoke one of the classic drought responses in roots, root growth suppression, and that this activity functions independently from the systemic acquired resistance pathway.

Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying regions that are differentially methylated (DMRs) between different conditions. A number of methods have been proposed which mainly focusing 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 DM region on a simulated dataset and an Arabidopsis dataset. 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 dataset following methylation throughout seed germination in Arabidopsis.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1–3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas—containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities—is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis., The authors describe an integrated atlas of the diverse cell types in the mouse primary motor cortex.

Single-cell transcriptome sequencing (scRNA-Seq) has allowed many new types of investigations at unprecedented and unique levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into potentially novel cell types. Many approaches build on the existing clustering literature to develop tools specific to single-cell applications. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters identified. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist to select these tuning parameters, most of them are quite ad hoc. In general, there is little assurance that any given set of parameters will represent an optimal choice in the ever-present trade-off between cluster resolution and replicability. For instance, it may be the case that another set of parameters will result in more clusters that are also more replicable, or in fewer clusters that are also less replicable.Here, we propose a new method calledDunefor optimizing the trade-off between the resolution of the clusters and their replicability across datasets. Our method takes as input a set of clustering results on a single dataset, derived from any set of clustering algorithms and associated tuning parameters, and iteratively merges clusters within partitions in order to maximize their concordance between partitions. As demonstrated on a variety of scRNA-Seq datasets from different platforms,Duneoutperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters. It provides an objective approach for identifying replicable consensus clusters most likely to represent common biological features across multiple datasets.

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.