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BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.
While single-cell experiments provide deep cellular resolution within a single sample, some single-cell experiments are inherently more challenging than bulk experiments due to dissociation difficulties, cost, or limited tissue availability. This creates a situation where we have deep cellular profiles of one sample or condition, and bulk profiles across multiple samples and conditions. To bridge this gap, we propose BuDDI (BUlk Deconvolution with Domain Invariance). BuDDI utilizes domain adaptation techniques to effectively integrate available corpora of case-control bulk and reference scRNA-seq observations to infer cell-type-specific perturbation effects. BuDDI achieves this by learning independent latent spaces within a single variational autoencoder (VAE) encompassing at least four sources of variability: 1) cell type proportion, 2) perturbation effect, 3) structured experimental variability, and 4) remaining variability. Since each latent space is encouraged to be independent, we simulate perturbation responses by independently composing each latent space to simulate cell-type-specific perturbation responses. We evaluated BuDDI's performance on simulated and real data with experimental designs of increasing complexity. We first validated that BuDDI could learn domain invariant latent spaces on data with matched samples across each source of variability. Then we validated that BuDDI could accurately predict cell-type-specific perturbation response when no single-cell perturbed profiles were used during training; instead, only bulk samples had both perturbed and non-perturbed observations. Finally, we validated BuDDI on predicting sex-specific differences, an experimental design where it is not possible to have matched samples. In each experiment, BuDDI outperformed all other comparative methods and baselines. As more reference atlases are completed, BuDDI provides a path to combine these resources with bulk-profiled treatment or disease signatures to study perturbations, sex differences, or other factors at single-cell resolution.
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Single-cell atlas reveals a high selection of IgA1- or IgG1-expressing plasma cells in patients with psoriasis.
Psoriasis has been considered as a T-cell driven common inflammatory skin disease. However, thus far, the pathogenetic role of B cells in psoriasis have largely been neglected. To explore the effect of psoriasis on B-cell mediated humoral immune response, we combined single-cell RNA-sequencing (scRNA-seq) and single-cell antigen receptor lineage (BCR)-sequencing (scBCR-seq) analysis to characterize human PBMC (Peripheral blood mononuclear cells). Merged PBMC from three healthy donors and three patients with psoriasis revealed 27 major cellular clusters in the UMAP plots. Interestingly, we found that follicular helper T cells (TFH) and plasma cells (PCs) obviously increased in patients with psoriasis. Further, we demonstrated that BCL6-expressing TFH cells and their helping B cell-derived IGHA1- or IGHG1-expressing PCs, were increased in patients with psoriasis. By analyzing scBCR-seq data, we found that BCR had a low diversity of IGHA and IGHG but not IGHM and IGHD in patients with psoriasis. In line with a low diversity, IGHA and IGHG frequently used IGHV3 in patients with psoriasis. In addition, we found that CDR silent mutations in IGHA and CDR replacement mutations in IGHG increased from patients with psoriasis. Finally, we showed that patients with psoriasis had high BCR selection pressure in CDR and framework regions (FWR) on IGHG and IGHA but not IGHM and IGHD. Altogether, our results suggest that patients with psoriasis had high BCR selection pressure, partly from helping of BCL6-expressing TFH cells, may result in increased IGHG1- or IGHA1-expressing PCs. Further exploration of high BCR selection pressure will provide valuable clues for the treatment of psoriasis.
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A spatially resolved multi-omic single-cell atlas of soybean development.
Cis-regulatory elements (CREs) precisely control spatiotemporal gene expression in cells. Using a spatially resolved single-cell atlas of gene expression with chromatin accessibility across ten soybean tissues, we identified 103 distinct cell types and 303,199 accessible chromatin regions (ACRs). Nearly 40% of the ACRs showed cell-type-specific patterns and were enriched for transcription factor (TF) motifs defining diverse cell identities. We identified de novo enriched TF motifs and explored the conservation of gene regulatory networks underpinning legume symbiotic nitrogen fixation. With comprehensive developmental trajectories for endosperm and embryo, we uncovered the functional transition of the three sub-cell types of endosperm, identified 13 sucrose transporters sharing the DNA binding with one finger 11 (DOF11) motif that were co-upregulated in late peripheral endosperm, and identified key embryo cell-type specification regulators during embryogenesis, including a homeobox TF that promotes cotyledon parenchyma identity. This resource provides a valuable foundation for analyzing gene regulatory programs in soybean cell types across tissues and life stages.
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Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.
The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.
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COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.
A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.
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Single-cell sequencing to multi-omics: technologies and applications.
Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.
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Spatial sorting enables comprehensive characterization of liver zonation.
The mammalian liver is composed of repeating hexagonal units termed lobules. Spatially resolved single-cell transcriptomics revealed that about half of hepatocyte genes are differentially expressed across the lobule, yet technical limitations impeded reconstructing similar global spatial maps of other hepatocyte features. Here, we show how zonated surface markers can be used to sort hepatocytes from defined lobule zones with high spatial resolution. We apply transcriptomics, miRNA array measurements and mass spectrometry proteomics to reconstruct spatial atlases of multiple zonated features. We demonstrate that protein zonation largely overlaps with mRNA zonation, with the periportal HNF4α as an exception. We identify zonation of miRNAs such as miR-122, and inverse zonation of miRNAs and their hepatocyte target genes, highlighting potential regulation of protein levels through zonated mRNA degradation. Among the targets we find the pericentral Wnt receptors Fzd7 and Fzd8 and the periportal Wnt inhibitors Tcf7l1 and Ctnnbip1. Our approach facilitates reconstructing spatial atlases of multiple cellular features in the liver and other structured tissues.
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Alteration of tumor-associated macrophage subtypes mediated by KRT6A in pancreatic ductal adenocarcinoma.
Pancreatic ductal adenocarcinoma (PDAC) is severely affecting the health and lives of patients. Clarifying the composition and regulatory factors of tumor immune microenvironment (TIME) is helpful for the treatment of PDAC. We analyzed the unique TIMEs and gene expression patterns between PDAC and adjacent normal tissue (ANT) using Gene Expression Omnibus (GEO) to find new immunotherapy targets. The Cancer Genome Atlas (TCGA) datasets were used to elucidate the possible mechanism of which tumor-associated macrophages (TAMs) changed in PDAC. We found that the composition of TAMs subtypes, including M0, M1, and M2, was different between PDAC and ANT, which was validated in recently published single-cell RNA-seq data. Many immune cells interacted with each other to affect the TIME. There were many DEGs enriched in some pathways that could potentially change the immune cell composition. KRT6A was found to be a DEG between PDAC and ANT that overlapped with DEGs between the M0-high group and the M0-low group in TCGA datasets, and it might alter and regulate TAMs via a collection of genes including COL5A2, COL1A2, MIR3606, SPARC, and COL6A3. TAMs, which could be a target of immunotherapy, might be influenced by genes through KRT6A and indicate an undesirable prognosis in PDAC.
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Single-cell chromatin accessibility and transcriptome atlas of mouse embryos.
Cis-regulatory elements regulate gene expression and lineage specification. However, the potential regulation of cis-elements on mammalian embryogenesis remains largely unexplored. To address this question, we perform single-cell assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA-seq in embryonic day 7.5 (E7.5) and E13.5 mouse embryos. We construct the chromatin accessibility landscapes with cell spatial information in E7.5 embryos, showing the spatial patterns of cis-elements and the spatial distribution of potentially functional transcription factors (TFs). We further show that many germ-layer-specific cis-elements and TFs in E7.5 embryos are maintained in the cell types derived from the corresponding germ layers at later stages, suggesting that these cis-elements and TFs are important during cell differentiation. We also find a potential progenitor for Sertoli and granulosa cells in gonads. Interestingly, both Sertoli and granulosa cells exist in male gonads and female gonads during gonad development. Collectively, we provide a valuable resource to understand organogenesis in mammals.
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Elimination of Reference Mapping Bias Reveals Robust Immune Related Allele-Specific Expression in Crossbred Sheep.
Pervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript, we describe an unbiased standardized computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open license. The analysis pipeline we present is designed to minimize reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel×Scottish Blackface sheep, using the sheep gene expression atlas data set. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited, and instead, they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programs for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq data sets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterization of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling to provide both a novel analysis of the multi-dimensional sheep gene expression atlas data set and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.