To examine global changes in breast heterogeneity across different states, we determined the single‐cell transcriptomes of > 340,000 cells encompassing normal breast, preneoplastic BRCA1+/– tissue, ...the major breast cancer subtypes, and pairs of tumors and involved lymph nodes. Elucidation of the normal breast microenvironment revealed striking changes in the stroma of post‐menopausal women. Single‐cell profiling of 34 treatment‐naive primary tumors, including estrogen receptor (ER)+, HER2+, and triple‐negative breast cancers, revealed comparable diversity among cancer cells and a discrete subset of cycling cells. The transcriptomes of preneoplastic BRCA1+/– tissue versus tumors highlighted global changes in the immune microenvironment. Within the tumor immune landscape, proliferative CD8+ T cells characterized triple‐negative and HER2+ cancers but not ER+ tumors, while all subtypes comprised cycling tumor‐associated macrophages, thus invoking potentially different immunotherapy targets. Copy number analysis of paired ER+ tumors and lymph nodes indicated seeding by genetically distinct clones or mass migration of primary tumor cells into axillary lymph nodes. This large‐scale integration of patient samples provides a high‐resolution map of cell diversity in normal and cancerous human breast.
Synopsis
To examine global changes in breast heterogeneity across different states, this gene expression resource integrates large‐scale patient samples from diverse tissue states and breast cancer subtypes, offering a refined high‐resolution map of cell diversity in the normal and cancerous human mammary gland.
Single‐cell transcriptome analyses profile > 340,000 cells encompassing normal breast, preneoplastic BRCA1+/– tissue, the major breast cancer subtypes, and metastatic lymph nodes.
Pre‐ to post‐menopause transition is associated with marked stromal changes, with decreased PDGFRb and matrix‐associated genes in fibroblasts.
Progression from preneoplasia to tumors correlates with increased immune infiltration in BRCA1 mutation carriers.
Tumor epithelial compartments show comparable diversity in different breast cancer subtypes.
Cycling CD8+ T‐cells are reduced in estrogen receptor (ER)+ tumors, suggesting different immunoregulatory patterns.
Both clonal selection and mass migration contribute to lymph node metastases in patients with ER+ cancer.
A large‐scale gene expression resource integrates diverse tissue samples and reveals unexpected heterogeneity of breast cancer subtypes.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Yarrowia lipolytica is a model oleaginous yeast with a strong capacity for lipid accumulation, yet its lipid metabolic pathways and regulatory mechanisms remain largely unexplored. The PAH1-encoded ...phosphatidate (PA) phosphatase governs lipid biosynthesis by its enzymatic activity and regulating the transcription of genes involved in phospholipid biosynthesis. In this work, we examined the effect of the loss of Pah1 (i.e., pah1Δ) on cell metabolism in cells growing in low- and high-glucose media. Multi-omics analyses revealed the global effect of the pah1Δ mutation on lipid and central carbon metabolism. Lipidomics analyses showed that the pah1Δ mutation caused a massive decrease in the masses of triacylglycerol (TAG) and diacylglycerol (DAG), and these effects were independent of glucose concentration in the media. Conversely, phospholipid levels declined in low-glucose media but increased in high-glucose media. The loss of Pah1 affected the expression of genes involved in key pathways of glucose metabolism, such as glycolysis, citric acid cycle, oxidative phosphorylation, and the pentose phosphate pathway, and these effects were more pronounced in high-glucose media. In lipid biosynthesis, the genes catalyzing phosphatidylcholine (PC) synthesis from phosphatidylethanolamine (PE) were upregulated within the CDP-DAG pathway. In contrast, PC synthesis through the Kennedy pathway was downregulated. The ethanolamine branch of the Kennedy pathway that synthesizes PE was also upregulated in pah1Δ. Interestingly, we noted a massive increase in the levels of lysophospholipids, consistent with the upregulation of genes involved in lipid turnover. Overall, this work identified novel regulatory roles of Pah1 in lipid biosynthesis and gene expression.
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•Lipidomics analyses showed that the pah1Δ mutation led to a significant reduction in the levels of triacylglycerol and diacylglycerol.•Transcriptomics analyses showed that the pah1Δ mutation affected the expression of genes involved in key pathways of glucose metabolism.•A significant increase in lysophospholipid levels indicated enhanced lipid turnover in pah1Δ.•This work identified novel regulatory roles of Pah1 in lipid biosynthesis and gene expression.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these ...technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.
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•DoubletDecon uses deconvolution to identify and remove doublets in scRNA-seq data•Retention of doublets can confound data analysis and cell population identification•DoubletDecon limits erroneous removal of transitional and progenitor cells•The algorithm identifies unique doublets relative to alternative approaches
Multiplets are a source of confounding gene expression in single-cell RNA sequencing (scRNA-seq) that result from the simultaneous capture of multiple cells in a droplet. DePasquale et al. introduce DoubletDecon to identify putative doublets and to consider unique gene expression inherent to transitional states and progenitors to “rescue” singlet captures from inaccurate classification.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Single-nucleus RNA sequencing (snRNA-seq) is used as an alternative to single-cell RNA-seq, as it allows transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-seq is able to ...detect cellular state in human tissue. Indeed, snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease. Our comparison of microglia from single cells and single nuclei of four human subjects reveals that, although most genes show similar relative abundances in cells and nuclei, a small population of genes (∼1%) is depleted in nuclei compared to whole cells. This population is enriched for genes previously implicated in microglial activation, including APOE, CST3, SPP1, and CD74, comprising 18% of previously identified microglial-disease-associated genes. Given the low sensitivity of snRNA-seq to detect many activation genes, we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.
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•A small set of genes is depleted in microglial nuclei relative to single cells•This set is enriched for microglial activation genes, including APOE and SPP1•This depletion is confirmed in publicly available datasets•Single-nucleus sequencing is not suited for the detection of human microglial activation
Thrupp et al. demonstrate the depletion of a small population of genes in nuclei relative to cells in human microglia by using single-nucleus and single-cell sequencing. This population is enriched for microglial activation genes, suggesting that single-nucleus sequencing is not suited for the detection of microglial activation in humans.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Since its establishment in 2009, single-cell RNA sequencing (RNA-seq) has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to ...profile single cells confers particular benefits. Although most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, with the same cell line and bioinformatics pipeline, we developed directly comparable datasets for each of three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. Although each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.
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•Comprehensive diagrams for comparing the features of the three systems•An open source versatile pipeline for all systems•Systematic comparison on sensitivity, precision, bias, and costs•Demonstration of Smart-seq2 protocols with inDrop platform
Zhang et al. compare three prevalent droplet-based high-throughput scRNA-seq systems using unified sample and bioinformatics pipeline. They provide detailed analyses on system designs and performance, which would guide both future experimental design and system improvement.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws ...collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.
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•Analysis of serial blood from 139 COVID-19 patients reveals immune coordination•A major immunological shift is seen between mild and moderate infection•Moderate and severe cases exhibit inflammation and a sharp drop in blood nutrients•Novel immune cell subsets emerge in moderate cases and increase with severity
Using serial blood draws from COVID-19 patients, Su et al. present an extensive multi-omics dataset of plasma and single PBMCs covering the first week of infection following clinical diagnosis, which includes information on plasma proteins, metabolites, and on PBMC transcriptomic and surface-protein data, immune receptor sequences, secreted proteins, and electronic health record data. Their integrated analysis identifies a major immunological shift between mild and moderate infection, which includes an increase in inflammation, drop in blood nutrients, and the emergence of novel immune cell subpopulations that intensify with disease severity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Large-scale single-cell transcriptomic datasets generated using different technologies contain batch-specific systematic variations that present a challenge to batch-effect removal and data ...integration. With continued growth expected in scRNA-seq data, achieving effective batch integration with available computational resources is crucial. Here, we perform an in-depth benchmark study on available batch correction methods to determine the most suitable method for batch-effect removal.
We compare 14 methods in terms of computational runtime, the ability to handle large datasets, and batch-effect correction efficacy while preserving cell type purity. Five scenarios are designed for the study: identical cell types with different technologies, non-identical cell types, multiple batches, big data, and simulated data. Performance is evaluated using four benchmarking metrics including kBET, LISI, ASW, and ARI. We also investigate the use of batch-corrected data to study differential gene expression.
Based on our results, Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives.
Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As ...more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial. This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.
This Tutorial details the steps of a typical single‐cell RNA‐seq analysis. Best‐practice recommendations are provided and illustrated with a workflow provided in the form of an open source code repository.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
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