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Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2 , TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2 , TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2 , TMPRSS2 and CTSL . Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention. An integrated analysis of over 100 single-cell and single-nucleus transcriptomics studies illustrates severe acute respiratory syndrome coronavirus 2 viral entry gene coexpression patterns across different human tissues, and shows association of age, smoking status and sex with viral entry gene expression in respiratory cell populations.

Background Principal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory. Results In this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq datasets. Our benchmark shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and more accurate than the other algorithms. Conclusion We develop a guideline to select an appropriate PCA implementation based on the differences in the computational environment of users and developers.

Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that further insights into T2D disease mechanisms can be obtained by network-based analysis of scRNAseq data from individual cell types. We developed differential gene coordination network analysis (dGCNA) and analyzed islet SmartSeq2 scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA reveals T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes are detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates beta cell microfilament organization and that CEPBG is an important regulator of the unfolded protein response. In addition, when comparing beta- and alpha cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides detailed insight into cell type-specific gene function and T2D pathophysiology. Single cell transcriptomics can be used to identify genes associated with type 2 diabetes (T2D) and computational models can be used to identify gene networks. Here the authors identify networks of dysregulated genes in T2D and the biological processes involved, further demonstrating the functional context of the identified genes.

Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q - diffusion , a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q -diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q -diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN- γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q -diffusion, yields interpretable structures of human cortical tissue. The q-diffusion method uses the full dimensionality of gene coexpression in transcriptomics and benchmarking shows improvment of scRNAseq analyses.

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. Graphical Abstract 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.

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.

Single-cell RNA sequencing (scRNA-seq) is the leading technique for characterizing the transcriptomes of individual cells in a sample. The latest protocols are scalable to thousands of cells and are being used to compile cell atlases of tissues, organs and organisms. However, the protocols differ substantially with respect to their RNA capture efficiency, bias, scale and costs, and their relative advantages for different applications are unclear. In the present study, we generated benchmark datasets to systematically evaluate protocols in terms of their power to comprehensively describe cell types and states. We performed a multicenter study comparing 13 commonly used scRNA-seq and single-nucleus RNA-seq protocols applied to a heterogeneous reference sample resource. Comparative analysis revealed marked differences in protocol performance. The protocols differed in library complexity and their ability to detect cell-type markers, impacting their predictive value and suitability for integration into reference cell atlases. These results provide guidance both for individual researchers and for consortium projects such as the Human Cell Atlas. A multicenter study compares 13 commonly used single-cell RNA-seq protocols.

Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a novel Bayesian method to estimate and remove contamination in individual cells. DecontX accurately predicts contamination levels in a mouse-human mixture dataset and removes aberrant expression of marker genes in PBMC datasets. We also compare the contamination levels between four different scRNA-seq protocols. Overall, DecontX can be incorporated into scRNA-seq workflows to improve downstream analyses.

The handling of single cells is of great importance in applications such as cell line development or single-cell analysis, e.g., for cancer research or for emerging diagnostic methods. This review provides an overview of technologies that are currently used or in development to isolate single cells for subsequent single-cell analysis. Data from a dedicated online market survey conducted to identify the most relevant technologies, presented here for the first time, shows that FACS (fluorescence activated cell sorting) respectively Flow cytometry (33% usage), laser microdissection (17%), manual cell picking (17%), random seeding/dilution (15%), and microfluidics/lab-on-a-chip devices (12%) are currently the most frequently used technologies. These most prominent technologies are described in detail and key performance factors are discussed. The survey data indicates a further increasing interest in single-cell isolation tools for the coming years. Additionally, a worldwide patent search was performed to screen for emerging technologies that might become relevant in the future. In total 179 patents were found, out of which 25 were evaluated by screening the title and abstract to be relevant to the field.

Trajectory inference approaches analyze genome-wide omics data from thousands of single cells and computationally infer the order of these cells along developmental trajectories. Although more than 70 trajectory inference tools have already been developed, it is challenging to compare their performance because the input they require and output models they produce vary substantially. Here, we benchmark 45 of these methods on 110 real and 229 synthetic datasets for cellular ordering, topology, scalability and usability. Our results highlight the complementarity of existing tools, and that the choice of method should depend mostly on the dataset dimensions and trajectory topology. Based on these results, we develop a set of guidelines to help users select the best method for their dataset. Our freely available data and evaluation pipeline (https://benchmark.dynverse.org) will aid in the development of improved tools designed to analyze increasingly large and complex single-cell datasets.The authors comprehensively benchmark the accuracy, scalability, stability and usability of 45 single-cell trajectory inference methods.