Single-Cell Sequencing Revealed Pivotal Genes Related to Prognosis of Myocardial Infarction Patients.

Objectives: Myocardial infarction (MI) is a common cardiovascular disease. Histopathology is a main molecular characteristic of MI, but often, differences between various cell subsets have been neglected. Under this premise, MI-related molecular biomarkers were screened using single-cell sequencing. Methods: This work examined immune cell abundance in normal and MI samples from GSE109048 and determined differences in the activated mast cells and activated CD4 memory T cells, resting mast cells. Weighted gene coexpression network analysis (WGCNA) demonstrated that activated CD4 memory T cells were the most closely related to the turquoise module, and 10 hub genes were screened. Single-cell sequencing data (scRNA-seq) of MI were examined. We used t-distributed stochastic neighbor embedding (t-SNE) for cell clustering. Results: We obtained 8 cell subpopulations, each of which had different marker genes. 7 out of the 10 hub genes were detected by single-cell sequencing analysis. The expression quantity and proportion of the 7 genes were different in 8 cell clusters. Conclusion: In general, our study revealed the immune characteristics and determined 7 prognostic markers for MI at the single-cell level, providing a new understanding of the molecular characteristics and mechanism of MI.

Single-cell RNA sequencing reveals the multi-cellular ecosystem in different radiological components of pulmonary part-solid nodules.

BACKGROUND: Early-stage lung adenocarcinoma that radiologically manifests as part-solid nodules, consisting of both ground-glass and solid components, has distinctive growth patterns and prognosis. The characteristics of the tumour microenvironment and transcriptional features of the malignant cells of different radiological phenotypes remain poorly understood. METHODS: Twelve treatment-naive patients with radiological part-solid nodules were enrolled. After frozen pathology was confirmed as lung adenocarcinoma, two regions (ground-glass and solid) from each of the 12 part-solid nodules and 5 normal lung tissues from 5 of the12 patients were subjected to single-cell sequencing by 10x Genomics. We used Seurat v3.1.5 for data integration and analysis. RESULTS: We comprehensively dissected the multicellular ecosystem of the ground-glass and solid components of part-solid nodules at the single-cell resolution. In tumours, these components had comparable proportions of malignant cells. However, the angiogenesis, epithelial-to-mesenchymal transition, KRAS, p53, and cell-cycle signalling pathways were significantly up-regulated in malignant cells within solid components compared to those within ground-glass components. For the tumour microenvironment, the relative abundance of myeloid and NK cells tended to be higher in solid components than in ground-glass components. Slight subtype composition differences existed between the ground-glass and solid components. The T/NK cell subsets' cytotoxic function and the macrophages' pro-inflammation function were suppressed in solid components. Moreover, pericytes in solid components had a stronger communication related to angiogenesis promotion with endothelial cells and tumour cells. CONCLUSION: The cellular landscape of ground-glass components is significantly different from that of normal tissue and similar to that of solid components. However, transcriptional differences exist in the vital signalling pathways of malignant and immune cells within these components.

Revealing the transcriptional heterogeneity of organ-specific metastasis in human gastric cancer using single-cell RNA Sequencing.

BACKGROUND: Deciphering intra- and inter-tumoural heterogeneity is essential for understanding the biology of gastric cancer (GC) and its metastasis and identifying effective therapeutic targets. However, the characteristics of different organ-tropism metastases of GC are largely unknown. METHODS: Ten fresh human tissue samples from six patients, including primary tumour and adjacent non-tumoural samples and six metastases from different organs or tissues (liver, peritoneum, ovary, lymph node) were evaluated using single-cell RNA sequencing. Validation experiments were performed using histological assays and bulk transcriptomic datasets. RESULTS: Malignant epithelial subclusters associated with invasion features, intraperitoneal metastasis propensity, epithelial-mesenchymal transition-induced tumour stem cell phenotypes, or dormancy-like characteristics were discovered. High expression of the first three subcluster-associated genes displayed worse overall survival than those with low expression in a GC cohort containing 407 samples. Immune and stromal cells exhibited cellular heterogeneity and created a pro-tumoural and immunosuppressive microenvironment. Furthermore, a 20-gene signature of lymph node-derived exhausted CD8+ T cells was acquired to forecast lymph node metastasis and validated in GC cohorts. Additionally, although anti-NKG2A (KLRC1) antibody have not been used to treat GC patients even in clinical trials, we uncovered not only malignant tumour cells but one endothelial subcluster, mucosal-associated invariant T cells, T cell-like B cells, plasmacytoid dendritic cells, macrophages, monocytes, and neutrophils may contribute to HLA-E-KLRC1/KLRC2 interaction with cytotoxic/exhausted CD8+ T cells and/or natural killer (NK) cells, suggesting novel clinical therapeutic opportunities in GC. Additionally, our findings suggested that PD-1 expression in CD8+ T cells might predict clinical responses to PD-1 blockade therapy in GC. CONCLUSIONS: This study provided insights into heterogeneous microenvironment of GC primary tumours and organ-specific metastases and provide support for precise diagnosis and treatment.

GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive ability of neural networks to capture complex dependencies between transcription factors and the corresponding genes they regulate, by developing a multitask learning framework. Second, to capture sparsity of GRNs observed in the real world, we design an unrolled algorithm technique for our framework. Our deep architecture requires supervision for training, for which we repurpose existing synthetic data simulators that generate scRNA-Seq data guided by an underlying GRN. Experimental results demonstrate that GRNUlar outperforms state-of-the-art methods on both synthetic and real data sets. Our study also demonstrates the novel and successful use of expression data simulators for supervised learning of GRN inference.

SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport.

Recent advances in sequencing technologies have allowed us to capture various aspects of the genome at single-cell resolution. However, with the exception of a few of co-assaying technologies, it is not possible to simultaneously apply different sequencing assays on the same single cell. In this scenario, computational integration of multi-omic measurements is crucial to enable joint analyses. This integration task is particularly challenging due to the lack of sample-wise or feature-wise correspondences. We present single-cell alignment with optimal transport (SCOT), an unsupervised algorithm that uses the Gromov-Wasserstein optimal transport to align single-cell multi-omics data sets. SCOT performs on par with the current state-of-the-art unsupervised alignment methods, is faster, and requires tuning of fewer hyperparameters. More importantly, SCOT uses a self-tuning heuristic to guide hyperparameter selection based on the Gromov-Wasserstein distance. Thus, in the fully unsupervised setting, SCOT aligns single-cell data sets better than the existing methods without requiring any orthogonal correspondence information.

A spatial and cellular distribution of rabies virus infection in the mouse brain revealed by fMOST and single-cell RNA sequencing.

BACKGROUND: Neurotropic virus infection can cause serious damage to the central nervous system (CNS) in both humans and animals. The complexity of the CNS poses unique challenges to investigate the infection of these viruses in the brain using traditional techniques. METHODS: In this study, we explore the use of fluorescence micro-optical sectioning tomography (fMOST) and single-cell RNA sequencing (scRNA-seq) to map the spatial and cellular distribution of a representative neurotropic virus, rabies virus (RABV), in the whole brain. Mice were inoculated with a lethal dose of a recombinant RABV encoding enhanced green fluorescent protein (EGFP) under different infection routes, and a three-dimensional (3D) view of RABV distribution in the whole mouse brain was obtained using fMOST. Meanwhile, we pinpointed the cellular distribution of RABV by utilizing scRNA-seq. RESULTS: Our fMOST data provided the 3D view of a neurotropic virus in the whole mouse brain, which indicated that the spatial distribution of RABV in the brain was influenced by the infection route. Interestingly, we provided evidence that RABV could infect multiple nuclei related to fear independent of different infection routes. More surprisingly, our scRNA-seq data revealed that besides neurons RABV could infect macrophages and the infiltrating macrophages played at least three different antiviral roles during RABV infection. CONCLUSION: This study draws a comprehensively spatial and cellular map of typical neurotropic virus infection in the mouse brain, providing a novel and insightful strategy to investigate the pathogenesis of RABV and other neurotropic viruses.

Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single-cell sequencing.

BACKGROUND: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. METHODS: In this study, we compared the single-cell transcriptomes of immune cells from 12 species. Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. RESULTS: Our data revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular crosstalks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. CONCLUSIONS: This study is the first to provide a comprehensive analysis of the cross-species single-cell transcriptome atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders.

Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2.

scDesign2 is a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. This article shows how to download and install the scDesign2 R package, how to fit probabilistic models (one per cell type) to real data and simulate synthetic data from the fitted models, and how to use scDesign2 to guide experimental design and benchmark computational methods. Finally, a note is given about cell clustering as a preprocessing step before model fitting and data simulation.

Single-Cell Multiomics Integration by SCOT.

Although the availability of various sequencing technologies allows us to capture different genome properties at single-cell resolution, with the exception of a few co-assaying technologies, applying different sequencing assays on the same single cell is impossible. Single-cell alignment using optimal transport (SCOT) is an unsupervised algorithm that addresses this limitation by using optimal transport to align single-cell multiomics data. First, it preserves the local geometry by constructing a k-nearest neighbor (k-NN) graph for each data set (or domain) to capture the intra-domain distances. SCOT then finds a probabilistic coupling matrix that minimizes the discrepancy between the intra-domain distance matrices. Finally, it uses the coupling matrix to project one single-cell data set onto another through barycentric projection, thus aligning them. SCOT requires tuning only two hyperparameters and is robust to the choice of one. Furthermore, the Gromov-Wasserstein distance in the algorithm can guide SCOT's hyperparameter tuning in a fully unsupervised setting when no orthogonal alignment information is available. Thus, SCOT is a fast and accurate alignment method that provides a heuristic for hyperparameter selection in a real-world unsupervised single-cell data alignment scenario. We provide a tutorial for SCOT and make its source code publicly available on GitHub.

Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model.

BACKGROUND: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. METHODS: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. RESULTS: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition. CONCLUSIONS: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).

Can single cell RNA sequencing reshape the clinical biochemistry of hematology: New clusters of circulating blood cells.

scRNA-seq is on track for use as a routine measurement of clinical biochemistry and to assist in clinical decision-making and guide the performance of molecular medicine, but there are still a large number of challenges to be overcome. In conclusion, scRNA-seq-based clusters and differentiation of circulating blood cells have been examined and informative in patients with various diseases, although the information generated from scRNA-seq varies between different conditions, technologies, and diseases. Most of the clinical studies published have focused on the landscape of circulating immune cells, disease-specific patterns of new clusters, understanding of potential mechanisms, and potential correlation between cell clusters, differentiations, cell interactions, and circulating and migrated cells. It is clear that the information from scRNA-seq advances the understanding of the disease, identifies disease-specific target panels, and suggests new therapeutic strategies. The adaptation of scRNA-seq as a routine clinical measurement will require standardization and normalization of scRNA-seq-based comprehensive information and validation in a large population of healthy and diseased patients. The integration of public databases on human circulating cell clusters and differentiations with an application of artificial intelligence and computational science will accelerate the application of scRNA-seq for clinical practice. Thus, we call special attention from scientists and clinicians to the clinical and translational discovery, validation, and medicine opportunities of scRNA-seq development.

Single-cell transcriptome atlas of human mesenchymal stem cells exploring cellular heterogeneity.

BACKGROUND: The heterogeneity of mesenchymal stem cells (MSCs) is poorly understood, thus limiting clinical application and basic research reproducibility. Advanced single-cell RNA sequencing (scRNA-seq) is a robust tool used to analyse for dissecting cellular heterogeneity. However, the comprehensive single-cell atlas for human MSCs has not been achieved. METHODS: This study used massive parallel multiplexing scRNA-seq to construct an atlas of > 130 000 single-MSC transcriptomes across multiple tissues and donors to assess their heterogeneity. The most widely clinically utilised tissue resources for MSCs were collected, including normal bone marrow (n = 3), adipose (n = 3), umbilical cord (n = 2), and dermis (n = 3). RESULTS: Seven tissue-specific and five conserved MSC subpopulations with distinct gene-expression signatures were identified from multiple tissue origins based on the high-quality data, which has not been achieved previously. This study showed that extracellular matrix (ECM) highly contributes to MSC heterogeneity. Notably, tissue-specific MSC subpopulations were substantially heterogeneous on ECM-associated immune regulation, antigen processing/presentation, and senescence, thus promoting inter-donor and intra-tissue heterogeneity. The variable dynamics of ECM-associated genes had discrete trajectory patterns across multiple tissues. Additionally, the conserved and tissue-specific transcriptomic-regulons and protein-protein interactions were identified, potentially representing common or tissue-specific MSC functional roles. Furthermore, the umbilical-cord-specific subpopulation possessed advantages in immunosuppressive properties. CONCLUSION: In summary, this work provides timely and great insights into MSC heterogeneity at multiple levels. This MSC atlas taxonomy also provides a comprehensive understanding of cellular heterogeneity, thus revealing the potential improvements in MSC-based therapeutic efficacy.

A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies.

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput sequencing technique for studying gene expressions at the cell level. Differential Expression (DE) analysis is a major downstream analysis of scRNA-seq data. DE analysis the in presence of noises from different sources remains a key challenge in scRNA-seq. Earlier practices for addressing this involved borrowing methods from bulk RNA-seq, which are based on non-zero differences in average expressions of genes across cell populations. Later, several methods specifically designed for scRNA-seq were developed. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to comprehensively study the performance of DE analysis methods. Here, we provide a review and classification of different DE approaches adapted from bulk RNA-seq practice as well as those specifically designed for scRNA-seq. We also evaluate the performance of 19 widely used methods in terms of 13 performance metrics on 11 real scRNA-seq datasets. Our findings suggest that some bulk RNA-seq methods are quite competitive with the single-cell methods and their performance depends on the underlying models, DE test statistic(s), and data characteristics. Further, it is difficult to obtain the method which will be best-performing globally through individual performance criterion. However, the multi-criteria and combined-data analysis indicates that DECENT and EBSeq are the best options for DE analysis. The results also reveal the similarities among the tested methods in terms of detecting common DE genes. Our evaluation provides proper guidelines for selecting the proper tool which performs best under particular experimental settings in the context of the scRNA-seq.

SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.

Clustering analysis is one of the most important technologies for single-cell data mining. It is widely used in the division of different gene sequences, the identification of functional genes, and the detection of new cell types. Although the traditional unsupervised clustering method does not require label data, the distribution of the original data, the setting of hyperparameters, and other factors all affect the effectiveness of the clustering algorithm. While in some cases the type of some cells is known, it is hoped to achieve high accuracy if the prior information about those cells is utilized sufficiently. In this study, we propose SCMAG (a semisupervised single-cell clustering method based on a matrix aggregation graph convolutional neural network) that takes into full consideration the prior information for single-cell data. To evaluate the performance of the proposed semisupervised clustering method, we test on different single-cell datasets and compare with the current semisupervised clustering algorithm in recognizing cell types on various real scRNA-seq data; the results show that it is a more accurate and significant model.

Confronting false discoveries in single-cell differential expression.

Differential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.