Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace.

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

Tracing the cell-type-specific modules of immune responses during COVID-19 progression using scDisProcema.

COVID-19 has caused severe threats to lives and damage to property worldwide. The immunopathology of the disease is of particular concern. Currently, researchers have used gene co-expression networks (GCNs) to deepen the study of molecular mechanisms of immune responses to COVID-19. However, most efforts have not fully explored dynamic changes of cell-type-specific molecular networks in the disease process. This study proposes a GCN construction pipeline named single-cell Disease Progression cellular module analysis (scDisProcema), which can trace dynamic changes of immune system response during disease progression using single-cell data. Here, scDisProcema considers changes in cell fate and expression patterns during disease development, identifying gene modules responsible for different immune cells. The hub genes are screened for each module by the specific expression level and the intercellular connectivity of modules. Based on functional items enriched by each gene module, we elucidate the biological processes of different cells involved in disease development and explain the molecular mechanisms underlying the process of cell depletion or proliferation caused by disease. Compared with traditional WGCNA methods, scDisProcema can make more convenient use of the heterogeneity information provided by scRNA-seq data and has great potential in exploring molecular changes during disease progression and organ development.

Multiplexing Methods for Simultaneous Large-Scale Transcriptomic Profiling of Samples at Single-Cell Resolution.

Barcoding technology has greatly improved the throughput of cells and genes detected in single-cell RNA sequencing (scRNA-seq) studies. Recently, increasing studies have paid more attention to the use of this technology to increase the throughput of samples, as it has greatly reduced the processing time, technical batch effects, and library preparation costs, and lowered the per-sample cost. In this review, the various DNA-based barcoding methods for sample multiplexing are focused on, specifically, on the four major barcoding strategies. A detailed comparison of the barcoding methods is also presented, focusing on aspects such as sample/cell throughput and gene detection, and guidelines for choosing the most appropriate barcoding technique according to the personalized requirements are developed. Finally, the critical applications of sample multiplexing and technical challenges in combinatorial labeling, barcoding in vivo, and multimodal tagging at the spatially resolved resolution, as well as, the future prospects of multiplexed scRNA-seq, for example, prioritizing and predicting the severity of coronavirus disease 2019 (COVID-19) in patients of different gender and age are highlighted.

Clinical differences in chest CT characteristics between the progression and remission stages of patients with COVID-19 pneumonia.

INTRODUCTION: Computed tomography (CT) can be effective for the early screening and diagnosis of COVID-19. This study aimed to investigate the distinctive CT characteristics of two stages of the disease (progression and remission). METHODS: We included all COVID-19 patients admitted to Wenzhou Central Hospital from January to February, 2020. Patients underwent multiple chest CT scans at intervals of 3-10 days. CT features were recorded, such as the lesion lobe, distribution characteristics (subpleural, scattered or diffused), shape of the lesion, maximum size of the lesion, lesion morphology (ground-glass opacity, GGO) and consolidation features. When consolidation was positive, the boundary was identified to determine its clarity. RESULTS: The ratios of some representative features differed between the remission stage and the progression phase, such as round-shape lesion (8.0% vs 34.4%), GGO (65.0% vs 87.5%), consolidation (62.0% vs 31.3%), large cable sign (59.0% vs 9.4%) and crazy-paving sign (20.0% vs 50.0%). Using these features, we pooled all the CT data (n = 132) and established a logistic regression model to predict the current development stage. The variables consolidation, boundary feature, large cable sign and crazy-paving sign were the most significant factors, based on a variable named "prediction of progression or remission" (PPR) that we constructed. The ROC curve showed that PPR had an AUC of 0.882 (cutoff value = 0.66, sensitivity = 0.75, specificity = 0.875). CONCLUSION: CT characteristics, in particular, round shape, GGO, consolidation, large cable sign, and crazy-paving sign, may increase the recognition of the intrapulmonary development of COVID-19.