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.

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.

Biparatopic antibody BA7208/7125 effectively neutralizes SARS-CoV-2 variants including Omicron BA.1-BA.5.

SARS-CoV-2 Omicron subvariants have demonstrated extensive evasion from monoclonal antibodies (mAbs) developed for clinical use, which raises an urgent need to develop new broad-spectrum mAbs. Here, we report the isolation and analysis of two anti-RBD neutralizing antibodies BA7208 and BA7125 from mice engineered to produce human antibodies. While BA7125 showed broadly neutralizing activity against all variants except the Omicron sublineages, BA7208 was potently neutralizing against all tested SARS-CoV-2 variants (including Omicron BA.1-BA.5) except Mu. By combining BA7208 and BA7125 through the knobs-into-holes technology, we generated a biparatopic antibody BA7208/7125 that was able to neutralize all tested circulating SARS-CoV-2 variants. Cryo-electron microscopy structure of these broad-spectrum antibodies in complex with trimeric Delta and Omicron spike indicated that the contact residues are highly conserved and had minimal interactions with mutational residues in RBD of current variants. In addition, we showed that administration of BA7208/7125 via the intraperitoneal, intranasal, or aerosol inhalation route showed potent therapeutic efficacy against Omicron BA.1 and BA.2 in hACE2-transgenic and wild-type mice and, separately, effective prophylaxis. BA7208/7125 thus has the potential to be an effective candidate as an intervention against COVID-19.

Distribution and impacts on the geological environment of antiviral drugs in major waters of Wuhan, China

This study investigated water samples collected from the surface water and groundwater in Wuhan City, Hubei Province, China in different stages of the outbreak of the coronavirus disease 2019 (hereinafter referred to as COVID-19) in the city, aiming to determine the distribution characteristics of antiviral drugs in the city’s waters. The results are as follows. The main hydrochemical type of surface water and groundwater in Wuhan was Ca-HCO3. The major chemical components in the groundwater had higher concentrations and spatial variability than those in the surface water. Two antiviral drugs and two glucocorticoids were detected in the surface water, groundwater, and sewage during the COVID-19 outbreak. Among them, chloroquine phosphate and cortisone had higher detection rates of 32.26% and 25.80%, respectively in all samples. The concentrations of residual drugs in East Lake were higher than those in other waters. The main drug detected in the waters in the later stage of the COVID-19 outbreak in Wuhan was chloroquine phosphate, whose detection rates in the surface water and the groundwater were 53.85% and 28.57%, respectively. Moreover, the detection rate and concentration of chloroquine phosphate were higher in East Lake than in Huangjia Lake. The groundwater containing chloroquine phosphate was mainly distributed along the river areas where the groundwater was highly vulnerable. The residual drugs in the surface water and the groundwater had lower concentrations in the late stage of the COVID-19 outbreak than in the middle of the outbreak, and they have not yet caused any negative impacts on the ecological environment.©2022 China Geology Editorial Office.

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.

Identifying potential treatments of COVID-19 from Traditional Chinese Medicine (TCM) by using a data-driven approach.

ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese Medicine (TCM) has been widely used as an approach worldwide. Chinese Medicines (CMs) had been used to treat and prevent viral infection pneumonia diseases for thousands of years and had accumulated a large number of clinical experiences and effective prescriptions. AIM OF THE STUDY: This research aimed to systematically excavate the classical prescriptions of Chinese Medicine (CM), which have been used to prevent and treat Pestilence (Wenbing, Wenyi, Shiyi or Yibing) for long history in China, to obtain the potential prescriptions and ingredients to alternatively treat COVID-19. MATERIALS AND METHODS: We developed the screening system based on data mining, molecular docking and network pharmacology. Data mining and association network were used to mine the high-frequency herbs and formulas from ancient prescriptions. Virtual screening for the effective components of high frequency CMs and compatibility Chinese Medicine was explored by a molecular docking approach. Furthermore, network pharmacology method was used to preliminarily uncover the molecule mechanism. RESULTS: 574 prescriptions were obtained from 96,606 classical prescriptions with the key words to treat "Warm diseases (Wenbing)", "Pestilence (Wenyi or Yibing)" or "Epidemic diseases (Shiyi)". Meanwhile, 40 kinds of CMs, 36 CMs-pairs, 6 triple-CMs-groups existed with high frequency among the 574 prescriptions. Additionally, the key targets of SARS-COV-2, namely 3CL hydrolase (Mpro) and angiotensin-converting enzyme 2(ACE2), were used to dock the main ingredients from the 40 kinds by the LigandFitDock method. A total of 66 compounds components with higher frequency were docked with the COVID-19 targets, which were distributed in 26 kinds of CMs, among which Gancao (Glycyrrhizae Radix Et Rhizoma), HuangQin (Scutellariae Radix), Dahuang (Rhei Radix Et Rhizome) and Chaihu (Bupleuri Radix) contain more potential compounds. Network pharmacology results showed that Gancao (Glycyrrhizae Radix Et Rhizoma) and HuangQin (Scutellariae Radix) CMs-pairs could also interact with the targets involving in immune and inflammation diseases. CONCLUSIONS: These results we obtained probably provided potential candidate CMs formulas or active ingredients to overcome COVID-19. Prospectively, animal experiment and rigorous clinic studies are needed to confirm the potential preventive and treat effect of these CMs and compounds.

Treating head and neck tumors during the SARS-CoV-2 epidemic, 2019 to 2020: Sichuan Cancer Hospital.

Since December 2019, a number of patients with novel coronavirus pneumonia (NCP) have been identified in Wuhan, Hubei Province, China. NCP has rapidly spread to other provinces and cities in China and other countries in the world. Due to the rapid increase in reported cases in China and around the world, on January 30, 2020, the World Health Organization (WHO) Emergency Committee announced that NCP is a Public Health Emergency of International Concern (PHEIC). However, there are relatively few suggestions and measures for tumor patients, especially patients with head and neck tumors. This article summarizes the prevention and control of disease in our medical institution to provide a reference for front-line head and neck surgeons.