Biological Control of Rapeseed Clubroot (Plasmodiophora brassicae) using the Endophytic Fungus Didymella macrostoma P2.

Didymella macrostoma P2 was isolated from rapeseed (Brassica napus), and it is an endophyte of rapeseed and an antagonist of three rapeseed pathogens, Botrytis cinerea, Leptosphaeria biglobosa and Sclerotinia sclerotiorum. However, whether or not P2 has a suppressive effect on infection of rapeseed by the clubroot pathogen Plasmodiophora brassicae remains unknown. This study was conducted to detect production of antimicrobials by P2 and to determine efficacy of the antimicrobials and P2 pycnidiospores in suppression of rapeseed clubroot. Results showed that cultural filtrates (CF) of P2 in potato dextrose broth and the substances in pycnidiospore mucilages exuded from P2 pycnidia were inhibitory to P. brassicae. In the indoor experiment, seeds of the susceptible rapeseed cultivar Zhongshuang No.9 treated with P2 CF and the P2 spore suspension (P2 SS, 1 × 107 spores/ml) reduced clubroot severity by 31% to 70% on the 30-day-old seedlings compared to the control (seeds treated with water). P2 was re-isolated from the roots of the seedlings in the treatment of P2 SS, the average isolation frequency in the healthy roots (26%) was much higher than that (5%) in the diseased roots. In the field experiment, seeds of another susceptible rapeseed cultivar Huayouza 50 (HYZ50) treated with P2 CF, P2 CE (chloroform extract of P2 CF, 30 µg/ml) and P2 SS reduced clubroot severity by 29% to 48% on 60-day-old seedlings and by 28% to 59% on adult plants (220 days old) compared to the control treatment. The three P2 treatments on HYZ50 produced significantly (P < 0.05) higher seed yield than the control treatment on this rapeseed cultivar, and they even generated seed yield similar to that produced by the resistant rapeseed cultivar Shengguang 165R in one of the two seasons. These results suggest that D. macrostoma P2 is an effective biocontrol agent against rapeseed clubroot.

Revealing spatial multimodal heterogeneity in tissues with SpaTrio.

Capturing and depicting the multimodal tissue information of tissues at the spatial scale remains a significant challenge owing to technical limitations in single-cell multi-omics and spatial transcriptomics sequencing. Here, we developed a computational method called SpaTrio that can build spatial multi-omics data by integrating these two datasets through probabilistic alignment and enabling further analysis of gene regulation and cellular interactions. We benchmarked SpaTrio using simulation datasets and demonstrated its accuracy and robustness. Next, we evaluated SpaTrio on biological datasets and showed that it could detect topological patterns of cells and modalities. SpaTrio has also been applied to multiple sets of actual data to uncover spatially multimodal heterogeneity, understand the spatiotemporal regulation of gene expression, and resolve multimodal communication among cells. Our data demonstrated that SpaTrio could accurately map single cells and reconstruct the spatial distribution of various biomolecules, providing valuable multimodal insights into spatial biology.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution.

Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

High-throughput transcriptional profiling of perturbations by Panax ginseng saponins and Panax notoginseng saponins using TCM-seq.

Panax ginseng (PG) and Panax notoginseng (PN) are highly valuable Chinese medicines (CM). Although both CMs have similar active constituents, their clinical applications are clearly different. Over the past decade, RNA sequencing (RNA-seq) analysis has been employed to investigate the molecular mechanisms of extracts or monomers. However, owing to the limited number of samples in standard RNA-seq, few studies have systematically compared the effects of PG and PN spanning multiple conditions at the transcriptomic level. Here, we developed an approach that simultaneously profiles transcriptome changes for multiplexed samples using RNA-seq (TCM-seq), a high-throughput, low-cost workflow to molecularly evaluate CM perturbations. A species-mixing experiment was conducted to illustrate the accuracy of sample multiplexing in TCM-seq. Transcriptomes from repeated samples were used to verify the robustness of TCM-seq. We then focused on the primary active components, Panax notoginseng saponins (PNS) and Panax ginseng saponins (PGS) extracted from PN and PG, respectively. We also characterized the transcriptome changes of 10 cell lines, treated with four different doses of PNS and PGS, using TCM-seq to compare the differences in their perturbing effects on genes, functional pathways, gene modules, and molecular networks. The results of transcriptional data analysis showed that the transcriptional patterns of various cell lines were significantly distinct. PGS exhibited a stronger regulatory effect on genes involved in cardiovascular disease, whereas PNS resulted in a greater coagulation effect on vascular endothelial cells. This study proposes a paradigm to comprehensively explore the differences in mechanisms of action between CMs based on transcriptome readouts.

Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution.

The clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic screening has been demonstrated as a powerful approach for unbiased functional genomics research. Single-cell CRISPR screening (scCRISPR) techniques, which result from the combination of single-cell toolkits and CRISPR screening, allow dissecting regulatory networks in complex biological systems at unprecedented resolution. These methods allow cells to be perturbed en masse using a pooled CRISPR library, followed by high-content phenotyping. This is technically accomplished by annotating cells with sgRNA-specific barcodes or directly detectable sgRNAs. According to the integration of distinct single-cell technologies, these methods principally fall into four categories: scCRISPR with RNA-seq, scCRISPR with ATAC-seq, scCRISPR with proteome probing, and imaging-based scCRISPR. scCRISPR has deciphered genotype-phenotype relationships, genetic regulations, tumor biological issues, and neuropathological mechanisms. This review provides insight into the technical breakthrough of scCRISPR by systematically summarizing the advancements of various scCRISPR methodologies and analyzing their merits and limitations. In addition, an application-oriented approach guide is offered to meet researchers' individualized demands.

De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution.

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms' biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space ( https://github.com/ZJUFanLab/bulk2space ), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

[Modern research on Chinese medicine based on single-cell omics: technologies and strategies].

As one of the most advanced technologies, single-cell omics technology develops rapidly in recent years. Based on different technical strategies, it enables unbiased and high-throughput access to multiple omics information at single-cell resolution. So far, single-cell omics technology, by virtue of its great powder in resolving tissue heterogeneity, has become a revolutionary tool to deeply understand the functional structure of tissues, reveal complex disease processes, and elucidate drug mechanisms of action. In view of the technical challenges in deconstructing the complexity of Chinese medicine and clarifying the modern scientific connotation of traditional Chinese medicine(TCM) theory, single-cell omics technology has huge application potential in the discovery of pharmacodynamic substances, construction of action networks, and elucidation of integrated regulatory mechanisms, which brings new opportunities for modern research in TCM. The present study briefly introduced three representative single-cell omics technologies, i.e., single-cell transcriptome sequencing, spatial transcriptomics, and single-cell multimodal omics, and their main application patterns. On this basis, an outlook was proposed on the strategies and applications for modern research in TCM using single-cell omics technology.

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk.

Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.

scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network.

Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a pre-trained cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network (GNN). Using human and mouse scRNA-seq data resources, we demonstrate the high performance and robustness of scDeepSort in labeling 764 741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed other known methods in annotating 76 external test datasets, reaching an 83.79% accuracy across 265 489 cells in humans and mice. Moreover, we demonstrate the universality of scDeepSort using more challenging datasets and using references from different scRNA-seq technology. Above all, scDeepSort is the first attempt to annotate cell types of scRNA-seq data with a pre-trained GNN model, which can realize the accurate cell-type annotation without additional references, i.e. markers or RNA-seq profiles.

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.

CellTalkDB: a manually curated database of ligand-receptor interactions in humans and mice.

Cell-cell communications in multicellular organisms generally involve secreted ligand-receptor (LR) interactions, which is vital for various biological phenomena. Recent advancements in single-cell RNA sequencing (scRNA-seq) have effectively resolved cellular phenotypic heterogeneity and the cell-type composition of complex tissues, facilitating the systematic investigation of cell-cell communications at single-cell resolution. However, assessment of chemical-signal-dependent cell-cell communication through scRNA-seq relies heavily on prior knowledge of LR interaction pairs. We constructed CellTalkDB (http://tcm.zju.edu.cn/celltalkdb), a manually curated comprehensive database of LR interaction pairs in humans and mice comprising 3398 human LR pairs and 2033 mouse LR pairs, through text mining and manual verification of known protein-protein interactions using the STRING database, with literature-supported evidence for each pair. Compared with SingleCellSignalR, the largest LR-pair resource, CellTalkDB includes not only 2033 mouse LR pairs but also 377 additional human LR pairs. In conclusion, the data on human and mouse LR pairs contained in CellTalkDB could help to further the inference and understanding of the LR-interaction-based cell-cell communications, which might provide new insights into the mechanism underlying biological processes.