MicroRNAs are released from cells in extracellular vesicles (EVs), representing an essential mode of cell-cell communication (CCC) via a regulatory effect on gene expression. Single-cell RNA-sequencing technologies have ushered in an era of elucidating CCC at single-cell resolution. Herein, we present miRTalk, a pioneering approach for inferring CCC mediated by EV-derived miRNA-target interactions (MiTIs). The benchmarking against simulated and real-world datasets demonstrates the superior performance of miRTalk, and the application to four disease scenarios reveals the in-depth MiTI-mediated CCC mechanisms. Collectively, miRTalk can infer EV-derived MiTI-mediated CCC with scRNA-seq data, providing new insights into the intercellular dynamics of biological processes.

Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data.

A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube (https://github.com/ZJUFanLab/scCube), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications. Unbiasedly benchmarking SRT computational methods remains a pressing challenge. Here, authors develop scCube, a simulation framework enabling scalable, reproducible, and unbiased generation of simulated SRT data, allowing researchers to benchmark and evaluate different methods.

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.

Gaining a better understanding of autoprotection against drug-induced liver injury (DILI) may provide new strategies for its prevention and therapy. However, little is known about the underlying mechanisms of this phenomenon. We used single-cell RNA sequencing to characterize the dynamics and functions of hepatic non-parenchymal cells (NPCs) in autoprotection against DILI, using acetaminophen (APAP) as a model drug. Autoprotection was modeled through pretreatment with a mildly hepatotoxic dose of APAP in mice, followed by a higher dose in a secondary challenge. NPC subsets and dynamic changes were identified in the APAP (hepatotoxicity-sensitive) and APAP-resistant (hepatotoxicity-resistant) groups. A chemokine (C-C motif) ligand 2 thorn endothelial cell subset almost disappeared in the APAP-resistant group, and an R-spondin 3 thorn endothelial cell subset promoted hepatocyte proliferation and played an important role in APAP autoprotection. Moreover, the dendritic cell subset DC-3 may protect the liver from APAP hepatotoxicity by inducing low reactivity and suppressing the autoimmune response and occurrence of inflammation. DC-3 cells also promoted angiogenesis through crosstalk with endothelial cells via vascular endothelial growth factor-associated ligand-receptor pairs and facilitated liver tissue repair in the APAP-resistant group. In addition, the natural killer cell subsets NK-3 and NK-4 and the Sca-1 -CD62L thorn natural killer T cell subset may promote autoprotection through interferon-g-dependent pathways. Furthermore, macrophage and neutrophil subpopulations with anti-inflammatory phenotypes promoted tolerance to APAP hepatotoxicity. Overall, this study reveals the dynamics of NPCs in the resistance to APAP hepatotoxicity and provides novel insights into the mechanism of autoprotection against DILI at a high resolution. & COPY; 2023 The Author(s). Published by Elsevier B.V. on behalf of Xi'an Jiaotong University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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.

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.

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.

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.

Spatially resolved transcriptomics (ST) 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 from ST data, 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, decomposed from ST data through a non-negative linear model and spatial mapping between single-cell RNA-sequencing and ST data. The performance of SpaTalk benchmarked on public single-cell ST datasets was superior to that of existing cell-cell communication inference methods. SpaTalk was then applied 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 ST data universally, providing new insights into spatial inter-cellular dynamics.

Background Microbiome big data from population-scale cohorts holds the key to unleash the power of microbiomes to overcome critical challenges in disease control, treatment and precision medicine. However, variations introduced during data generation and processing limit the comparisons among independent studies in respect of interpretability. Although multiple databases have been constructed as platforms for data reuse, they are of limited value since only raw sequencing files are considered. Description Here, we present MetaGeneBank, a standardized database that provides details on sample collection and sequencing, and abundances of genes, microbiota and molecular functions for 4470 raw sequencing files (over 12 TB) collected from 16 studies covering over 10 types of diseases and 14 countries using a unified data-processing pipeline. The incorporation of tools that enable browsing and searching with descriptive attributes, gene sequences, microbiota and functions makes the database user-friendly. We found that the source of specimen contributes more than sequencing centers or platforms to the variations of microbiota. Special attention should be paid when re-analyzing sequencing files from different countries. Conclusions Collectively, MetaGeneBank provides a gateway to utilize the untapped potential of gut metagenomic data in helping fighting against human diseases. With the continuous updating of the database in terms of data volume, data types and sample types, MetaGeneBank would undoubtedly be the benchmarking database in the future in respect of data reuse, and would be valuable in translational science.

ABSTRACT The heterogeneity of liver non-parenchymal cells (NPCs) is essential for liver structure and function. However, the current understanding of liver NPCs, especially in different liver diseases, remains incompletely elucidated. Here, a single-cell transcriptome atlas of 171,814 NPCs from healthy and 5 typical liver disease mouse models, including alcoholic liver disease, nonalcoholic steatohepatitis (NASH), drug-induced liver injury, cholestatic, and ischemia-reperfusion liver injury is constructed. The inter- and intra-group heterogeneity of 12 types (and numerous subtypes) of NPCs involving endothelial cells, hepatic stellate cells (HSCs), neutrophils, T cells, and mononuclear phagocytes (MPs) are summarized. A protective subtype of neutrophils characterized by Chil3 high is validated and found significantly increasing only in drug-induced and cholestatic liver injury models. Transcriptional regulatory network analysis reveals disease-specific transcriptional reprogramming. Metabolic activity analysis indicates that fibrosis is accompanied by increases in glycolysis and retinol metabolism in activated HSCs and MPs. Moreover, we found that cell-cell interactions between cholangiocytes and immune cells contribute more to cholestatic liver fibrosis compared with NASH, while HSCs are more important for NASH fibrosis. Our atlas, together with an interactive website provides a systematic view of highly heterogeneous NPCs and a valuable resource to better understand pathological mechanisms underlying liver diseases.

The study aimed to investigate the multi-constituent, multi-target mechanism of Xuanfei Baidu Tang(XFBD) in the treatment of coronavirus disease 2019(COVID-19), through exploring the main ingredients and effective targets of XFBD, as well as analyzing the correlation between XFBD targets and COVID-19. The compounds of each herb in XFBD were collected from TCM-PTD, ETCM, TCMSP and SymMap database. Next, the information of meridian tropisms was collected from Chinese Pharmacopoeia(2015 edition), and the target information of the major constituents of XFBD were obtained from TCM-PTD, ETCM, TCMSP and TargetNet database. Subsequently, the target network model and the major modules were generated by Cytoscape, and the functional enrichment analysis of XFBD targets were completed by DAVID and STRING. As a result, ten of the 13 herbs in XFBD belonged to the lung meridian, and 326 of the 1 224 putative XFBD targets were associated with the disease target of COVID-19, among which 109 targets were enriched in the disease pathways of viral infection and lung injury. The main biological pathways regulated by the key XFBD targets included viral infection, energy metabolism, immunity and inflammation, parasites and bacterial infections. In conclusion, the therapeutic mechanism of XFBD in COVID-19 showed a multi-herb, multi-constituent, multi-target pattern, with lung as the chief targeted organ. By regulating a series of biological pathways closely related to the occurrence and development of diseases, XFBD plays a role in balancing immunity, eliminating inflammation, regulating hepatic and biliary metabolism and recovering energy metabolism balance.