Integrative analysis of spatial transcriptome with single-cell transcriptome and single-cell epigenome in mouse lungs after immunization

Mycoplasma ovipnuemoniae impairs the immune response of sheep and suppresses neutrophil function by inhibiting S100A9

Mycoplasma pneumonia is a chronic respiratory disease that seriously affects the health of sheep. To date, little information is available about the damage caused by Mycoplasma ovipneumoniae (MO) pneumonia to host lungs. Here, after sheep were infected with MO for 28 days, severe inflammatory reactions and pathological damage occurred. By using single-cell RNA sequencing (scRNA-seq), all the transcriptome changes in 11 cell types in sheep lung tissue were systematically analyzed, and the key biological processes regulating inflammation and immunity were identified. Moreover, we constructed both intercellular communication models and differential expression maps of key regulatory genes for each cell subgroup. We also specifically focused on the response of T cell subpopulations and neutrophils to MO infection. Long-term infection may affect an organism's immune response, inhibit intercellular communication, and highlight the important role of the cyclophilin A (CypA) and macrophage migration inhibitory factor (MIF) pathways in intercellular communication. Notably, MO infection decreased the toxicity of CD8 effector T cells and depleted regulatory T cells, thus inhibiting normal cell function. Subsequently, emphasis was placed on the important role of the neutrophil marker gene S100A9 in promoting neutrophil clearance of MO through activation of the ERK signaling pathway and reactive oxygen species (ROS) burst in vitro. These results contribute to understanding the progression of MO infection in the lungs and provide a rich database on the molecular basis of the response to different cell types in MO infection.

Eosinophils Enhance Granuloma-Mediated Control of Persistent Salmonella Infection

Salmonella enterica can persist asymptomatically within tissues for extended periods. This remarkable feat is achieved through intricate host-pathogen interactions in immune cell aggregates called granulomas, wherein Salmonella find favorable cellular niches to exploit while the host limits its expansion and tissue dissemination. Here, using a mouse model of persistent Salmonella infection, we identify a host-protective role of eosinophils in control of Salmonella Typhimurium (STm) infection within the mesenteric lymph nodes (MLN), the main lymphoid tissue of STm persistence. Combining spatial transcriptomics and experimental manipulations, we found that macrophages responding to STm infection recruited eosinophils in a C-C motif chemokine ligand 11 (CCL11)-dependent manner and enhanced their activation. Eosinophil deficiencies increased Salmonella burdens, which was associated with altered granuloma size and impaired type-1 immunity in the MLN. Thus, eosinophils play a vital role in restraining Salmonella exploitation of granuloma macrophages at a key site of bacterial persistence.

scMoresDB: A comprehensive database of single-cell multi-omics data for human respiratory system

The human respiratory system is a complex and important system that can suffer a variety of diseases. Single-cell sequencing technologies, applied in many respiratory disease studies, have enhanced our ability in characterizing molecular and phenotypic features at a single-cell resolution. The exponentially increasing data from these studies have consequently led to difficulties in data sharing and analysis. Here, we present scMoresDB, a single-cell multi-omics database platform with extensive omics types tailored for human respiratory diseases. scMoresDB re-analyzes single-cell multi-omics datasets, providing a user-friendly interface with cross-omics search capabilities, interactive visualizations, and analytical tools for comprehensive data sharing and integrative analysis. Our example applications highlight the potential significance of BSG receptor in SARS-CoV-2 infection as well as the involvement of HHIP and TGFB2 in the development and progression of chronic obstructive pulmonary disease. scMoresDB significantly increases accessibility and utility of single-cell data relevant to human respiratory system and associated diseases.

Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression

Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model's accuracy, unveiling complex structure-functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Single-cell and spatial multi-omics in the plant sciences: Technical advances, applications, and perspectives

Plants contain a large number of cell types and exhibit complex regulatory mechanisms. Studies at the single-cell level have gradually become more common in plant science. Single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics techniques have been combined to analyze plant development. These techniques have been used to study the transcriptomes and metabolomes of plant tissues at the single-cell level, enabling the systematic investigation of gene expression and metabolism in specific tissues and cell types during defined developmental stages. In this review, we present an overview of significant breakthroughs in spatial multi-omics in plants, and we discuss how these approaches may soon play essential roles in plant research.

Single-Cell RNA Sequencing Reveals New Basic and Translational Insights in the Cystic Fibrosis Lung

Cystic fibrosis (CF) is a multisystemic, autosomal recessive disorder caused by mutations in the CFTR (cystic fibrosis transmembrane conductance regulator) gene, with the majority of morbidity and mortality extending from lung disease. Single-cell RNA sequencing (scRNA-seq) has been leveraged in the lung and elsewhere in the body to articulate discrete cell populations, describing cell types, states, and lineages as well as their roles in health and disease. In this translational review, we provide an overview of the current applications of scRNA-seq to the study of the normal and CF lungs, allowing the beginning of a new cellular and molecular narrative of CF lung disease, and we highlight some of the future opportunities to further leverage scRNA-seq and complementary single-cell technologies in the study of CF as we bridge from scientific understanding to clinical application.