In addition to protecting the body from infections, cancers, and other diseases, the immune system also produces a larger number of inflammatory cytokines and auto-antibodies that induce complex autoimmune disorders (AD), such as Type I diabetes (T1D), primary biliary cirrhosis (PBC), rheumatoid arthritis (RA), multiple sclerosis (MS), autoimmune liver disease (ALD), systemic lupus erythematosus (SLE), ankylosing spondylitis (AS), Sjogren syndrome (SS), polymyositis/dermatomyositis, etc. Although it has been reported that different factors, such as genetic, epigenetic, microbiome, and environmental factors might trigger autoimmune diseases, the pathogenesis of AD remains elusive and needs to be illuminated systematically. Besides, the diagnosis, treatment, and prognosis of autoimmune diseases are challenging.
Single-cell sequencing technology refers to high-throughput sequencing analysis of the genome, transcriptome, and epigenomics at the level of a single cell to reflect the heterogeneity between cells in diseases. Spatial transcriptomics is a groundbreaking molecular profiling method that allows scientists to measure all genes activities in a tissue sample and map where the activities are occurring. Metagenomic sequencing refers to the sequencing of the entire genomes of all microbes present in a sample in order to explore taxonomic, functional, and evolutionary aspects. Although the human gut microbiome has been the focus in recent years, the importance of the human microbiome in other parts of the human body is gaining weight. Although these techniques are generally used to reveal the links of genetic, epigenetic, microbiota and environmental factors for different kinds of disease, the universal, accurate, and context-adaptive biological markers for diagnosis, treatment, and prognosis in AD diseases are still lacking. To tackle the above-mentioned problem, more investigations on AD diseases with different layers of information deriving from various technologies are required. Meanwhile, data mining and bioinformatics tools are also needed to decode multi-dimensional and multi-modal data.
In this Research Topic, we encourage submissions of both Original Research and Review articles of basic research and clinical applications that address these existing challenges in AD from the immunological perspective. Hence, the following subtopics are especially welcome:
• Single-cell sequencing methods to reveal the pathological and physiological characteristics of AD patients at the single-cell level
• Multi-modal learning methods in inferring immune microenvironment by integrating single-cell sequencing, spatial transcriptomics, cell-level imaging data like hematoxylin-eosin staining images, and so on
• Spatial transcriptomics methods to reveal the spatial-temporal gene expression and metabolic characteristics of AD
• Metagenomic sequencing method and analytical tools to help better interpret multi-omics data to understand the complex inner micro-environment interaction in AD
• Immune microenvironment changes in parallel comparison between AD and healthy subjects or patients before and after drug intervention
• Investigation of biomarkers in diagnosis, treatment, and prognosis in AD patients under different technologies
In addition to protecting the body from infections, cancers, and other diseases, the immune system also produces a larger number of inflammatory cytokines and auto-antibodies that induce complex autoimmune disorders (AD), such as Type I diabetes (T1D), primary biliary cirrhosis (PBC), rheumatoid arthritis (RA), multiple sclerosis (MS), autoimmune liver disease (ALD), systemic lupus erythematosus (SLE), ankylosing spondylitis (AS), Sjogren syndrome (SS), polymyositis/dermatomyositis, etc. Although it has been reported that different factors, such as genetic, epigenetic, microbiome, and environmental factors might trigger autoimmune diseases, the pathogenesis of AD remains elusive and needs to be illuminated systematically. Besides, the diagnosis, treatment, and prognosis of autoimmune diseases are challenging.
Single-cell sequencing technology refers to high-throughput sequencing analysis of the genome, transcriptome, and epigenomics at the level of a single cell to reflect the heterogeneity between cells in diseases. Spatial transcriptomics is a groundbreaking molecular profiling method that allows scientists to measure all genes activities in a tissue sample and map where the activities are occurring. Metagenomic sequencing refers to the sequencing of the entire genomes of all microbes present in a sample in order to explore taxonomic, functional, and evolutionary aspects. Although the human gut microbiome has been the focus in recent years, the importance of the human microbiome in other parts of the human body is gaining weight. Although these techniques are generally used to reveal the links of genetic, epigenetic, microbiota and environmental factors for different kinds of disease, the universal, accurate, and context-adaptive biological markers for diagnosis, treatment, and prognosis in AD diseases are still lacking. To tackle the above-mentioned problem, more investigations on AD diseases with different layers of information deriving from various technologies are required. Meanwhile, data mining and bioinformatics tools are also needed to decode multi-dimensional and multi-modal data.
In this Research Topic, we encourage submissions of both Original Research and Review articles of basic research and clinical applications that address these existing challenges in AD from the immunological perspective. Hence, the following subtopics are especially welcome:
• Single-cell sequencing methods to reveal the pathological and physiological characteristics of AD patients at the single-cell level
• Multi-modal learning methods in inferring immune microenvironment by integrating single-cell sequencing, spatial transcriptomics, cell-level imaging data like hematoxylin-eosin staining images, and so on
• Spatial transcriptomics methods to reveal the spatial-temporal gene expression and metabolic characteristics of AD
• Metagenomic sequencing method and analytical tools to help better interpret multi-omics data to understand the complex inner micro-environment interaction in AD
• Immune microenvironment changes in parallel comparison between AD and healthy subjects or patients before and after drug intervention
• Investigation of biomarkers in diagnosis, treatment, and prognosis in AD patients under different technologies
