Immune-mediated disorders encompass conditions in which disruptions in immune regulation lead to unintended attacks on healthy tissues, as observed in autoimmune diseases or infections, or the failure to recognize abnormal cellular growth seen in cancers. The complexity of immune responses involves intricate interactions among diverse immune cells, molecular signaling pathways, and regulatory proteins. Although significant strides have been made in understanding components of immune pathogenesis, a comprehensive view of disease mechanisms remains elusive. The scarcity of robust, multi-dimensional datasets has limited the depth and accuracy of investigations, impeding precise clinical applications and therapies.
Emerging multi-omics technologies, characterized by rapidly decreasing data generation costs, have provided unprecedented insights into complex disease features across multiple biological scales, from bulk tissues to single-cell resolution and spatial mapping. Studies leveraging such omics profiles have identified key molecular pathways implicated in immune disorders, pinpointed potential therapeutic targets, and uncovered biomarkers associated with treatment response and prognosis. However, challenges related to accurate integration and interpretation of data generated from distinct sources and platforms persist, underscoring an urgent need for robust strategies facilitating the harmonization, normalization, and analysis of omics data.
This Research Topic aims to bridge the gap between multi-scale omics data integration techniques and their practical translation into precision diagnostic, prognostic, and therapeutic interventions within immune-mediated disorders. It seeks to foster advancements in computational algorithms, biomarker discovery methodologies, and translation of integrated multi-omics insights into applicable clinical outcomes.
To gather further insights within the boundaries of multi-scale and multi-omics data integration and its translational applications for immune-mediated disorders, we welcome articles addressing, but not limited to, the following themes:
• Innovative approaches integrating multi-scale clinical and molecular profiles for disease diagnosis, prognosis, and therapeutic monitoring.
• Development of machine learning and deep learning algorithms emphasizing clinical predictability, interpretability, reliability, and transparency.
• Identification, validation, and biological characterization of robust and reproducible biomarkers based on multi-omics data.
• Methodologies advancing the rigor of data normalization, correction of batch effects, and integration across omics platforms to improve biomarker stability and sample comparability.
• Real-world translational research, including clinical trials and patient-level validation, demonstrating clinical benefit from integrated multi-omics analysis.
• Creation and validation of publicly available computational toolkits, pipelines, databases, and standardized platforms designed explicitly for comprehensive immunomics studies.
We welcome the submission of Original Research papers, Methods papers, and Review articles addressing these areas. Studies that include clinical outcome prediction should provide appropriate validation of findings.
Keywords: Immune-mediated disorders, Biomarker discovery, Multi-omics integration, Data normalization techniques, Precision medicine
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
