The Immune Signatures Data Resource 2.0: An Expanded Compendium of Systems Vaccinology Datasets 2304506

Abstract Introduction Vaccines are among the most impactful public health interventions. Systems vaccinology leverages high-dimensional omics data to elucidate mechanisms of vaccine-induced immunity, but these data are often fragmented across studies with heterogeneous metadata, limiting cross-study analyses. The NIH/NIAID Human Immunology Project Consortium (HIPC) previously addressed this challenge by releasing the Immune Signatures Data Resource (ISDR). Here, we present ISDR 2.0, an expanded and standardized framework that harmonizes human systems vaccinology datasets using ImmPort metadata. ISDR 2.0 broadens vaccine coverage and introduces a robust, reproducible analysis pipeline for consistent data processing, quality control, and immune response interpretation. Methods We developed an automated pipeline to integrate experimental design, clinical metadata and serological response data from ImmPort with linked transcriptional profiling data from GEO. Using these standardized metadata, we constructed a MultiAssayExperiment object that unifies molecular data with subject demographics and vaccine details for seamless analysis. Results The ISDR 2.0 provides a harmonized collection of 9,638 gene expression samples from 2,544 subjects across 51 studies, covering 36 different vaccines. Through the newly developed automated harmonization pipeline, this comprehensive dataset incorporates extensive RNA-seq data, offering a computationally ready platform with enhanced statistical power for identifying pan-vaccine immune signatures. Conclusion The ISDR 2.0 provides the systems vaccinology community with a standardized dataset for analyzing human vaccine response data. By harmonizing a large number of samples, standardizing metadata via ImmPort, and processing data through a reproducible pipeline, this resource will accelerate the identification of robust immune signatures and enable the development of powerful, predictive models critical for next-generation vaccine design. Funding Source NIH grants U01AI167892 Topic Categories Vaccines and Immunotherapy (VAC)