ImmuneSpace (immunespace.org) is a freely accessible database that hosts curated human immune-profiling data from a wide range of studies. It was created as the central data repository of the Human Immunology Project Consortium (HIPC), a multi-center NIH-funded program to characterize the diverse states of the human immune system and its regulation, using consistently formatted data [PMID: 23648045]. The overarching goal is to investigate human immune perturbations using state-of-the-art systems-level profiling technologies and innovative methodologies, and to make these data available to the scientific community, accessible for both humans and machines.
ImmuneSpace hosts data related to immunological exposure, demographics, cytokine profiling, cytometry, and neutralizing antibody assays. It builds on the ImmPort data model, a long-term archive of research and clinical data for the NIH [PMID: 29485622], but implements additional standardization and normalization rules, following the HIPC Data Standards initiative [PMID: 22343568, 26861911, 31272390, 32283555].
ImmuneSpace is continuously updated with data and features. Each study undergoes enhanced curation to ensure consistent use of ontology-based terminology, enabling more efficient queries both within and across studies. Study components stored in different repositories, unparsed raw data, and computationally inaccessible elements are integrated through manual curation, such as study timelines and author-determined ‘immune signatures’. Recently, we have added the ‘Finder Feature’ which simplifies complex searches through a hierarchical, tree-based interface that allows users to visually browse, search with autocomplete, or select entire branches of categorized filters while providing definitions, synonyms, and ontology links.
The HIPC Project and the ImmuneSpace platform demonstrate the feasibility and benefits of a structured approach to representing human immunological studies to elucidate system-level phenomena.
U01 AI167892
Computational and Systems Immunology (COMP)
Kerstin Westendorf, M. Kojima, James A. Overton et al.· Journal of Immunology· 0 citations
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.
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.
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.
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.
NIH grants U01AI167892
Vaccines and Immunotherapy (VAC)
Jian Xing, Gisela Gabernet, Anthony Melillo et al.· Journal of Immunology· 0 citations
Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.
Pramod Shinde, Lisa Willemsen, Jiyeun Lee et al.· bioRxiv· 0 citations
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