ImProVIT

Transforming big data into knowledge: for deep immunoprofiling in vaccination, infectious diseases, and transplantation

Facts

Management

Prof. (Univ. Simón Bolívar) Dr. Maria-Esther Vidal

Funding

„Big Data in den Lebenswissenschaften der Zukunft" der VolkswagenStiftung und des Niedersächsischen Ministeriums für Wissenschaft und Kultur (MWK)

Duration

October 2019 – September 2022

Cooperation

  • Medizinische Hochschule Hannover (MHH)
  • Helmholtz-Zentrum für Infektionsforschung (HZI)
  • TWINCORE Zentrum für Experimentelle und Klinische Infektionsforschung (TWINCORE) 

ImProVIT is embedded in the TRAIN Omics initiative of the Translational Alliance in Lower Saxony (TRAIN).

Description

The immune system plays a key role in health and disease. However, the knowledge about functions of the human immune cells is still limited. This is due to insufficient technologies that are applied for immunmonitoring. So far, mostly peripheral blood mononuclear cells (PBMC) are immunlabeled with specific antibodies, the samples are acquired by a fluorescence activated cell analyzer (FACS), and finally the data are examined manually. This is a costly and time-consuming approach, which nevertheless is not highly reproducible. Furthermore, such FACS data collections can only inform on the distribution of immune cells subsets, but not on their functions. Here we aim at improving immunmonitoring to an extent that data acquisition and knowledge retrieval is a highly standardized process that informs on cell distributions as well as on immune cell functions. Such an approach would be suitable to characterize the immune status of a subject, irrespective of his or her current health conditions. ImProVIT aims at developing a knowledge-driven framework able to combine heterogeneous data sets coming from diverse analytical methods. Specifically, ImProVIT will integrate data from conventional FACS-based immunmonitoring with information from cutting-edge technologies such as chip cytometry, T cell and B cell receptor repertoire analysis, single-cell sequencing, cytokine arrays, transcriptomics, and whole genome sequencing. Additionally, knowledge encoded in biomedical ontologies, e.g., the Human Phenotype Ontology (HPO), and open source data databases such as Online Mendelian Inheritance in Man (OMIM) will be mined and represented at the knowledge graph. The resulting knowledge graph of the human immune system will inform on traits of the “normal” immune system under conditions of homeostasis, as well as on the activated immune system after vaccination, during infection, or after transplantation. ImProVIT vision is that every newly retrieved data collection that is included in the knowledge graph will increase our information on the human immune system. Knowledge management and discovery technologies will enable the exploration and uncovering of patterns that provide the basis for understanding main characteristics of the human immune system. Eventually, this process will result in improved deep immunprofiling strategies, which will not only help to improve diagnostics, and as a consequence the efficacy of individualized therapies, but also to enhance our knowledge about human immune processes in order to develop new interventive strategies and vaccines.

Generating the ImProVIT knowledge graph demands the extraction of knowledge from a wide variety of data sources collected from diverse analytical. Additionally, the traversal of the ImProVIT knowledge graph requires the development of knowledge-driven methods that enable query processing and visualization. TIB will lead the development of cutting-edge techniques able to uncover patterns that empower the understanding about the human immune system. Furthermore, the TIB team participated in dissemination and training to circulate the outcomes of ImProVIT across different scientific communities and stakeholders.

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