INsTRuCT: Call for applications from Early Stage Researcher for Doctoral (PhD) Training Positions in Basic, Translational and Computational Immunotherapy

INsTRuCT is an Innovative Training Network (ITN) funded by the European Union Horizon 2020 Programme. The INsTRuCT Consortium is a research network of leading European scientists from academia and industry focused on fundamental research and clinical translation of myeloid regulatory cell (MRC)-based immunotherapies. A total of 15 doctoral research projects (PhD projects) for Early Stage Researchers (ESR) have been offered across 13 institutes in Europe. Research topics range from fundamental to translational science, all at the forefront in the field of MRC-based immunotherapies. The successful applicants will receive expert training in basic immunological research, preclinical pharmacological development, manufacturing processes, clinical trial design, healthcare economics, ethics and marketing. The interaction of biologists, computational scientists, physicians and engineers is central to INsTRuCT and will challenge ESR to explore complex issues in bench-to-bedside research from different perspectives. Graduates of INsTRuCT will be well-prepared to enter the workplace with an innovative and beyond state-of-the-art view on fundamental and translational science of cell-based therapies that will accelerate the future of the field.

Participating in INsTRuCT offers early stage researchers many unique opportunities, including:

• A project as Marie Skłodowska Curie trainee in one of the participating institutions with the intention of receiving a doctoral degree (PhD).
• State-of-the art, exciting research in an international consortium with highly integrated research projects.
• Expert training in preclinical and clinical development of cell-based medicinal products.
• At least two months of research training in the lab of another consortium member, mostly in a different EU country than the country where most of the project will take place.
• Training in both academic and commercial research environments.
• Salary according to EU guidelines for Marie Skłodowska Curie trainees, including mobility payments and family allowances where applicable.

Eligible candidates must:

• hold a Master’s degree or equivalent in a field of science relevant to their chosen project (see below)
• demonstrate a history of academic excellence
• demonstrate an affinity for translational research in basic immunology, computational biology and data science
• have programming experience (R, python, or similar) and ideally have applied machine learning algorithms for single cell analysis previously
• speak and write fluently in English
• have less than 4 years’ previous research experience and not hold a doctoral (PhD) title
• have not been resident in the country where the host institution is located for more than 12 months in the 3 years before recruitment.
• be available to start their project no later than 1^st September, 2021.

ESR12 Classification of Human Myeloid Regulatory Cell Networks by Machine Learning from Multiparametric Immunofluorescence Images

Hosting Institution: Miltenyi Biotec BV & Co. KG , Bergisch Gladbach, Germany

Supervisor Dr. KOLLET, Jutta

Email address for the applications jutta.kollet@miltenyi.com

Subject area: Computational Biology; Image Analysis

Project-specific selection criteria: Degree in Bioinformatics , Computer Science, or Biology with project-relevant subject.

Introduction: It is critical to understand differences between immunoregulatory cell (IRC) populations in the context their surrounding tissue microenvironments. This project will identify and systematically characterize the IRC networks in tissues through development of computational methods to analyze multiparametric immunofluorescence image data (MACSima^TM technology). The composition and spatial distribution of IRC subsets in tissues will be classified using machine learning on a knowledge database.

Aims: A1: To apply model-based machine learning algorithms on multiparametric immunofluorescence image data of cancer biopsy sections to identify and characterize the immune regulatory network. A2: To generate experimental image datasets and matched single-cell sequencing data from unrelated tissue sections to validate and optimize the initially generated classifier. A3: To optimize predictions and analysis tools by inclusion of self-learning components.

Expected Results: R1: The phenotype and spatial network of IRC in tissues (eg. tumour, transplant) will be characterized; thereby phenotypically distinct subpopulations of IRC will be identified. R2: Understanding of the correlation of transcriptome and proteome data and of the function of cell types will be obtained from collaborations with UHREG. This is particularly important, since public NGS datasets are frequently used for biomarker/ target discovery. R3: Data-driven marker collections (‘self-learning’ algorithms will automatically update a data base) will be used for identification and prediction of cell composition. R4: Optimization and commercialization of antibody panels for kits and diagnostics for use in identifying or isolating IRC subsets using the newly developed software tools. R5: Software for automated annotation and cell type classification in immunofluorescence images and novel visualization methods for multiparametric datasets will be established.

Planned secondment: A 2-month secondment to the labs of Professors Spang and Rehli is planned to train the ESR in analyzing different types of ‘Big Data’ in order to make correlations with imaging-based proteome data.

Enrolment in Doctoral degree(s): University of Regensburg

Project-specific selection criteria: Degree in Bioinformatics , Computer Science, or Biology with project-relevant subject.

Recommended reading: Lin et al., 2018: eLife 2018;7:e31657; Ronneberger et al., 2015: arXiv:1505.04597; Chamier, L., Laine, R.F., Jukkala, J. et al., 2021: Nature Comm. https://doi.org/10.1038/s41467-021-22518-0; MACSima technology:

https://www.miltenyibiotec.com/DE-en/products/macs-imaging-and-microscopy/ultra-high-content-imaging.html