1. Master thesis position: High-throughput in vitro screening of hybridoma libraries for the identification of HER2-targeting agonistic antibodies
Our laboratory is currently developing and validating novel platforms and models for agonist antibody discovery, utilizing HER2+ breast cancer as a primary model system. To fuel this pipeline, we have successfully generated comprehensive hybridoma libraries as a robust source of potential therapeutic antibodies. However, to rigorously validate our new discovery platforms, we currently lack established positive controls that can serve as reliable biological benchmarks for our HER2 model. Therefore, isolating baseline binding and agonistic antibodies from our newly generated libraries is a critical hurdle for the ongoing development of our screening technologies.
We propose a project centered on the systematic in vitro screening and characterization of our existing hybridoma libraries to identify novel antibodies targeting HER2+ breast cancer cell lines. This project aims to deploy both classical and novel high-throughput screening approaches to isolate and validate hybridoma clones producing antibodies that successfully bind to HER2+ cells. Furthermore, the student will perform functional cell-based assays to evaluate these binding candidates, with the ultimate goal of identifying at least one validated agonistic antibody to serve as a positive control for our broader antibody discovery pipeline. The project will involve hybridoma handling, high-throughput binding assays (e.g., flow cytometry, ELISA), and downstream functional assays to assess receptor activation and pathway signaling.
Expertise Gained: The project will give the student the opportunity to gain expertise in:
• Mammalian cell culture (handling of cancer cell lines and hybridomas)
• High-throughput in vitro screening methodologies (ELISA, flow cytometry/FACS)
• Antibody binding validation and characterization techniques
• Functional cell-based assays for evaluating receptor agonism and signaling cascades
• Analysis and management of high-throughput biological screening data
Type of work: 15% literature study and assay planning, 60% experimental work (in vitro screening, cell culture, and functional assays), 25% data analysis and results reporting.
Duration: 4 – 6 months
Prerequisites:
• Ideally someone with a background in Bioengineering, Molecular Biology, Immunology, Biotechnology, or Life Sciences with relevant hands-on laboratory experience.
• Full competence with mammalian cell culture and standard wet-lab instrumentation. Experience with basic immunological assays (such as ELISA or flow cytometry) is highly advantageous.
• A strong motivation and a hands-on, problem-solving attitude are required regardless of the specific background.
Do not hesitate to contact us by email in case of interest: [email protected], [email protected]
2. Master thesis position: Integration of a signature matching algorithm into a scRNAseq bioinformatic pipeline for agonistic antibody discovery
Single-cell RNA sequencing (scRNAseq) is extensively used to characterize highly specific cellular responses to therapeutics, including the identification of novel agonistic antibodies derived from functional pathway changes. A crucial step in identifying these therapeutic candidates is mapping the transcriptomic shifts to specific biological functions. Current analytical methods predominantly rely on clustering cells prior to performing pathway enrichment analysis. However, this intermediate clustering step can obscure rare cellular states and dilute fine-grained transcriptomic shifts, limiting the sensitivity required to detect nuanced agonistic effects. To overcome this limitation, our lab has developed a novel method that relies on the direct matching of individual cellular profiles to established pathways in public databases, bypassing the need for prior clustering and retaining true single-cell resolution.
We propose to develop and validate the full integration of this approach by combining our existing R-based bioinformatic pipeline with a newly developed package for signature matching. This project aims to seamlessly embed the available signature matching package into our standard scRNAseq workflow, enabling automated, high-throughput identification of agonistic antibodies. The system will exploit direct matching algorithms to map cellular profiles to functional databases, allowing for the rapid identification of pathway activation at the single-cell level. The project will involve code refactoring, optimizing data structures for computational efficiency, and benchmarking the integrated pipeline against traditional clustering-based methods to maximize discovery accuracy while ensuring the robust handling of large-scale biological datasets.
Expertise Gained: The project will give the student the opportunity to gain expertise in:
•Advanced R programming and bioinformatic software integration
•Single-cell RNA sequencing (scRNAseq) data analysis and workflow optimization
•Algorithm benchmarking and computational efficiency testing
•Pathway enrichment and transcriptomic signature matching methodologies
•Computational immunology and data-driven antibody discovery
Type of work: 15% literature study and codebase familiarization, 45% software integration and pipeline development, 25% computational benchmarking and dataset testing, 15% data analysis and results reporting.
Duration: 4 – 6 months
Prerequisites:
•A background in Bioinformatics, Computational Biology, Data Science, Bioengineering, or a related field with relevant hands-on computational experience.
•Strong proficiency in R programming and familiarity with standard scRNAseq analysis ecosystems (e.g., Seurat, Bioconductor).
•A strong motivation and a hands-on, problem-solving attitude are required regardless of the specific background.
Do not hesitate to contact us by email in case of interest: [email protected], [email protected]
3. Microfluidics-guided personalized cancer immunotherapy
We have previously established a microfluidic platform for testing therapy options directly on tumor biopsies from patients (Eduati F et al., Nature Communications 2018, Utharala R et al., Nature Protocols 2022, Mathur L et al., Nature Communications 2022). However, these platforms are not yet suited for predicting outcomes of immune therapies. The aim of this project is to establish different kinds of assays for assessing the efficient killing of cancer cells by T-cells. The primary readout will be based on light-sheet imaging. The successful candidate will optimize staining of different cell types using fluorescent antibodies and establish functional fluorescence assays. Prior experience with cell culture, imaging and/or image analysis is a strong plus.
Keywords: cancer immune therapy, microfluidics, imaging
Supervisor: Christoph Merten
Co-supervisor: Tianhao Li
Contact: [email protected], [email protected]