Next-Generation High-Throughput Light Sheet Microscopy for Quantitative Analysis of Immune–Cancer Cell Interactions

PROJECT OVERVIEW

Understanding immune–cancer interactions requires experimental platforms that capture the enormous heterogeneity of cell behaviour. While most immune cells fail to eliminate tumour cells, rare but highly effective interactions often determine the overall response. These biologically important events are frequently obscured by population averages, making conventional microscopy poorly suited to uncovering the mechanisms that drive anti-tumour immunity. This DPhil project aims to develop a next-generation high-throughput light-sheet imaging platform that enables quantitative analysis of immune–cancer interactions across unprecedented numbers of cells while automatically identifying and interrogating rare events at high spatial resolution.

This project builds on a fully developed first-generation light-sheet microscope designed to overcome the limited field of view and throughput of commercial imaging systems. The platform enables high-speed imaging across a 5 × 5 mm field of view, corresponding to an imaging area approximately 40-fold larger than that of standard confocal microscopes, while simultaneously tracking up to 100,000 individual cells. Using this technology, we demonstrated that the tails of behavioural distributions, rather than population averages, contain critical information for understanding immune cell cytotoxicity and tumour cell killing. These findings established that large-scale imaging is essential for uncovering mechanisms governed by cellular heterogeneity and rare functional events. However, translating these discoveries into clinically relevant applications requires comparing multiple therapeutic conditions and patient samples in parallel. Understanding how different immunotherapies, including CAR T cells, reshape immune population dynamics, or how responses vary between patients, demands substantially greater experimental throughput.

To address this challenge, the project will develop a second-generation light-sheet imaging platform capable of imaging multiple sample chambers simultaneously, enabling parallel analysis of immune–tumour interactions across therapeutic perturbations, CAR T cell designs, and patient-derived samples. The system will integrate real-time, machine-learning-based image analysis for automated cell tracking, rare event detection, and on-the-fly identification of biologically relevant interactions. This will enable statistically robust comparisons across large experimental cohorts while maintaining single-cell resolution.

A key innovation will be the integration of adaptive imaging workflows in which real-time analysis directs the microscope to automatically acquire targeted high-resolution images of selected cells and interactions. This hierarchical imaging strategy combines the statistical power of population-scale imaging with the structural detail of high-resolution microscopy, linking rare cellular phenotypes directly to their underlying biological mechanisms.
The platform will initially be validated using established immune–tumour model systems before being applied to patient-derived immune cells, including CAR T cell therapies. This approach will enable systematic investigation of patient-specific heterogeneity, identification of rare but highly effective cytotoxic responses, and quantitative assessment of therapeutic performance, supporting the development of next-generation immunotherapies.

This interdisciplinary project combines optics, computational bioimage analysis, artificial intelligence, and cancer immunology. The DPhil student will gain expertise in advanced microscopy, live-cell imaging, machine learning, automated microscopy, and quantitative immune assays. By establishing a scalable, intelligent imaging platform that connects population-scale measurements with targeted high-resolution analysis, this work will deliver both transformative imaging technology and fundamental insights into the cellular mechanisms that govern effective anti-tumour immune responses.

KEYWORDS

Biophysics, Microscopy, Artificial intelligence, Cancer immunology

TRAINING OPPORTUNITIES

The prospective DPhil candidate will be involved in the development and optimisation of a next-generation high-throughput light-sheet microscopy platform, including the design and implementation of advanced optical instrumentation, real-time image analysis, and adaptive workflows that enable automated high-resolution imaging of biologically relevant events. In parallel, the candidate will establish and perform immune–cancer functional imaging assays and contribute to the development of machine-learning-based bioimage analysis pipelines for large-scale cell tracking, rare event detection, and quantitative analysis of immune cell behaviour. The training will cover advanced optical microscopy, optical system design, microscope instrumentation, computational image analysis, machine learning, and cancer immunology, equipping the candidate with a unique combination of biological, engineering, and analytical skills.

Supervision will be provided by Dr. Veronika Pfannenstill and Prof. Marco Fritzsche, with additional support from senior postdoctoral researchers and technical staff in the Biophysical Immunology (BPI) Laboratory. The BPI Lab fosters a collaborative and interdisciplinary research environment, with a strong focus on advanced microscopy, computational image analysis, and immune cell biology. The DPhil candidate will benefit from extensive training opportunities, attend seminars led by world-renowned scientists, and regularly present their work at weekly BPI lab meetings. Co-supervision and guidance in advanced optical engineering and microscope design will be provided by Dr. Narain Karedla, whose expertise in optical instrumentation and cutting-edge microscopy will support the development and optimisation of the next-generation imaging platform, ensuring the project is underpinned by state-of-the-art optical engineering alongside its biological applications.

KEY PUBLICATIONS

Pfannenstill, V., Barbotin, A., Colin-York, H., & Fritzsche, M. (2021). Quantitative methodologies to dissect immune cell mechanobiology. Cells, 10(4), 851. https://doi.org/10.3390/cells10040851

Dekkers, J. F., Alieva, M., Cleven, A., Keramati, F., Wezenaar, A. K. L., van Vliet, E. J., Puschhof, J., Brazda, P., Johanna, I., Meringa, A. D., Rebel, H. G., Buchholz, M. B., Barrera Román, M., Zeeman, A. L., de Blank, S., Fasci, D., Geurts, M. H., Cornel, A. M., Driehuis, E., Millen, R., … Rios, A. C. (2023). Uncovering the mode of action of engineered T cells in patient cancer organoids. Nature Biotechnology, 41(1), 60–69. https://doi.org/10.1038/s41587-022-01397-w

Fritzsche, M. (2020). Thinking multi-scale to advance mechanobiology. Communications Biology, 3, 469. https://doi.org/10.1038/s42003-020-01197-5

THEMES

Advanced microscopy, Cancer immunology, Biophysics, High-throughput imaging

CONTACT INFORMATION OF ALL SUPERVISORS

Marco Fritzsche  

Narain Karedla

Veronika Pfannenstill