Single-cell and spatial transcriptomics

Cellular heterogeneity within tumours and their microenvironment implies major obstacles for therapy. We use single-cell and spatial transcriptomics to resolve functional cell states and their interactions across diverse cancers, identifying cellular drivers of disease processes.

Selected publications:

Krieger et al. (2022), Krieger et al. (2021), Zowada et al. (2021)

Computational pathology

While generating single-cell and spatial transcriptomics data is costly and labour intensive, histopathology images are routinely acquired for many diseases as part of diagnostic procedures. We develop approaches to integrate routine clinical images with single-cell data, aiming to ultimately accelerate diagnosis and refine treatment decisions.

Selected publications:

tba

High-throughput imaging screens

In vitro systems are a versatile tool to model human diseases, test candidate mechanisms or screen for novel drugs. We develop computational workflows for acquiring, processing and analysing 3D images of in vitro disease models. These also have potential clinical applications for personalised medicine.

Selected publications:

Krieger et al. (2020), Eismann, Krieger et al. (2020)

Developmental trajectories

In earlier projects, we analysed single-cell lineage tracing data from mouse and human model systems to understand how the collective behaviour of individual cells underlies development and disease. Borrowing methods from non-equilibrium statistical physics, we derived models of cellular behaviour during neurogenesis and their disruption in neurodevelopmental disorders.

Selected publications:

Krieger et al. (2019), Basak et al. (2018),

Gao, Postiglione et al. (2014)