The function of a biological system is determined by the composition, interactions, and spatial organisation of cells within the tissue. Understanding how these cells behave in the context of this environment is key to further our understanding of cancer initiation and progression. As such, we have been interested in the use of spatial transcriptomics to answer our research questions.
Through the use of morphological markers, the NanoString GeoMx Digital Spatial Profiler allows for the generation of cell-type specific (e.g. tumour and immune cells) spatial transcriptomic profiles. This technology enables us to investigate the interplay between tumour and immune cells. More recently, we have been using the NanoString CosMx Spatial Molecular Imager to investigated these changes in single-cell spatial transcriptomics.
Computational pathology is an emerging technique, combining technologies including image analysis and machine learning, to augment the traditional pathology pipeline and aid pathologists in making more detailed and efficient diagnoses.
Computational Pathology forms a core part of our lab: we use cutting edge techniques to computationally analyse Whole Slide Images from biopsies, resections and excision from surgery.
We are currently working on applications to support and guide breast cancer treatment, including identification of systemic immune features in lymph nodes, analysis of the aging profile of breast tissue, and identification of histological features of genomic instability in triple negative breast cancer.
People-Powered Research – Zooniverse
We also have a people-powered research project: Node Code Breakers:
We are looking for patterns in lymph nodes” and calling for volunteers to help us find the germinal centres (shallow pink circles). These dots become visible within the lymph node, signalling a location where new immune cells are produced and produce immunoglobulins to fight with foreign molecules to keep our body healthy. We have found that the appearance of these dots (germinal centres) in lymph nodes can help to identify breast cancer patients who will overall live longer. The collected annotations will be used for improving our AI model to detect the germinal centres and will be possible to assist our doctors in future diagnostics.