New AI technology to help research into cancer metastasis

DeepScratch is a new AI technology that can be used to analyse how cells move in response to wounds, building on the latest advances in deep learning

Network of biological cells

Cell migration is the process of cells moving around the body, such as immune cells moving through the body’s tissues to fight off disease, or the cells that move to fill the gap where a tissue has been injured. Whilst cell migration is an important process for regeneration and growth, it is also the process that allows cancer cells to invade and spread across the body.

Therefore, understanding the factors that regulate and instruct cells to move is an important part of understanding how we can prevent the metastasis of many cancers. One method of doing this is through scratch assays, which as the title suggests, involves inflicting a wound or ‘scratch’ on cells grown in a petri-dish and analysing how the surrounding cells react and migrate to ‘heal’ the scratch under a microscope.

Although cell migration is intensively studied, we still do not have efficient therapies to target it in the context of cancer metastasis. Observing cancer cell behaviour to artificial wounding and how this can be altered in response to pharmacological drug treatment or gene editing is important to fully understand the factors that drive this process in tumours and provide insights on the processes that drive such behaviours. Whilst current microscopic analysis methods of wound healing data are hindered by the limited image resolution in these assays. Therefore, there is a need to develop new methods that overcome current challenges and help to answer these questions.

Dr Heba Sailem a Research Fellow from the Department of Engineering, has led a study to develop a new deep learning technology known as DeepScratch. DeepScratch can detect cells from heterogenous image data with a limited resolution, allowing researchers to better characterise changes in tissue arrangement in response to wounding and how this affect cell migration.

Tests using the technology have found that DeepScratch can accurately detect cells in both membrane and nuclei images under different treatment conditions that affected cell shape or adhesion, with over 95% accuracy. This out-performs traditional analysis methods, and can also be used when the scratch assays in question are applied to genetically mutated cells or under the influence of pharmaceutical drugs – which makes this technology applicable to cancer cell research too.

Dr Heba Saliem says, “Scratch assays are prevalent tool in biomedical studies, however only the wound area is typically measured in these assays. The change in wound area does not reflect the cellular mechanisms that are affected by genetic or pharmacological treatments.

 

“By analysing the patterns formed by single cells during healing process, we can learn much more on the biological mechanisms influenced by certain genetic or drug treatments than what we can learn from the change in wound area alone.”

Using this technology, the team have already observed that cells respond to wounds by changing their spatial organisation, whereby cells that are more distant from the wound have higher local cell density and are less spread out. Such reorganisation is affected differently when perturbing different cellular mechanisms. This approach can be useful for identifying more specific therapeutic targets and advance our understanding of mechanisms driving cancer invasion.

The team predicts that DeepScratch will prove useful in cancer research that studies changes in cell structures during migration and improve the understanding of various disease processes and engineering regenerative medicine therapies. You can read more about DeepScratch and its applications in a recent study published in Computational and Structural Biotechnology.

 

About Heba

Dr Heba Sailem is a Sir Henry Wellcome Research Fellow at the Big Data Institute and Institute of Biomedical Engineering at the University of Oxford. Her research is focused on developing intelligent systems that help further biological discoveries in the field of cancer.