New software to visualise data generated by imaging of cells could help scientists understand more about cancer.
These avatars simultaneously display nine important cellular features, such as ‘texture’ and ‘ruffliness’, which represents inconsistencies in cell shape, in a more intuitive way than graphs or charts.
Systems like PhenoPlot could help clinicians make faster and more accurate diagnoses based on imaging of patients’ cancer cells.
Just like CCTV can be used track the movements of millions of people, automated microscopy now allows researchers to image millions of cells very quickly, such as in genetic or chemical screens.
Robotic microscopes are also being implemented in clinical settings to identify cancer cells in tissues, but because the datasets generated by automated microscopy often contain images of millions of different cells, it is difficult for humans to spot the differences between cells.
While computers can quickly analyse these images, the large and complex datasets they generate are difficult for scientists to interpret using conventional means such as spreadsheets, bar charts, or heat maps. PhenoPlot now allows researchers to analyse these big datasets very quickly.
Preliminary tests by lead author Dr Heba Sailem, who designed PhenoPlot, show how the software can be used to display differences in cell shape between 18 different breast cancer types.
Dr Sailem and her colleagues in the Dynamical Cell Systems laboratory at the ICR used PhenoPlot to portray the phenotypic, or observable, differences in cell shape between aggressive and non-aggressive cancers. Using PhenoPlot allowed the researchers to more easily spot which of the cancer types was the most aggressive and metastatic.
The work was published in Nature Communications and funded by the BBSRC and Cancer Research UK.
Download the software here.
Study leader Dr Chris Bakal, Leader of the Dynamical Cell Systems Team at The Institute of Cancer Research, London, said:
“Visual aids can have real power. Florence Nightingale used graphs of time and cause of death in the Crimean War to persuade Queen Victoria of the need for hospital reform. But we have lacked a robust way to visualise and quantify the unique traits of cancer cells.
“Here we have developed software that uses avatars to visually display multiple important features of cancer cells, extracted from huge amounts of data, in a compact and intuitive fashion.”
“Tumour biology varies from patient to patient, so having the correct tools to interpret these key differences will be crucial in allowing doctors to tailor treatment for the individual. PhenoPlot can help in the interpretation of cellular imaging data and removes human bias.
“Another attractive aspect of PhenoPlot is that it doesn’t require extensive biological expertise to interpret the images – anyone can use PhenoPlot. Finally, I believe that PhenoPlot avatars are making it very easy to communicate complex datasets about cancer cells to non-scientists.”
Visualizing cellular imaging data using PhenoPlot, Heba Z. Sailem, Julia E. Sero & Chris Bakal, Nature Communications, doi:10.1038/ncomms6825, published 8 January 2015.