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More accurate understanding of single-cell DNA change

New software makes it clear more accurately which DNA changes are present in an individual cell. Comparing the DNA of cells, for example before and after treatment, becomes easier and provides new insights. The computer program was developed by bioinformaticians from the Van Boxtel Group of the Princess Máxima Center. Machine learning, a form of artificial intelligence, played an important role in this.

DNA is stored in every cell. This genetic code is copied when new cells are made. This can result in errors, also known as mutations. Sometimes these DNA mutations cause a cell to divide uncontrollably, which can cause cancer. The DNA of cancer cells is different from that of healthy cells. It can also be changed by treatment, such as chemotherapy.

By comparing the DNA of different cells, researchers learn about tumor origin and development, the different types of cancer cells within a tumor and the effect of treatments on DNA, among other things. In addition, the effect of cell therapies, such as stem cell transplantation, can be better explained.

Cracking the code

A single cell contains very small amounts of DNA. Reading the DNA code is therefore a major challenge. Thanks to a new technology called PTA, reading the DNA individually for all types is more and more possible. The result is a very long code of billions of letters. This code often contains reading errors that make it difficult to understand the DNA code properly. To turn this large amount of data into a workable result, bioinformatician Dr. Sjors Middelkamp developed the computer program PTATO together with colleagues from the Van Boxtel group. Sjors Middelkamp: 'Previously, the results obtained were not very accurate and there was no appropriate technology to improve this. We used machine learning to train the PTATO program to automatically recognize and filter reading errors in DNA codes. Thanks to this form of artificial intelligence, we get a much more accurate picture of the DNA code in a cell. We also gain new insights faster because the program easily compares the results of different cells.'

Blood stem cells

Using PTATO, Middelkamp and his colleagues analyzed stem cells from children with Fanconi anemia (FA). The blood stem cells of children with this inherited disorder become damaged. As a result, they have a greatly increased risk of leukemia or other forms of cancer. The children are treated at the Máxima with, among other things, stem cell transplantation.

Middelkamp: ‘With traditional techniques, we could only study the DNA of healthy stem cells. With PTATO we can now also properly map the DNA of individual stem cells from children with hereditary disorders, such as Faconi anemia. Thanks to PTATO, we saw that these blood stem cells are missing pieces of the DNA code. This gives us insights into the biological processes that go wrong and possibly lead to cancer. Hopefully, in the future we will be able to closely monitor these processes in every child and even target treatments to them.’

The results of this research were published in the scientific journal Cell Genomics.

Single cell DNA

Dr. Ruben van Boxtel, research group leader at Máxima and Oncode researcher is very pleased with the development of PTATO and has high expectations: 'Because the results of DNA analysis of an individual cell are now much more accurate, I expect that we will learn a lot about the different types of cancer cells in a tumor and the evolution of cancer. This knowledge can then be turned into new and better treatments. This will enable us to achieve our mission to cure every child with cancer, with optimal quality of life.

PTATO is available as open-source software. For more information contact Ruben van Boxtel.

This research was made possible thanks to funding from KWF, the European Research Council and the New York Stem Cell Foundation.

Sjors Middelkamp recently received a Veni grant to continue his research on DNA changes in blood stem cells. He is doing this in collaboration with the Van Boxtel group and Hugo Snippert's lab at UMC Utrecht, where Middelkamp is now working.