The human body relies on carefully orchestrated genetic instructions that guide how cells grow and function. Cancer can occur when these instructions are disrupted. Over time, cells accumulate genetic mistakes that allow them to escape the normal controls that limit their growth and division. One of the earliest warning signs in this process is the presence of chromosomal abnormalities, including changes in chromosome number or structure. These defects can cause healthy cells to become cancerous.
Researchers from the Kobel Group at EMBL Heidelberg have now developed a powerful AI-based tool to help scientists investigate how these chromosomal abnormalities arise. By uncovering the conditions under which these errors occur, this technology could help researchers better understand how cancer develops.
“Chromosome aberrations are a major factor in particularly aggressive cancers and are strongly associated with patient mortality, metastasis, recurrence, chemotherapy resistance, and early tumor development,” said Jan Kobel, a senior researcher at EMBL and lead author of the new paper published in the journal. nature. “We wanted to understand what determines the likelihood that a cell will undergo such chromosomal changes, and how quickly such abnormalities occur when still normal cells divide.”
A century-old theory about cancer
A link between abnormal chromosomes and cancer has been suspected for more than 100 years. German scientist Theodor Boveri first proposed the idea in the early 20th century after studying cells under a microscope. His observations suggested that abnormal chromosomal content within cells may be involved in the development of cancer.
Despite long-standing theories, these anomalies have been difficult to study. There will always be a small number of cells that exhibit chromosomal defects, and many of those cells will (or will) die due to natural cell selection. For this reason, researchers have traditionally had to search for them manually using a microscope. This process allowed scientists to isolate only a few cells at a time for further study.
Marco Cosenza, a researcher in the Korbel group, began considering a solution after collaborating with other EMBL teams facing similar technical limitations. Along with his colleagues, he helped design an automated platform that integrates microscopy, single-cell sequencing, and artificial intelligence. This system is called Machine Learning-Assisted Genomics and Imaging Convergence (MAGIC).
“Laser tag” for cells using AI
MAGIC works like a highly automated version of laser tag. The system scans the cells and identifies those that display certain visible characteristics. In this study, the researchers focused on structures known as “micronuclei.”
Micronuclei are small compartments within cells that contain DNA fragments that are separated from the main genome. Cells containing micronuclei are more likely to develop further chromosomal abnormalities and are more likely to become cancerous.
When the system detects cells containing micronuclei, it uses a laser to mark them. This tagging process relies on photoconverting dyes, fluorescent molecules that change the color of the light they emit after being exposed to light.
“This project brings together many of my interests,” Cosenza says. “This includes genomics, microscopic imaging, and robotic automation. During the 2020 coronavirus disease (COVID-19)-related lockdown, I was able to spend significant time learning and applying AI computer vision technology to previously collected biological image data. I then designed experiments to validate and further develop it.”
How the MAGIC system works
The system works in several automated steps. First, an automated microscope acquires a large number of images from a cell sample. A machine learning algorithm trained using examples of micronuclei containing manually labeled cells analyzes the images.
When the algorithm detects a cell containing micronuclei, its location is sent to the microscope. The microscope then shines a beam of light on that particular cell, permanently tagging it with a photoconverting dye. Researchers can later separate these tagged cells from the living cell population using techniques such as flow cytometry. Once the cells are isolated, more detailed studies such as genome analysis are possible.
MAGIC replaces the slow and labor-intensive process of manually searching for micronuclei, allowing scientists to examine far more cells than previously possible. This system can analyze nearly 100,000 cells within a day.
Find out how often chromosomal errors occur
Researchers used MAGIC to study chromosomal abnormalities in cultured cells originally derived from normal human cells. Their analysis revealed that just over 10% of cell divisions result in spontaneous chromosomal abnormalities. The rate almost doubles when the gene p53, a well-known tumor suppressor, is mutated.
The research team also looked at other factors that may influence the formation of chromosomal abnormalities. These include the presence and location of double-stranded DNA breaks within chromosomes.
Broad possibilities for biological discovery
This research included collaborations within and outside of EMBL. The main contributors include the Advanced Light Microscopy Facility (ALMF) and the Peppercock team at EMBL Heidelberg, Isidro Cortés-Siriano’s group at EMBL-EBI, and Andreas Krosik’s team at the German Cancer Research Center (DKFZ), which is also part of the Molecular Medicine Partnership Unit (MMPU) at EMBL and the University of Heidelberg.
MAGIC is designed to be flexible and adaptable. Although the researchers trained an AI to detect micronuclei in this study, the underlying AI could also be trained to identify many other cell features.
“As long as there are features that visually distinguish them from ‘normal’ cells, we can train the system to detect them thanks to AI. Our system therefore has the potential to advance future discoveries in many areas of biology,” Kobel said.
