Computational Tool Can Map Gene Expression in Space

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Scientists at Lausanne’s EPFL institute have developed a computational algorithm that can visualize and map where genes are expressed without the aid of imaging tools such as microscopes.

Instead, the tool uses tissue measurement and messenger (m)RNA data to give the tool—known as ‘Tomographer’—the information it needs to create an image.

This new technique could eventually replace older methods such as in situ hybridization, where a gene is tagged with a fluorescent marker in a section of tissue and a specialized microscope is then used to locate where the gene is being expressed.

In a time when genetic information is being used more and more often in research and medicine, locating where a gene is expressed can be important. For example, if a clinician is trying to assess whether malignant cells remain after a patient has been treated for cancer.

Gioele La Manno, Ph.D., a principal investigator at EPFL led the project, which is now published in the journal Nature Biotechnology.

“The Tomographer algorithm opens a promising and robust path to ‘spatialize’ different genomics measurement techniques,” he commented in a press statement.

More specifically, the new technique works by cutting tissue along the required axis and then into sections. Different strips cut at different angles are then measured and the information entered into the algorithm along with mRNA information about gene expression in the different cells along the strips.

The team used their tool to create a molecular map of the brain of the Australian Bearded Dragon to test the validity of their tool. They found it provided an accurate representation of gene expression compared with other methods, although it has a few drawbacks.

“One limit of our approach is its relatively low resolution when compared to imaging-based methods and its inability to reveal discontinuous, checkerboard-like patterns,” write the authors.

“For example, our method does not allow for discrimination of the signal at the single-cell level. Instead, it is designed to capture broader tissue-level patterns of expression. For these reasons, we envision the method to be most useful as a way to link single-cell resolved data to its spatial context, to profile anatomically structured samples and to study the molecular anatomy of non-model organisms.”

A big advantage of this new technique is that it can be set up at a low cost and without the need of specialized and complex imaging technology. It is also compatible with different types of genomics measurements.

“Ever since I started med school, I have been admiring the way computer tomography revolutionized the way we examine organs and body parts,” says Christian Gabriel Schneider, a student researcher who worked on the project and was one of the study’s lead authors.

“Today, I am very proud to be part of a team that has developed a molecular tomography technology. So far, we have focused on applications in neurodevelopmental biology, but in the future, we can certainly imagine molecular tomography becoming a constituent in personalized medicine.”

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