High-resolution 3D maps of diseased tissues at the subcellular level

Molecular maps from patient tissue samples can be created with subcellular precision using an open-source platform created by researchers in the lab of Nikolaus Rajewsky at the Max Delbrück Center. This allows for in-depth analysis and may improve routine clinical pathology. The research was published in the journal Cell.

Researchers in Professor Nikolaus Rajewsky's Systems Biology Lab created a spatial transcriptomics platform known as Open-ST. It allows researchers to reconstruct gene expression in individual cells within a tissue in three dimensions.

Because of the platform's exceptional resolution, scientists can now see molecular and (sub)cellular structures that are frequently obscured in conventional 2D representations.

Open-ST was able to reconstruct cell types at subcellular resolution in mouse brain tissues. The platform captured the diversity of immune, stromal, and tumor cell populations in tumor tissue and in a healthy and metastatic lymph node from a head and neck cancer patient.

Additionally, it demonstrated how these cell populations were arranged in the original tumor around communication hotspots, but this arrangement was broken up in the metastasis.

These discoveries may pave the way for future research into the ways that cancer cells interact with their environment and sidestep the immune system. Additionally, data can be used to forecast possible therapeutic targets for specific patients. The platform can be used to study any kind of tissue and organism, not just cancer.

We think these types of technologies will help researchers discover drug targets and new therapies.”

Dr. Nikos Karaiskos, Senior Scientist and Study Corresponding Author, Berlin Institute for Medical Systems Biology, Max Delbrück Center

Unveiling the Spatial Complexity of Tissues

The study of gene expression in a single cell or in a population of cells is known as transcriptomics; however, spatial information is typically not included. On the other hand, spatial transcriptomics quantifies the spatial expression of RNA in a particular tissue sample.

Open-ST provides a high-resolution, user-friendly, and reasonably priced technique for capturing the spatial transcriptomics and tissue morphology of a tissue section. By aligning successive 2D maps, the tissue can be rebuilt as 3D "virtual tissue blocks."

Understanding the spatial relationships among cells in diseased tissues is crucial for deciphering the complex interactions that drive disease progression. Open-ST data allow to systematically screen cell-cell interactions to discover mechanisms of health and disease and potential ways to reprogram tissues.”

Nikolaus Rajewsky, Director, Max Delbrück Center

Potential biomarkers at the 3D tumor/lymph node boundary that could be new targets for therapeutic interventions were also highlighted by open-ST images from cancer tissues. “These structures were not visible in 2D analyses and could only be seen in such an unbiased reconstruction of the tissue in 3D,” states Daniel León-Periñán, co-first author of the paper.

Rajewsky added, “We have achieved a completely different level of precision. One can virtually navigate to any location in the 3D reconstruction to identify molecular mechanisms in individual cells, or the boundary between healthy and cancerous cells, for example, which is crucial for understanding how to target disease.”

Cost-Effective and Accessible Technology

Cost is one of Open-ST's main benefits. Commercially available spatial transcriptomics tools can be extremely costly. On the other hand, Open-ST efficiently captures RNA with standard lab equipment, saving substantial money.

Reduced expenses also allow researchers to expand their studies to examine patient cohorts, for example, with larger sample sizes.

The researchers have made the entire computational and experimental workflow publicly available to facilitate widespread use. León-Periñán highlights the platform's modularity, which allows Open-ST to be customized to meet unique requirements. León-Periñán notes, “All the tools are flexible enough that anything can be tweaked or changed.”

A key goal was to create a method that is not only powerful but also accessible. By reducing the cost and complexity, we hope to democratize the technology and accelerate discovery.”

Marie Schott, Technician and Study Co-First Author, Rajewsky Lab, Max Delbrück Center