Spatial Transcriptomics Platform Promises Low-Cost, High-Res 3D Mapping

Researchers at the Max Delbrück Center have developed an open-source spatial transcriptomics (ST) platform, called Open-ST, that creates 3D molecular maps from patient tissue samples with subcellular precision, allowing scientists to reconstruct gene expression in cells within a tissue in three dimensions.

Developed by scientists in the Systems Biology Lab of Professor Nikolaus Rajewsky, PhD, Open-ST produces these maps with such high resolution that researchers can see molecular and (sub)cellular structures that are often lost in traditional 2D representations. The team says as well as enabling highly detailed study, the platform could potentially help to enhance routine clinical pathology.

Reported studies showed that in tissues from the brains of mice, Open-ST was able to reconstruct cell types at subcellular resolution. In tumor tissue and a healthy and metastatic lymph node from a patient with head and neck cancer, the platform captured the diversity of immune, stromal, and tumor cell populations. It also showed that these cell populations were organized around communication hotspots within the primary tumor, but this organization was disrupted in the metastasis.

Such insights could help researchers understand how cancer cells interact with their surroundings and, potentially begin exploring how they evade the immune system. The resulting data might also be used to predict potential drug targets for individual patients. The Open-ST platform is not restricted to cancer and can be used to study any type of tissue and organism.

“We think these types of technologies will help researchers discover drug targets and new therapies,” said Nikos Karaiskos, PhD, a senior scientist in the Rajewsky lab at the Berlin Institute for Medical Systems Biology of the Max Delbrück Center (MDC-BIMSB). Karaiskos is a corresponding author of the team’s published paper in Cell, titled “Open-ST: High-resolution spatial transcriptomics in 3D,” in which the authors describe the platform as “an end-to-end experimental and computational workflow for do-it-yourself  subcellular spatial transcriptomics in 2D or 3D at low cost.”

Transcriptomics is the study of gene expression in a cell or a population of cells, but it usually does not include spatial information. In contrast, spatial transcriptomics (ST) measures RNA expression in space, within a given tissue sample. “Unlike standard single-cell methods, ST retains the spatial context of the captured transcriptome and thus allows the direct observation of the arrangements of cells and their interactions in tissue space,” the authors wrote. However, they pointed out, commercially available ST technologies can be limited by their relatively high costs and/or limited resolution. “… there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods.”

The team’s Open-ST platform offers a cost-effective, high-resolution and easy-to-use method that captures both tissue morphology and spatial transcriptomics of a tissue section. Serial 2D maps can be aligned, reconstructing the tissue as 3D “virtual tissue blocks.” Added Rajewsky, who is also director of MDC-BIMSB, “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.”

Open-ST images from cancer tissues also highlighted potential biomarkers at the 3D tumor/lymph node boundary that might serve as new drug targets. “These structures were not visible in 2D analyses and could only be seen in such an unbiased reconstruction of the tissue in 3D,” said co-first author Daniel León-Periñán, PhD. The authors further commented, “In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST … Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary.”

Open-ST images from cancer tissues highlighted potential biomarkers at the 3D tumor/lymph node boundary that might serve as new drug targetsOpen-ST images from cancer tissues highlighted potential biomarkers at the 3D tumor/lymph node boundary that might serve as new drug targets

Rajewsky stated, “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.” The authors concluded, “In summary, Open-ST provides a versatile and powerful framework for comprehensive analysis of gene expression in 2D and 3D, including the unveiling of molecular and/or cellular spatial structures obscured in traditional 2D representations.” They also noted limitations and outlined future developments.

One significant advantage of Open-ST is cost. Commercially available spatial transcriptomics tools can be prohibitively expensive. Open-ST, however, uses only standard lab equipment and captures RNA efficiently, reducing costs significantly. Lower costs also mean that researchers Importantly, the platform is modular, said León-Periñán, so Open-ST can be adapted to suit specific needs. “All the tools are flexible enough that anything can be tweaked or changed.” can scale up their studies to include large sample sizes, to study patient cohorts, for example.

The researchers have made the entire experimental and computational workflow freely available to enable widespread use. “Due to its ease of use, cost-effectiveness, and wide applicability, we envision Open-ST to become a valuable method for spatial omics studies.” To aid researchers in implementing Open-ST, the developers have set up an online resource with detailed experimental and computational protocols/software, at https://rajewskylab.github.io/openst.

“A key goal was to create a method that is not only powerful but also accessible,” says Marie Schott, a technician in the Rajewsky lab and co-first author on the paper. “By reducing the cost and complexity, we hope to democratize the technology and accelerate discovery.”