New 3D mapping technology offers unprecedented insight into tissue architecture at subcellular level
Researchers at the Max Delbrück Center have developed an open-source platform called Open-ST that creates high-resolution 3D maps of gene expression in tissue samples, potentially revolutionising our understanding of disease processes and enhancing clinical pathology.
A groundbreaking new technology developed by scientists at the Max Delbrück Center for Molecular Medicine in the Helmholtz Association is set to transform our ability to visualise and analyse tissue architecture at a molecular level. The open-source platform, named Open-ST, enables the creation of three-dimensional maps of gene expression within tissue samples with subcellular precision, offering unprecedented insights into cellular organisation and interactions.
The research, published in the journal Cell [1], describes how Open-ST combines spatial transcriptomics with high-resolution imaging to produce detailed 3D reconstructions of tissue samples. This novel approach allows researchers to observe molecular and subcellular structures that are often obscured in traditional two-dimensional analyses.
Unveiling cellular complexity in health and disease
The Open-ST platform has demonstrated its versatility and power across a range of tissue types. In mouse brain samples, the technology successfully reconstructed cell types at subcellular resolution, providing a detailed map of neuronal organisation. Perhaps even more significantly, when applied to tumour tissue and lymph nodes from a patient with head and neck cancer, Open-ST revealed the intricate interplay between immune cells, stromal cells, and tumour cells. One of the most intriguing findings was the identification of communication ‘hotspots’ within the primary tumour, where different cell populations appeared to be strategically organised. Notably, this organisation was disrupted in metastatic tissue, potentially offering clues about the mechanisms of cancer spread.
Dr Nikos Karaiskos, a senior scientist in Professor Nikolaus Rajewsky’s lab at the Berlin Institute for Medical Systems Biology of the Max Delbrück Center and corresponding author on the paper, highlighted the potential impact of this technology: “We think these types of technologies will help researchers discover drug targets and new therapies.”
Advancing beyond traditional transcriptomics
While traditional transcriptomics has long been a valuable tool for studying gene expression, it typically lacks spatial context. Spatial transcriptomics addresses this limitation by measuring RNA expression within the physical structure of a tissue sample. Open-ST takes this concept further by offering a cost-effective, high-resolution method that captures both tissue morphology and spatial gene expression data. The platform’s ability to align serial 2D maps into 3D ‘virtual tissue blocks’ represents a significant advance in tissue analysis. Prof. Rajewsky, who is also Director of MDC-BIMSB, emphasised the importance of this capability: “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.”
Uncovering hidden structures and potential biomarkers
One of the most exciting aspects of Open-ST is its ability to reveal structures and interactions that remain invisible in conventional 2D analyses. In cancer tissue samples, the technology highlighted potential biomarkers at the three-dimensional tumour/lymph node boundary that could serve as new drug targets.
Daniel León-Periñán, co-first author on the paper, explained: “These structures were not visible in 2D analyses and could only be seen in such an unbiased reconstruction of the tissue in 3D.” Prof. 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.”
Significant cost reduction
A key advantage of Open-ST is its accessibility. While commercially available spatial transcriptomics tools can be prohibitively expensive, Open-ST utilises standard lab equipment and employs efficient RNA capture methods, significantly reducing costs. This cost-effectiveness allows researchers to scale up their studies to include larger sample sizes, potentially facilitating the analysis of patient cohorts.
Open science
In line with the principles of open science, the researchers have made the entire experimental and computational workflow freely available. Marie Schott, a technician in the Rajewsky lab and co-first author on the paper, emphasised the importance of this approach: “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 democratise the technology and accelerate discovery.” The modular nature of the platform also allows for customisation, with León-Periñán noting that “all the tools are flexible enough that anything can be tweaked or changed”.
Implications for future research and clinical practice
While the current study focused primarily on cancer tissues, the researchers stress that Open-ST is not limited to oncology. The platform’s versatility means it can be applied to any type of tissue and organism, opening up a wide range of potential applications in both basic research and clinical settings.
As the technology continues to develop and be adopted by the scientific community, it has the potential to significantly enhance our understanding of tissue architecture in both health and disease. This could lead to the identification of new drug targets, the development of more personalised treatment approaches, and potentially even improvements in routine clinical pathology.
Reference:
1. Schott, M., León-Periñán, D., Splendiani, E., et al. (2024). Open-ST: High-resolution spatial transcriptomics in 3D. Cell.
https://doi.org/10.1016/j.cell.2024.05.055
The colors indicate expression of select genes.
Photo credit: N. Rajewsky Lab, Max Delbrück Center
