Dr Todd Dickinson, CEO, Stellaromics on 3D spatial biology and functional genomics.

Understanding how genes are regulated and expressed in complex tissue environments is fundamental to modern drug discovery. Traditionally, researchers have relied on 2D spatial genomics methods, analysing thin slices of tissue on a single plane to study gene expression. However, 3D spatial genomics is unlocking a far richer view.

By capturing how intact tissues are organised in three dimensions, this technology helps researchers map gene expression, cellular neighborhoods, and molecular interactions, at subcellular resolution, throughout entire tissue samples. This breakthrough is ushering in a new era for functional genomics, with major implications for target discovery, therapeutic validation, and disease modelling.

The power of 3D context

Unlike 2D approaches, which analyse biological structures as a single layer, 3D spatial genomics preserves a tissue’s complete architecture. This allows scientists to observe which genes are expressed and how gene expression patterns vary across spatial gradients, layered anatomical features, and long-range cellular connections. This context is critical: the literature shows1 that gene regulation is influenced by intrinsic cellular programs and physical proximity to other cells, vasculature, and structural boundaries within tissues.

In neuroscience, 3D spatial technologies are providing unprecedented insights into how neurons organise, communicate, and degenerate across brain regions – advances that would be invisible in 2D slices.

Enabling technologies behind the 3D revolution

Several converging technologies have made 3D spatial genomics possible. Tissue clearing methods help make biological samples optically transparent without disrupting their structure. Meanwhile, in situ transcriptomic techniques, such as STARmap2 and RIBOmap3, help researchers detect and localize RNA molecules within intact tissues. High-resolution, volumetric imaging platforms, including confocal and light-sheet microscopy, now provide the speed and resolution to capture entire tissue volumes at single-cell or even subcellular levels. Multiplexed probe designs can detect hundreds or even thousands of RNA species in 3D space. platforms, including confocal and light-sheet microscopy, now provide the speed and resolution to capture entire tissue volumes at single-cell or even subcellular levels. Multiplexed probe designs can detect hundreds or even thousands of RNA species in 3D space.

Together, these tools are expanding the frontier of how researchers study complex biology at scale.

Unlocking new drug discovery insights 

The ability to map gene expression in 3D is transforming drug discovery. Spatially-resolved data help researchers validate therapeutic targets by answering crucial questions: Where is the target gene expressed? In which cell types? How does its activity vary within the tissue’s architecture?

Moreover, 3D spatial genomics can assess therapeutic delivery and efficacy. Researchers can track whether a viral vector, monoclonal antibody, or RNA therapeutic reaches its intended destination in the tissue, and whether it initiates the expected molecular response.

Another emerging application, spatial analysis of translation, measures RNA presence and active protein production across tissue volumes. Technologies like RIBOmap offer a window into the functional state of cells in situ, helping researchers better understand post-transcriptional regulation and drug mechanisms of action.

Expanding the impact: from oncology to organoids

While 3D spatial biology is valuable across many fields, it is particularly transformative in those where spatial context is critical. Oncology, immunology, neuroscience, and developmental biology are leading early adopters.

In cancer research, spatial genomics provides insights into tumour heterogeneity, immune microenvironments, and metastatic pathways. In immunology, it reveals how immune cells coordinate across tissues during health and disease. In developmental biology, it captures how complex structures, like the brain or heart, form through dynamic, spatially-regulated programs.

Researchers are also applying 3D spatial genomics to organoid models and in vivo systems, paving the way for more accurate preclinical testing and therapeutic validation.

The challenge ahead: managing big spatial data

Despite these advances, 3D spatial genomics faces significant technical challenges, particularly in data management and analysis. A single 3D dataset can range from hundreds of gigabytes to several terabytes of raw data, necessitating robust computational infrastructure.

Recent progress in GPU-accelerated computing, deep learning, and purpose-built analysis pipelines is helping address these hurdles. Specialised software tools now allow researchers to visualise, quantify, and interpret vast 3D datasets with greater ease, but ongoing innovation in data compression, visualisation, and machine learning integration remains a priority in the field.

Looking forward: the future of multi-modal 3D biology

Multimodal integration –  combining spatial RNA, protein, and functional analyses in the same tissue sample – is the next frontier in 3D spatial genomics. This integrated approach promises to create holistic maps of tissue biology, offering deeper insights into how molecular and cellular systems operate in space and time.

Researchers are also working towards generating comprehensive 3D disease atlases, evergreen reference maps that can serve as foundations for machine learning applications, therapeutic discovery, and precision medicine.

The importance of collaboration

Advancing 3D spatial genomics requires a collaborative ecosystem. Academic researchers, biotech companies, and pharmaceutical partners are working together to refine protocols, validate use cases, and share high-impact datasets. Cross-sector collaboration accelerates innovation and ensures that new spatial technologies address real-world biological and clinical questions.

As spatial genomics continues to evolve, its integration into mainstream drug discovery pipelines is both inevitable and essential, providing a powerful new technology to help us better understand biology and develop new therapies.

From DDW Volume 26 – Issue 3, Summer 2025 Read the digital issue

References:

  1. https://pmc.ncbi.nlm.nih.gov/articles/PMC8475179/ 
  2. STARmap reference publication: Wang X, Allen WE, Wright MA, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018 Jul 27;361(6400):eaat5691.
  3. RIBOmap reference publication: Zeng H, Huang J, Ren J, et al. Spatially resolved single-cell translatomics at molecular resolution. Science. 2023 Jun 30;380(6652):eadd3067.

 

Dr Todd DickinsonAbout the author:

Dr Todd Dickinson is a genomics pioneer with 20+ years leading breakthrough life sciences companies, including Illumina, Bionano Genomics, and Cantata Bio. Now President & CEO of Stellaromics, he is driven to build purpose-driven teams and technologies that meaningfully advance human health and leave a lasting impact.