Holding space for spatial biology Open Access

Imagine being able to see inside our bodies at a microscopic level, not just observing individual cells but understanding exactly how they work together, communicate, and organise themselves—like watching a bustling city from above, but at a cellular level. This is the fascinating world of spatial biology, a revolutionary field that is transforming how we understand life and disease. Historically, methods of studying cells were akin to taking a city, putting all its buildings and people in a blender, and then trying to understand how the city functions from the resulting mixture. However, spatial biology is different—it enables scientists to see cells in their natural environment, preserving their locations and relationships with neighbouring cells. Tissues can be thought of a land map, where individual cells are landmarks and their molecular interactions form a dynamic landscape, looking for “X marks the spot” to deepen our understanding of biology (Figure 1).

The ability to analyse individual cells at the molecular level while preserving their natural spatial organisation is progressing rapidly. Experimental techniques, such as DNA barcoding, immunohistochemistry (IHC) for protein identification, and in situ hybridisation (ISH) for nucleic acid detection, have recently facilitated the expansion of spatial omics technologies, enabling them to target a wider array of molecules and regions. These technologies provide insights into various cellular characteristics, with factors such as spatial resolution, scale, coverage, throughput, and multiplexing capacity tailored to the specific scientific question. As of 2025, a wide array of platforms is available for spatial biology exploration, reflecting the growing diversity and sophistication of technologies in this field.

IHC is a longest-standing technique first developed in the 1940s and still widely used today, enabling the detection of specific proteins within tissue samples. It remains an invaluable tool in clinical practice. Single-plex IHC is a well-established method used routinely in pathology departments, playing a crucial role in diagnostic workflows. However, the growing need for multiple biomarkers to improve patient diagnosis and prognosis has placed increasing pressure on clinical pathology services—a challenge that is expected to intensify as advanced therapies continue to emerge. An example of a clinical function of IHC is to identify T cells within tumours, which are strong predictors of colorectal cancer outcomes through a system known as the Immunoscore®. This approach relies on IHC to detect CD3+ and CD8+ T cells, generating a score based on their densities within the tumour core and invasive margin. Higher Immunoscore® values are associated with improved patient survival and may also help distinguish which patients are most likely to benefit from immunotherapy intervention.

The field has advanced rapidly, with the development of even more powerful tools capable of examining multiple proteins simultaneously. Examples include the PhenoImager™ HT, which can track up to six different biomarkers at once, Imaging Mass Cytometry (IMC) can monitor up to 40 different biomarkers and, the PhenoCycler® which can follow an impressive 100+ different biomarkers, showcasing ultra-high-plex capabilities. These technological advances have already significantly contributed to our understanding of disease. For instance, Wang et al. (2022) examined the effects of immune checkpoint blockade (ICB) on triple-negative breast cancer, observing that while ICB offers benefits to some patients, the factors distinguishing responders from non-responders remain ambiguous. Since ICB targets cell–cell interactions, the authors focused on the role of multicellular spatial organisation in response to treatment and how ICB might remodel the tumour microenvironment. This study used IMC to profile the expression of 43 proteins in tumour samples from patients enrolled in a randomised trial of neoadjuvant ICB. Samples were collected at three distinct timepoints: baseline, early on-treatment, and post-treatment, with robust representation at each stage. Multivariate modelling revealed that the proportions of proliferating CD8+ TCF1+ T cells and MHCII+ cancer cells were the strongest predictors of response to ICB. Other important factors included cancer–immune interactions, particularly with B cells and granzyme B+ T cells. Responsive tumours were found to have a high presence of granzyme B+ T cells, while resistant tumours showed a predominance of CD15+ cancer cells. This study reveals the true power of spatial biology in revolutionising precision immunotherapy. By integrating tissue features from both pre-treatment and on-treatment biopsies, researchers achieved the most accurate prediction of treatment response—highlighting the immense potential of early biopsies in shaping adaptive therapies. Crucially, the findings emphasise that the spatial organisation of multiple cell types within the tumour microenvironment is a key driver of ICB effectiveness. With systematic in situ analysis, spatial biology could unlock a new age of precision immuno-oncology, optimising treatment strategies and improving patient outcomes.

ISH is a powerful technique that addresses the resolution limitations often encountered with various spatial transcriptomics methods. It allows for the direct visualisation of RNA/DNA molecules within tissue by using probes designed to specifically bind to the target transcripts. These probes can simultaneously detect one or more RNA/DNA species, offering subcellular resolution, making it ideal for pinpointing cell-type-specific gene expression or complementing RNA sequencing data to spatially map the cells of interest. Spatial transcriptomics is a key RNA-based technology that maps gene expression across entire tissue samples, providing insights into cell types and their gene expression within their native spatial context. It offers a comprehensive view of the molecular landscape, highlighting functional differences and cell interactions. However, it faces limitations, including limited sequencing depth which can prevent detection of low-abundance transcripts. Furthermore, BaseScope™ (ACD) is an advanced ISH assay designed to detect short RNA and DNA targets at single-cell resolution. BaseScope™ offers enhanced specificity for splice variants, point mutations, and short RNA sequences. This enables precise spatial visualisation of nucleic acids within tissue, providing researchers with a versatile tool to investigate how genetic changes influence disease and pathology across a wide range of biological contexts.

Examples of this technique are highlighted in seminal work by Lomakin et al. (2022), the authors found that genome sequencing of cancers often reveals a mosaic of subclones within a tumour, but the spatial growth patterns and mechanisms behind this are not fully understood (14). To address this, the authors developed a workflow that creates detailed maps of genetic subclone composition across tumour sections, enabling the study of clonal growth, histological features, and the tumour microenvironment. This approach combines whole-genome sequencing, base-specific in situ sequencing, single-cell resolved spatial transcriptomics, and algorithms to link these data layers. When applied to eight tissue sections from two multifocal primary breast cancers, this method revealed intricate sub-clonal growth patterns. In ductal carcinoma in situ, polyclonal expansions were observed at the macroscopic level but were restricted to specific microanatomical regions. The subclonal territories in carcinoma in situ, invasive cancer, and lymph node metastasis displayed distinct transcriptional features and cellular environments, demonstrating the value of spatial genomics in understanding cancer evolution. The BaSISS pipeline, which combines multiplexed fluorescence microscopy and advanced algorithms, mapped and characterised the subclonal architecture of breast cancer progression. Fascinatingly, every sample showed a spatial organisation of subclones with unique gene expression, stromal interactions, and microenvironments. This suggests that subclonal diversification plays a functional role in tumour biology. Mapping clonal growth and the genetic drivers of the tumour microenvironment is crucial for understanding cancer evolution and its interactions with the microenvironment.

With all of these advances however, many spatial transcriptomics platforms struggle to recapitulate the granularity of reads, which single-cell RNA sequencing offers. Bruker Spatial Biology explores this in a recent publication by Khafizov et al. (2024), showcasing the CosMx® Spatial Molecular Imager Whole Transcriptome Panel (WTx). This platform enables high-resolution, sub-cellular mapping of the entire human protein-coding transcriptome through a comprehensive set of imaging barcodes. In their study spanning six diverse human FFPE tissue types—including colon, pancreas, hippocampus, skin, breast, and kidney—they achieved deep spatial profiling across millions of cells, revealing rich transcriptomic landscapes with thousands of transcripts and hundreds of unique genes captured per cell. The technology enables single-cell imaging with over 10,000 subcellularly imaged transcripts, surpassing traditional single-cell RNAseq in terms of transcript and gene coverage.

As tissue staining becomes more complex and the number of markers continues to grow, a diverse array of advanced spatial analysis tools is emerging to accurately identify non-random cell associations (Figure 2). Bull et al. (2024), for example, have developed a powerful new tool called MuSpAn (www.muspan.co.uk) that fundamentally changes how researchers interpret complex cell patterns in tissues. This sophisticated software is multifaceted, capable of examining everything from tiny details within individual cells to broad patterns across entire tissues. MuSpAn stands out for its remarkable versatility and accessibility. The software provides researchers with a comprehensive set of mathematical tools to investigate crucial questions about cellular behaviour and organisation. Scientists can explore how cells communicate through physical contact, examine how cell shapes vary across different tissue regions, or understand, for example, how immune cells organise themselves during disease responses. What makes this tool particularly valuable is its compatibility with a wide range of cellular imaging technologies, from traditional microscopy images to advanced multiplex-IHC and spatial transcriptomic methods. MuSpAn holds such potential in helping scientists understand the biological significance of patterns in cell images, complimenting the use of artificial intelligence tools which can be used for pattern recognition. By providing clear, interpretable metrics, MuSpAn gives insight into why certain cellular arrangements exist and what they might indicate for health and disease. This mechanistic insight is vital for unlocking the complexities of cell–cell interactions and maximising the potential of the vast spatial data now obtainable from single tissue sections.

Spatial biology is revolutionising how we understand disease by combining advanced imaging with molecular analysis to observe how cells work together in health and disease. This innovative approach enables us to examine genes, proteins, and other key molecules while maintaining their spatial context. This opens a plethora of potential in the field of personalised medicine. By understanding how cells organise and interact in diseased tissues, clinicians could tailor treatments to suit each patient’s unique ‘cellular interactome’. However, challenges remain before this technology can be widely adopted in clinical settings. The main obstacles include managing the vast amount of data these techniques generate and developing efficient tools to analyse it. Scientists are actively working on solutions, such as creating standardised data management systems (MITI standards). While widespread implementation may take time, spatial biology represents a fundamental shift in how we understand and treat disease at the tissue level. The future of medicine is increasingly (and excitingly) spatial.

Eoghan Mulholland earned an MEng in Chemical Engineering and a PhD in Pharmacy from Queen’s University Belfast. From 2021 to 2024, he was the Lee Placito Research Fellow in Gastrointestinal Cancer at the University of Oxford, where he investigated cellular dynamics and spatial biology approaches in colorectal (pre)cancers and invasive carcinomas. He is now the Colorectal Cancer Team Lead within the GSK-Oxford Cancer Immuno-Prevention Programme at the University of Oxford. His research focuses on exploring precancer biology and uncovering key insights into cancer development in humans, with the goal of informing novel approaches to cancer vaccination. Bluesky handle: eoghanjm.bsky.social. Email: [email protected].