Mapping Molecules in Space:
The Rise of Spatial Biology

Biological analysis has entered a new era, powered by artificial intelligence. In a previous article, I discussed how biology is evolving toward the creation of a virtual cell, a computational model capable of enabling truly personalized medicine. One of the most important factors making this vision feasible is the rapid advancement of transcriptomic technologies; the ability to measure the complete set of RNA molecules (or transcripts) produced by a cell at any given time.

Last month, I highlighted the role of mRNA in reading out a cell’s gene expression levels… what’s turned on or off, and under which circumstances. But understanding a single cell in isolation is only part of the story. What about its neighbors? Its environment? How are cells communicating with each other? This is the domain of Spatial Biology.

What Is Spatial Biology?

This technology allows us to study DNA, RNA, and proteins in their physical context; across 2D and 3D space. It moves beyond the snapshot of a single cell and asks bigger questions: What’s happening across the entire tissue? Are neighboring cells influencing gene expression? How is the architecture of the tissue contributing to disease?

This isn’t just about whether genes are turned up or down. It’s about seeing where those changes happen and how they influence other regions, across space and structure.

What Kind of Information Does Spatial Biology Provide?

Spatial biology gives us insight at multiple levels, including:

• Subcellular localisation of DNA, RNA, and proteins

• Single-cell resolution of cell-cell interactions and signaling

• Cellular neighborhoods, regions, and microenvironments

• Tissue architecture and organization within organs. 

Together, these layers enable us to pinpoint spatially-resolved signatures of disease progression, elucidating patterns that would be invisible in bulk or single-cell RNA-seq alone.

The Rise of Spatial Transcriptomics

While spatial biology spans all molecular layers, spatial transcriptomics in particular has revolutionized the field over the past decade. Researchers could get information from individual cells but what about if we could get that information, while keeping the spatial structure of the tissue? Basically, can we provide a structural representation of RNA expression? 

This became a reality in 2016, when researchers in Sweden published a landmark study in Science. They developed a method using microarray slides embedded with unique spatial barcodes to track RNA expression in space. This foundational work coined the term "spatial transcriptomics", and was quickly picked up by industry. In 2018, the technology was acquired by 10x Genomics and became what we now know as Visium.

Then in 2019, the Broad Institute released Slide-seq, which significantly improved spatial resolution, shrinking it to ~10μm per spot compared to Visium’s ~55μm. This meant fewer cells per spot, inching us closer to true single-cell resolution while retaining spatial structure.

The impact was so profound that Nature Methods named spatial transcriptomics the 2020 “Method of the Year.”

Beyond RNA: Spatial Proteomics

Just as spatial transcriptomics dominated 2020, spatial proteomics is stepping into the spotlight. In 2024, it was Nature Methods’ new “Method of the Year.” These technologies now let researchers observe protein localization and signaling cascades at sub-cellular levels with unprecedented resolution, further expanding the spatial toolkit.

From Research to Patient Care

Later in 2020, spatial biology took a huge step into clinical care. A team at the Max Planck Institute used spatial analysis to guide treatment for seven patients with toxic epidermal necrolysis (SJS–TEN overlap), a rare and severe skin condition. Based on their molecular findings, the team administered JAK inhibitors, which targeted the disease’s specific immune responses.

All seven patients fully recovered with no side effects. 

Just as baffling, and in a rare move for early-phase clinical work, the study included photographic documentation for all patients, rather than a single “representative” image. This drastically positive outcome highlights the promise of spatial-guided medicine. 

The Role of AI: Enter SpatialAgent

To streamline the spatial pipeline further, AI is now stepping in. In a 2025 preprint published on bioRxiv, researchers introduced SpatialAgent; a fully autonomous AI agent designed to handle the end-to-end spatial biology workflow. It assists with experimental design, data processing, and even hypothesis generation.

While still under peer review, the implications are enormous. Autonomous systems like SpatialAgent could help democratize spatial biology, making it faster, cheaper, and more widely applicable.

Why It Matters

Why is all of this revolutionary? In biological research, the aim is always specificity. With every answered question, there are 100 new questions that come from it.

Spatial biology moves us beyond the simple up/down regulation of genes. It provides context, structure, and mechanism. It empowers scientists to ask and answer more specific questions: Do certain pathways activate only in specific regions? Are all fibroblasts involved in fibrosis, or just a subpopulation? How do immune cells behave differently depending on where they are?

Each layer of resolution brings us closer to understanding our biology and what happens in disease. The more specific the answer, the more precise the treatment.