The human genome encodes ~20,000 protein-coding genes, but only a small fraction of these are currently targeted or considered viable for drug development. 95% of drugs target proteins, and around 700–800 unique human proteins are targeted by FDA-approved small-molecule and biologic drugs, whereas 3,000–4,000 proteins are considered druggable. For some diseases such as Parkinson’s disease, amyotrophic lateral sclerosis, triple negative breast cancer, and many others, effective drug targets remain rare or unavailable.
Pathologists can recognize hundreds of diseases from patients’ biopsy, surgical, or postmortem samples under a microscope, where unique morphological features reflect indisputable phenotypes of the corresponding diseases. Ideally, for drug discovery, we would like to know exactly what goes wrong with the proteins at the locations of the unique morphological features, so a short list of potential causes or unique biomarkers of the disease can be obtained. Identification of disease-associated proteins can then lead to druggable targets.
Imagine if we knew the protein components of Lewy bodies in Parkinson’s disease, or proteins at the nuclear bodies of lung cancer. It could shed light on pathways related to the disease and open the door to new first-in-class drug targets.
Spatial proteomics is an emerging discipline that exactly fills this gap. Spatial proteomics captures complexity by revealing proteins in their native spatial context. Unlike traditional proteomics, which analyzes homogenized samples, spatial proteomics preserves tissue architecture and cellular microenvironments. It allows researchers to ask not just what proteins are present, but where they are, how they co-localize, and how their distribution changes in disease.
Drug discovery has long been driven by the search for molecular targets: proteins that, when modulated, can correct a disease phenotype. Therefore, spatial proteomics is transforming how scientists understand pathology, identify drug targets, and evaluate therapeutic responses — and it’s poised to become a foundational tool in modern drug discovery. One can even use high-throughput spatial proteomics to screen which drug can result in a proteomic correction at disease-specific locations.
With this new spatial insight, researchers can:
- Identify targets that are functionally relevant in specific tissue compartments
- Discover biomarker patterns that correlate with disease states or therapeutic responses
- Understand heterogeneity within tissues — for example, why some tumor cells resist therapy while others respond
- Screen and identify good drug candidates that recover the proteomic signature to a healthy state
The implications are profound. Spatial proteomics helps de-risk early-stage R&D by offering a more faithful representation of disease biology — and therefore more reliable starting points for intervention.
Despite significant advances in proteomics, many current tools fall short when it comes to capturing the full biological context.
Targeted proteomics techniques, such as antibody panels with predefined targets, are limited by their scope. They require prior knowledge of what to look for, which assumes we already know the key players. This is hugely limiting because in complex, multifactorial diseases, that’s rarely the case.
We can only find what we’re looking for — and often, the most critical disease mechanisms are the ones we’re not expecting. This tunnel vision can lead to false starts, overlooked targets, or therapies that work in vitro but fail in clinical settings.
Unbiased spatial proteomics overcomes these limitations by combining spatial protein isolation and downstream mass spectrometry — without requiring a predefined hypothesis. This enables true discovery-driven research, where new biology can emerge from the data itself.
Techniques like mass spectrometry imaging, laser micro-dissection-based deep visual proteomics, and high-throughput photo-induced proximity labeling now allow researchers to profile dozens to thousands of proteins simultaneously, while maintaining information about their cellular and subcellular localization.
These approaches open the door to several key benefits:
- New target identification: Observing aberrant protein contents in diseased tissues
- Mechanistic insight: Understanding how spatial disruptions drive pathology; for example, how immune cell exclusion affects tumor progression
- Biomarker development: Finding plasma markers through spatial signatures that correlate with prognosis or predict therapeutic response
- Drug mechanism-of-action (MoA) validation: Evaluating how a drug reshapes proteomic “health state” through further mechanism studies
These different unbiased spatial proteomics technologies address different markets, where organelle or sub-organelle features that dictate disease phenotypes require high-precision unbiased spatial proteomics, while clusters of cells behaving aberrantly require large-volume unbiased spatial proteomics.
Recent rapid advances in mass spectrometry, robotics, and AI technologies further benefit the field of unbiased spatial proteomics, pushing the limit for high-throughput proteomics-based drug screening. It is foreseeable that spatial proteomics-based drug discovery will become an essential element in pharmaceutical and biotech industry.
We’re entering an era where we can no longer afford to ignore spatial biology. Unbiased spatial proteomics gives us the resolution, context, and systems-level insight we need to make better drugs, faster.
Spatial proteomics represents a critical leap forward for drug discovery. By bridging the gap between molecular data and tissue context, it provides a richer, more actionable understanding of disease biology. As tools become more scalable and integrated with AI, the field will continue to push boundaries — uncovering hidden mechanisms, de-risking pipelines, and driving smarter, more effective therapies.
For pharmaceutical scientists facing ever-growing complexity, this approach offers more than just a new set of data — it offers a new way of thinking.
