Introduction to Spatial Proteomics And The Spatial -Omics Revolution

Illuminating cell states at high resolution with information from spatial proteomics on top of the more established spatial transcriptomics

Seeing is believing, but regular images aren’t enough in modern biology. In order to achieve our finest understanding of the mechanisms of life as well as the origins of disease — and ultimately how to treat them — we need to understand how the molecules arising from the (epi)genome distribute. The spatially resolved concentrations of RNA transcripts, proteins, metabolites and other molecules that define a cell’s state, function and fate, can be measured through dedicated Spatial -Omics techniques that are bringing about a revolution in biology and medicine. Among them, the rapidly emerging spatial proteomics technologies enable exploration of the complex protein landscapes of cells and tissues, to inform on their current and future states at higher resolution than ever before and possibly with richer information than alternatives such as spatial transcriptomics.

Let us introduce you in this blog post to Spatial Proteomics, which joins the spatial -Omics revolution and was chosen by Nature Methods as “Method of the Year 2024”.

Spatial Proteomics: Nature Methods’ “Method of the Year 2024”

Spatial proteomics consists in depicting maps of the inter-cellular and intra-cellular concentrations of each protein present in a cell or piece of tissue, spread over 2D or 3D images that relate one-to-one to the shape of the sample being analyzed. To achieve this, various techniques can be used including cyclic immunofluorescence, co-detection by indexing, multiplexed ion beam imaging, and imaging mass cytometry, all of them detecting proteins and their locations at cellular and subcellular levels. Among these techniques, those powered by mass spectrometry have important advantages; for example, they are less biased toward specific proteins and more broadly applicable, and they don’t require labeling. A recent and particularly exciting innovation in spatial proteomics based on mass spectrometry is Deep Visual Proteomics (DVP), which combines laser microdissection with mass spectrometry to retain spatial context while offering an unparalleled proteome coverage. Unlike antibody-based methods, DVP is not limited by reagent availability, providing a richer and less biased proteomic landscape. We comment in the next section some exciting applications of spatial proteomics and one special success case of DVP.

Quickly approaching spatial transcriptomics in relevance and possibly going to outrun it as a superior technique that provides richer information, spatial proteomics is reaching the forefront of modern discoveries in fundamental biology and in medicine. This is so because while RNA is the messenger connecting genomic information with its main molecular products, it is these products — proteins — that play most of the actual primary functional roles in cells, directly driving all main biological processes. It is then no wonder that looking at protein abundancies, especially when resolved in space, can illuminate more biology than looking exclusively at RNA.

A Disruptive Technique: Spatial Proteomics Identifies a Treatment for a Lethal Skin Disease

Spatial proteomics is very new, but it is already driving disrupting studies and producing novel tools for work in biological and clinical research. On one hand, it is enabling large-scale atlas projects similar to those built on transcriptomics but focusing on proteins. For example, the Human BioMolecular Atlas Program (HuBMAP), which attempts to map the human body at single cell resolution with its molecular components, recently started to incorporate spatial proteomics data. Likewise, the National Cancer Institute (NCI)-funded Human Tumor Atlas Network (HTAN) which intends to construct 3D atlases of the dynamic cellular, morphological, and molecular features of several human cancers, is now also incorporating protein distributions.

On the other hand, researchers are using spatial proteomics as a new tool to identify markers of cell states including states related to disease, intoxication, etc. For example, this 2024 paper by Nordmann et al in Nature exemplifies how spatial proteomics enabled the development of a life-saving therapy for toxic epidermal necrolysis (TEN), a rare but fatal skin condition triggered by adverse drug reactions. Until recently, the molecular drivers of TEN had remained totally elusive, and no effective treatments existed. By leveraging a DVP-based method for spatial proteomics, the disease could be dissected at single-cell resolution.

More specifically, the researchers analyzed archived skin tissue samples from TEN patients and uncovered hyperactivation of the JAK/STAT inflammatory pathway, revealing a potential therapeutic target. Using this insight, they tested JAK inhibitors (JAKi), a class of drugs already approved for autoimmune diseases on preclinical models and even on human patients. The results were truly groundbreaking. JAKi treatment significantly reduced keratinocyte cytotoxicity in vitro, while in vivo JAKi therapy ameliorated disease severity in mouse models. Even more impactful was that seven patients treated with JAK inhibitors experienced very rapid recovery and full re-epithelialization.

The study illustrates the power of spatial proteomics in precision medicine, and it is hopefully just the first of its type. By mapping diseases and other cell states at the protein level in situ, researchers will be able to identify actionable therapeutic targets more efficiently, as the technique looks directly at the ultimate molecular entities whose function or stability is being compromised. At Nexco we provide the bioinformatics expertise necessary to drive such discoveries, from raw data processing to actionable insights.

The Future of Spatial-Omics: A Multi-Layered Approach

Mapping Cell States in Unprecedented Detail

We have seen here and in previous posts how in the last 5 years the spatial -omics have emerged to allow scientists to explore cells in their native tissue environments, not just describing their shapes and structures but actually also knowing their states in terms of genetic and genetically encoded material. As of early 2025 this is mainly embodied in spatial transcriptomics, which maps the expression of different RNA transcripts, and spatial proteomics, which profiles the localization of different proteins, both at high spatial resolution.

Spatial transcriptomics and proteomics can illuminate in their own ways quite complete and detailed “atlases” of a piece of tissue, as of today approaching subcellular resolution. This information about how different RNA transcripts and proteins spread throughout a tissue turns out critical to define states, understand cellular mechanisms, predict the fate of a cell given its state and the environmental conditions, and ultimately the origin of many diseases — information key to the development of therapeutic opportunities.

We have previously covered Spatial Transcriptomics and its exciting applications in previous posts of our blog, and you can know more about our Spatial Transcriptomics services here, including our expertise in high-resolution data collection, analysis, and visualization; and you can know more about the technique and its applications in our previous blog posts:

Now, the designation of spatial proteomics as Method of the Year by Nature not only acknowledges its success but also forecasts where the field is headed. We are positive that integrating spatial proteomics with spatial transcriptomics and spatial metabolomics in the context of genomics will unlock even deeper layers of biological understanding. Whereas on one hand these will be actionable directly on basic and applied studies on specific systems, as in the case of TEN presented above, the deposition of spatially resolved -Omics into datasets such as atlases will be fundamental for the development of multimodal foundational AI systems and agents that can server as “digital twins” on which researchers can perform complex biological simulations and predictions, as we advanced in these two blog posts:

The future is bright with spatial -Omics surely leading to more groundbreaking findings. In cancer research, for example, they will allow scientists to better understand tumor microenvironments, possibly leading to more precise immunotherapy strategies. In neuroscience, mapping molecular compositions, particularly proteins with certain post-translational modifications and aggregation states, will provide new insights into neurodegenerative diseases, their causes and ways to stop or slow down their progression. In regenerative medicine, full spatially resolved -Omics can aid in designing targeted tissue engineering strategies that enable better tissue and organ growth as well as smoother adaptation upon transplant.

Nexco is Here to Help you With (Spatial) Omics

We have explored here how the spatial -Omics revolution is starting to transform biological and medical research, with spatial transcriptomics and proteomics leading the charge along with more traditional techniques. But as we explain in the opening to this post, all modern techniques come with a huge computational cost and highly technical expertise. That’s where our services come in.

At Nexco we are committed to staying at the cutting edge of spatial-omics, applying and developing personalized bioinformatics pipelines and tools to carry out those complex analyses that separate your data from conclusions and actions. Whether you’re exploring spatial transcriptomics, proteomics, or integrated multi-omics approaches, our expertise ensures that you maximize the value of your research.

We master raw data processing, compilation and visualization, as well as a large array of computational tools for data archival, retrieval, visualization, and analysis. As bioinformatics experts, we can also develop personalized algorithms and workflows tailored to your specific needs and datasets.

Nexco Analytics - Life Sciences AI experts

In parallel, we have also released a fully-fledged web-based software, ONex, that abstracts all the simpler tasks related to data processing and analysis in bioinformatics, that you can work with even without being an expert — and eventually follow up with our customized services for more complex endeavors: