While the field of proteomics continues to gain speed, depth, and quantitative power, many bulk workflows still struggle to retain information on the origin of proteins within complex tissues. This challenge becomes especially acute in systems such as tumors or brain tissue, where multiple cell types coexist and drive biology in highly localized ways.
For bottom-up liquid chromatography–mass spectrometry (LC–MS) workflows, this loss of spatial information is a well-recognized but often unavoidable trade-off. However, spatial proteomics approaches based on imaging mass spectrometry (IMS) are designed to address this limitation by preserving molecular information directly within intact tissue sections.
“IMS gives you spatial resolution that is now very close to the cellular level,” explains Pierre Chaurand, Professor at the Université de Montréal. “It allows you to ask which proteins come from which cell type within a heterogeneous tissue, and how those cells interact with each other and with their environment.”
This shift changes the nature of proteomics experiments. Instead of focusing solely on whether a protein is present or changing in abundance, spatial proteomics enables researchers to ask where those changes occur and how protein organization evolves with disease, development, or treatment.
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From Intact Proteins to Peptide-Based Spatial Proteomics
Early proteomic imaging focused on intact proteins. The approach worked, but only to a point. “With intact protein imaging, you mainly detect lower-molecular-weight, abundant, and easily ionizable proteins,” Chaurand notes. “In practice, that represents a small fraction of the proteome.”
Comparative studies often produced similar spectra across conditions, limiting biological insight. That constraint pushed the field toward a familiar proteomics solution: digestion. Importantly, this shift also enables peptide-level identification strategies that are directly compatible with established database search and validation workflows.
“The shift came with spatially resolved digestion, where you digest proteins directly on the tissue using trypsin and then image the resulting peptides,” he explains. “Peptides fall into a narrower mass range and are much more compatible with tandem mass spectrometry, which increases identification confidence and depth.”
This transition aligned imaging workflows with mainstream proteomics logic and made sample preparation the central determinant of data quality.
Sample Preparation as the Gatekeeper of Spatial Fidelity
“To achieve cell-level resolution, every step of sample preparation has to be perfectly controlled,” says Chaurand. “Anything that causes molecules to move on the tissue surface will destroy spatial information.”
Matrix and enzyme deposition, humidity, and incubation conditions all matter. Too much solvent creates delocalization. Too little prevents digestion. Uneven digestion or local analyte delocalization can easily be misinterpreted as biological heterogeneity if preparation variables are not tightly controlled.
“You want to avoid creating a swimming pool on the tissue,” he explains. “Humidity and wetness have to be just right. For digestion, we incubate at very high humidity so the reaction happens, but nothing moves.”
Automation improves consistency but does not eliminate the need for expertise. “Automated sprayers help control how much material you deposit and under what conditions,” Chaurand adds. “But you still need to understand the chemistry and the mass spectrometry. These systems are more accessible now, but they still require expertise and careful method development.”
Targeted Proteomics and High-Plex Spatial Analysis
Spatial proteomics increasingly relies on targeted strategies, often informed by transcriptomics. “You can use prior information to focus on a defined panel of proteins that are known to be important for a given disease,” describes Chaurand. “Instead of measuring one marker at a time, you can measure dozens in the same tissue section. In practice, assay design still depends heavily on antibody specificity, reporter chemistry, and the ability to control background signal across the tissue.”
Imaging mass spectrometry removes a key bottleneck that fluorescence-based methods face. “With fluorescence, you are quickly limited by spectral overlap, usually to four or five reporters,” he asserts. “With mass spectrometry, each antibody is tagged with a different reporter ion. You only need a one-mass-unit difference to distinguish them.”
The result is highly multiplexed proteomics with spatial information intact. In extreme cases, more than one hundred protein targets can be measured simultaneously.
Resolution Versus Coverage: A Proteomics Trade-Off
Higher spatial resolution is appealing, but it carries a direct sensitivity penalty. “As you push to finer spatial resolution, you generate fewer ions,” clarifies Chaurand. “At some point, you hit a sensitivity wall.”
That trade-off forces strategic choices. “In our lab, we often start with a survey scan at lower spatial resolution to maximize signal,” he shares. “Then we go back to regions of interest at higher resolution, sometimes using serial sections.”
Ion statistics still govern proteomics, even in spatial form. At very fine spatial scales, stochastic sampling effects become increasingly prominent, particularly for lower-abundance peptides. “Going from 25 microns to 10 microns can cost you half your signal. Going to five microns costs you another factor of two,” Chaurand adds. “Resolution without ions does not give you meaningful proteomics.”
Data Analysis: The True Bottleneck
Acquiring spatial proteomics data is demanding. Interpreting it often takes longer. “Data analysis is more of a bottleneck than data acquisition in our lab,” Chaurand says. “It takes three to four times longer to analyze the data than to generate it.” Identification, statistical analysis, and validation dominate timelines. Vendor software helps, but flexibility matters.
Confirming peptide identity often requires follow-up tandem MS or orthogonal methods, further extending analysis time. “We export data into an in-house pipeline built in R so we can control preprocessing and apply different statistical tools,” he explains. “The way you analyze the data depends entirely on the biological question you started with.”
For students and early-career scientists, his advice is direct. “If you work in this area, you need training in statistics and data analysis. Spatial proteomics produces big data, and you have to know how to interrogate it.”
Expanding What Spatial Proteomics Can Measure
Beyond protein abundance, spatial proteomics increasingly targets molecular features that are difficult to resolve in bulk workflows. “Post-translational modifications, especially glycans, are very informative,” Chaurand notes. “You can enzymatically release sugars from proteins and image them. Their spatial distribution can be highly specific to disease state.”
Other targets remain challenging. “Steroids are important in diseases like prostate or breast cancer, but they are low abundance, poorly soluble, and hard to ionize,” he notes. “One strategy is on-target chemical derivatization, where you add a fixed charge and a UV-absorbing group to improve detectability.”
These advances rely less on new instruments and more on chemistry.
“The next gains will come from better sample preparation and smarter chemistry,” Chaurand predicts. “That is what will expand the chemical space we can access.”
Spatial Proteomics Rewards Discipline
Spatial proteomics does not simplify proteomics; it raises the bar. “Imaging mass spectrometry gives you access to information you cannot get any other way,” Chaurand concludes. “But the method is unforgiving. If sample preparation, experimental design, or data analysis are weak, the biology will not be there.” For laboratories willing to invest in rigor, the payoff is clear. Spatial proteomics restores context to protein measurements—and in doing so, changes the questions proteomics can answer.
