Purdue researchers propose a faster route to multiomics analysis
A team at Purdue University says it has developed a way to remove one of the most time-consuming parts of many laboratory workflows: sample preparation. The method, called surface touch extraction imaging, or STEi, is designed for use with liquid chromatography-tandem mass spectrometry (LC-MS/MS) and aims to let researchers analyze metabolites, lipids, environmental compounds and proteins directly from complex surfaces.
The advance matters because conventional preparation steps often consume a large share of lab time and budget before any instrument begins generating data. In the Purdue team’s framing, those steps can also reshape the sample itself. Mixing, centrifuging, transferring and drying are routine parts of many analytical workflows, but they can destroy the original structure of a specimen and erase information about where specific chemicals were located.
That loss is especially important for spatial analysis. When scientists want to study an uneven or irregular surface, such as a piece of tissue or a fish fillet, traditional methods may require them to section and flatten the material before extraction and analysis. Purdue’s researchers say STEi is meant to avoid that tradeoff by working directly on the sample surface.
How the approach works
According to the supplied source text, the method uses a pipette tip preloaded with extraction solution that briefly touches the surface of a tissue or food sample. That contact is intended to recover target molecules without putting the specimen through the usual multi-step preparation sequence. The concept is straightforward, but its practical significance is larger: it treats spatial chemistry as something to preserve at the moment of collection, rather than something reconstructed later from a heavily processed sample.
The researchers describe the technique as part of a broader multiomics workflow. In practice, that means a single approach could support multiple layers of biological or chemical information, including proteins and lipids, while also handling environmental compounds and metabolites. That breadth is one reason the team positions the technique as useful across several fields rather than as a niche laboratory trick.
The source text identifies food science and toxicology among the areas that could benefit. Those are logical early targets because they often involve chemically complex, nonuniform materials that are difficult to standardize for analysis. A tissue section in a biomedical setting may be irregular. A food sample may vary across its surface. Environmental residues may appear in localized pockets rather than in neat, uniform distributions. Methods that preserve where a compound sits can therefore change not just speed, but the kind of questions a lab can answer.
Why sample preparation is such a bottleneck
Sample preparation is rarely the most visible part of laboratory science, but it often determines throughput, reproducibility and cost. A workflow that appears simple at the instrument stage can depend on repeated manual or semi-manual handling beforehand. Each additional step adds time, consumables and opportunities for variation between runs or operators.
In the Purdue team’s description, STEi is built to reduce that burden. Removing preparation steps does not just accelerate turnaround. It can also change the economics of analysis, especially in settings where researchers need to process many samples or generate multiomics datasets from difficult materials. If the method proves broadly transferable, labs could spend less time reshaping specimens for instrument compatibility and more time on interpretation and follow-up work.
There is also a data-quality argument in Purdue’s presentation of the method. Traditional preparation can discard positional context by blending or altering the sample before measurement. Once that happens, a result may still show what compounds are present, but not where they were concentrated in the original material. For studies where location matters, preserving that information can be as valuable as improving speed.
Built for irregular surfaces and broader lab use
The source text emphasizes that STEi was developed and tested for multiple applications. That matters because many analytical innovations perform well only under tightly controlled conditions or for a narrow class of samples. Purdue’s pitch is broader: a method that can operate across irregular surfaces and diverse instrumentation while generating multiomics data.
The work is being led by Christina Ferreira, a research assistant professor at Purdue’s Bindley Bioscience Center with a courtesy appointment in food science, and Ryan Hilger, assistant director of the Jonathan Amy Facility for Chemical Instrumentation in Purdue’s chemistry department. Their collaboration reflects the hybrid nature of the problem. This is not just a chemistry question, and not just a workflow question. It sits at the intersection of instrumentation, biology, food science and practical lab operations.
The article also notes that Purdue has moved to protect the intellectual property. Ferreira disclosed the innovation to the Purdue Innovates Office of Technology Commercialization, which applied for a patent. That does not validate the technology on its own, but it does signal that the university sees potential for the technique to move beyond a single academic paper or demonstration and into broader use.
What comes next
For now, STEi should be viewed as an enabling method rather than a finished platform with every operational question settled. The source text makes a strong case for why eliminating preparation could matter, but laboratories considering adoption would still need to evaluate performance, reproducibility, instrument compatibility and throughput under their own conditions. In analytical science, the gap between a promising workflow and a widely deployed standard can be substantial.
Even so, the Purdue announcement points to a real pressure point in modern research. Multiomics has expanded what scientists can measure, but the front end of that process often remains slow and labor-intensive. Techniques that compress handling time while preserving spatial information could help unlock more routine use of high-value analyses on real-world samples that are messy, uneven and difficult to process.
If STEi performs as Purdue expects, its significance will not rest on novelty alone. It will rest on whether labs can ask richer spatial questions without paying the usual penalty in prep time, sample destruction and workflow friction. That is the kind of improvement that can quietly reshape scientific practice: not by changing what instruments are, but by changing how much useful information reaches them in the first place.
This article is based on reporting by Phys.org. Read the original article.
Originally published on phys.org
