High-resolution accurate-mass mass spectrometry has long helped researchers see deeper into complex samples. Thermo Fisher Scientific now sees a broader role for Orbitrap-based HRAM MS in development, biopharma, environmental testing, and food safety.
“It is still a discovery tool,” notes Shane Elliott, Vice President and General Manager, Life Science Mass Spectrometry at Thermo Fisher Scientific. But as HRAM MS moves into new applications, he adds that Thermo Fisher must make the technology “more accessible and easier to use” for customers.
That shift depends on more than instrument performance. Elliott points to software, AI, and machine learning as tools that can help scientists interpret complex datasets and make faster decisions.
Aaron Robitaille, Ph.D., Senior Director, Product & Vertical Marketing of Mass Spectrometry at Thermo Fisher Scientific, describes the expansion as a way to bring research-grade data into development and applied labs.
“We’re taking Orbitrap technology, which has been the gold standard for years in research, and now we’re enabling labs in development and the applied sector to have that same sort of high-quality data,” he notes.
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Seeing Beyond the Target List
Traditional targeted workflows focus on known compounds. HRAM MS still captures those targets, but it also gives labs a broader view of the sample.
“The biggest difference is more of a mindset shift,” Robitaille explains. “When you’re doing a targeted workflow, you know what you’re looking for.”
With HRAM MS, labs can collect data around the targets and revisit it later. In environmental monitoring, for example, a molecule may appear at low levels, then increase over time. Scientists can then ask whether it is a pollutant, where it came from, or whether it has a natural source.
“You see more of what’s in your sample, not just the targeted things you care about, but also the unknowns,” Robitaille adds.
Elliott captures the advantage more simply: “You get the unknowns for free.”
That added context gives labs a way to answer today’s method-driven questions while preserving data for future investigations. As contaminant lists grow and biological questions become more complex, that ability to return to existing data could become a major advantage.
Workflow Gaps Still Matter
As LC-MS moves into oligonucleotides, peptides, and other complex therapeutics, sample handling and separation remain critical. Sticky molecules can create adsorption, recovery, and reproducibility challenges that limit what even a powerful mass spectrometer can report.
Elliott points to Thermo Fisher’s Vanquish Amplify UHPLC system and related columns as part of the company’s response to those needs, especially for oligonucleotide workflows. He positions these tools around molecules that can prove difficult to handle as Orbitrap technology moves further into biopharma applications.
Robitaille frames the approach as an end-to-end solution, spanning sample preparation, UHPLC, columns, Orbitrap detection, and Biopharma Finder software. For oligonucleotide analysis, he emphasizes the need to help customers preserve more of what is in the sample and generate stronger analytical confidence.
“It’s all of that together, which really adds the value for the customer doing oligonucleotide analysis,” he notes.
AI Supports Faster Interpretation
As HRAM instruments generate larger datasets, interpretation becomes a major pressure point. Elliott sees AI and machine learning playing their strongest role in helping scientists manage that complexity.
“The parts of the workflow that lend themselves really well to AI and machine learning are… the interpretation of that data,” he explains.
The goal is to help scientists move from data to decisions with less delay. Elliott identifies interpretation as a key focus area because richer datasets can otherwise slow analysis.
Robitaille highlights AI-native proteomic search tools as one example of where the field is heading. These tools can support interpretation and prediction, especially as datasets grow in size and complexity. He points to precision medicine and large cohort studies as areas where AI could become especially important.
“We really see this as the future as customer data sets get larger, particularly around precision medicine and large-scale cohorts,” he notes.
AI does not remove the need for scientific judgment. Instead, it can help scientists handle larger datasets, identify meaningful patterns, and focus attention where expertise matters most.
Regulated Labs Need Confidence
As methods move from development into regulated QA and QC, Robitaille identifies three priorities: “Reproducibility number one, productivity number two, and of course the confidence to be able to accelerate their drug discovery timeline.”
That combination will shape how HRAM MS expands into routine biopharma workflows. Labs need advanced analytical depth, but they also need methods that support consistency, throughput, and defensible results.
For complex therapeutics, the workflow around the instrument becomes as important as the mass spectrometer itself. Sample preparation, separation, detection, and software all need to work together so teams can generate data they trust across development stages.
That need for confidence also connects back to AI-enabled interpretation. As datasets grow, software must help scientists review results with speed while preserving the judgment required for regulated decision-making.
Applied Testing Faces Expanding Demands
Environmental and food safety labs face growing contaminant lists, lower limits, and rising concern around PFAS, pesticides, dioxins, and persistent organic pollutants.
Elliott emphasizes the value of collecting “all of the data, all of the time.” That approach supports retrospective analysis as regulations and compound lists change.
Robitaille highlights two applied testing areas: GC-MS workflows for dioxins and persistent organic pollutants, and LC-MS workflows for liquid-based compounds such as PFAS. He describes the goal as end-to-end workflows that help customers move “from sample to knowledge.”
For emerging contaminants, the right platform depends on the lab’s purpose. A municipality may need targeted monitoring for water safety. A research group may need to explore unknowns, trace sources, and assess broader environmental or biological impact.
If a lab needs to measure a fixed list of compounds, a triple quadrupole workflow may fit. If it also wants to track unknown changes over time, HRAM MS offers another layer of insight.
“If they say, ‘I need to measure these 20 compounds, and I want to be able to look over time if things are changing,’ that’s where the Orbitrap and its HRAM ability to do retrospective analysis would be a better fit,” Robitaille explains.
A Broader Role for Orbitrap MS
Elliott describes the expansion of Orbitrap technology as intentional. Thermo Fisher continues to invest in high-end research, but it also sees opportunity “further down the value chain,” where HRAM MS can support applied testing and routine decisions.
For labs, the shift moves HRAM MS beyond discovery alone. It gives scientists a way to answer targeted questions today while keeping the data needed to investigate tomorrow’s unknowns.
