Georgia Tech researchers Andrei Fedorov and John Sentmanat have developed a framework to reduce trial and error in mass spectrometry imaging (MSI), according to a 2026 report. The method, published in Analytical Chemistry, aims to streamline molecular analysis by providing a predictive model for optimal experimental conditions. Fedorov, Associate Chair for Graduate Studies and Rae S. and Frank H. Neely Chair in the George W. Woodruff School of Mechanical Engineering, led the project alongside Ph.D. student John Sentmanat. The research was supported by a $1.2 million National Institutes of Health (NIH) grant awarded to Fedorov in 2020 for the development of a new imaging instrument.
The Framework’s Development
MSI maps molecular distributions in tissue but requires extensive trial and error to determine optimal conditions. Mass spectrometry imaging is probably the most powerful tool to enable new discoveries in cell biology and protein biochemistry,
Fedorov said. The team distilled complex physics governing molecular extraction into a simplified model, which they validated against experimental data from prior studies. The framework, described as a “recipe” for researchers, allows customization of experimental conditions without prescribing exact solutions.
The project began as an effort to improve BeamMap but expanded into a broader solution for MSI. During development, Fedorov and Sentmanat faced inconsistencies: under some conditions, they successfully extracted molecules, while others damaged samples without yielding data. As we continued developing the instrument, we realized we needed to develop a theory to identify the optimal operating conditions,
Sentmanat said. The framework’s adaptability was confirmed across different MSI approaches, with Fedorov stating, It’s all measurable data that any scientist in this field would be able to use.
Implications for Medical Research
The framework could accelerate discoveries in cell biology and disease diagnosis by reducing the time researchers spend adjusting experimental conditions. MSI is used to search for molecular clues associated with cancer, investigate the chemical composition of the brain, or determine where a drug travels after entering the body. Fedorov emphasized its potential to support next-generation treatments, noting, More and more, physicians base their diagnoses on much more specific molecular information.
The model’s flexibility could aid in improving existing technologies or developing new approaches, according to the team.
Adaptability and Future Use
While designed for MSI, the framework is intended as a starting point for researchers to refine their own experiments. The model’s validation against prior studies demonstrated its utility beyond the team’s lab. Fedorov likened the approach to shooting arrows in the dark,
emphasizing its role in reducing uncertainty. You don’t need to be a chef, or in this case, be able to write complicated equations,
he said. It even allows you to come up with a new recipe using our model as the base.
Source Details
The research was detailed in a 2026 Analytical Chemistry paper by Sentmanat and Fedorov (DOI: 10.1021/acs.analchem.6c03821). The study builds on work supported by the NIH, with additional context provided by Georgia Tech. The framework’s development followed a 2020 NIH grant to Fedorov for the development of a new imaging instrument, which aimed to advance tumor growth, disease progression, and cell function research. The team’s findings highlight MSI’s potential to provide direct insights into disease mechanisms, as noted by Fedorov: It helps provide a direct insight not only to how the body works but also the underlying cause of disease.
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