Untargeted small molecule machine learning for MSI

Cameron Shedlock (until 14:00 )

Mass spectrometry imaging returns large dimensionally complex datasets. Traditional analysis in imaging utilizes a mindset focused on target molecule identification that parses data with a narrow view of molecular exploration. These data analysis methods are specifically focused on exploring differences between a traditional organic acid matrix when compared to using nanoparticles for MSI, which results in very different ionization of small molecules. Analysis methods have taken a shotgun approach using both supervised and unsupervised machine learning to reveal critical trends in MSI datasets. A workflow is being prepared which enables select regions of interest to be compared using powerful machine learning algorithms to offer a holistic approach to data analysis and class comparison.

Mass spectrometry imaging using gold nanoparticles

Kate Stumpo, PhD

Mass spectrometry imaging (MSI) is a powerful analytical method for the simultaneous analysis of hundreds of compounds within a biological sample. Despite the broad applicability of this technique, there is a critical need for advancements in methods for small molecule detection. Some molecular classes of small molecules are more difficult than others to ionize, e.g., neurotransmitters (NTs). The chemical structure of NTs (i.e., primary, secondary, and tertiary amines) affects ionization and has been a noted difficulty in the literature. In order to achieve detection of NTs using MSI, strategies must focus on either changing the chemistry of target molecules to aid in detection or focus on new methods of ionization. This presentation will introduce a new method of ionization, using gold nanoparticles (AuNPs), and the bigger picture of NPs for MSI.