Anya Eyres

Many biologically important molecules are structural isomers that share the same molecular mass yet differ in shape, bonding and function. This creates an ambiguity for mass spectrometry imaging (MSI), where chemically distinct species can be indistinguishable on the mass-to-charge ( m/z) scale and therefore cannot be reliably mapped in heterogeneous materials. Here we demonstrate that Orbitrap secondary ion mass spectrometry (OrbiSIMS) can provide an orthogonal contrast that enables both identification and spatial separation of isomeric molecules directly on surfaces. By exploiting structure-dependent secondary-ion dissociation and gas-phase collisional effects during ion transmission, we discriminate isomeric trisaccharides raffinose, maltotriose and melezitose and spatially resolve raffinose and maltotriose in mixtures. We benchmark these measurements against ion mobility spectrometry and observe consistent trends between OrbiSIMS transmission behaviour and ion mobility separation. These results establish OrbiSIMS as an approach for isomer-resolved MSI at surfaces and interfaces, opening opportunities to map isomeric biomolecules in complex systems where spatial distribution is central to function.

Orbitrap mass spectrometry is widely used in the life-sciences. However, like all mass spectrometers, non-uniform (heteroscedastic) noise introduces bias in multivariate analysis complicating data interpretation. Here, we study the noise structure of an Orbitrap mass analyser integrated into a secondary ion mass spectrometer (OrbiSIMS). Using a stable primary ion beam to provide a well-controlled source of ions from a silver sample, we find that noise has three characteristic regimes: at low signals the Orbitrap detector noise and a censoring algorithm dominates; at intermediate signals counting noise specific to the ion emission process is most significant; and at high signals additional sources of measurement variation become important. Using this understanding, we developed a generative model for Orbitrap data that accounts for the noise distribution and introduce a scaling method, termed WSoR, to reduce the effects of noise bias in multivariate analysis. We compare WSoR performance with no-scaling and existing scaling methods for three biological imaging data sets including drosophila central nervous system, mouse testis and a desorption electrospray ionisation (DESI) image of a rat liver. WSoR consistently performed best at discriminating chemical information from noise. The performance of the other methods varied on a case-by-case basis, complicating the analysis.