Abstract
Strategies that do not require additional characterization to be performed on the sample or the collection of additional secondary ion signals are needed to depth correct 3D SIMS images of cells. Here, we develop a depth correction strategy that uses the pixel intensities in the secondary electron images acquired during negative-ion NanoSIMS depth profiling to reconstruct the sample morphology. This morphology reconstruction was then used to depth correct the 3D SIMS images that show the components of interest in the sample. As a proof of concept, we applied this approach to NanoSIMS depth profiling data that show the (15)N-enrichment and (18)O-enrichment from (15)N-sphingolipids and (18)O-cholesterol, respectively, within a metabolically labeled Madin-Darby canine kidney cell. Comparison of the cell morphology reconstruction to the secondary electron images collected with the NanoSIMS revealed that the assumption of a constant sputter rate produced small inaccuracies in sample morphology after approximately 0.66 μm of material was sputtered from the cell. Nonetheless, the resulting 3D renderings of the lipid-specific isotope enrichments better matched the shapes and positions of the subcellular compartments that contained (15)N-sphingolipids and (18)O-cholesterol than the uncorrected 3D SIMS images. This depth correction of the 3D SIMS images also facilitated the detection of spherical cholesterol-rich compartments that were surrounded by membranes containing cholesterol and sphingolipids. Thus, we expect this approach will facilitate identifying the subcellular structures that are enriched with biomolecules of interest in 3D SIMS images while eliminating the need for correlated analyses or additional secondary ion signals for the depth correction of 3D NanoSIMS images.