Development of a Topographic Z-Correction Algorithm for 3D Depth Profiling Nanosims Images

Abstract

Seasonal epidemics caused by the influenza virus, are responsible for thousands of deaths each year throughout the world. Anti-viral therapeutics that target the host cell components critical for its infectivity could reduce antiviral resistance. Cellular lipids such as sphingolipids and cholesterol are attractive targets because influenza a virus infectivity is sensitive to their abundance. Studies indicate the influenza a viral envelope is enriched with cholesterol and sphingolipids, and depletion of these components is detrimental to virus infectivity. Interestingly, influenza virus infection is also correlated with an accumulation of cholesterol within certain organelles in cells. The ability to produce intracellular three-dimensional images that show the abundance of cholesterol within distinct organelles could improve understanding of the role of intracellular cholesterol in influenza virus infectivity. Metabolically incorporated, rare stable isotope-labeled cholesterol and sphingolipids within intracellular compartments can be visualized by performing NanoSIMS imaging in a depth profiling mode. This depth profiling produces a series of NanoSIMS images that show the same location on the cell, but at increasing depth from its surface. Unfortunately, stacking the NanoSIMS images creates a three-dimensional (3D) image in which all the component-specific signals detected in a single scan are positioned at the same height. In contrast, the components that produced these ions were located at different distances from the substrate due to the cell's nonplanar topography. This poster describes an algorithm we developed that enables reshaping the 3D NanoSIMS images stacks to correct for sample topography in the absence of atomic force microscopy (AFM) data.