High-resolution imaging mass spectrometry of large biological samples is the goal of several research groups. In mosaic imaging, the most common method, the large sample is divided into a mosaic of small areas that are then analyzed with high resolution. Here we present an automated alignment routine that uses principal component analysis to reduce the uncorrelated noise in the imaging datasets, which previously obstructed automated image alignment. An additional signal quality metric ensures that only those regions with sufficient signal quality are considered. We demonstrate that this algorithm provides superior alignment performance than manual stitching and can be used to automatically align large imaging mass spectrometry datasets comprising many individual mosaic tiles. (J Am Soc Mass Spectrom 2008, 19, 823-832) © 2008 American Society for Mass Spectrometry I maging mass spectrometry is a rapidly developing analytical tool because it provides the ability to map the profiles of specific biomolecules, in which the intrinsic mass of the molecule differentiates between any modified forms; to record the distributions of multiple analytes in parallel; and to perform these analyses without a label and with clinical samples [1]. This combination of specificity, parallel detection, and non-targeted analysis has led to great excitement for its potential as a discovery tool.It is the goal of many research groups to be able to perform high-resolution analysis of large samples, thus combining the ability to examine distributions between organs/tumors and their surroundings as well as to investigate the subcellular/intracellular locations of the biomolecules. The central premise of this approach is that the subcellular locations will provide some of the information required to explain differences in the more global patterns.High spatial resolution measurements are a wellestablished ability of secondary ion mass spectrometry (SIMS). Recent advances in both ionization efficiency (polyatomic primary ions) and sample preparation have significantly improved the sensitivity for detecting intact, medium-sized molecular ions (Ͻ1000 Da) from tissues and cells [1][2][3][4][5][6]. High-resolution images of small peptides, lipids, cholesterol, vitamins, and pharmaceuticals have all been reported, and through the use of large polyatomic primary ions three-dimensional (3D) molecular imaging results are beginning to appear [7][8][9].The images are normally created by moving the ionization beam in a set pattern across the sample and performing mass analysis at each point of the raster (spatially correlated mass spectrometry). The raster pattern typically uses 8-or 10-bit encoding; as a result the maximum analysis field contains 256 ϫ 256 (or 1024 ϫ 1024) pixels. In a typical high spatial resolution SIMS measurement the pixel size is about 200 nm, meaning maximum analysis areas of approximately 50 or 200 m, respectively. This is much smaller than many of the biological samples of interest; for example, a tissue section of an adult rat ...