High-Resolution Image Stitching as a Tool to Assess Tissue-Level Protein Distribution and Localization

Abstract

High-resolution microscopy has traditionally come at the expense of field of view, resulting in suboptimal interpretation of protein distribution throughout large or complex samples. Likewise, a low-resolution microscopic approach inhibits the ability of researchers to precisely localize proteins of interest at the subcellular level. Until recently, the ability to combine the strengths of these approaches was limited and technically impractical for most laboratories to implement. Continued advances in microscope automation, sophisticated software applications, and modern workstations have enabled expansion of such combinatorial approaches to researchers outside computationally focused fields. Through image stitching, researchers can acquire large field-of-view, multidimensional datasets, at the diffraction limit of high-numerical aperture objectives to effectively map protein distribution in large samples with high precision. Here, we outline a protocol for acquisition of such datasets with the purpose of introducing inexperienced researchers to the methodology of large image stitching using the widely available technology of laser point-scanning confocal microscopy in combination with basic microscope automation and freely available software for post-acquisition processing.