Customizable Automated High Content Image Acquisition and Analysis for Tissue Biopsies

Abstract

Abstract:Advancements in high content image analysis have led to an increase in the adoption of these techniques in basic science and clinical research. High-throughput approaches to imaging and image analysis require minimal user interventions, circumventing the often prohibitively time-consuming and unreliable standard manual analysis. In this study, we demonstrate how high content imaging (HCI) techniques, in combination with high content analysis (HCA), can be paired with more traditional manual analysis to quantify both micro- and macro-level features of biopsied tissue sections. High-resolution, full-color images of stained tissue were acquired and stitched together to reconstruct the entire tissue section, which enabled analyses that required accurate identification of a given region's location within the tissue section. A custom region of interest grid was generated that followed the curvature of the tissue. The composite images were used in two separate analyses: tissue layer thickness as a macro-level approach, and nuclei density as a micro-level approach. Ultimately, the flexibility of the HCI and HCA methodologies used in this study allowed for complex analysis of tissue that would not have been otherwise feasible.