Algorithm-Driven Image Analysis Solutions for RNA ISH Quantification in Human Clinical Tissues

Abstract

RNA in situ hybridization (ISH) offers an unprecedented advantage of detecting RNA transcript expression in tissues at cellular resolution while maintaining critical information on tissue morphology and architecture. Lack of standardized scoring paradigms for RNA ISH poses a challenge to translation of RNA ISH into clinical settings. Thus, development of novel tools for RNA ISH quantification and analysis is of significant interest. Here, we demonstrate a novel and proprietary algorithm-driven image analysis-based approach for quantification and analysis of RNA ISH in human clinical tissue samples. This approach allows RNA ISH analysis on cell by cell basis for whole tissues. We discuss in detail methods by which image analysis can be utilized for successful detection and interpretation of ISH signal, and provision of pertinent biological endpoints. Strategies discussed herein highlight the potential of combining RNA ISH and image analysis tools to build scoring paradigms and high-throughput approaches for the analysis and quantification of gene expression in tissues. Once such methods are adopted, RNA ISH may become a frequently utilized technique in clinical settings and biomarker and drug discovery.