Confocal microscopy is integral to molecular and cellular biology, enabling high-resolution imaging and colocalization studies to elucidate biomolecular interactions in cells. Despite its utility, challenges in handling large datasets, particularly in preprocessing Z-stacks and calculating colocalization metrics like the Manders coefficient, limit efficiency and reproducibility. Manually processing large numbers of imaging data for colocalization analysis is prone to observer bias and inefficiencies. This study presents an automated workflow integrating Python-based preprocessing with Fiji ImageJ's BIOP-JACoP plugin to streamline Z-stack refinement and colocalization analysis. We generated an executable Windows application and made it publicly available on GitHub (https://github.com/weiyue99/Yue-Colocalization), allowing even those without Python experience to directly run the Python code required in the current protocol. The workflow systematically removes signal-free Z-slices that sometimes exist at the beginning and/or end of the Z-stacks using auto-thresholding, creates refined substacks, and performs batch analysis to calculate the Manders coefficient. It is designed for high-throughput applications, significantly reducing human error and hands-on time. By ensuring reproducibility and adaptability, this protocol addresses critical gaps in confocal image analysis workflows, facilitating efficient handling of large datasets and offering broad applicability in protein colocalization studies.

Semi-quantitative immunohistochemistry (IHC) is a powerful method for investigating protein expression and localization within tissues that involves using software, such as the freely available Fiji (ImageJ), to conduct deconvolution and downstream analysis. Currently, there is lack of an integrated protocol that includes a detailed procedure on how to measure or compare protein expression. Publications that use semi-quantitative methods to ascertain protein expression often don’t provide enough details in their methods section, which makes it difficult for the reader to reproduce their data. The current protocol provides an example and detailed steps of conducting semi-quantitative analysis of IHC images using Fiji software.

Organic anion transporting polypeptide (OATP) 1B1 is a liver-specific transport protein that plays an important role in hepatic drug disposition. It transports many drugs from the blood into the liver, including lipid-lowering statins. The c. 521 T>C polymorphism of OATP1B1 has reduced transport activity and is associated with statin-induced myopathy. Formalin-fixed paraffin-embedded (FFPE) liver tissues can be an enriched source for genotyping of this clinically significant OATP1B1 polymorphism in retrospective studies. The successfulness of genotyping using Sanger-sequencing of a PCR product from FFPE tissue relies on a successful PCR amplification using genomic DNA extracted from the FFPE tissues. Such PCR amplification is often limited by the quality of DNA extracted from the FFPE tissue. An optimized method for high-quality DNA extraction and efficient PCR amplification is highly needed in order to genotype polymorphisms such as the c. 521 T>C polymorphism using FFPE tissues. The current study established an optimized and reproducible method for a Sanger-sequencing-based genotyping method using FFPE human liver tissues that is applicable to even small FFPE tissues such as needle-core biopsy specimens.