QuPath Digital Immunohistochemical Analysis of Placental Tissue

Authors

Ashley L. Hein, University of Nebraska Medical Center
Maheswari Mukherjee, University of Nebraska Medical CenterFollow
Geoffrey A. Talmon, University of Nebraska Medical CenterFollow
Sathish Kumar Natarajan, University of Nebraska - LincolnFollow
Tara M. Nordgren, University of California Riverside
Elizabeth Lyden, University of Nebraska Medical CenterFollow
Corrine K. Hanson, University of Nebraska Medical CenterFollow
Jesse L. Cox, University of Nebraska Medical CenterFollow
Annelisse Santiago‑Pintado, University of Nebraska Medical Center
Mariam A. Molani, University of Texas‑Southwestern Medical Center
Matthew Van Ormer, University of Nebraska Medical CenterFollow
Maranda Thompson, University of Nebraska Medical Center, Omaha
Melissa K. Thoene, University of Nebraska Medical CenterFollow
Aunum Akhter, University of Michigan - Ann Arbor
Ann Anderson‑Berry, University of Nebraska Medical Center
Ana G. Yuil-Valdes, University of Nebraska Medical CenterFollow

Date of this Version

11-1-2021

Citation

Hein AL, Mukherjee M, Talmon GA, Natarajan SK, Nordgren TM, Lyden E, et al. QuPath digital immunohistochemical analysis of placental tissue. J Pathol Inform 2021;12:40.

Comments

Open Access

Abstract

Background: QuPath is an open‑source digital image analyzer notable for its user‑friendly design, cross‑platform compatibility, and customizable functionality. Since it was first released in 2016, at least 624 publications have reported its use, and it has been applied in a wide spectrum of settings. However, there are currently limited reports of its use in placental tissue. Here, we present the use of QuPath to quantify staining of G‑protein coupled receptor 18 (GPR18), the receptor for the pro‑resolving lipid mediator Resolvin D2, in placental tissue. Methods: Whole slide images of vascular smooth muscle (VSM) and extravillous trophoblast (EVT) cells stained for GPR18 were annotated for areas of interest. Visual scoring was performed on these images by trained and in‑training pathologists, while QuPath scoring was performed with the methodology described herein. Results: Bland–Altman analyses showed that, for the VSM category, the two methods were comparable across all staining levels. For EVT cells, the high‑intensity staining level was comparable across methods, but the medium and low staining levels were not comparable. Conclusions: Digital image analysis programs offer great potential to revolutionize pathology practice and research by increasing accuracy and decreasing the time and cost of analysis. Careful study is needed to optimize this methodology further.