Abstract 4698: Reproducibility in spatial biology: reducing variables to improve the reliability of insight generation

Abstract

Abstract There is an ongoing crisis in reproducibility in scientific studies, and studies in spatial biology are no exception. Cell DIVETM Multiplex imaging solution (Leica Microsystems) provides reliable workflow solutions to minimize variability from study to study. Cell DIVE allows probing and imaging of dozens of biomarkers on a whole tissue section with an iterative staining and dye inactivation workflow. At its core, Cell DIVE is designed to provide methods reproducibility, from tissue preparation, antigen retrieval, and sample imaging and slide storage. Cell DIVE is designed to work with directly conjugated primary antibodies, another source of variability. However, conjugated antibodies from Cell Signaling Technology (CST) are rigorously validated using stringent acceptance criteria to reduce variability. In addition, the use of recombinant antibodies, the consistent conjugate brightness and antibody degree-of-labeling reduce lot to lot variability, ensuring reliable conjugated antibodies for spatial biology studies. High resolution imaging results are obtained by consistent round to round imaging, consistent calibration and corrections, and reduction of human error using the BAB 200 liquid handling solution (Advanced Solutions Life Sciences). We present here, an iterative biomarker study using adjacent tissue sections probed with distinct lots of CST antibody panels, imaged in temporally separated batches using a Cell DIVE imager fitted with a BAB200 liquid handler. Following robust analysis (segmentation, phenotyping and statistical analysis), we report the reproducibility findings across parameters. Methods and results reproducibility in spatial biology is essential for reliable insight generation, giving confidence in the quality of future studies aimed at improving patient outcomes. Citation Format: Lisa Arvidson, Reginaldo Prioli, Samuel Jensen, James B. Hoying, Michael W. Golway, Michael J. Smith, Katie O. White, Richard A. Heil-Chapdelaine, Chi-Chou Huang, Tuan H. Phan, Hideki Sasaki, Melinda Angus-Hill. Reproducibility in spatial biology: reducing variables to improve the reliability of insight generation. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4698.