1. Enhancing reproducibility in single cell research with biocytometry: An inter-laboratory study.
- Author
-
Pavel Fikar, Laura Alvarez, Laura Berne, Martin Cienciala, Christopher Kan, Hynek Kasl, Mona Luo, Zuzana Novackova, Sheyla Ordonez, Zuzana Sramkova, Monika Holubova, Daniel Lysak, Lyndsay Avery, Andres A Caro, Roslyn N Crowder, Laura A Diaz-Martinez, David W Donley, Rebecca R Giorno, Irene K Guttilla Reed, Lori L Hensley, Kristen C Johnson, Audrey Y Kim, Paul Kim, Adriana J LaGier, Jamie J Newman, Elizabeth Padilla-Crespo, Nathan S Reyna, Nikolaos Tsotakos, Noha N Al-Saadi, Tayler Appleton, Ana Arosemena-Pickett, Braden A Bell, Grace Bing, Bre Bishop, Christa Forde, Michael J Foster, Kassidy Gray, Bennett L Hasley, Kennedy Johnson, Destiny J Jones, Allison C LaShall, Kennedy McGuire, Naomi McNaughton, Angelina M Morgan, Lucas Norris, Landon A Ossman, Paollette A Rivera-Torres, Madeline E Robison, Kathryn Thibodaux, Lescia Valmond, and Daniel Georgiev
- Subjects
Medicine ,Science - Abstract
Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples.
- Published
- 2024
- Full Text
- View/download PDF