Fikar Pavel, Alvarez Laura, Berne Laura, Cienciala Martin, Kan Christopher, Kasl Hynek, Luo Mona, Novackova Zuzana, Ordonez Sheyla, Sramkova Zuzana, Holubova Monika, Lysak Daniel, Avery Lyndsay, Caro Andres A, Crowder Roslyn N, Diaz-Martinez Laura A, Donley David W, Giorno Rebecca R, Reed Irene K Guttilla, Hensley Lori L, Johnson Kristen C, Kim Audrey Y, Kim Paul, LaGier Adriana J, Newman Jamie J, Padilla-Crespo Elizabeth, Reyna Nathan S, Tsotakos Nikolaos, Al-Saadi Noha N, Appleton Tayler, Arosemena-Pickett Ana, Bell Braden A, Bing Grace, Bishop Bre, Forde Christa, Foster Michael J, Gray Kassidy, Hasley Bennett L, Johnson Kennedy, Jones Destiny J, LaShall Allison C, McGuire Kennedy, McNaughton Naomi, Morgan Angelina M, Norris Lucas, Ossman Landon A, Rivera-Torres Paollette A, Robison Madeline E, Thibodaux Kathryn, Valmond Lescia, Georgiev Daniel
Department of Research & Development, Sampling Human Inc., Berkeley, California, United States of America.
Department of Hematology and Oncology, University Hospital Pilsen, Pilsen, Czech Republic.
PLoS One. 2024 Dec 9;19(12):e0314992. doi: 10.1371/journal.pone.0314992. eCollection 2024.
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.
当今生物医学正朝着分散式数据收集转变，这有望在不同实验室环境中提高可重复性和协作性。这项实验室间研究评估了生物细胞计数法的性能，该方法利用工程化生物颗粒根据细胞表面抗原模式对细胞进行计数。在集中式和汇总式实验室间研究中，生物细胞计数法在区分不同浓度的靶细胞数量方面显示出显著的统计功效，低至每100,000个背景细胞中有1个靶细胞。用户技能水平从专家到初学者各不相同，涵盖了一系列熟练程度。测量是在分散式环境中进行的，除了基本说明书外，没有进行任何仪器交叉校准或高级用户培训。结果证实，生物细胞计数法是免疫表型分析应用的可行解决方案，该应用需要灵敏度以及可扩展性和可重复性，并为异质样本中稀有细胞的分散式分析铺平了道路。
