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.

Chemistry Department Hendrix College Conway Arkansas United States of America

Department of Biological Sciences Grambling State University Grambling Louisiana United States of America

Department of Biological Sciences Jacksonville State University Jacksonville Alabama United States of America

Department of Biological Sciences School of Science Engineering and Technology Penn State Harrisburg Middletown Pennsylvania United States of America

Department of Biology College of Social and Natural Sciences Grand View University Des Moines Iowa United States of America

Department of Biology Gonzaga University Spokane Washington United States of America

Department of Biology Harding University Searcy Arkansas United States of America

Department of Biology Ouachita Baptist University Arkadelphia Arkansas United States of America

Department of Biology Saint Michael's College Colchester Vermont United States of America

Department of Biology Stetson University Deland Florida United States of America

Department of Biology University of Saint Joseph West Hartford Connecticut United States of America

Department of Hematology and Oncology University Hospital Pilsen Pilsen Czech Republic

Department of Life Sciences University of New Hampshire Manchester New Hampshire United States of America

Department of Research and Development Sampling Human Inc Berkeley California United States of America

Department of Science and Technology Inter American University of Puerto Rico Aguadilla Aguadilla Puerto Rico United States of America

Faculty of Applied Sciences University of West Bohemia Pilsen Czech Republic

School of Biological Sciences Louisiana Tech University Ruston Louisiana United States of America

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Baker M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;533(7604). doi: 10.1038/533452a PubMed DOI

Gibson G. Perspectives on rigor and reproducibility in single cell genomics. PLoS Genetics. 2022. May 10;18(5):e1010210. doi: 10.1371/journal.pgen.1010210 PubMed DOI PMC

Maecker HT, Rinfret A, D’Souza P, Darden J, Roig E, Landry C, et al.. Standardization of cytokine flow cytometry assays. BMC immunology. 2005. Dec;6:1–8. PubMed PMC

Wood BL. Principles of minimal residual disease detection for hematopoietic neoplasms by flow cytometry. Cytometry Part B: Clinical Cytometry. 2016. Jan;90(1):47–53. doi: 10.1002/cyto.b.21239 PubMed DOI

Begley CG, Ellis LM. Raise standards for preclinical cancer research. Nature. 2012. Mar 29;483(7391):531–3. PubMed

Kalina T. Reproducibility of flow cytometry through standardization: opportunities and challenges. Cytometry Part A. 2020. Feb;97(2):137–47. doi: 10.1002/cyto.a.23901 PubMed DOI

Ticchioni M, Brouzes C, Durrieu F, Lambert C, Association Française de Cytométrie accreditation working group (AFC‐AG). Acceptable “Real‐Life” Variability for Lymphocyte Counts by Flow Cytometry. Cytometry Part B: Clinical Cytometry. 2019. Sep;96(5):379–88. PubMed

Donnenberg AD, Donnenberg VS. Rare-event analysis in flow cytometry. Clinics in laboratory medicine. 2007. Sep 1;27(3):627–52. doi: 10.1016/j.cll.2007.05.013 PubMed DOI

Sanoja-Flores L, Flores-Montero J, Pérez-Andrés M, Puig N, Orfao A. Detection of circulating tumor plasma cells in monoclonal gammopathies: methods, pathogenic role, and clinical implications. Cancers. 2020. Jun 8;12(6):1499. doi: 10.3390/cancers12061499 PubMed DOI PMC

Ried K, Eng P, Sali A. Screening for circulating tumour cells allows early detection of cancer and monitoring of treatment effectiveness: an observational study. Asian Pacific journal of cancer prevention: APJCP. 2017;18(8):2275. PubMed PMC

Peinelt A, Bremm M, Kreyenberg H, Cappel C, Banisharif-Dehkordi J, Erben S, et al.. Monitoring of circulating CAR T cells: validation of a flow cytometric assay, cellular kinetics, and phenotype analysis following tisagenlecleucel. Frontiers in immunology. 2022. Mar 2;13:830773. doi: 10.3389/fimmu.2022.830773 PubMed DOI PMC

Chatterjee T, Mallhi RS, Venkatesan S. Minimal residual disease detection using flow cytometry: Applications in acute leukemia. medical journal armed forces india. 2016. Apr 1;72(2):152–6. doi: 10.1016/j.mjafi.2016.02.002 PubMed DOI PMC

Cienciala M, Alvarez L, Berne L, Chena D, Fikar P, Holubova M, et al.. Massively parallel identification of single-cell immunophenotypes. bioRxiv. 2024:2024–04.

Allan AL, Keeney M. Circulating tumor cell analysis: technical and statistical considerations for application to the clinic. Journal of oncology. 2010. Oct;2010. doi: 10.1155/2010/426218 PubMed DOI PMC