A model for harmonizing flow cytometry in clinical trials
Open accessAbstract
Complexities in sample handling, instrument setup and data analysis are barriers to the effective use of flow cytometry to monitor immunological parameters in clinical trials. The novel use of a central laboratory may help mitigate these issues.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Individual laboratories typically experience ~20% CV in data analysis, whereas a central laboratory shows ~4% variance.
“Inter-laboratory variations are also an issue, with Maecker et al. having reported that individual laboratories typically experience a ~20% CV in the data analysis, whereas data from a central laboratory only shows a variance of ~4% [19].”
From Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition) (2021) · cites this paper as [19]
In “Instrument setup and data analysis issues,” the paper reports mean coefficients of variation of 20.5% for individual-laboratory analyses and 4% for central analysis. Those figures come from one multisite standardization study of four samples, so the claim that laboratories *typically* experience those levels goes beyond what the passage establishes. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Seventeen-colour flow cytometry: unravelling the immune system
Perfetto, S. P., Chattopadhyay, P. K., & Roederer, M. (2004) · Nature Reviews Immunology · doi:10.1038/nri1416 · Cited by 1,019 · see citing works
Spectral compensation for flow cytometry: Visualization artifacts, limitations, and caveats
Roederer, M. (2001) · Cytometry · doi:10.1002/1097-0320(20011101)45:3 · Cited by 561 · see citing works
MIFlowCyt: The minimum information about a flow cytometry experiment
Lee, J. A., Spidlen, J., Boyce, K., Cai, J., Crosbie, N., Dalphin, M., Furlong, J., Gasparetto, M., Goldberg, M., Goralczyk, E. M., et al. (2008) · Cytometry Part A · doi:10.1002/cyto.a.20623 · Cited by 533 · see citing works
Flow cytometry controls, instrument setup, and the determination of positivity
Maecker, H. T., & Trotter, J. (2006) · Cytometry Part A · doi:10.1002/cyto.a.20333 · Cited by 365 · see citing works
Self-citationA Prescription for Human Immunology
Davis, M. M. (2008) · Immunity · doi:10.1016/j.immuni.2008.12.003 · Cited by 346 · see citing works
Standardization of cytokine flow cytometry assays
Maecker, H. T., Rinfret, A., D’Souza, P., Darden, J. M., Roig, E., Landry, C., Hayes, P., Birungi, J., Anzala, O. A., Garcia, M., et al. (2005) · BMC Immunology · doi:10.1186/1471-2172-6-13 · Cited by 242 · see citing works
Self-citationSelecting fluorochrome conjugates for maximum sensitivity
Maecker, H. T., Frey, T., Nomura, L. E., & Trotter, J. (2004) · Cytometry Part A · doi:10.1002/cyto.a.20092 · Cited by 219 · see citing works
Self-citationSleep associated regulation of T helper 1/T helper 2 cytokine balance in humans
Dimitrov, S., Lange, T., Tieken, S., Fehm, H. L., & Born, J. (2004) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2003.08.004 · Cited by 209 · see citing works
Weinberg, A., Song, L., Wilkening, C. L., Sevin, A. D., Blais, B., Louzao, R., Stein, D., Defechereux, P. A., Durand, D., Riedel, E., et al. (2009) · Clinical and Vaccine Immunology · doi:10.1128/cvi.00342-08 · Cited by 194 · see citing works
“MIATA”—Minimal Information about T Cell Assays
Janetzki, S., Britten, C. M., Kalos, M. D., Levitsky, H. I., Maecker, H. T., Melief, C. J. M., Old, L. J., Romero, P. J., Hoos, A., & Davis, M. M. (2009) · Immunity · doi:10.1016/j.immuni.2009.09.007 · Cited by 192 · see citing works
Self-citationSleep-dependent activity of T cells and regulatory T cells
Bollinger, T., Bollinger, A., Skrum, L., Dimitrov, S., Lange, T., & Solbach, W. (2009) · Clinical and Experimental Immunology · doi:10.1111/j.1365-2249.2008.03822.x · Cited by 167 · see citing works
Evidence supporting a circadian control of natural killer cell function
Arjona, A., & Sarkar, D. K. (2005) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2005.10.002 · Cited by 150 · see citing works
Analysis of T cell subsets in normal adults
RENZI, P. M., & Ginns, L. C. (1987) · Journal of Immunological Methods · doi:10.1016/0022-1759(87)90434-0 · Cited by 129 · see citing works
Lévi, F. A., Canon, C., Touitou, Y., Reinberg, A., & Mathé, G. (1988) · Journal of Clinical Investigation · doi:10.1172/jci113333 · Cited by 104 · see citing works
Lymphocyte immunophenotyping by flow cytometry in normal adults
AssumpcióRomeu, M., Mestre, M., González, L., Valls, A., Verdaguer, J., Corominas, M., Bas, J., Massip, E., & Buendia, E. (1992) · Journal of Immunological Methods · doi:10.1016/0022-1759(92)90206-9 · Cited by 90 · see citing works
Weinberg, A., Betensky, R. A., Zhang, L., & Ray, G. (1998) · Clinical and Diagnostic Laboratory Immunology · doi:10.1128/cdli.5.6.804-807.1998 · Cited by 83 · see citing works
Toward quantitative fluorescence measurements with multicolor flow cytometry
Wang, L., Gaigalas, A. K., Marti, G. E., Abbasi, F., & Hoffman, R. A. (2007) · Cytometry Part A · doi:10.1002/cyto.a.20507 · Cited by 72 · see citing works
Hodge, G. L., Flower, R. L., & Han, P. (1999) · Journal of Immunological Methods · doi:10.1016/s0022-1759(99)00025-3 · Cited by 57 · see citing works
Immuno-monitoring of CD8+ T cells in whole blood versus PBMC samples
Appay, V., Reynard, S., Voelter, V., Romero, P. J., Speiser, D. E., & Leyvraz, S. (2005) · Journal of Immunological Methods · doi:10.1016/j.jim.2005.11.007 · Cited by 57 · see citing works
Performance of plate-based cytokine flow cytometry with automated data analysis
Suni, M. A., Dunn, H. S., Orr, P. L., de Laat, R., Sinclair, E., Ghanekar, S., Bredt, B. M., Dunne, J. F., Maino, V. C., & Maecker, H. T. (2003) · BMC Immunology · doi:10.1186/1471-2172-4-9 · Cited by 53 · see citing works
Self-citationMultiparameter Flow Cytometry Monitoring of T Cell Responses
Maecker, H. T. (2009) · Methods in molecular biology · doi:10.1007/978-1-59745-170-3_25 · Cited by 46 · see citing works
Self-citationHoffmeister, B., Bunde, T., Rudawsky, I. M., Volk, H., & Kern, F. (2003) · European Journal of Immunology · doi:10.1002/eji.200324223 · Cited by 42 · see citing works
Increased immunofluorescence sensitivity using 532 nm laser excitation
Perfetto, S. P., & Roederer, M. (2007) · Cytometry Part A · doi:10.1002/cyto.a.20358 · Cited by 38 · see citing works
Shalekoff, S., Page‐Shipp, L., & Tiemessen, C. T. (1998) · Clinical and Diagnostic Laboratory Immunology · doi:10.1128/cdli.5.5.695-702.1998 · Cited by 30 · see citing works
Effects of cryopreservation on CD4+ CD25+ T cells of HIV‐1 infected individuals
Seale, A. C., de Jong, B. C., Zaidi, I., Duvall, M. G., Whittle, H. C., Rowland‐Jones, S., & Jaye, A. (2008) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.20234 · Cited by 27 · see citing works
Kumar, P., & Satchidanandam, V. (2000) · Clinical and Diagnostic Laboratory Immunology · doi:10.1128/cdli.7.4.578-583.2000 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Standardizing immunophenotyping for the Human Immunology Project
Maecker, H. T., McCoy, J. P., & Nussenblatt, R. B. (2012) · Nature reviews. Immunology · doi:10.1038/nri3158 · Cited by 1,174 · see citing works
Self-citationGuidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
Cossarizza, A., Chang, H., Radbruch, A., Acs, A., Adam, D., Adam‐Klages, S., Agace, W. W., Aghaeepour, N., Akdiş, M., Allez, M., et al. (2019) · European Journal of Immunology · doi:10.1002/eji.201970107 · Cited by 982 · see citing works
Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)
Cossarizza, A., Chang, H., Radbruch, A., Abrignani, S., Addo, R., Akdis, M., Andrä, I., Andreata, F., Annunziato, F., Arranz, E., et al. (2021) · European Journal of Immunology · doi:10.1002/eji.202170126 · Cited by 531 · see citing works
Separation, characterization and identification of microplastics and nanoplastics in the environment
Fu, W., Min, J., Jiang, W., Li, Y., & Zhang, W. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137561 · Cited by 367 · see citing works
Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium
Finak, G., Langweiler, M., Jaimes, M. C., Malek, M., Taghiyar, M. J., Korin, Y. D., Raddassi, K., Devine, L., Obermoser, G., Pękalski, M. Ł., et al. (2016) · Scientific Reports · doi:10.1038/srep20686 · Cited by 286 · see citing works
“We transformed the proportion, (1): yrij=logit(prij), and model (2):”
MethodSelf-citationAdvances in Human B Cell Phenotypic Profiling
Kaminski, D. A., Wei, C., Yu, Q., Rosenberg, A. F., & Sanz, I. (2012) · Frontiers in Immunology · doi:10.3389/fimmu.2012.00302 · Cited by 271 · see citing works
“…described elsewhere are instrument set-up considerations and the extent of controls needed, which are familiar to many flow cytometry users (Perfetto et al., 2006; Maecker et al., 2010, 2012; Chattopadhyay and Roederer, 2012), but become extremely important to maintain consistency in large studies.”
BackgroundFlow Cytometric Analysis of Myeloid Cells in Human Blood, Bronchoalveolar Lavage, and Lung Tissues
Yu, Y.-R. A., Hotten, D. F., Malakhau, Y., Volker, E., Ghio, A. J., Noble, P. W., Kraft, M., Hollingsworth, J. W., Gunn, M. D., & Tighe, R. M. (2015) · American Journal of Respiratory Cell and Molecular Biology · doi:10.1165/rcmb.2015-0146oc · Cited by 238 · see citing works
Novel technologies and emerging biomarkers for personalized cancer immunotherapy
Yuan, J. D., Hegde, P. S., Clynes, R., Foukas, P. G., Harari, A., Kleen, T. O., Kvistborg, P., Maccalli, C., Maecker, H. T., Page, D. B., et al. (2016) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-016-0107-3 · Cited by 231 · see citing works
“ever, there have been recent efforts to recommend best practices for such multicenter studies [176].”
BackgroundSelf-citationStreitz, M., Miloud, T., Kapinsky, M., Reed, M. R., Magari, R. T., Geissler, E. K., Hutchinson, J. A., Vogt, K., Schlickeiser, S., Kverneland, A. H., et al. (2013) · Transplantation Research · doi:10.1186/2047-1440-2-17 · Cited by 215 · see citing works
“The variables that need to be controlled to ensure standardization have been reviewed elsewhere and different models for standardization of sample handling, instrument setup, data acquisition, and data analysis have been proposed [17-20].”
BackgroundMethodInfluentialBarcoding of Live Human Peripheral Blood Mononuclear Cells for Multiplexed Mass Cytometry
Mei, H. E., Leipold, M. D., Schulz, A. R., Chester, C., & Maecker, H. T. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1402661 · Cited by 163 · see citing works
Self-citationPorta, M. G. D., Picone, C., Pascutto, C., Malcovati, L., Tamura, H., Handa, H., Czader, M., Freeman, S. D., Vyas, P., Porwit‐MacDonald, A., et al. (2012) · Haematologica · doi:10.3324/haematol.2011.048421 · Cited by 164 · see citing works
A Review of the Application of Inflammatory Biomarkers in Epidemiologic Cancer Research
Brenner, D. R., Scherer, D., Muir, K. R., Schildkraut, J. M., Boffetta, P., Spitz, M. R., Marchand, L. L., Chan, A. T., Goode, E. L., Ulrich, C. M., et al. (2014) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-14-0064 · Cited by 163 · see citing works
“These differences across study sites are known to affect outcome measurements (91, 93, 94) and have been shown to affect results in multicentered projects (95).”
Albers, R., Bourdet-Sicard, R., Braun, D., Calder, P. C., Herz, U., Lambert, C., Lenoir-Wijnkoop, I., Méheust, A., Ouwehand, A., Phothirath, P., et al. (2013) · British Journal of Nutrition · doi:10.1017/s0007114513001505 · Cited by 150 · see citing works
T Cell Assays and MIATA: The Essential Minimum for Maximum Impact
Britten, C. M., Janetzki, S., Butterfield, L. H., Ferrari, G., Gouttefangeas, C., Huber, C. G., Kalos, M. D., Levitsky, H. I., Maecker, H. T., Melief, C. J. M., et al. (2012) · Immunity · doi:10.1016/j.immuni.2012.07.010 · Cited by 130 · see citing works
Self-citationInter‐laboratory assessment of flow cytometric monocyte HLA‐DR expression in clinical samples
Demaret, J., Walencik, A., Jacob, M., Timsit, J., Venet, F., Lepape, A., & Monneret, G. (2012) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21043 · Cited by 106 · see citing works
Advanced model systems and tools for basic and translational human immunology
Wagar, L. E., DiFazio, R. M., & Davis, M. M. (2018) · Genome Medicine · doi:10.1186/s13073-018-0584-8 · Cited by 102 · see citing works
Harmonization of Immune Biomarker Assays for Clinical Studies
van der Burg, S. H., Kalos, M. D., Gouttefangeas, C., Janetzki, S., Ottensmeier, C. H. H., Welters, M. J. P., Romero, P. J., Britten, C. M., & Hoos, A. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002785 · Cited by 97 · see citing works
Diks, A. M., Bonroy, C., Teodósio, C., Groenland, R. J., de Mooij, B., Maertelaere, E. D., Neirynck, J., Philippé, J., Orfao, A., van Dongen, J. J. M., et al. (2019) · Journal of Immunological Methods · doi:10.1016/j.jim.2019.06.007 · Cited by 96 · see citing works
Immunomodulatory therapy to preserve pancreatic β-cell function in type 1 diabetes
Waldron-Lynch, F., & Herold, K. C. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3402 · Cited by 92 · see citing works
Venet, F., Lepape, A., & Monneret, G. (2011) · Critical Care · doi:10.1186/cc10333 · Cited by 90 · see citing works
“In ICU patients, therefore, the measure ment of Treg and conventional T-cell frequency or absolute count (CD4+ T-cell enumeration is doable on the same tube used for Treg identifi cation) may represent simple and valuable surrogate markers of lymphocyte anergy usable on a routine basis for the monitoring of sepsis-ind…”
InfluentialEpigenetic immune cell counting in human blood samples for immunodiagnostics
Baron, U., Werner, J., Schildknecht, K., Schulze, J., Mulu, A., Liebert, U.-G., Sack, U., Speckmann, C., Gossen, M., Wong, R. J., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aan3508 · Cited by 88 · see citing works
In utero arsenic exposure and fetal immune repertoire in a US pregnancy cohort
Nadeau, K. C., Li, Z., Farzan, S. F., Koestler, D. C., Robbins, D. J., Fei, D. L., Malipatlolla, M. B., Maecker, H. T., Enelow, R. I., Korrick, S., et al. (2014) · Clinical Immunology · doi:10.1016/j.clim.2014.09.004 · Cited by 84 · see citing works
“This procedure demonstrates approximately 90% viability after thawing [11] and has high reproducibility in our laboratory [30] and other laboratories [31].”
BackgroundSelf-citationQuantitative Flow Cytometry: Concerns and Recommendations in Clinic and Research
Mizrahi, O., Ish‐Shalom, E., Baniyash, M., & Klieger, Y. (2017) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21515 · Cited by 84 · see citing works
Jahan-Tigh, R. R., Ryan, C., Obermoser, G., & Schwarzenberger, K. (2012) · Journal of Investigative Dermatology · doi:10.1038/jid.2012.282 · Cited by 83 · see citing works
Deep phenotyping of immune cell populations by optimized and standardized flow cytometry analyses
Pitoiset, F., Cassard, L., Soufi, K. E., Boselli, L., Grivel, J., Roux, A., Klatzmann, D., Chaput, N., & Rosenzwajg, M. (2018) · Cytometry Part A · doi:10.1002/cyto.a.23570 · Cited by 82 · see citing works
“But multiple reasons led us to choose flow cytometry including: (1) the time acquisition with CyTOF that is much slower (a maximum of around 1,000 events per second) that would not be suitable for routine use or for a trial like Transimmmunom or clinical trials including patients treated with ICB as at Gustave Roussy…”
Reproducibility of Flow Cytometry Through Standardization: Opportunities and Challenges
Kalina, T. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23901 · Cited by 82 · see citing works
“, percentage of various immune system cells in blood) (2).”
BackgroundIdentification of Immune Traits Correlated with Dairy Cow Health, Reproduction and Productivity
Banos, G., Wall, E., Coffey, M. P., Bagnall, A., Gillespie, S., Russell, G. C., & McNeilly, T. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065766 · Cited by 77 · see citing works
“However, with increasing use of flow cytometry in clinical settings using highthroughput and robust methods [62,63] it is now feasible to perform such measures reproducibly on a large scale and at minimal cost.”
Heidt, S., Segundo, D. S., Shankar, S., Mittal, S., Muthusamy, A. S. R., Friend, P. J., Fuggle, S. V., & Wood, K. J. (2011) · Transplantation · doi:10.1097/tp.0b013e318218e978 · Cited by 75 · see citing works
“However, because flow cytometer configuration and setup, and data acquisition and analysis, will be different among centers, standardization to enable multiparameter phenotyping to be used in biomarker studies is crucial (84).”
BackgroundSmith, S. G., Smits, K., Joosten, S. A., van Meijgaarden, K. E., Satti, I., Fletcher, H. A., Caccamo, N., Dieli, F., Mascart, F., McShane, H., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138042 · Cited by 74 · see citing works
“To this may be added optimization of instrument set-up to ensure standardized performance [40], the need for careful compensation between fluorescence channels, the use of fluorescence-minus-one (FMO) control stains and for the acquisition of an adequate number of positive events.”
Millán, O., Rafael-Valdivia, L., Torrademé, E., López, A., Fortuna, V., Sánchez-Cabús, S., López‐Púa, Y., Rimola, A., & Brunet, M. (2012) · Cytokine · doi:10.1016/j.cyto.2012.10.026 · Cited by 72 · see citing works
“However, the impact of the biological matrix, the type and timing of stimulation, instrument setup and data analysis are important considerations in the implementation of these techniques in multicenter clinical trials and subsequently in routine clinical practice [7].”
MethodAghaeepour, N., Chattopadhyay, P. K., Ganesan, A., O’Neill, K., Zare, H., Jalali, A., Hoos, H. H., Roederer, M., & Brinkman, R. R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts082 · Cited by 69 · see citing works
on behalf of the Cancer Immunotherapy Trials Network (CITN), Kohrt, H. E. K., Tumeh, P. C., Benson, D., Bhardwaj, N., Brody, J., Formenti, S. C., Fox, B. A., Galon, J., June, C. H., et al. (2016) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-016-0118-0 · Cited by 68 · see citing works
“Such effort has involved either approaches to standardize assays [120, 121] or efforts to harmonize assays across laboratories by defining and ultimately implementing critical platformand assay-specific variables important for quality [122, 123].”
MethodSelf-citationBiomarkers of Immunotoxicity for Environmental and Public Health Research
Duramad, P., & Holland, N. (2011) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph8051388 · Cited by 68 · see citing works
The Antibody-Secreting Cell Response to Infection: Kinetics and Clinical Applications
Carter, M. J., Mitchell, R. M., Sauteur, P. M. M., Kelly, D. F., & Trück, J. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.00630 · Cited by 65 · see citing works
“As such, optimized panels of reagents for identifying specific populations and automated gating strategies have been developed (12).”
BackgroundUnraveling the autoimmune translational research process layer by layer
Blumberg, R. S., Dittel, B. N., Hafler, D. A., von Herrath, M., & Nestle, F. O. (2012) · Nature Medicine · doi:10.1038/nm.2632 · Cited by 65 · see citing works
RIPOSTE: a framework for improving the design and analysis of laboratory-based research
Masca, N. G. D., Hensor, E. M. A., Cornelius, V. R., Buffa, F. M., Marriott, H. M., Eales, J., Messenger, M. P., Anderson, A. E., Boot, C., Bunce, C., et al. (2015) · eLife · doi:10.7554/elife.05519 · Cited by 57 · see citing works
“Ultimately, some level of variation should be anticipated to occur amongst operators or sites and this needs to be reported and accounted for (Barnhart et al., 2007; Maecker et al., 2011).”
BackgroundEosinophils as potential biomarkers in respiratory viral infections
Macchia, I., Sorsa, V. L., Urbani, F., Moretti, S., Antonucci, C., Afferni, C., & Schiavoni, G. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1170035 · Cited by 56 · see citing works
Lanuti, P., Simeone, P., Rotta, G., Almici, C., Avvisati, G., Azzaro, R., Bologna, G., Budillon, A., Cerbo, M. D., Gennaro, E. D., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-24234-0 · Cited by 54 · see citing works
Relevance of Antibody Validation for Flow Cytometry
Kalina, T., Lundsten, K., & Engel, P. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23895 · Cited by 54 · see citing works
“A requirement for the successful use of computational methods is that an assumption can be made that any changes detected in the data are related to the biological question (and are not due to technical variation) (8,27).”
BackgroundHarmonization of the intracellular cytokine staining assay
Welters, M. J. P., Gouttefangeas, C., Ramwadhdoebé, T. H., Letsch, A., Ottensmeier, C. H. H., Britten, C. M., & van der Burg, S. H. (2012) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-012-1282-9 · Cited by 50 · see citing works
“the HIV vaccination field [7, 8] or to use a central laboratory [9].”
BackgroundIvison, S., Malek, M., Garcia, R. V., Broady, R., Halpin, A., Richaud, M., Brant, R. F., Wang, S.-I., Goupil, M., Guan, Q., et al. (2018) · JCI Insight · doi:10.1172/jci.insight.121867 · Cited by 49 · see citing works
Hasan, M., Beitz, B., Rouilly, V., Libri, V., Urrutia, A., Duffy, D., Cassard, L., Santo, J. P. D., Mottez, E., Quintana‐Murci, L., et al. (2015) · Clinical Immunology · doi:10.1016/j.clim.2014.12.008 · Cited by 49 · see citing works
“These include the EuroFlow Consortium, which focuses on laboratory procedures for the phenotyping of malignant leukocytes [7,8]; the Human Immunology Project Consortium (HIPC) and European Network for Translational Immunology Research and Education (ENTIRE), which are working together to develop panels for the phenoty…”
BackgroundMethodMulti-center harmonization of flow cytometers in the context of the European “PRECISESADS” project
Jamin, C., Lann, L. L., Álvarez‐Errico, D., Barbarroja, N., Cantaert, T., Ducreux, J., Dufour, A. M., Gerl, V., Kniesch, K., Neves, E., et al. (2016) · Autoimmunity Reviews · doi:10.1016/j.autrev.2016.07.034 · Cited by 49 · see citing works
Lemieux, J., Jobin, C., Simard, C., & Néron, S. (2016) · Journal of Immunological Methods · doi:10.1016/j.jim.2016.04.010 · Cited by 47 · see citing works
van der Strate, B., Longdin, R., Geerlings, M., Bachmayer, N., Cavallin, M., Litwin, V., Patel, M. B., Passe-Coutrin, W., Schoelch, C., Companjen, A. R., et al. (2017) · Bioanalysis · doi:10.4155/bio-2017-0093 · Cited by 46 · see citing works
Mandala, W. L., Harawa, V., Munyenyembe, A., Soko, M., & Longwe, H. (2021) · Current Research in Immunology · doi:10.1016/j.crimmu.2021.10.002 · Cited by 44 · see citing works
Guo, J., Amini, S., Lei, Q., Yuan, P., Agola, J. O., Wang, L., Zhou, L., Cao, J., Franco, S., Noureddine, A., et al. (2022) · ACS Nano · doi:10.1021/acsnano.1c08103 · Cited by 41 · see citing works
Report from the second cytomegalovirus and immunosenescence workshop
Wills, M. R., Akbar, A. N., Beswick, M., Bosch, J. A., Caruso, C., Colonna‐Romano, G., Dutta, A., Franceschi, C., Fülöp, T., Gkrania‐Klotsas, E., et al. (2011) · Immunity & Ageing · doi:10.1186/1742-4933-8-10 · Cited by 40 · see citing works
“A goal for presentation at the next meeting is to determine a set of recommendations for standardization along the lines outlined in [26,27].”
BackgroundMa, C., Fan, R., & Elitaş, M. (2013) · Frontiers in Oncology · doi:10.3389/fonc.2013.00133 · Cited by 36 · see citing works
“Multiple clinical centers have established centralized flow cytometry facilities (Maecker and McCoy, 2010).”
BackgroundKlinger, M., Kong, K. A., Moorhead, M., Weng, L., Zheng, J., & Faham, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074231 · Cited by 36 · see citing works
“Finally there have been reports of substantial variability in the quantitative readouts of several of these assays, particularly those relying on T cell activation [6–9].”
BackgroundDeep Immunophenotyping of Human Whole Blood by Standardized Multi-parametric Flow Cytometry Analyses
Gao, J., Luo, Y., Li, H., Zhao, Y., Zhao, J., Han, X., Han, J., Lin, H., & Qian, F. (2023) · Phenomics · doi:10.1007/s43657-022-00092-9 · Cited by 35 · see citing works
“However, the increased complexity of immunophenotypic approaches requires optimized antibody panels and fully standardized procedures (Maecker et al. 2010; Maecker et al. 2012).”
Macchia, I., Urbani, F., & Proietti, E. (2013) · BioMed Research International · doi:10.1155/2013/726239 · Cited by 34 · see citing works
Calarota, S. A., Aberle, J. H., Puchhammer‐Stöckl, E., & Baldanti, F. (2015) · Journal of Clinical Virology · doi:10.1016/j.jcv.2015.07.299 · Cited by 33 · see citing works
Rossi, D., Dannhauser, D., Telesco, M., Netti, P. A., & Causa, F. (2019) · Lab on a Chip · doi:10.1039/c9lc00695h · Cited by 32 · see citing works
Dannhauser, D., Rossi, D., Ripaldi, M., Netti, P. A., & Causa, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-12990-4 · Cited by 30 · see citing works
Craven, T., Avlonitis, N., McDonald, N., Walton, T., Scholefield, E., Akram, A. R., Walsh, T. S., Haslett, C., Bradley, M., & Dhaliwal, K. (2018) · Scientific Reports · doi:10.1038/s41598-018-31391-9 · Cited by 28 · see citing works
Effects of Platelet Agonists and Priming on the Formation of Platelet Populations
Veninga, A., Baaten, C. C. F. M. J., Simone, I. D., Tullemans, B. M. E., Kuijpers, M. J. E., Heemskerk, J. W. M., & van der Meijden, P. E. J. (2021) · Thrombosis and Haemostasis · doi:10.1055/s-0041-1735972 · Cited by 29 · see citing works
Veluchamy, J. P., Delso-Vallejo, M., Kok, N., Bohme, F., Seggewiß-Bernhardt, R., van der Vliet, H. J., de Gruijl, T. D., Huppert, V., & Spanholtz, J. (2017) · Scientific Reports · doi:10.1038/srep43873 · Cited by 29 · see citing works
McCausland, M. M., Lin, Y., Nevers, T., Groves, C. J., & Decman, V. (2021) · Bioanalysis · doi:10.4155/bio-2021-0201 · Cited by 28 · see citing works
Cyt‐Geist: Current and Future Challenges in Cytometry: Reports of the CYTO 2018 Conference Workshops
Czechowska, K., Lannigan, J. A., Wang, L., Arcidiacono, J. A., Ashhurst, T. M., Barnard, R. M., Bauer, S. R., Bispo, C., Bonilla, D. L., Brinkman, R. R., et al. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23777 · Cited by 28 · see citing works
“Additional samples need to be included as controls both for the stimulation and for correct gating of the population of interest (111).”
BackgroundKuuliala, K., Kuuliala, A., Koivuniemi, R., Oksanen, S., Hämäläinen, M., Moilanen, E., Kautiainen, H., Leirisalo‐Repo, M., & Repo, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0137385 · Cited by 27 · see citing works
A user's guide to multicolor flow cytometry panels for comprehensive immune profiling
Holmberg-Thydén, S., Grønbæk, K., Gang, A. O., Fassi, D. E., & Hadrup, S. R. (2021) · Analytical Biochemistry · doi:10.1016/j.ab.2021.114210 · Cited by 26 · see citing works
“The performance of flow cytometers varies [4], even within models with similar laser-and filter configuration.”
Immuno-Oncology biomarkers 2010 and beyond: Perspectives from the iSBTc/SITC biomarker task force
Butterfield, L. H., Disis, M. L., Khleif, S. N., Balwit, J. M., & Marincola, F. M. (2010) · Journal of Translational Medicine · doi:10.1186/1479-5876-8-130 · Cited by 26 · see citing works
“Simplification and standardization of methodology will be necessary for clinically useable tests [17].”
MethodReview of flow cytometry as a tool for cell and gene therapy
Lazarski, C. A., & Hanley, P. J. (2023) · Cytotherapy · doi:10.1016/j.jcyt.2023.10.005 · Cited by 25 · see citing works
Hensley‐McBain, T., Heit, A., Rosa, S. C. D., McElrath, M. J., & Andersen‐Nissen, E. (2014) · Journal of Immunological Methods · doi:10.1016/j.jim.2014.06.002 · Cited by 25 · see citing works
“maintain cell counts—Standardized acquisition parameters are vital for collection of consistent clinical trial data, yet standardization across different flow cytometers and laboratories presents many obstacles that can be difficult to overcome (Maecker et al., 2010; Perfetto et al., 2012).”
BackgroundHersey, S., Keller, S., Mathews, J. A., King, L., Bandukwala, A., Berisha, F., Birchler, M., Bower, J., Clausen, V. A., Duarte, J., et al. (2022) · Bioanalysis · doi:10.4155/bio-2022-0080 · Cited by 25 · see citing works
Co-Infection of the Epstein–Barr Virus and the Kaposi Sarcoma-Associated Herpesvirus
Böni, M., Rieble, L., & Münz, C. (2022) · Viruses · doi:10.3390/v14122709 · Cited by 25 · see citing works
Biajoux, V., Bignon, A., Freitas, C., Martinez, V., Thelen, M., Lima, G., Jakez‐Ocampo, J., Émilie, D., Llorente, L., & Balabanian, K. (2012) · Journal of Translational Medicine · doi:10.1186/1479-5876-10-251 · Cited by 25 · see citing works
“The reasons for discrepancies on CXCR4 profiles between our work and principally that of Wang and coworkers are unclear but may relate to differences in sample handling or ethnical origin of patients [51].”
Abraham, R. S. (2011) · Clinical and Molecular Allergy · doi:10.1186/1476-7961-9-6 · Cited by 26 · see citing works
“However, quality control and standardization of techniques, methods, platforms and reference values is essential to successful and accurate outcomes for immunological analyses within the laboratory, and clinical trial models may provide a frame of reference for such endeavors [145,146].”
BackgroundReagents and Cell Staining for Immunophenotyping by Flow Cytometry
Tario, J. D., & Wallace, P. K. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-386456-7.07104-5 · Cited by 22 · see citing works
Gouttefangeas, C., Chan, C. C. W., Attig, S., Køllgaard, T., Rammensee, H., Stevanović, S., Wernet, D., Straten, P. T., Welters, M. J. P., Ottensmeier, C. H. H., et al. (2015) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-014-1649-1 · Cited by 21 · see citing works
“One approach to improve the reproducibility of FCM results is to establish core facilities that perform centralized immune monitoring for single- and multicenter studies [21].”
Pelák, O., Stuchlý, J., Król, L., Hubáček, P., Keslová, P., Sedláček, P., Formánková, R., Starý, J., Hrušák, O., & Kalina, T. (2015) · Cytometry Part B Clinical Cytometry · doi:10.1002/cyto.b.21348 · Cited by 21 · see citing works
“Implementation of functional studies in a multi-laboratory setting is cumbersome, however multiple measures to improve pre-analytical phase with lyophilized reagents (39), standardized flow cytometry settings (40), automated data analysis (41), or employing a central laboratory (42) has been published.”
BackgroundToward Clinical Application of Leukocyte Counts Based on Targeted DNA Methylation Analysis
Sontag, S., Bocova, L., Hubens, W. H. G., Nüchtern, S., Schnitker, M., Look, T., Schröder, K. M., Plümäkers, B., Tharmapalan, V., Wessiepe, M., et al. (2022) · Clinical Chemistry · doi:10.1093/clinchem/hvac006 · Cited by 20 · see citing works
Chun, B., Pucilowska, J., Chang, S., Kim, I., Nikitin, B. M., Koguchi, Y., Redmond, W. L., Bernard, B. M., Rajamanickam, V., Polaske, N. W., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-004033 · Cited by 19 · see citing works
Villanova, F., Meglio, P. D., Inokuma, M. S., Aghaeepour, N., Perucha, E., Mollon, J., Nomura, L. E., Hernández-Fuentes, M. P., Cope, A. P., Prevost, A. T., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0065485 · Cited by 19 · see citing works
“As part of the Federation of Clinical Immunology Societies (FOCIS) Human Immunophenotyping Consortium, we have recently highlighted three main areas impeding the widespread use of flow cytometry in clinical trials: sample handling, instrument setup, and data analysis [12].”
BackgroundAnother step on the path to mass cytometry standardization
Leipold, M. D. (2015) · Cytometry Part A · doi:10.1002/cyto.a.22661 · Cited by 19 · see citing works
“As flow cytometry became used more in research settings, interest was raised for standardization between machines and locations (4,5).”
MethodImmunological Monitoring to Rationally Guide AAV Gene Therapy
Britten, C. M., Walter, S., & Janetzki, S. (2013) · Frontiers in Immunology · doi:10.3389/fimmu.2013.00273 · Cited by 18 · see citing works
“The critical variables were identified by large scale proficiency programs conducted by the Cancer Research Institute’s Cancer Immunotherapy consortium (CRI-CIC), the Cancer Immunotherapy Immunoguiding Program (CIMT-CIP) (14), and the Human Immunophenotyping Consortium (15).”
MethodWestera, L., van Viegen, T., Jeyarajah, J., Azad, A., Bilsborough, J., BRINK, G. R. V. D., Cremer, J., Danese, S., D’Haens, G., Eckmann, L., et al. (2017) · Clinical and Translational Gastroenterology · doi:10.1038/ctg.2017.52 · Cited by 17 · see citing works
“of reliable FC, yet remains the largest single contributor to interlaboratory variability.(5) Such variability may be reduced by the use of a shared gating template among laboratories, or the use of centralized gating strategies, but the detailed procedures and degree of achievable improvement remain to be firmly esta…”
BackgroundMethodAn Update on Flow Cytometry Analysis of Hematological Malignancies: Focus on Standardization
Holl, E. K., Kapinsky, M., & Larbi, A. (2025) · Cancers · doi:10.3390/cancers17122045 · Cited by 16 · see citing works
“It has proven to reduce significantly the site-to-site variability as demonstrated in the ONE Study and the Human Immune Phenotyping Consortium [90,91].”
Coinfection of EBV with other pathogens: a narrative review
Ebrahimi, F., Rasizadeh, R., Sharaflou, S., Aghbash, P. S., Shamekh, A., Jafari-Sales, A., & Baghi, H. B. (2024) · Frontiers in Virology · doi:10.3389/fviro.2024.1482329 · Cited by 16 · see citing works
Nullens, S., Man, J. G. D., Bridts, C., Ebo, D., Francque, S., & Winter, B. D. (2018) · Mediators of Inflammation · doi:10.1155/2018/5130463 · Cited by 16 · see citing works
““ Fluorescence minus one ” samples (FMO) were included for determining the correct negative populations and gate settings [45 – 47].”
Macchia, I., Sorsa, V. L., Ruspantini, I., Sanchez, M., Tirelli, V., Carollo, M., Fedele, G., Leone, P., Schiavoni, G., Buccione, C., et al. (2020) · Journal of Immunology Research · doi:10.1155/2020/1938704 · Cited by 16 · see citing works
“Here, we present a FCM harmonisation workflow directed by ISS among laboratories belonging to the immunomonitoring working group of the Lazio regional network for translational medicine, based on a previously described “mixed model” [35], to simulate a real multicentre study, where an operator’s individual capacity in…”
Grudzinska‐Goebel, J., Braun, M., Chen, L., Gupta, S., Lohmann, S., Morais, P., Niu, T., Tarcsa, E., Tourdot, S., Youssef, A. S., et al. (2025) · Nucleic Acid Therapeutics · doi:10.1177/21593337251387423 · Cited by 16 · see citing works
Predictive immunomonitoring — The COST ENTIRE initiative
Popadic, D. M., Anegón, I., Baeten, D., Eibel, H., Giese, T., Marits, P., Martínez-Cáceres, E. M., Mascart, F., Nestle, F. O., Pujol‐Borrell, R., et al. (2013) · Clinical Immunology · doi:10.1016/j.clim.2013.01.013 · Cited by 15 · see citing works
“HIP-C has briefly reviewed the above mentioned areas and proposed a model tominimize variation inmulti center studies [12].”
BackgroundA roadmap for translational cancer glycoimmunology at single cell resolution
Peixoto, A., Miranda, A., Santos, L. L., & Ferreira, J. A. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02335-z · Cited by 14 · see citing works
Immune Monitoring for Advanced Cell Therapy Trials in Transplantation: Which Assays and When?
Stark, H. L., Wang, C., Kuburic, J., Alzhrani, A., Hester, J., & Issa, F. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.664244 · Cited by 14 · see citing works
“It provides rapid multiparametric analysis of single cells in solution and is a powerful tool for immune monitoring as it can measure multiple parameters in parallel (example of data shown in Figure 2A) (9).”
MethodDannhauser, D., Rossi, D., Palatucci, A. T., Rubino, V., Carriero, F., Ruggiero, G., Ripaldi, M., Toriello, M., Maisto, G., Netti, P. A., et al. (2021) · Lab on a Chip · doi:10.1039/d1lc00651g · Cited by 14 · see citing works
Systems Biology Approach for Cancer Vaccine Development and Evaluation
Circelli, L., Petrizzo, A., Tagliamonte, M., Tornesello, M. L., Buonaguro, L., & Buonaguro, L. (2015) · Vaccines · doi:10.3390/vaccines3030544 · Cited by 13 · see citing works
“However, many of these methods suffer from low sensitivity, and they show a significant variability in the quantitative readouts [61–63].”
BackgroundSetting objective thresholds for rare event detection in flow cytometry
Richards, A., Staats, J. S., Enzor, J., McKinnon, K. M., Frelinger, J., Denny, T. N., Weinhold, K. J., & Chan, C. C. W. (2014) · Journal of Immunological Methods · doi:10.1016/j.jim.2014.04.002 · Cited by 13 · see citing works
Comprehensive phenotyping of circulating immune cell subsets in people living with HIV
Navas, A., de Wijer, L. V., Cleophas, M. C. P., Schimmel-Naber, A. M., van Cranenbroek, B., van der Heijden, W. A., van der Lei, R. J., Vergara, Z., Netea, M. G., van der Ven, A. J. A. M., et al. (2022) · Journal of Immunological Methods · doi:10.1016/j.jim.2022.113307 · Cited by 12 · see citing works
Shapiro, J. R., Corrado, M., Perry, J. A., Watts, T. H., & Bolotin, S. (2024) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2024.2395679 · Cited by 12 · see citing works
“152 In addition, at the height of the COVID-19 pandemic, certain labs implemented a two-step approach, whereby higher throughput methods like ELISpots were used to survey cellular responses in large cohorts, and then a subset of samples were analyzed with more in-depth techniques.”
MethodStandardized Multi-Color Flow Cytometry and Computational Biomarker Discovery
Schlickeiser, S., Streitz, M., & Sawitzki, B. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3139-2_15 · Cited by 12 · see citing works
Sources of diversity in T cell epitope discovery
Chang, C. X. L. (2011) · Frontiers in bioscience · doi:10.2741/3895 · Cited by 11 · see citing works
Happle, C., Meyer-Decking, L., Dreier, A., Wetzke, M., Gläsener, S., Grychtol, R. M., Braubach, P., Dopfer‐Jablonka, A., Haid, S., Jirmo, A. C., et al. (2018) · Experimental Lung Research · doi:10.1080/01902148.2018.1432721 · Cited by 11 · see citing works
Generation of TCR-Engineered T Cells and Their Use To Control the Performance of T Cell Assays
Bidmon, N., Attig, S., Rae, R., Schröder, H., Omokoko, T., Simon, P., Kuhn, A. N., Kreiter, S., Şahin, U., Gouttefangeas, C., et al. (2015) · The Journal of Immunology · doi:10.4049/jimmunol.1400958 · Cited by 10 · see citing works
Improving Reliability of Immunological Assays by Defining Minimal Criteria for Cell Fitness
Ivison, S. M., Boucher, G., Zheng, G., García, R., Kohen, R., Bitton, A., Rioux, J. D., & Levings, M. K. (2024) · ImmunoHorizons · doi:10.4049/immunohorizons.2300095 · Cited by 11 · see citing works
Eight-color panel for immune phenotype monitoring by flow cytometry
Cardoso, C. C., & Santos‐Silva, M. C. (2019) · Journal of Immunological Methods · doi:10.1016/j.jim.2019.03.010 · Cited by 9 · see citing works
CytOpT: Optimal transport with domain adaptation for interpreting flow cytometry data
Freulon, P., Bigot, J., & Hejblum, B. P. (2023) · The Annals of Applied Statistics · doi:10.1214/22-aoas1660 · Cited by 12 · see citing works
“For instance, differences in the staining procedure, in the data acquisition settings or cytometers performances are very likely to happen and to lead to undesirable variability between flow cytometry data [Maecker and McCoy, 2010].”
Koguchi, Y., Gonzalez, I. L., Meeuwsen, T. L., Miller, W. L., Haley, D. P., Tanibata-Branham, A. N., & Bahjat, K. S. (2016) · Journal of Visualized Experiments · doi:10.3791/53485 · Cited by 8 · see citing works
Flow Cytometry as a Platform for Biomarker Discovery and Clinical Validation
Millán, O., & Brunet, M. (2014) · Springer eBooks · doi:10.1007/978-94-007-7740-8_16-1 · Cited by 7 · see citing works
- Authors
- the FOCIS Human Immunophenotyping Consortium, Maecker, H. T., & McCoy, J. P.
- Affiliations
- Stanford University (US)
- National Institutes of Health (US)
- Corresponding author
- Maecker, Holden T
- Venue
- Nature Immunology
- Published
- 2010-10-19
- Volume
- 11
- Issue
- 11
- Pages
- 975-978
- ISSN
- 1529-2908 (ISSN-L)
- 1529-2916
- PMC
- PMC3400260
- OpenAlex
- W1497374306
- Cited by
- 155
- References
- 26
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 7 · 2023: 7 · 2022: 9 · 2021: 10 · 2020: 6 · 2019: 14 · 2018: 10 · 2017: 8 · 2016: 12 · 2015: 16 · 2014: 10 · 2013: 11 · 2012: 14
- FWCI
- 8.01
- Type
- journal article
- Language
- en
- Update notices
- correction (2011-02-15)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Immunology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DukeSpace (Duke University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- National Institutes of Health (Z99 HL999999)
- National Heart, Lung, and Blood Institute (Z01HL005905, ZICHL005905)
- Topics
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- flow cytometry, clinical trials, harmonization, central laboratory, sample handling, instrument setup, data analysis, immune monitoring, standardization
- MeSH terms
- Clinical Trials as Topic, Flow Cytometry, Humans, Specimen Handling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
