Agreement of lymphocyte subsets detection permits reference intervals transference between flow cytometry systems: direct validation using established reference intervals
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Mei Liu
und Hong Shang
Abstract
Objectives
With the increasing demand and application of lymphocyte subsets detection in clinical laboratories, different single-platform flow cytometer (FCM) systems have been developed. There is an urgent need to establish the reference intervals (RIs) for different single-platform FCMs and transferring them from one FCM system to another provides a much more feasible and convenient approach. This study aimed to explore the transferability of RIs for lymphocyte subsets across different flow cytometry platforms.
Methods
We first conducted the pairwise method comparison across four FCM platforms, including NovoCyte, BriCyteE6, DxFLEX, and FACSCantoII systems. Next, the transferability of RIs of lymphocyte subsets was evaluated. Furthermore, we conducted the RIs transference based on the FACSCantoII system, BriCyteE6 system and DxFLEX system, except for NK cells. The transferred RIs were further verified by calculating the bias (CV) between the established ones.
Results
The results of lymphocyte subsets detection based on the NovoCyte, BriCyteE6, DxFLEX, and FACSCantoII systems were comparable and it was feasible to transfer the RIs of lymphocyte subsets detected by the four FCM systems. The RIs of lymphocyte subsets detection using FACSCantoII, DxFLEX, and BriCyteE6 systems were established. Upon transferring the RIs of lymphocyte subsets from the FACSCantoII system to the BriCyteE6 system, and DxFLEX system except for NK cells, the CV between the transferred RIs and the established ones was below 20 % for all parameters.
Conclusions
The present study illustrated that the RIs of lymphocyte subsets could be transferred across different flow cytometry systems except for NK cells with different definition strategies.
Funding source: National Key Technologies R&D Program provided by Ministry of Science and Technology of the People’s Republic of China
Award Identifier / Grant number: Project Grant # 2022YFC3602300, Sub-project Grant #2022YFC3602302
Acknowledgments
We would like to thank Prof. Wei Wang and Jia Li of the Physical Examination Center of the First Hospital of China Medical University for their assistance and help during the process of volunteer recruitment and sample collection. We also thank all the individuals for their participation in this study.
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Research ethics: The study was conducted in accordance with the Declaration of Helsinki, and the study protocol was approved by the local Ethics Committee.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: This study was supported by National Key Technologies R&D Program provided by Ministry of Science and Technology of the People’s Republic of China (Project Grant # 2022YFC3602300, Sub-project Grant # 2022YFC3602302).
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Data availability: The raw data can be obtained on request from the corresponding author.
References
1. Douek, DC, Brenchley, JM, Betts, MR, Ambrozak, DR, Hill, BJ, Okamoto, Y, et al.. HIV preferentially infects HIV-specific CD4+ T cells. Nature 2002;417:95–8. https://doi.org/10.1038/417095a.Suche in Google Scholar PubMed
2. Mandala, WL, Gondwe, EN, Molyneux, ME, MacLennan, JM, MacLennan, CA. Leukocyte counts and lymphocyte subsets in relation to pregnancy and HIV infection in Malawian women. Am J Reprod Immunol 2017;78. https://doi.org/10.1111/aji.12678.Suche in Google Scholar PubMed PubMed Central
3. Wang, F, Hou, H, Yao, Y, Wu, S, Huang, M, Ran, X, et al.. Systemically comparing host immunity between survived and deceased COVID-19 patients. Cell Mol Immunol 2020;17:875–7. https://doi.org/10.1038/s41423-020-0483-y.Suche in Google Scholar PubMed PubMed Central
4. Akbari, H, Tabrizi, R, Lankarani, KB, Aria, H, Vakili, S, Asadian, F, et al.. The role of cytokine profile and lymphocyte subsets in the severity of coronavirus disease 2019 (COVID-19): a systematic review and meta-analysis. Life Sci 2020;258:118167. https://doi.org/10.1016/j.lfs.2020.118167.Suche in Google Scholar PubMed PubMed Central
5. Wang, YY, Zhou, N, Liu, HS, Gong, XL, Zhu, R, Li, XY, et al.. Circulating activated lymphocyte subsets as potential blood biomarkers of cancer progression. Cancer Med 2020;9:5086–94. https://doi.org/10.1002/cam4.3150.Suche in Google Scholar PubMed PubMed Central
6. Yakovlev, P, Klyushin, D. Lymphocyte count in peripheral blood is a sensitive tool in pretreatment assessment of patients with urological cancer. Exp Oncol 2018;40:119–23. https://doi.org/10.31768/2312-8852.2018.40(2):119-123.10.31768/2312-8852.2018.40(2):119-123Suche in Google Scholar
7. Lübbers, J, van Beers-Tas, MH, Vosslamber, S, Turk, SA, de Ridder, S, Mantel, E, et al.. Changes in peripheral blood lymphocyte subsets during arthritis development in arthralgia patients. Arthritis Res Ther 2016;18:205. https://doi.org/10.1186/s13075-016-1102-2.Suche in Google Scholar PubMed PubMed Central
8. Tong, C, Guo, Z, Lou, JX, Liu, XD, Yang, K, He, XP, et al.. [Clinical significance of monitoring T lymphocyte subsets after allogeneic hematopoietic stem cell transplantation]. Zhongguo Shi Yan Xue Ye Xue Za Zhi 2016;24:167–72. https://doi.org/10.7534/j.issn.1009-2137.2016.01.032.Suche in Google Scholar PubMed
9. Llinàs-Mallol, L, Redondo-Pachón, D, Pérez-Sáez, MJ, Raïch-Regué, D, Mir, M, Yélamos, J, et al.. Peripheral blood lymphocyte subsets change after steroid withdrawal in renal allograft recipients: a prospective study. Sci Rep 2019;9:7453. https://doi.org/10.1038/s41598-019-42913-4.Suche in Google Scholar PubMed PubMed Central
10. Gossez, M, Malcus, C, Demaret, J, Frater, J, Poitevin-Later, F, Monneret, G. Evaluation of a novel automated volumetric flow cytometer for absolute CD4+ T lymphocyte quantitation. Cytometry, Part B 2017;92:456–64. https://doi.org/10.1002/cyto.b.21360.Suche in Google Scholar PubMed
11. Whitby, L, Whitby, A, Fletcher, M, Helbert, M, Reilly, JT, Barnett, D. Comparison of methodological data measurement limits in CD4⁺ T lymphocyte flow cytometric enumeration and their clinical impact on HIV management. Cytometry, Part B 2013;84:248–54. https://doi.org/10.1002/cyto.b.21094.Suche in Google Scholar PubMed
12. Sun, H, Kang, X, Chen, X, Cai, L, Li, Y, Yu, J, et al.. Immunosenescence evaluation of peripheral blood lymphocyte subsets in 957 healthy adults from 20 to 95 years old. Exp Gerontol 2022;157:111615. https://doi.org/10.1016/j.exger.2021.111615.Suche in Google Scholar PubMed
13. Wei, B, Guo, Y, Zhang, L, Zhong, H, Miao, Q, Yan, L, et al.. Reference ranges of T lymphocyte subsets by single-platform among healthy population in southwest China. BMC Immunol 2021;22:80. https://doi.org/10.1186/s12865-021-00474-0.Suche in Google Scholar PubMed PubMed Central
14. Liu, W, Xu, J, Pu, Q, Lan, M, Zhang, X, Gu, Y, et al.. The reference ranges and characteristics of lymphocyte parameters and the correlation between lymphocyte parameters and routine health indicators in adults from China. Immun Ageing 2022;19:42. https://doi.org/10.1186/s12979-022-00298-5.Suche in Google Scholar PubMed PubMed Central
15. Xu, K, Miao, L, Chen, W, Wu, H, Gong, Y, Tu, X, et al.. Establishment of the reference intervals of lymphocyte subsets for healthy Chinese Han adults and its influencing factors. Ann Transl Med 2021;9:1495. https://doi.org/10.21037/atm-21-4031.Suche in Google Scholar PubMed PubMed Central
16. CLSI. Defining, establishing, and verifying reference intervals in the clinical laboratory; approved guideline, 3rd ed. Wayne, PA: Clinical and Laboratory Standards Institute; 2010.Suche in Google Scholar
17. CLSI. Measurement procedure comparison and bias estimation using patient samples; approved guideline, 3rd ed. Wayne, PA: Clinical and Laboratory Standards Institute; 2013.Suche in Google Scholar
18. Higgins, V, Truong, D, Woroch, A, Chan, MK, Tahmasebi, H, Adeli, K. CLSI-based transference and verification of CALIPER pediatric reference intervals for 29 Ortho VITROS 5600 chemistry assays. Clin Biochem 2018;53:93–103. https://doi.org/10.1016/j.clinbiochem.2017.12.011.Suche in Google Scholar PubMed
19. Mu, R, Yun, K, Yu, X, Cheng, S, Ma, M, Zhang, X, et al.. A study on reference interval transference via linear regression. Clin Chem Lab Med 2019;58:116–29. https://doi.org/10.1515/cclm-2019-0055.Suche in Google Scholar PubMed
20. Ezra, S, Winstone, TML, Singh, R, Orton, DJ. Agreement of LC-MS assays for IGF-1 traceable to NIST and WHO standards permits harmonization of reference intervals between laboratories. Clin Biochem 2023;116:75–8. https://doi.org/10.1016/j.clinbiochem.2023.04.002.Suche in Google Scholar PubMed
21. Kalla, GCM, Voundi, EV, Tanghu, FM, Sike, CM, Noubom, M, Nkodo, JM, et al.. [Evaluation of the performances of the “MUSE AUTO CD4/CD4%” flow cytometer vs “GUAVA AUTO CD4/CD4%” flow cytometer for measuring the rate of CD4 lymphocytes in patients infected with HIV in Cameroon]. Pan Afr Med J 2019;32:2. https://doi.org/10.11604/pamj.2019.32.2.16990.Suche in Google Scholar PubMed PubMed Central
22. Mbopi-Kéou, FX, Sagnia, B, Ngogang, J, Angwafo, FF3rd, Colizzi, V, Montagnier, L, et al.. Validation of a single-platform, volumetric, flow cytometry for CD4 T cell count monitoring in therapeutic mobile unit. J Transl Med 2012;10:22. https://doi.org/10.1186/1479-5876-10-22.Suche in Google Scholar PubMed PubMed Central
23. Pattanapanyasat, K, Phuang-Ngern, Y, Sukapirom, K, Lerdwana, S, Thepthai, C, Tassaneetrithep, B. Comparison of 5 flow cytometric immunophenotyping systems for absolute CD4+ T-lymphocyte counts in HIV-1-infected patients living in resource-limited settings. J Acquir Immune Defic Syndr 2008;49:339–47. https://doi.org/10.1097/qai.0b013e31818c1721.Suche in Google Scholar PubMed
24. Dieye, TN, Vereecken, C, Diallo, AA, Ondoa, P, Diaw, PA, Camara, M, et al.. Absolute CD4 T-cell counting in resource-poor settings: direct volumetric measurements versus bead-based clinical flow cytometry instruments. J Acquir Immune Defic Syndr 2005;39:32–7. https://doi.org/10.1097/01.qai.0000160515.20581.ad.Suche in Google Scholar PubMed
25. Mossoro-Kpinde, CD, Kouabosso, A, Mboumba Bouassa, RS, Longo, JD, Kokanzo, E, Féissona, R, et al.. Performance evaluation of the touchscreen-based Muse™ Auto CD4/CD4% single-platform system for CD4 T cell numeration in absolute number and in percentage using blood samples from children and adult patients living in the Central African Republic. J Transl Med 2016;14:326. https://doi.org/10.1186/s12967-016-1082-7.Suche in Google Scholar PubMed PubMed Central
26. Ichihara, K, Ozarda, Y, Klee, G, Straseski, J, Baumann, N, Ishikura, K. Utility of a panel of sera for the alignment of test results in the worldwide multicenter study on reference values. Clin Chem Lab Med 2013;51:1007–25. https://doi.org/10.1515/cclm-2013-0248.Suche in Google Scholar PubMed
27. Schnizlein-Bick, CT, Mandy, FF, O’Gorman, MR, Paxton, H, Nicholson, JK, Hultin, LE, et al.. Use of CD45 gating in three and four-color flow cytometric immunophenotyping: guideline from the National Institute of Allergy and Infectious Diseases, Division of AIDS. Cytometry 2002;50:46–52. https://doi.org/10.1002/cyto.10073.Suche in Google Scholar PubMed
28. Degandt, S, Peeters, B, Jughmans, S, Boeckx, N, Bossuyt, X. Analytical performance of an automated volumetric flow cytometer for quantitation of T, B and natural killer lymphocytes. Clin Chem Lab Med 2018;56:1277–88. https://doi.org/10.1515/cclm-2017-0638.Suche in Google Scholar PubMed
29. Araújo, PA, Thomas, D, Sadeghieh, T, Bevilacqua, V, Chan, MK, Chen, Y, et al.. CLSI-based transference of the CALIPER database of pediatric reference intervals to Beckman Coulter DxC biochemical assays. Clin Biochem 2015;48:870–80. https://doi.org/10.1016/j.clinbiochem.2015.06.002.Suche in Google Scholar PubMed
30. Xu, P, Zhou, Q, Xu, J. Reference interval transference of common clinical biomarkers. Scand J Clin Lab Invest 2021;81:264–71. https://doi.org/10.1080/00365513.2021.1907858.Suche in Google Scholar PubMed
31. Estey, MP, Cohen, AH, Colantonio, DA, Chan, MK, Marvasti, TB, Randell, E, et al.. CLSI-based transference of the CALIPER database of pediatric reference intervals from Abbott to Beckman, Ortho, Roche and Siemens Clinical Chemistry Assays: direct validation using reference samples from the CALIPER cohort. Clin Biochem 2013;46:1197–219. https://doi.org/10.1016/j.clinbiochem.2013.04.001.Suche in Google Scholar PubMed
32. Abou El Hassan, M, Stoianov, A, Araújo, PA, Sadeghieh, T, Chan, MK, Chen, Y, et al.. CLSI-based transference of CALIPER pediatric reference intervals to Beckman Coulter AU biochemical assays. Clin Biochem 2015;48:1151–9. https://doi.org/10.1016/j.clinbiochem.2015.05.002.Suche in Google Scholar PubMed
33. Ichihara, K, Ceriotti, F, Kazuo, M, Huang, YY, Shimizu, Y, Suzuki, H, et al.. The Asian project for collaborative derivation of reference intervals: (2) results of non-standardized analytes and transference of reference intervals to the participating laboratories on the basis of cross-comparison of test results. Clin Chem Lab Med 2013;51:1443–57. https://doi.org/10.1515/cclm-2012-0422.Suche in Google Scholar PubMed
34. Lykkeboe, S, Nielsen, CG, Christensen, PA. Indirect method for validating transference of reference intervals. Clin Chem Lab Med 2018;56:463–70. https://doi.org/10.1515/cclm-2017-0574.Suche in Google Scholar PubMed
35. Ozarda, Y, Higgins, V, Adeli, K. Verification of reference intervals in routine clinical laboratories: practical challenges and recommendations. Clin Chem Lab Med 2018;57:30–7. https://doi.org/10.1515/cclm-2018-0059.Suche in Google Scholar PubMed
36. Wood, B, Jevremovic, D, Béné, MC, Yan, M, Jacobs, P, Litwin, V. Validation of cell-based fluorescence assays: practice guidelines from the ICSH and ICCS – part V – assay performance criteria. Cytometry, Part B 2013;84:315–23. https://doi.org/10.1002/cyto.b.21108.Suche in Google Scholar PubMed
37. Cooper, MA, Fehniger, TA, Caligiuri, MA. The biology of human natural killer-cell subsets. Trends Immunol 2001;22:633–40. https://doi.org/10.1016/s1471-4906(01)02060-9.Suche in Google Scholar PubMed
38. Parks, DR, Moore, WA, Brinkman, RR, Chen, Y, Condello, D, El Khettabi, F, et al.. Methodology for evaluating and comparing flow cytometers: a multisite study of 23 instruments. Cytometry, Part A 2018;93:1087–91. https://doi.org/10.1002/cyto.a.23605.Suche in Google Scholar PubMed PubMed Central
39. López-Ruíz, R, Martín-Torres, S, Jiménez-Carvelo, AM, Romero-González, R, Cuadros-Rodríguez, L. Instrument-agnostizing methodology for liquid chromatography-mass spectrometry systems. Methods Mol Biol 2023;2571:257–69. https://doi.org/10.1007/978-1-0716-2699-3_22.Suche in Google Scholar PubMed
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2024-0603).
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- Frontmatter
- Editorial
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- Reviews
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- Perspectives
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- Comparative analysis of BCR::ABL1 p210 mRNA transcript quantification and ratio to ABL1 control gene converted to the International Scale by chip digital PCR and droplet digital PCR for monitoring patients with chronic myeloid leukemia
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- IVDCheckR – simplifying documentation for laboratory developed tests according to IVDR requirements by introducing a new digital tool
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- The effects of drone transportation on routine laboratory, immunohematology, flow cytometry and molecular analyses
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