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Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets

  • Kai Guo ORCID logo , Xiaoran Feng , Lei Xu , Chenbin Li ORCID logo , Yating Ma and Mingting Peng EMAIL logo
Published/Copyright: May 31, 2024
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 11

Abstract

Objectives

This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design.

Methods

Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals.

Results

Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/μL), with 23.7 %.

Conclusions

This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

Keywords: biological variation; lymphocyte deep immunophenotyping; monocyte subset; full spectrum flow cytometry; analytical performance specification
Corresponding author: Prof. Mingting Peng, National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, Beijing 100730, P.R. China; and National Center for Clinical Laboratories, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, P.R. China, E-mail:

Funding source: National Special Project for Science and Technology Basic Work of Ministry of Science and Technology of China

Award Identifier / Grant number: 2013FY113800

Funding source: Discipline Construction Project of Peking Union Medical College

Award Identifier / Grant number: No. 201920102202

Funding source: National High Level Hospital Clinical Research Funding of China

Award Identifier / Grant number: BJ-2022-139

Acknowledgments

We are grateful to all the volunteers and colleagues participating in the BV study. The authors thank Mrs. Hong Lu, Dr. Zhongli Du, Dr. Gaofeng Hu, and Dr. Chengshan Xu from the National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, China, for all their invaluable efforts and help. We also thank Thomas Røraas and their team for their invaluable contribution to statistical methods and data analysis.

  1. Research ethics: The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The Ethics Committee of Beijing Hospital gave expedited approval to this study involved human samples. The authors have no other ethical conflicts to disclose.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: This study was supported by the grant from National HighLevel Hospital Clinical Research Funding of China (BJ-2022-139), the Discipline Construction Project of Peking Union Medical College (No. 201920102202), and the National Special Project for Science and Technology Basic Work of Ministry of Science and Technology of China (2013FY113800).

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2024-0371).

Received: 2024-03-21
Accepted: 2024-05-05
Published Online: 2024-05-31
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2024-0371
Keywords for this article
biological variation; lymphocyte deep immunophenotyping; monocyte subset; full spectrum flow cytometry; analytical performance specification

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Circulating tumor DNA measurement: a new pillar of medical oncology?
  4. Reviews
  5. Circulating tumor DNA: current implementation issues and future challenges for clinical utility
  6. Circulating tumor DNA methylation: a promising clinical tool for cancer diagnosis and management
  7. Opinion Papers
  8. The final part of the CRESS trilogy – how to evaluate the quality of stability studies
  9. The impact of physiological variations on personalized reference intervals and decision limits: an in-depth analysis
  10. Computational pathology: an evolving concept
  11. Perspectives
  12. Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine
  13. General Clinical Chemistry and Laboratory Medicine
  14. Macroprolactin in mothers and their babies: what is its origin?
  15. The influence of undetected hemolysis on POCT potassium results in the emergency department
  16. Quality control in the Netherlands; todays practices and starting points for guidance and future research
  17. QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories
  18. OILVEQ: an Italian external quality control scheme for cannabinoids analysis in galenic preparations of cannabis oil
  19. Using Bland-Altman plot-based harmonization algorithm to optimize the harmonization for immunoassays
  20. Comparison of a two-step Tempus600 hub solution single-tube vs. container-based, one-step pneumatic transport system
  21. Evaluating the HYDRASHIFT 2/4 Daratumumab assay: a powerful approach to assess treatment response in multiple myeloma
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  24. Reference values for plasma and urine trace elements in a Swiss population-based cohort
  25. Stimulating thyrotropin receptor antibodies in early pregnancy
  26. Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets
  27. Diurnal and day-to-day biological variation of salivary cortisol and cortisone
  28. Web-accessible critical limits and critical values for urgent clinician notification
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  33. Diagnostic performance of a point of care high-sensitivity cardiac troponin I assay and single measurement evaluation to rule out and rule in acute coronary syndrome
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