Roy Spijkerman1, Lillian Hesselink2, Pien Hellebrekers3, Nienke Vrisekoop4, Falco Hietbrink5, Luke P H Leenen6, Leo Koenderman7. 1. Department of Trauma Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands; Laboratory of Translational Immunology (LTI) and Department of Respiratory Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. 2. Department of Trauma Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands; Laboratory of Translational Immunology (LTI) and Department of Respiratory Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. Electronic address: L.Hesselink@umcutrecht.nl. 3. Department of Trauma Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands; Laboratory of Translational Immunology (LTI) and Department of Respiratory Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. Electronic address: P.Hellebrekers@umcutrecht.nl. 4. Laboratory of Translational Immunology (LTI) and Department of Respiratory Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. Electronic address: N.Vrisekoop@umcutrecht.nl. 5. Department of Trauma Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. Electronic address: F.Hietbrink@umcutrecht.nl. 6. Department of Trauma Surgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. Electronic address: L.P.H.Leenen@umcutrecht.nl. 7. Laboratory of Translational Immunology (LTI) and Department of Respiratory Medicine, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands. Electronic address: l.koenderman@umcutrecht.nl.
Abstract
INTRODUCTION: Phagocytes such as granulocytes and monocytes are fundamental players in the innate immune system. Activation of these cells can be quantified by the measurement of activation marker expression using flow cytometry. Analysis of receptor expression on inflammatory cells facilitates the diagnosis of inflammatory diseases and can be used to determine the extent of inflammation. However, several major limitations of this analysis precludes application of inflammation monitoring in clinical practice. Fast and automated analysis would minimalize ex vivo manipulation and allow reproducible processing. The aim of this study was to evaluate a fully automated "load & go" flow cytometer for analyzing activation of granulocytes and monocytes in a clinically applicable setting. METHODS: Blood samples were obtained from 10 anonymous and healthy volunteers between the age of 18 and 65 years. Granulocyte and monocyte activation was determined by the use of the markers CD35, CD11b and CD10 measured in the automated AQUIOS CL® "load & go" flow cytometer. This machine is able to pierce the tube caps, add antibodies, lyse and measure the sample within 20 min after vena puncture. Reproducibility tests were performed to test the stability of activation marker expression on phagocytes. The expression of activation markers was measured at different time points after blood drawing to analyze the effect of bench time on granulocyte and monocyte activation. RESULTS: The duplicate experiments demonstrate a high reproducibility of the measurements of the activation state of phagocytes. Healthy controls showed a very homogenous expression of activation markers at T = 0 (immediately after vena puncture). Activation markers on neutrophils were already significantly increased after 1 h (T = 1) depicted as means (95%Cl) CD35: 2.2× (1.5×-2.5×) p = .028, CD11b: 2.5× (1.7×-3.1×) p = .023, CD10: 2.5× (2.1×-2.7×) p = .009) and a further increase in activation markers was observed after 2 and 3 h. Monocytes also showed a increase in activation markers in 1 h (mean (95%Cl) CD35: 1.8× (1.3×-2.2×) p = .058, CD11b: 2.13× (1.6×-2.4×) p = .025) and also a further significant increase in 2 and 3 h was observed. CONCLUSION: This study showed that bench time of one hour already leads to a significant upregulation and bigger variance in activation markers of granulocytes and monocytes. In addition, it is likely that automated flow cytometry reduces intra-assay variability in the analysis of activation markers on inflammatory cells. Therefore, we found that it is of utmost importance to perform immune activation analysis as fast as possible to prevent drawing wrong conclusions. Automated flow cytometry is able to reduce this analysis from 2 h to only 15-20 min without the need of dedicated personnel and in a point-of-care context. This now allows fast and automated inflammation monitoring in blood samples obtained from a variety of patient groups. FUND: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
INTRODUCTION: Phagocytes such as granulocytes and monocytes are fundamental players in the innate immune system. Activation of these cells can be quantified by the measurement of activation marker expression using flow cytometry. Analysis of receptor expression on inflammatory cells facilitates the diagnosis of inflammatory diseases and can be used to determine the extent of inflammation. However, several major limitations of this analysis precludes application of inflammation monitoring in clinical practice. Fast and automated analysis would minimalize ex vivo manipulation and allow reproducible processing. The aim of this study was to evaluate a fully automated "load & go" flow cytometer for analyzing activation of granulocytes and monocytes in a clinically applicable setting. METHODS: Blood samples were obtained from 10 anonymous and healthy volunteers between the age of 18 and 65 years. Granulocyte and monocyte activation was determined by the use of the markers CD35, CD11b and CD10 measured in the automated AQUIOS CL® "load & go" flow cytometer. This machine is able to pierce the tube caps, add antibodies, lyse and measure the sample within 20 min after vena puncture. Reproducibility tests were performed to test the stability of activation marker expression on phagocytes. The expression of activation markers was measured at different time points after blood drawing to analyze the effect of bench time on granulocyte and monocyte activation. RESULTS: The duplicate experiments demonstrate a high reproducibility of the measurements of the activation state of phagocytes. Healthy controls showed a very homogenous expression of activation markers at T = 0 (immediately after vena puncture). Activation markers on neutrophils were already significantly increased after 1 h (T = 1) depicted as means (95%Cl) CD35: 2.2× (1.5×-2.5×) p = .028, CD11b: 2.5× (1.7×-3.1×) p = .023, CD10: 2.5× (2.1×-2.7×) p = .009) and a further increase in activation markers was observed after 2 and 3 h. Monocytes also showed a increase in activation markers in 1 h (mean (95%Cl) CD35: 1.8× (1.3×-2.2×) p = .058, CD11b: 2.13× (1.6×-2.4×) p = .025) and also a further significant increase in 2 and 3 h was observed. CONCLUSION: This study showed that bench time of one hour already leads to a significant upregulation and bigger variance in activation markers of granulocytes and monocytes. In addition, it is likely that automated flow cytometry reduces intra-assay variability in the analysis of activation markers on inflammatory cells. Therefore, we found that it is of utmost importance to perform immune activation analysis as fast as possible to prevent drawing wrong conclusions. Automated flow cytometry is able to reduce this analysis from 2 h to only 15-20 min without the need of dedicated personnel and in a point-of-care context. This now allows fast and automated inflammation monitoring in blood samples obtained from a variety of patient groups. FUND: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Authors: Roy Spijkerman; Lillian Hesselink; Suzanne Bongers; Karlijn J P van Wessem; Nienke Vrisekoop; Falco Hietbrink; Leo Koenderman; Luke P H Leenen Journal: Crit Care Explor Date: 2020-07-17
Authors: Lillian Hesselink; Ruben J Hoepelman; Roy Spijkerman; Mark C H de Groot; Karlijn J P van Wessem; Leo Koenderman; Luke P H Leenen; Falco Hietbrink Journal: J Clin Med Date: 2020-01-10 Impact factor: 4.241
Authors: Roy Spijkerman; Nikita K N Jorritsma; Suzanne H Bongers; Bas J J Bindels; Bernard N Jukema; Lillian Hesselink; Falco Hietbrink; Luke P H Leenen; Harriët M R van Goor; Nienke Vrisekoop; Karin A H Kaasjager; Leo Koenderman Journal: Scand J Immunol Date: 2021-02-01 Impact factor: 3.889
Authors: B N Jukema; K Smit; M T E Hopman; C C W G Bongers; T C Pelgrim; M H Rijk; T N Platteel; R P Venekamp; D L M Zwart; F H Rutten; L Koenderman Journal: Front Allergy Date: 2022-07-27
Authors: Leo Koenderman; Maarten J Siemers; Corneli van Aalst; Suzanne H Bongers; Roy Spijkerman; Bas J J Bindels; Giulio Giustarini; Harriët M R van Goor; Karin A H Kaasjager; Nienke Vrisekoop Journal: Cells Date: 2021-05-05 Impact factor: 6.600
Authors: Roy Spijkerman; Lillian Hesselink; Carlo Bertinetto; Coen C W G Bongers; Falco Hietbrink; Nienke Vrisekoop; Luke P H Leenen; Maria T E Hopman; Jeroen J Jansen; Leo Koenderman Journal: Cytometry B Clin Cytom Date: 2021-03-08 Impact factor: 3.248
Authors: Aaron Nauth; Frank Hildebrand; Heather Vallier; Timothy Moore; Luke Leenen; Todd Mckinley; Hans-Christoph Pape Journal: OTA Int Date: 2021-02-23