Validation study of the Helena V8 UltraCE capillary elecrophoresis analyser
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Joris R. Delanghe
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Maaike Godefroid
Abstract
Objectives
Serum protein electrophoresis is one of the keystone investigations for screening for monoclonal proteins and assessing the serum protein profile. The newly released Helena V8ultra capillary electrophoresis system has been evaluated.
Methods
In total, 164 serum samples were assessed on the Helena V8 UltraCE system and compared with the Sebia Capillarys™ instrument. Abnormalities suggestive of monoclonal proteins were confirmed by immunosubtraction. Imprecision studies intervals were determined. Special attention was paid to complement C3 polymorphism.
Results
The imprecision of the Helena V8ultra was inferior or equal from 0.73 % (albumin) to 4.32 % (alpha2 globulin). The mean bias of Helena V8ultraCE vs. Sebia Capillarys was about −0.225 % for albumin; −0.791 % for alpha1 globulins; 1.353 % for alpha2 globulins; −2.317 % for beta globulins; 0.066 % for gamma globulins. Among the 6 samples with monoclonal proteins confirmed by immunofixation, all were seen on both methods, with only 1 discordant result. The theoretical plate number (albumin fraction) was 2,371 ± 718 (Helena V8UltraCE) vs. 445 ± 115 (Sebia Capillarys 2). The high resolution also allows to distinguish Complement C3 phenotypes. CV values for the various fractions were low, ranging from 0.47 % (albumin) to 0.89 % (gamma globulins). IgG, IgA and IgM M-proteins could be detected in the electropherogram with an excellent sensitivity (<0.1 g/L).
Conclusions
Our evaluation confirms the good analytical performance of the Helena V8 analyzer as a suitable alternative to the Sebia Capillarys instrument. The high resolution allows detailed analysis of individual protein fractions which is an excellent basis for studying microheterogeneity.
Acknowledgments
Yana Dewitte and Christa Desmet are thanked for skillful assistance.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: JD: carried out experiments, statistics, writing; MG: experimental work; TM: supervising, editing.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Data are available upon request.
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