Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Cytometry Part Aarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Cytometry Part A
Article . 2012 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
Cytometry Part A
Other literature type . 2013
versions View all 2 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

In the realm for standardization in immunophenotyping

Authors: Attila Tárnok; Attila Tárnok;

In the realm for standardization in immunophenotyping

Abstract

STANDARDIZATION and quality control is the keystone of reliable and trustworthy diagnosis. This is particularly true for cellular diagnostics where the preanalytics, instrumentation, and reagents are very diverse and prone to innumerable variations affecting the results (1). Furthermore, as critical are the objects of interest—the cells from biological sources—that are fine-tuned in their composition and behavior in the organism and may be sensitive to various modifications of environmental changes once outside the body. So defining the best and most reproducible assays for their detailed analysis is of utmost relevance for trustworthy diagnosis. Roussel and colleagues from Rennes, France, Bangor, Maine and Seattle, Washington USA (this issue, page 973) scrutinized on behalf of the International Council for Standardization in Hematology (ICSH) three different flow cytometric methods in order to find the reference method for leukocyte differential counts in human blood. In clinical laboratories, nowadays hematology analyzers are routinely used to obtain whole blood differential leukocyte counts. Unfortunately, each of the systems has its own bias as has recently been shown (1) when it comes to rare or unusual cells. In this study, the ICSH study group compared in a multicenter endeavor (three countries), different instruments including automated analyzers, manual counting and in addition two different standard flow cytometers were used. The immunophenotype panels and whole blood, no wash staining and analysis protocols were adapted to each of the flow cytometers from different providers. There are several conclusions from this thorough study. First of all, the bias of hematological automates is that up to 50% of the analysis has to be confirmed by tedious and time consuming manual counting on a microscope. This is not necessary when using flow cytometry analysis. The use of staining of the cell nucleus by DNA specific dyes is useful in order to detect all nucleated cells including premature red blood cells but is difficult to standardize. Finally, the protocols need to be extended to quantify the clinically relevant immature leukocytes and blasts by extending the antibody panel. Also, the characterization of other specific clinically relevant subsets like resident and activated monocytes may be of relevance (2). So in summary, new higher level consensus protocols need evaluation in a clinical setting to approach clinical cytomics in diagnosis. In this respect, various research groups took the effort to increase the depth of analysis by combining a multitude of differentially colorized monoclonal antibodies in a painful and tedious development of optimized multicolor immunofluorescence panels (OMIPs). As examples, recently a 10-color OMIP for the differentiation of hematopoietic cell samples (3) or for memory B-cells (4) were introduced. Now, Mahnke and colleagues from Bethesda, Maryland, USA (this issue, page 935) present a new 13-color 12-antibody panel in order to detail the differentiation of human T-cells. This panel has the advantage of combining phenotypic markers for the unequivocal identification of specific cell types with differentiation and activation markers for their further characterization. Many of the published OMIPs have the potential of giving invaluable input for future standardized clinical panels and many more are to come. Next to the identification of the right instrument and the staining panel, the correct way to isolate and store cells until the

Keywords

Cryopreservation, Quality Control, Antibodies, Monoclonal, Reproducibility of Results, Flow Cytometry, Immunophenotyping, Leukocyte Count, Leukocytes, Humans, Biomarkers

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!