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Lymphocyte enrichment using CD81‐targeted immunoaffinity matrix
Author(s) -
Pelák Ondřej,
Kužílková Daniela,
Thürner Daniel,
Kiene MarieLuise,
Stanar Kristian,
Stuchlý Jan,
Vášková Martina,
Starý Jan,
Hrušák Ondřej,
Stadler Herbert,
Kalina Tomáš
Publication year - 2017
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22918
Subject(s) - mass cytometry , cytometry , computational biology , ficoll , immune system , flow cytometry , computer science , chromatography , chemistry , biology , microbiology and biotechnology , immunology , peripheral blood mononuclear cell , biochemistry , in vitro , gene , phenotype
In mass cytometry, the isolation of pure lymphocytes is very important to obtain reproducible results and to shorten the time spent on data acquisition. To prepare highly purified cell suspensions of peripheral blood lymphocytes for further analysis on mass cytometer, we used the new CD81+ immune affinity chromatography cell isolation approach. Using 21 metal conjugated antibodies in a single tube we were able to identify all basic cell subsets and compare their relative abundance in final products obtained by density gradient (Ficoll‐Paque) and immune affinity chromatography (CD81+ T‐catch™) isolation approach. We show that T‐catch isolation approach results in purer final product than Ficoll‐Paque ( P values 0.0156), with fewer platelets bound to target cells. As a result acquisition time of 10 5 nucleated cells was 3.5 shorter. We then applied unsupervised high dimensional analysis viSNE algorithm to compare the two isolation protocols, which allowed us to evaluate the contribution of unsupervised analysis over supervised manual gating. ViSNE algorithm effectively characterized almost all supervised cell subsets. Moreover, viSNE uncovered previously overseen cell subsets and showed inaccuracies in Maxpar™ Human peripheral blood phenotyping panel kit recommended gating strategy. These findings emphasize the use of unsupervised analysis tools in parallel with conventional gating strategy to mine the complete information from a set of samples. They also stress the importance of the impurity removal to sensitively detect rare cell populations in unsupervised analysis. © 2016 International Society for Advancement of Cytometry