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Novel Serial Positive Enrichment Technology Enables Clinical Multiparameter Cell Sorting

Overview of attention for article published in PLOS ONE, April 2012
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Title
Novel Serial Positive Enrichment Technology Enables Clinical Multiparameter Cell Sorting
Published in
PLOS ONE, April 2012
DOI 10.1371/journal.pone.0035798
Pubmed ID
Authors

Christian Stemberger, Stefan Dreher, Claudia Tschulik, Christine Piossek, Jeannette Bet, Tori N. Yamamoto, Matthias Schiemann, Michael Neuenhahn, Klaus Martin, Martin Schlapschy, Arne Skerra, Thomas Schmidt, Matthias Edinger, Stanley R. Riddell, Lothar Germeroth, Dirk H. Busch

Abstract

A general obstacle for clinical cell preparations is limited purity, which causes variability in the quality and potency of cell products and might be responsible for negative side effects due to unwanted contaminants. Highly pure populations can be obtained best using positive selection techniques. However, in many cases target cell populations need to be segregated from other cells by combinations of multiple markers, which is still difficult to achieve--especially for clinical cell products. Therefore, we have generated low-affinity antibody-derived Fab-fragments, which stain like parental antibodies when multimerized via Strep-tag and Strep-Tactin, but can subsequently be removed entirely from the target cell population. Such reagents can be generated for virtually any antigen and can be used for sequential positive enrichment steps via paramagnetic beads. First protocols for multiparameter enrichment of two clinically relevant cell populations, CD4(high)/CD25(high)/CD45RA(high) 'regulatory T cells' and CD8(high)/CD62L(high)/CD45RA(neg) 'central memory T cells', have been established to determine quality and efficacy parameters of this novel technology, which should have broad applicability for clinical cell sorting as well as basic research.

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The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Czechia 2 3%
United States 1 1%
United Kingdom 1 1%
Unknown 69 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 29%
Researcher 18 25%
Student > Doctoral Student 6 8%
Student > Master 5 7%
Student > Bachelor 4 5%
Other 11 15%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 22%
Immunology and Microbiology 13 18%
Medicine and Dentistry 11 15%
Biochemistry, Genetics and Molecular Biology 9 12%
Engineering 8 11%
Other 4 5%
Unknown 12 16%