A versatile platform for comprehensive chip-based explorative cytometry

Hennig, Christian, Adams, Nico, and Hansen, Gesine (2009) A versatile platform for comprehensive chip-based explorative cytometry. Cytometry Part A, 75A (4). pp. 362-370.

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Analysis of the immense complexity of the immune system is increasingly hampered by technical limitations of current methodologies, especially for multiparameter- and functional analysis of samples containing small numbers of cells. We here present a method, which is based on the stepwise functional manipulation and analysis of living immune cells that are self-immobilized within microfluidic chips using automated epifluorescence microscopy overcoming current limitations for comprehensive immunophenotyping. Crossvalidation with flow cytometry revealed a 10-fold increased sensitivity and a comparable specificity. By using small sample volumes and cell numbers (2–10 μl, down to 20,000 cells), we were able to analyze a virtually unlimited number of intracellular and surface markers even on living immune cells. We exemplify the scientific and diagnostic potential of this method by (1) identification and phenotyping of rare cells, (2) comprehensive analysis of very limited sample volume, and (3) deep immunophenotyping of human B-cells after in vitro differentiation. Finally, we propose an informatic model for annotation and comparison of cytometric data by using an ontology-based approach. The chip-based cytometry introduced here turned out to be a very useful tool to enable a stepwise exploration of precious, small cell-containing samples with an virtually unlimited number of surface- and intracellular markers.

Item ID: 74841
Item Type: Article (Research - C1)
ISSN: 1552-4930
Keywords: image cytometry, ontology, microfluidic, chip-based cytometry, immunophenotyping
Copyright Information: © 2008 International Society for Advancement of Cytometry.
Date Deposited: 28 Mar 2024 06:33
FoR Codes: 31 BIOLOGICAL SCIENCES > 3101 Biochemistry and cell biology > 310199 Biochemistry and cell biology not elsewhere classified @ 80%
31 BIOLOGICAL SCIENCES > 3102 Bioinformatics and computational biology > 310299 Bioinformatics and computational biology not elsewhere classified @ 20%
SEO Codes: 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280103 Expanding knowledge in the biomedical and clinical sciences @ 100%
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