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High‐resolution 3‐D imaging of living cells in suspension using confocal axial tomography
Author(s) -
Renaud Olivier,
Viña Jose,
Yu Yong,
Machu Christophe,
Trouvé Alain,
Van der Voort Hans,
Chalmond Bernard,
Shorte Spencer L.
Publication year - 2008
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.200700188
Subject(s) - confocal , computer science , cytometry , fluorescence lifetime imaging microscopy , suspension (topology) , flow cytometry , deconvolution , biomedical engineering , detector , confocal microscopy , tomography , computer vision , artificial intelligence , fluorescence , optics , physics , biology , medicine , mathematics , genetics , homotopy , pure mathematics , telecommunications , algorithm
Conventional flow cytometry (FC) methods report optical signals integrated from individual cells at throughput rates as high as thousands of cells per second. This is further combined with the powerful utility to subsequently sort and/or recover the cells of interest. However, these methods cannot extract spatial information. This limitation has prompted efforts by some commercial manufacturers to produce state‐of‐the‐art commercial flow cytometry systems allowing fluorescence images to be recorded by an imaging detector. Nonetheless, there remains an immediate and growing need for technologies facilitating spatial analysis of fluorescent signals from cells maintained in flow suspension. Here, we report a novel methodological approach to this problem that combines micro‐fluidic flow, and microelectrode dielectric‐field control to manipulate, immobilize and image individual cells in suspension. The method also offers unique possibilities for imaging studies on cells in suspension. In particular, we report the system's immediate utility for confocal “axial tomography” using micro‐rotation imaging and show that it greatly enhances 3‐D optical resolution compared with conventional light reconstruction (deconvolution) image data treatment. That the method we present here is relatively rapid and lends itself to full automation suggests its eventual utility for 3‐D imaging cytometry.