@article{LukešGlatzovaKvičalovaetal.2017,
  author    = {Lukeš, Tom{\´a}š and Glatzov{\´a}, Daniela and Kv{\´i}čalov{\´a}, Zuzana and Levet, Florian and Benda, Aleš and Letschert, Sebastian and Sauer, Markus and Brdička, Tom{\´a}š and Lasser, Theo and Cebecauer, Marek},
  title     = {Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging},
  series = {Nature Communications},
  volume    = {8},
  journal   = {Nature Communications},
  doi       = {10.1038/s41467-017-01857-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172993},
  year      = {2017},
  abstract  = {Quantitative approaches for characterizing molecular organization of cell membrane molecules under physiological and pathological conditions profit from recently developed super-resolution imaging techniques. Current tools employ statistical algorithms to determine clusters of molecules based on single-molecule localization microscopy (SMLM) data. These approaches are limited by the ability of SMLM techniques to identify and localize molecules in densely populated areas and experimental conditions of sample preparation and image acquisition. We have developed a robust, model-free, quantitative clustering analysis to determine the distribution of membrane molecules that excels in densely labeled areas and is tolerant to various experimental conditions, i.e. multiple-blinking or high blinking rates. The method is based on a TIRF microscope followed by a super-resolution optical fluctuation imaging (SOFI) analysis. The effectiveness and robustness of the method is validated using simulated and experimental data investigating nanoscale distribution of CD4 glycoprotein mutants in the plasma membrane of T cells.},
  language  = {en}
}