+1 Recommend
0 collections
      • Record: found
      • Abstract: found
      • Article: not found

      Quantification of fluorescence intensity of labeled human mesenchymal stem cells and cell counting of unlabeled cells in phase-contrast imaging: an open-source-based algorithm.

      Tissue Engineering. Part C, Methods

      Algorithms, Cell Count, methods, Cells, Cultured, Flow Cytometry, Fluorescence, Fluorescent Dyes, analysis, metabolism, Green Fluorescent Proteins, Humans, Image Enhancement, Mesenchymal Stromal Cells, cytology, Microscopy, Fluorescence, Microscopy, Phase-Contrast, Staining and Labeling

      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          Assessment of cell fate is indispensable to evaluate cell-based therapies in regenerative medicine. Therefore, a widely used technique is fluorescence labeling. A major problem still is the standardized, noninvasive, and reliable quantification of fluorescence intensity of adherent cell populations on single-cell level, since total fluorescence intensity must be correlated to the cell number. Consequently, the aim of the present study was to produce and validate an open-source-based algorithm, capable of measuring the total fluorescence intensity of cell populations and assessing the total cell number in phase-contrast images. To verify the algorithms' capacity to assess fluorescence intensity, human mesenchymal stem cells were transduced to stably express enhanced green fluorescent protein and results produced by the algorithm were compared to flow cytometry analysis. No significant differences could be observed at any time (p ≥ 0.443). For validation of the algorithm for cell counting in phase-contrast images, adherent human mesenchymal stem cells were manually counted and compared to results produced by the algorithm (correlation coefficient [CC] r = 0.975), nuclei staining (CC r = 0.997), and hemocytometer (CC r = 0.629). We conclude that applying the developed algorithm in routine practice allows robust, fast, and reproducible assessment of fluorescence intensity and cell numbers in simple large-scale microscopy. The method is easy to perform and open source based.

          Related collections

          Author and article information



          Comment on this article