title: Trypan blue as an affordable marker for automated live-dead cell analysis in image cytometry.
abstract: The aim of the present study was to combine image cytometry and trypan blue (TB) exclusion staining for a reproducible high-throughput detection of dead cells, enabling TB as an inexpensive marker, to be affordable for many studies and creating the possibility to combine fluorochromes without or with less spectral overlap. Capillary blood was drawn from a healthy volunteer, red blood cells were lysed and leukocyte cell death was induced. Samples were stained with CD45-FITC, CD14-PE, TB and DAPI, and then analyzed using image cytometry (iCys). TB quenching control tests were performed using DAPI and CD45-FITC. Images were generated in .TIF and .JPEG format using iCys image cytometer. The images were analyzed using CellProfiler (CP) modules to optimize the analysis based on the aims of each phase of this study. CellProfiler Analyst (CPA) was used to classify cells throughout machine learning and to calculate sensibility of the classification. A sensitivity of 0.94 for dead cells and 0.99 for live cells was calculated using CPA. We did not see any quenching effects of the FITC staining. DAPI signal was reduced in the presence of TB. The results of the present study revealed that TB serves as a dead cell marker in an image cytometric analysis, being able to be combined with other fluorescence markers without loss of fluorescence intensity signal or overlapping emission spectrum. SCANNING 38:857-863, 2016. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Pediatric Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
authors.forename: Susanne
authors.initials: S
authors.lastname: Melzer
authors.affiliation: Master Program in Pharmaceutical Sciences, University of Vila Velha, Vila Velha, Brazil.
authors.forename: Celio Siman Mafra
authors.initials: CS
authors.lastname: Nunes
authors.affiliation: Master Program in Pharmaceutical Sciences, University of Vila Velha, Vila Velha, Brazil.
authors.forename: Denise Coutinho
authors.initials: DC
authors.lastname: Endringer
authors.affiliation: Master Program in Pharmaceutical Sciences, University of Vila Velha, Vila Velha, Brazil.
authors.forename: Tadeu Uggere
authors.initials: TU
authors.lastname: de Andrade
authors.affiliation: Department of Pediatric Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
authors.forename: Attila
authors.initials: A
authors.lastname: Tarnok
authors.affiliation: Master Program in Pharmaceutical Sciences, University of Vila Velha, Vila Velha, Brazil.
authors.forename: Dominik
authors.initials: D
authors.lastname: Lenz
mesh_terms: D000328:Adult; D015415:Biomarkers; D049109:Cell Proliferation; D005260:Female; D006801:Humans; D019044:Image Cytometry / Q000379:methods*; D013194:Staining and Labeling; D014343:Trypan Blue
chemical_list: D015415:Biomarkers; D014343:Trypan Blue
keywords: image cytometry; live-dead analysis; machine-based learning; trypan blue