title: Replacement of specific markers for apoptosis and necrosis by nuclear morphology for affordable cytometry.
abstract: OBJECTIVE
  The objective of the present study was to employ high throughput image analysis to detect necrosis and apoptosis. Specific markers were replaced by morphological parameters of cells and nuclei.
  
  
  METHODS
  Fresh blood was taken from a healthy female and given a treatment to induce cell necrosis and apoptosis. Afterward, the samples were stained with AnnexinV-FITC, DRAQ5 and DAPI. Slides were made and analyzed using the cytometer iCys. Pictures were scanned. The analyzed sample consisted of 73 sets of images of DAPI, DRAQ5 and AnnexinV-FITC, respectively. For image analysis and subsequent statistical processing, the CellProfiler and CellProfilerAnalyst were used. Each sample was analyzed twice. The first analysis was conducted using the markers (DAPI, DRAQ5 and Annexin) for an unequivocal identification and subsequent count of necrotic, apoptotic and live cells (gold standard). Thereafter, a second analysis was performed for the nuclear morphology and texture (morphometric analysis). After the machine learning process was completed, the software calculated the quantity of cells in each of the three groups. A comparison between the result of the gold standard and the morphometric analysis was performed using linear regression and a Bland-Altman test.
  
  
  RESULTS
  The linear regression between the two compared analyses was r(2)=0.57 for apoptosis, r(2)=0.84 for necrosis and r(2)=0.79 for living cells.
  
  
  CONCLUSIONS
  It may be concluded that it is possible to replace specific markers against morphology without losing the reproducible high-throughput character of a cytometric analysis.
authors.affiliation: University of Vila Velha, Pharmacology, Brazil.
authors.forename: Aline R
authors.initials: AR
authors.lastname: Buzin
authors.affiliation: University of Vila Velha, Pharmaceutical Sciences, Brazil.
authors.forename: Fernanda E
authors.initials: FE
authors.lastname: Pinto
authors.affiliation: University of Leipzig, Heart Center Leipzig, Department of Pediatric Cardiology, Germany.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Nieschke
authors.affiliation: University of Leipzig, Heart Center Leipzig, Department of Pediatric Cardiology, Germany.
authors.forename: Anja
authors.initials: A
authors.lastname: Mittag
authors.affiliation: University of Vila Velha, Pharmaceutical Sciences, Brazil.
authors.forename: Tadeu U
authors.initials: TU
authors.lastname: de Andrade
authors.affiliation: University of Vila Velha, Pharmaceutical Sciences, Brazil.
authors.forename: Denise C
authors.initials: DC
authors.lastname: Endringer
authors.affiliation: University of Leipzig, Heart Center Leipzig, Department of Pediatric Cardiology, Germany.
authors.forename: Attila
authors.initials: A
authors.lastname: Tarnok
authors.affiliation: University of Vila Velha, Pharmaceutical Sciences, Brazil. Electronic address: dominik.lenz@gmail.com.
authors.forename: Dominik
authors.initials: D
authors.lastname: Lenz
mesh_terms: D000328:Adult; D017209:Apoptosis / Q000276:immunology*; D015415:Biomarkers; D001773:Blood Cells / Q000276:immunology* / Q000473:pathology; D002467:Cell Nucleus / Q000276:immunology* / Q000473:pathology; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D009336:Necrosis / Q000276:immunology / Q000473:pathology
chemical_list: D015415:Biomarkers
keywords: Cellular diagnostics; Image cytometry; Machine learning