title: Fully Automated Islet Cell Counter (ICC) for the Assessment of Islet Mass, Purity, and Size Distribution by Digital Image Analysis.
abstract: For isolated pancreatic islet cell preparations, it is important to be able to reliably assess their mass and quality, and for clinical applications, it is part of the regulatory requirement. Accurate assessment, however, is difficult because islets are spheroid-like cell aggregates of different sizes (<50 to 500 μm) resulting in possible thousandfold differences between the mass contribution of individual particles. The current standard manual counting method that uses size-based group classification is known to be error prone and operator dependent. Digital image analysis (DIA)-based methods can provide less subjective, more reproducible, and better-documented islet cell mass (IEQ) estimates; however, so far, none has become widely accepted or used. Here we present results obtained using a compact, self-contained islet cell counter (ICC3) that includes both the hardware and software needed for automated islet counting and requires minimal operator training and input; hence, it can be easily adapted at any center and could provide a convenient standardized cGMP-compliant IEQ assessment. Using cross-validated sample counting, we found that for most human islet cell preparations, ICC3 provides islet mass (IEQ) estimates that correlate well with those obtained by trained operators using the current manual SOP method ( r2= 0.78, slope = 1.02). Variability and reproducibility are also improved compared to the manual method, and most of the remaining variability (CV = 8.9%) results from the rearrangement of the islet particles due to movement of the sample between counts. Characterization of the size distribution is also important, and the present digitally collected data allow more detailed analysis and coverage of a wider size range. We found again that for human islet cell preparations, a Weibull distribution function provides good description of the particle size.
authors.affiliation: Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL, USA.|Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Buchwald
authors.affiliation: Bioniko Consulting LLC, Miami, FL, USA.
authors.forename: Andres
authors.initials: A
authors.lastname: Bernal
authors.affiliation: Biorep Technologies Inc., Miami, FL, USA.
authors.forename: Felipe
authors.initials: F
authors.lastname: Echeverri
authors.affiliation: Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL, USA.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Tamayo-Garcia
authors.affiliation: Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL, USA.
authors.forename: Elina
authors.initials: E
authors.lastname: Linetsky
authors.affiliation: Diabetes Research Institute, Miller School of Medicine, University of Miami, Miami, FL, USA.
authors.forename: Camillo
authors.initials: C
authors.lastname: Ricordi
mesh_terms: D001331:Automation; D002452:Cell Count / Q000379:methods*; D002469:Cell Separation; D048429:Cell Size; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007515:Islets of Langerhans / Q000166:cytology*
keywords: Digital image analysis (DIA); Islet characterization; Islets of Langerhans