Carmelo Militello1, Leonardo Rundo2, Vincenzo Conti3, Luigi Minafra4, Francesco Paolo Cammarata4, Giancarlo Mauri5, Maria Carla Gilardi4, Nunziatina Porcino4. 1. Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM-CNR), Cefalù, PA, Italy. Electronic address: carmelo.militello@ibfm.cnr.it. 2. Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM-CNR), Cefalù, PA, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Milano, Italy. 3. Facoltà di Ingegneria e Architettura, Università degli Studi di Enna Kore, Enna, Italy. 4. Istituto di Bioimmagini e Fisiologia Molecolare - Consiglio Nazionale delle Ricerche (IBFM-CNR), Cefalù, PA, Italy. 5. Dipartimento di Informatica, Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Milano, Italy.
Abstract
BACKGROUND: The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent. METHOD: Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability. RESULTS: To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone. CONCLUSIONS: The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.
BACKGROUND: The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent. METHOD: Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability. RESULTS: To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone. CONCLUSIONS: The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.
Authors: Francesco P Cammarata; Filippo Torrisi; Giusi I Forte; Luigi Minafra; Valentina Bravatà; Pietro Pisciotta; Gaetano Savoca; Marco Calvaruso; Giada Petringa; Giuseppe A P Cirrone; Anna L Fallacara; Laura Maccari; Maurizio Botta; Silvia Schenone; Rosalba Parenti; Giacomo Cuttone; Giorgio Russo Journal: Int J Mol Sci Date: 2019-09-24 Impact factor: 5.923
Authors: Leonardo Rundo; Andrea Tangherloni; Darren R Tyson; Riccardo Betta; Carmelo Militello; Simone Spolaor; Marco S Nobile; Daniela Besozzi; Alexander L R Lubbock; Vito Quaranta; Giancarlo Mauri; Carlos F Lopez; Paolo Cazzaniga Journal: Appl Sci (Basel) Date: 2020-09-06 Impact factor: 2.679