Julien Savatier1, J Vigo, J-M Salmon. 1. Laboratoire de Biologie Physico-Chimique des Systèmes Intégrés, Université de Perpignan, 52 Avenue de Villeneuve, 66860 Perpignan Cedex, France. savatier@univ-perp.fr
Abstract
BACKGROUND: The study of the cell cycle of living cells is often based on quantification of nuclear DNA. These studies may be improved by multifactorial analysis evaluating several parameters for each cell. METHODS: Single lymphoblastoid living cells were labeled with three fluorescent markers: Hoechst 33342 for nuclear DNA, Rhodamine 123 for mitochondria, and Nile Red for plasma membrane. Numerical image analysis allowed us to obtain, for each cell, morphological parameters (e.g., cell size, nuclear size, and shape) and functional information (e.g., nuclear DNA content, level of mitochondria energetic state, and the amount and properties of the plasma membrane) by fluorescence intensity. These parameters were used in a typological analysis that separated control cells into four groups. RESULTS: A discriminant factorial analysis (DFA) confirmed the four groups: G0-G1, S, G2+M, and polyploid cells called Gn. These groups were significantly different, with a classification probability of 0.9999; these control cells defined a learning population. Different populations of untreated and adriamycin-treated cells were analyzed as additional individuals within a DFA and were classified into the G0-G1, S, G2+M, and Gn groups by their probability of belonging to each of the groups. CONCLUSIONS: This approach is particularly efficient when it is used to determine variations in cellular properties and to objectively study cellular populations. Copyright 2003 Wiley-Liss, Inc.
BACKGROUND: The study of the cell cycle of living cells is often based on quantification of nuclear DNA. These studies may be improved by multifactorial analysis evaluating several parameters for each cell. METHODS: Single lymphoblastoid living cells were labeled with three fluorescent markers: Hoechst 33342 for nuclear DNA, Rhodamine 123 for mitochondria, and Nile Red for plasma membrane. Numerical image analysis allowed us to obtain, for each cell, morphological parameters (e.g., cell size, nuclear size, and shape) and functional information (e.g., nuclear DNA content, level of mitochondria energetic state, and the amount and properties of the plasma membrane) by fluorescence intensity. These parameters were used in a typological analysis that separated control cells into four groups. RESULTS: A discriminant factorial analysis (DFA) confirmed the four groups: G0-G1, S, G2+M, and polyploid cells called Gn. These groups were significantly different, with a classification probability of 0.9999; these control cells defined a learning population. Different populations of untreated and adriamycin-treated cells were analyzed as additional individuals within a DFA and were classified into the G0-G1, S, G2+M, and Gn groups by their probability of belonging to each of the groups. CONCLUSIONS: This approach is particularly efficient when it is used to determine variations in cellular properties and to objectively study cellular populations. Copyright 2003 Wiley-Liss, Inc.