The paper presents a novel approach to the processing of flow cytometry data sequences. It consists in decomposing a sequence of multidimensional probability density functions by using multilinear block tensor decomposition approach \cite{DeLath08_1,DeLath08_2}. Also a formal link between flow cytometry data and fluorescence spectra is provided allowing the joint processing of both data. To illustrate the effectiveness of the approach, a study of the T47D cell line mitochondrial membrane potential as a function of the CCCP decoupling agent concentration is performed. The main advantages of the method are : (i) the flow cytometry data compensation is no longer necessary, (ii) the cell sorting capacity of the method is significantly improved as compared to classical clustering methods. As a byproduct, it was possible to observe directly on the result of the processing, the dependence of the cell mitochondrial membrane potential with respect to the cell cycle phase. The proposed method is quite general provided that it is possible to design an experiment allowing to observe the response of cell populations to an environmental/chemical/biological parameter.