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01.12.2016 | Research article | Ausgabe 1/2016 Open Access

BMC Cancer 1/2016

Time course decomposition of cell heterogeneity in TFEB signaling states reveals homeostatic mechanisms restricting the magnitude and duration of TFEB responses to mTOR activity modulation

BMC Cancer > Ausgabe 1/2016
Paula Andrea Marin Zapata, Carsten Jörn Beese, Anja Jünger, Giovanni Dalmasso, Nathan Ryan Brady, Anne Hamacher-Brady
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12885-016-2388-9) contains supplementary material, which is available to authorized users.



TFEB (transcription factor EB) regulates metabolic homeostasis through its activation of lysosomal biogenesis following its nuclear translocation. TFEB activity is inhibited by mTOR phosphorylation, which signals its cytoplasmic retention. To date, the temporal relationship between alterations to mTOR activity states and changes in TFEB subcellular localization and concentration has not been sufficiently addressed.


mTOR was activated by renewed addition of fully-supplemented medium, or inhibited by Torin1 or nutrient deprivation. Single-cell TFEB protein levels and subcellular localization in HeLa and MCF7 cells were measured over a time course of 15 hours by multispectral imaging cytometry. To extract single-cell level information on heterogeneous TFEB activity phenotypes, we developed a framework for identification of TFEB activity subpopulations. Through unsupervised clustering, cells were classified according to their TFEB nuclear concentration, which corresponded with downstream lysosomal responses.


Bulk population results revealed that mTOR negatively regulates TFEB protein levels, concomitantly to the regulation of TFEB localization. Subpopulation analysis revealed maximal sensitivity of HeLa cells to mTOR activity stimulation, leading to inactivation of 100 % of the cell population within 0.5 hours, which contrasted with a lower sensitivity in MCF7 cells. Conversely, mTOR inhibition increased the fully active subpopulation only fractionally, and full activation of 100 % of the population required co-inhibition of mTOR and the proteasome. Importantly, mTOR inhibition activated TFEB for a limited duration of 1.5 hours, and thereafter the cell population was progressively re-inactivated, with distinct kinetics for Torin1 and nutrient deprivation treatments.


TFEB protein levels and subcellular localization are under control of a short-term rheostat, which is highly responsive to negative regulation by mTOR, but under conditions of mTOR inhibition, restricts TFEB activation in a manner dependent on the proteasome. We further identify a long-term, mTOR-independent homeostatic control negatively regulating TFEB upon prolonged mTOR inhibition. These findings are of relevance for developing strategies to target TFEB activity in disease treatment. Moreover, our quantitative approach to decipher phenotype heterogeneity in imaging datasets is of general interest, as shifts between subpopulations provide a quantitative description of single cell behaviour, indicating novel regulatory behaviors and revealing differences between cell types.
Additional file 1: Figure S1. Work flow for classification of cell subpopulations. Initially, cells subjected to FM or Torin1 treatments are classified into three groups/clusters (denoted as activation phenotypes) using agglomerative clustering on the base-clustering-feature “Mean Pixel Nuc/Cyto” (See Fig. 4a). The resulting classification criteria, consisting of thresholds on the base-clustering-feature, constitute our data-based model for cell classification. This model is estimated separately for HeLa and MCF7 cells, and thus, is cell line specific. The activation phenotypes are further characterized by identifying additional phenotypic differences between the cell groups. To this end, the three clusters are statistically compared based on a set of features which were not used in the generation of the cell classification model. Besides identifying features which are specific to each activation phenotype, significant differences between the cell groups report correlations between the evaluated features and the base clustering feature (exemplified by the correlation coefficients and correlation plots on the top right corners of the grey panels). Finally, the FM and Torin1 data-based model is used as a basis for cell classification in response to other treatments such as nutrient deprivation, and inhibition of ERK, proteasome or protein translation (model extrapolation). (JPG 7051 kb)
Additional file 4: Figure S4. Positive clustering example. To demonstrate the reproducibility of the clustering outcome, we separately present the curves from three independent experiments (rows 1 to 3) and the combined mean response ± SEM (row 4), which corresponds to the subpopulation dynamics presented in Fig. 4f. The result was obtained using three clusters and the feature “Mean Pixel Nuc/Cyto” as input. Importantly, treatments with FM and Torin1 induced a clear redistribution of the cell population among the different phenotypes (clusters). This distribution was consistent among the three repetitions and displayed independent dynamics for each cluster, thus adhering to our first and second evaluation criteria, respectively. (JPG 1499 kb)
Additional file 7: Figure S7. Mean population response of nuclear/cytoplasmic ratio predicted from subpopulation distributions. Dots and error bars: mean population response ± SD found experimentally, as presented in Fig. 3b, d. Dotted lines: mean population response predicted based on subpopulation distributions. The predicted mean population response (F) was estimated as follows  F = ( αIFI + αMFM + αAFA )/100, where  FI, FM and FA are the mean “Mean Pixel Nuc/Cyto” of the “Inactive”, “Medium” and “Active” clusters, respectively, and αI, αM and αA are the percentage of cells in each cluster.  FI, FM and FA are constants (presented in Fig. 4b, d), while and αI, αM and αA are functions of time given by the curves in Fig. 4f, g. (JPG 657 kb)
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