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
- Paula Andrea Marin Zapata1,
- Carsten Jörn Beese†1,
- Anja Jünger†1, 2,
- Giovanni Dalmasso1,
- Nathan Ryan Brady2, 3, 4Email author and
- Anne Hamacher-Brady1, 4Email author
© The Author(s). 2016
Received: 17 December 2015
Accepted: 26 May 2016
Published: 7 June 2016
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.
KeywordsTranscription Factor EB (TFEB) Mammalian target of rapamycin (mTOR) Autophagy Lysosomes Proteasome Systems biology Subpopulation dynamics Single cell Multispectral imaging cytometry
Autophagy, a process of lysosomal degradation essential for cellular homeostasis, is transcriptionally regulated by Transcription Factor EB (TFEB) [1–3], which coordinates the expression of genes involved in lysosome biogenesis, autophagy and endocytosis [1, 2, 4]. Under normal growth conditions TFEB is transiently recruited to the lysosomes through its interaction with active RAG GTPases at the lysosomal membrane . Active RAG GTPases also recruit the anabolic kinase complex mTOR, which phosphorylates TFEB at serine S211 to promote its dissociation from the lysosome and binding with 14-3-3 protein family members, which retain TFEB in the cytoplasm and inhibit its transcriptional activity [5–7]. Upon amino acid starvation, RAG GTPases are inactivated  resulting in the loss of lysosomal recruitment of TFEB and mTOR. Consequently, the cytoplasmic pool of TFEB becomes dephosphorylated, leading to the dissociation from 14-3-3 proteins and ultimately to nuclear accumulation of TFEB. Besides amino acid starvation, pharmacological inhibition of mTOR and lysosomal stresses result in TFEB dephosphorylation and nuclear accumulation [7, 9]. In the nucleus, TFEB activates the transcription of the CLEAR network (Coordinated Lysosomal Expression and Regulation), which is composed of at least 471 direct targets, including a battery of lysosomal and autophagy genes .
Abnormalities in autophagic processes can lead to neurodegenerative diseases and cancer . Moreover, recent studies have identified TFEB and other family members as key players for metabolic reprogramming in pancreatic cancer [11, 12]. Thus, TFEB presents an attractive target for manipulating the cellular autophagic capacity in disease treatment. To date, studies on TFEB have primarily focused on the role of mTOR-mediated regulation of nuclear-cytoplasmic TFEB shuttling. Intriguingly, transcription of TFEB-controlled autophagosomal and lysosomal genes is increased in cells overexpressing TFEB [1, 2, 6] and overall cellular TFEB protein levels are reduced following TFEB activation via long-term (15 hours) chloroquine-induced lysosomal stress , suggesting TFEB concentration changes may contribute to the regulation of TFEB signaling. However, the relationship between mTOR activity states and temporal changes in TFEB subcellular localization and concentration has not been elucidated. To that end, we performed time course analysis over 15 hours of TFEB levels and localization by quantitative Western blotting and imaging cytometry. We activated mTOR by fresh addition of fully-supplemented medium (FM), or inhibited mTOR by Torin1 treatment  or nutrient deprivation . We report that overall cellular TFEB levels transiently decrease in response to small increases in mTOR activation, and transiently increase in response to mTOR inhibition. Both Western blot and population-averaged imaging results displayed high variability, suggesting that heterogeneous TFEB responses within the cell population may cache important information on these complex dynamics. We therefore analyzed single-cell imaging cytometry data using spanning-tree progression analysis of density-normalized events (SPADE) agglomerative clustering , as a basis for unbiased and quantitative detection of spatial and temporal dynamics of subpopulations. Using unsupervised clustering, we identified three TFEB phenotype subpopulations, with low, medium and high nuclear TFEB concentrations. We found that total cellular TFEB levels and subcellular localization are directly under control of a short-term rheostat controlled by mTOR. mTOR inhibition rapidly activates TFEB in a fraction of cells, for a limited duration, with distinct TFEB subpopulation re-inactivation dynamics in response to Torin1 vs. nutrient deprivation. Moreover, time course subpopulation analysis identified a correlation between TFEB protein levels and nuclear localization, and revealed differences between HeLa and MCF7 cells in the sensitivity of TFEB to mTOR regulation. Finally, subpopulation analysis revealed that in response to mTOR inhibition, maximal nuclear localization of TFEB is negatively regulated by the proteasome, independently of TFEB concentration.
Cell culture reagents were obtained from Invitrogen, Sigma, Lonza and PAN Biotech. Methanol-free paraformaldehyde was obtained from Alfa Aesar. Torin1 was purchased from Merck, DMSO from Genaxxon Biosciences and U0126 was from Biovision. Hoechst 33342 was purchased from ImmunoChemistry.
Cell culture and treatments
The human cervical cancer cell line HeLa Kyoto and the human breast cancer cell line MCF7 (obtained from CLS Cell lines service, Heidelberg) were cultured in DMEM (1 g/L D-glucose, 0.11 g/L sodium pyruvate), supplemented with 2 mM L-Glutamine, 10 % Fetal Bovine Serum, non-essential amino acids and penicillin/streptomycin/amphotericin B. Cells were routinely tested for mycoplasma contamination using Hoechst 33342. Transient transfections were performed using jetPRIME (Polyplus) according to the manufacturer’s instructions. Transfection complexes were removed after 6 hours and experiments performed at 24 hours of expression. Nutrient deprivation (ND) was introduced using glucose-containing HBSS (Life Technologies; no. 14025), supplemented with penicillin/streptomycin/amphotericin B. For drug treatments, cells were incubated in FM or HBSS, containing one or a combination of the following reagents: Torin1 (2 μM), U0126 (10 μM), epoxomicin (1 μM), and actinomycin D (1 μg/ml). Co-treatments with epoxomicin, actinomycin D or DMSO included a pre-treatment period (Fig. 7c-e). Cells were pretreated with Epox, ActD or vehicle control (DMSO) for 1 hour, and subsequently treated with FM supplemented with Torin1 in combination with the respective pretreatment reagent for 1 hour. For pre-treatments the drugs were directly added to the culture medium, without addition of fresh FM.
Entry Clones were obtained from the German cDNA Consortium of the German Cancer Research Center. N-terminally tagRFP-tagged clone of 14-3-3 protein isoform YWHAG, RFP-YWHAG, was generated using the Gateway Cloning System (Life Technologies). TFEB wild type was cloned using forward primer: 5′-gtaAAGCTTcgatggcgtcacgcatagggttgcgcatg-3′ and reverse primer 5′- tacGGTACCttacagcacatcgccctcctccat-3′ and inserted into pEGFP (Invitrogen) generating TFEB with N-terminal GFP fusion, GFP-TFEB.
Immunofluorescence and fluorescence microscopy
Fifty thousand cells were plated per well of an 8 well μ-slide microscopy chamber (ibidi) 24 hours before treatment. Following drug treatments, cells were fixed with 4 % paraformaldehyde in PBS for 15 minutes, permeabilized with 0.3 % Triton X-100 in PBS for 10 minutes and blocked with 3 % BSA in 0.3 % Triton X-100/PBS for 1 hour. Cells were then incubated with primary antibodies against LAMP1 (Hybridoma Bank; #H4A3-s), TFEB (Cell Signaling; #4240S), or p-4E-BP1 (Cell Signaling; #2855S) in 0.3 % Triton X-100/PBS at 4 °C overnight. Fluorescence staining was performed using anti-rabbit Alexa Fluor 488 or 594 secondary antibodies (Life Technologies; #A11008, #A11012) in 0.3 % Triton X-100/PBS at room temperature for 1 hour. Fluorescence microscopy was performed with a DeltaVision microscope system (Applied Precision) using a 60x oil immersion objective (Olympus) and a digital CCD camera (Hamamatsu Photonics). Following acquisition, images were deconvolved with Softworks V3.5.1 (Applied Precision) to increase spatial resolution. Images were prepared using ImageJ (rsbweb.nih.gov/ij/). Representative images shown are total intensity projections (Z-axis scans).
Six hundred thousand cells/well were plated in 6-well plates, 24 hours prior drug treatment. Following drug treatments whole cell lysates were prepared of adherent and floating cells with RIPA lysis buffer containing 1X EDTA-free protease inhibitor cocktail (Roche) and 2X PhosphoSTOP (Roche). Dosed protein samples were separated on pre-cast 4–12 % Bis-Tris gels (Invitrogen) and transferred to nitrocellulose using the iBlot dry blotting system (Invitrogen). Blocked membranes were incubated with primary antibodies against TFEB (#101532; Santa Cruz), LAMP1 (# H4A3-s; Hybridoma Bank), LC3 (#2775; Cell Signaling), 4E-BP1 (#9452; Cell Signaling), phospho-4E-BP1 (#9459S; Cell Signaling), p70-S6K1 rabbit IgG (#9202S; Cell Signaling), phospho-p70-S6K1 (#9205S; Cell Signaling) and GAPDH (#25778; Santa Cruz). HRP-conjugated anti-rabbit IgG (#213110-01; GeneTex) and anti-mouse IgG (#213111-01; GeneTex) antibodies were used as secondary antibodies. For immunodetection membranes were incubated with peroxide and luminol solution (1:1) and analyzed with a chemiluminescence imager (Intas). Protein bands were quantified using the gel analysis tool of ImageJ and normalized to the loading control GAPDH. Blots shown are representative of at least three independent experiments.
Multispectral imaging cytometry
Flow cytometry coupled to high resolution imaging was performed using the ImageStreamX cytometer operated with INSPIRE 4.1.501.0 software (Amnis), using a 40X air objective.
Two hundred fifty thousand cells per well of a 12-well plate were plated on the day before drug treatments. Following drug treatments, cells were trypsinized, harvested by centrifugation at 800 g for 5 minutes at 4 °C and fixed with 4 % paraformaldehyde for 15 minutes at room temperature. For detection of endogenous TFEB, cells were immunostained as stated above. Nuclei were labeled with 1 μg/mL Hoechst 33342 in PBS for 10 minutes. Compensation controls were generated from single-color control cells. Endogenous TFEB was immunostained with an antibody against TFEB and Alexa Fluor 594. Lysosomes were immunostained with an antibody against LAMP1 and Alexa Fluor 647. For measurements cells were resuspended in PBS. Fluorescence signal of Hoechst 33342 was excited using the 405 nm laser and detected in channel 1 (420–480 nm). Alexa Fluor 594 was excited using the 561 nm laser and the fluorescence signal was detected in channel 4 (595–642 nm).
All data processing was performed using the IDEAS v6.0 software (Amnis). For each treatment and time point a total of 10000 cells were collected. Following compensation, cells were gated as single (based on the area and aspect ratio of the bright filed mask) and in-focus (based on the Gradient RMS of the bright filed image). With the exception of 15 hours, at least 2000 cells were analyzed after gating. The nuclear, cytoplasmic and cellular masks of gated cells were calculated based on the following morphological and logical operations: Cell, default mask for TFEB channel OR 5-pixel erosion of default bright field mask; Nucleus, 70 % Threshold mask on Hoechst channel; Cytoplasm: Cell AND NOT Nucleus. The features “Intensity Cell, Nucleus and Cytoplasm” were calculated as the total intensities (background subtracted) in their respective masks. The features “Concentration Cell, Nucleus and Cytoplasm” were calculated by dividing the intensity features by the area of their respective masks (in μm 2). The feature “Nuclear percentage” was calculated as the ratio between the features “Intensity Nucleus” and “Intensity Cell”, multiplied by 100. The feature “Max Contour Position” was calculated with a build in function available in IDEAS software . The feature “Mean Pixel Nu/Cyto” was calculated based on masks which underestimated the nuclear and cytoplasmic compartments in order to avoid including cytoplasmic pixels in the nuclear signal or including background or nuclear pixels in the cytoplasmic signal. Underestimated masks were obtained by morphological erosion of the original masks. These feature values were exported to.fcs-files for further processing with the clustering software SPADE V2.0 (Spanning-tree Progression Analysis of Density-normalized Events) . The number of clusters and combination of input features was optimized as presented on the Results Section. The remaining SPADE input parameters were set to default values (arcsinh with cofactor = 5, neighborhood size = 5, local density approximation factor = 1.5, max allowable cells in pooled down-sampled data = 50000, fixed number of cells remained = 20000, Algorithm: K-means). Clustering results were exported to.fcs files and subsequently converted to .txt files using the IDEAS software. Text files were imported into MATLAB R2014a for data representation and further analysis.
Mean population responses were obtained by averaging the single-cell data from a specific treatment, time point and repetition. All subpopulations were identified according to the classification model obtained from FM and Torin1 data in Fig. 4 (Refer to Additional file 1: Figure S1 for further explanation of the classification work flow).
Statistical comparisons were performed with Student’s two-tailed t-test or the non-parametric test Wilcoxon-rank-sum (two-sided). The latter was used in inter-cluster comparisons of non-normally distributed variables, including the features “Mean Pixel Nuc/Cyto” (Figs. 4b, d and 5b) and the discrete variable “LAMP1 Max Contour Position” (Fig. 6d).
Regulation of TFEB localization and protein levels by mTOR
Time course quantification of TFEB response to modulations in mTOR activity by Western blot analysis
Torin1 (2 μM) treatment on the other hand initially resulted in stable TFEB protein levels, which increased significantly after 3 hours (Fig. 2d, e), similar to as observed by imaging (Fig. 1d). Similar to following long-term (15 hour) mTOR inhibition with chloroquine , following 5 and 15 hours of Torin1 treatment TFEB levels were reduced (Fig. 2d, e), albeit with a high degree of variation between experiments. Importantly, Torin1 inhibition of 4E-BP1 phosphorylation was maintained also at 15 hours (Fig. 2f). These findings suggest that total TFEB levels are oppositely regulated by Torin1 and FM treatments during the initial 3-hour treatment period, followed by a prolonged TFEB recovery to initial levels. However, for most time points, the variability of immunoblotting data was too high to infer dynamic behavior of TFEB.
Mean population time course quantification of TFEB response to modulations in mTOR activity by multispectral imaging cytometry
Multispectral imaging cytometry for quantification of endogenous TFEB in cell populations
TFEB exerts its activity in the nucleus, and thus spatial dynamics of TFEB signaling contain relevant information. We therefore performed single-cell analysis of TFEB subcellular localization and protein levels in cell populations using the imaging cytometer, ImageStreamX (ISX) . For each obtained single-cell image, the nuclear and cytoplasmic compartments were segmented, and based on these masks and the total fluorescence intensity of endogenous TFEB, two normalized features were calculated to report spatial concentration states. The first feature, “Mean Pixel Nuc/Cyto”, reflects the nuclear/cytoplasmic ratio of TFEB concentration, and was calculated as the ratio of the mean pixels from each compartment. The second feature, referred to as “Concentration”, was determined by normalizing the total intensity of TFEB to the cell area (described in Methods).
Time course quantification of mean TFEB responses to FM and Torin1 treatments
Next, we assessed the mean population responses in HeLa cells treated under the conditions and time points reported in Fig. 2. Initially (t = 0), TFEB displayed a slightly higher concentration in the nuclear compartment, with a nuclear/cytoplasmic ratio of 1.4 (Fig. 3b). Upon treatment with fresh FM, at 0.5 hours the nuclear/cytoplasmic ratio rapidly decreased (from 1.4 to 1.0), and then gradually increased back to initial levels during the later time points. Consistent with Western blot findings, fresh FM induced a rapid, 14 % decrease in mean total cell TFEB concentrations within 0.5 hours, which was maintained up to 15 hours (Fig. 3c).
Upon treatment with Torin1, at 0.5 hours the nuclear/cytoplasmic ratio increased (from 1.4 to 1.9), peaking at 1 hour, and following 1.5 hours was gradually reduced to a final distribution of 1.4 at 15 hours, similar to time point 0 (Fig. 3b). Consistent with Western blot findings, Torin1 increased cellular TFEB concentration (Fig. 3c), significantly at 1.5 (45 %) and 3 hours (38 %), after which concentrations were reduced to approximately initial (t = 0) levels.
We further evaluated the effect of fresh FM and Torin1 treatments in MCF7 cells. Consistent with the findings in HeLa cells, within 1 hour of treatment with fresh FM the TFEB nuclear/cytoplasmic ratio slightly but non-significantly decreased from 1.8 to 1.7 (Fig. 3d) and overall TFEB protein levels were reduced by 5 % (Fig. 3e). Conversely, Torin1 treatment increased the nuclear/cytoplasmic ratio to 2.2 (Fig. 3d) and increased TFEB levels by 20 % (Fig. 3e). As in HeLa cells, TFEB nuclear localization and cellular concentration increased transiently in response to Torin1 and, after approximately 1 hour, decreased gradually. Of note, in MCF7 cells, at 15 hours of Torin1 treatment TFEB concentration decreased below the initial values.
Taken together, ISX-based analysis of mean population responses support Western blot findings, wherein mTOR inhibition by Torin1 increases TFEB protein levels in HeLa (Figs. 2e and 3c) as well as in MCF7 cells (Fig. 3e). Conversely, addition of fresh FM, to mildly increase mTOR activity (Fig. 1d), led to a slight, but significant, reduction in TFEB levels (Figs. 2b and 3c, e). Furthermore, these results indicate that changes in TFEB protein levels correlate with significant shifts of TFEB between nuclear and cytoplasmic compartments.
Agglomerative clustering analysis of single cell multispectral imaging cytometry data identifies underlying subpopulation dynamics and elucidates temporal TFEB regulation
As Western blot and population-averaged ISX analyses report bulk population dynamics of TFEB, we hypothesized that cell-to-cell heterogeneity in TFEB signaling may contribute to time point variability for both approaches, and thereby contain relevant information on TFEB dynamics. Therefore, we sought to quantify subpopulation TFEB responses from single cell multispectral imaging cytometry data using SPADE-based agglomerative clustering .
Analytical framework for subpopulation analysis of TFEB distribution in time course datasets
Criterion 1: The temporal evolution of the percentage of cells in each cluster should be consistent among repetitions, to assure the reproducibility of subpopulation dynamics. (See Additional file 4: Figure S4 and Additional file 5: Figure S5a).
Criterion 2: The distribution of cells in each cluster should follow independent dynamics. We assume that if the distribution of cells in two or more clusters are affected in the same way by the treatments, this would indicate that the clusters are redundant (See Additional file 5: Figure S5b).
Identification of three TFEB activation phenotypes/subpopulations
We applied this framework to identify subpopulations in the response to FM and Torin1 treatments. The features used for this analysis included the nuclear/cytoplasmic ratio, and areas, concentrations and total intensities in the segmented compartments (cellular, nuclear and cytoplasmic masks).
After evaluating different feature combinations and number of clusters, we determined that our evaluation criteria were satisfied by the single feature “Mean Pixel Nuc/Cyto”, i.e. nuclear/cytoplasmic ratios, and a total of three, statistically different clusters. Based on the nuclear/cytoplasmic ratios, the three clusters were classified as “Inactive”, moderately active (denoted as “Medium”), and “Active”. The “Active” cluster has the highest nuclear localization and the “Inactive” cluster has the lowest nuclear localization, i.e. highest cytoplasmic retention (Fig. 4b).
We characterized the predicted clusters based on the frequency distribution and mean values of a subset of features that were not included in the cluster generation. TFEB nuclear percentage and cellular concentration features (described in Methods) yielded normal distributions within predicted clusters, with statistically-different means (Fig. 4c), further indicating that the predicted clusters represent biologically-meaningful subpopulations. Interestingly, the “Active” cluster contained the highest total cellular TFEB concentration. We confirmed this positive correlation between TFEB nuclear localization and TFEB protein levels through statistical analysis, with a correlation coefficient of 0.53 (See Additional file 1: Figure S1, panel II). To test whether TFEB protein levels and localization were correlated independently of mTOR, we increased cellular TFEB concentration by ectopic expression of GFP-TFEB, and quantified the percentage of activated cells (mainly nuclear TFEB) by fluorescence microscopy (Additional file 6: Figure S6). The amount of activated cells was significantly increased by GFP-TFEB expression compared to endogenous TFEB levels. Furthermore, the effect of overexpression was partially reversed by enhanced sequestration of TFEB in the cytosol through overexpression of 14-3-3 isoform ɣ (YWHAG), for which TFEB has a high binding affinity . These results suggest that increased cellular TFEB protein levels can trigger nuclear localization and override regulation by mTOR, and that this effect is partially dependent on 14-3-3 protein levels.
We identically applied our analysis framework to define TFEB subpopulations in the MCF7 cells time course data (the workflow for defining cell line-specific subpopulations is represented in Additional file 1: Figure S1). Similar to HeLa cells, statistically-different clusters were obtained, and the cluster with the highest nuclear localization (“Active”) had the highest TFEB concentration, while the “Inactive” cluster displayed the lowest TFEB levels (Fig. 4d, e).
Time course subpopulation responses to FM and Torin1 treatments
The temporal impact of fresh FM and Torin1 on the population distribution among predicted activity states was calculated for HeLa cells (Fig. 4f). Results from three independent experiments are shown in Additional file 4: Figure S4. At time point 0, cells were equally distributed between the “Inactive” (45 %) and “Medium” (46 %) subpopulations, while the fraction of “Active” cells amounted to only 9 % (Fig. 4f). Thus, under basal metabolism TFEB activity is inactive-to-moderately active for most cells. In response to fresh FM, at 0.5 hours the “Inactive” subpopulation fraction increased from 45 % to nearly 100 % of the cell population, concurrent with a decrease in the “Medium” subpopulation (from 46 to 6 %) and a depletion of the “Active” subpopulation (region R1). Beginning at 1 hour, the “Inactive” subpopulation decreased, concomitant with an increased “Medium” subpopulation. At 15 hours a distribution of 21 %, 76 % and 3 % for the “Inactive”, “Medium” and “Active” subpopulations was reached, respectively (region R2). The decrease of the “Inactive” subpopulation is consistent with the reduction in mTOR activity detected 5 and 15 hours post FM treatment (Fig. 2c).
In contrast, Torin1 treatment induced a differential subpopulation response, and did not drive 100 % of the population towards a single activation state (Fig. 4f). Within 1.5 hours Torin1 reduced the “Inactive” subpopulation to marginal levels (from 45 to 2 %), slightly reduced the “Medium” subpopulation (from 46 to 41 %), and increased the “Active” subpopulation from 9 to 57 % (region R1). Following 1.5 hours, the “Inactive” and “Medium” subpopulations increased, concurrent to a decrease in “Active” subpopulation, and by 15 hours all subpopulations were similarly re-distributed to time point 0 levels, with 47 %, 45 % and 8 % of the cells in the “Inactive”, “Medium” and “Active” subpopulations, respectively (region R2).
Similar, but blunted, subpopulation redistributions were observed in MCF7 cells (Fig. 4g). FM increased the percentage of cells in the “Inactive” subpopulation (from 57 to 67 %), concomitantly decreasing the “Medium” (from 33 to 26 %) and “Active” subpopulations (from 11 to 6 %), while Torin1 decreased the “Inactive” fraction (to 14 %) and increased the “Active” (to 42 %) and “Medium” subpopulations (to 44 %). As observed in HeLa cells, TFEB activation was gradually reversed starting 1.5 hours after Torin1 treatment, nearly reaching the initial subpopulation distribution after 15 hours (region R2). Notably, differing from HeLa cell results, FM treatment did not inactivate 100 % of the population, suggesting that TFEB activity is less sensitive to mTOR activation in MCF7 cells. This is in accordance with the recently described lower mTOR control over TFEB in pancreatic cancer cells .
Importantly, the mean population response of the nuclear/cytoplasmic ratio could be accurately predicted based on the subpopulation distributions (see Additional file 7: Figure S7), confirming the consistency of our analysis at all time points.
Overall, the results from HeLa and MCF7 cells show that subpopulation analysis reveals highly accurate cell type-, condition-, and time-dependent phenotype dynamics. The above findings reveal that mTOR maximally induced cytoplasmic TFEB retention in all HeLa cells, but fractionally in MCF7 cells. Conversely in both cell types, Torin1 induced maximal TFEB nuclear concentration only in a fraction of cells, and following 1.5 hours of treatment, TFEB began a re-localization to the cytoplasm, suggesting an early rheostat control by mTOR followed by TFEB re-inactivation uncoupled from mTOR activity.
Time course quantification of TFEB mean population and subpopulation responses to nutrient deprivation
Next, we evaluated the TFEB subpopulation response to nutrient deprivation. Cells were classified into “Active”, “Medium” and “Inactive” phenotypes using the classification model obtained above for FM and Torin1 treatments (see Additional file 1: Figure S1 for a schematic of the classification workflow). Consistent with FM and Torin1, the mean features obtained from the nutrient deprivation data set showed that the “Active” cluster, displayed the highest TFEB concentration, while the “Inactive” cluster contained the lowest TFEB levels (Fig. 5b).
Subsequently, we calculated the temporal impact of nutrient deprivation on the distribution of TFEB subpopulations (Fig. 5c). Within 1 hour, nutrient deprivation induced an initial activation wave (region R1), which reduced the “Inactive” subpopulation (from 80 to 18 %) and increased the “Medium” and “Active” subpopulations from 17 % and 3 % to 56 % and 25 %, respectively. Following 1 hour, TFEB was re-inactivated, moving 60 % of the cells to the “Inactive” phenotype after 5 hours, and leading to full loss of the “Active” subpopulation (region R2). Finally, between 5 and 15 hours TFEB subpopulation dynamics shifted again towards increasing “Medium” and “Active” phenotypes (region R3).
Thus, similar to Torin1 treatment, nutrient deprivation rapidly activated TFEB, increasing its nuclear localization. However, in contrast to Torin1 treatment, the subsequent reduction to TFEB nuclear localization was more rapid, and then reversed.
Single cell correlation of TFEB activity states, LAMP1 concentration and lysosomal positioning in response to nutrient deprivation
In addition, we analyzed the subcellular positioning of lysosomes, which is intricately linked to the cellular nutrient state . To that end, we employed the “LAMP1 Max Contour Position” feature, defined as the location of the contour in the cell with the highest LAMP1 intensity concentration . As shown in Fig. 6d and Additional file 8: Figure S8, values close to 0 indicate a centered (perinuclear) lysosomal distribution, while values close to 1 indicate a peripheral (cytoplasmic) lysosomal distribution. The mean population response (Fig. 6e) demonstrates that within 0.5 hours, as expected , nutrient deprivation significantly redistributed lysosomes to the nuclear region. This perinuclear lysosomal distribution was maintained up to 5 hours, and after 15 hours lysosomes re-localized towards the cell periphery. Notably, the three clusters displayed statistically different mean “LAMP1 Max Contour Position” values (Fig. 6f) and clear differences between lysosomal positioning distributions (Fig. 6g), indicating that cells in the “Inactive” cluster contain the most peripheral lysosomes and cells in the “Active” cluster contain the most perinuclear lysosomes. Consistent with the inter-cluster differences, “LAMP1 Max Contour Position” negatively correlated with TFEB nuclear/cytoplasmic ratio in single cells, with a correlation coefficient of −0.25 (see Additional file 1: Figure S1, panel III). Overall, LAMP1 concentration dynamics and spatial distribution within subpopulations indicate that the TFEB-defined subpopulations are of physiological relevance for the cellular lysosomal state.
Maximal activation of TFEB under conditions of mTOR inhibition is negatively regulated by the proteasome, independently of TFEB concentration
ERK signaling does not impact TFEB activation in response to Torin1 or nutrient deprivation
Maximal TFEB activation by Torin1 is negatively regulated by the proteasome
As increased TFEB activation correlated with increased TFEB protein levels (see Additional file 1: Figure S1, panel II), we next sought to determine whether the initial changes in TFEB protein levels could be attributed to proteasomal degradation or protein synthesis. Therefore, we treated HeLa cells with the proteasome inhibitor epoxomicin (1 μM) or the transcriptional inhibitor actinomycin D (1 μg/mL), alone or in combination with Torin1 for 1 hour (Fig. 7c). Neither epoxomicin nor actinomycin D had a significant effect on TFEB levels for all tested conditions, indicating that, within the period of rapid TFEB concentration changes, proteasomal degradation and transcriptional regulation do not play a significant role. We thus speculate that TFEB changes might be regulated via lysosomal degradation. However, as impairment of lysosomal function activates TFEB , an unbiased assessment of the involvement of lysosomal degradation on TFEB levels is not readily possible and requires future investigation.
Of note, while epoxomicin had no impact on the nuclear/cytoplasmic ratio under control conditions, nuclear TFEB localization was significantly increased under epoxomicin and Torin1 co-treatment (Fig. 7d). Subpopulation analysis demonstrates that this effect was due to a significant increase in the subpopulation with “Active” TFEB (to 96 %) (Fig. 7d), and was not related to changes in total TFEB levels (Fig. 7e).
Subpopulation analysis reveals accurate TFEB signaling behavior
We achieved highly accurate analysis of spatial and temporal TFEB dynamics by subpopulation analysis. To decompose cellular heterogeneity into discrete subpopulations of TFEB activation states we used agglomerative clustering [19–21] of single-cell imaging datasets, and established criteria to optimize the number of phenotypes and input features. We identified “Active”, “Medium” and “Inactive” TFEB subpopulations which significantly and reproducibly correlated with TFEB signaling. Consistent with TFEB activation of lysosomal biogenesis , the “Active” subpopulation reported higher lysosomal content than “Medium” and “Inactive” subpopulations (Fig. 6c). Moreover, “Active”, “Medium” and “Inactive” subpopulations reported significant differences in lysosomal positioning (Fig. 6f, g), an indicator of the cellular metabolic state. In response to nutrient deprivation, the “Active” subpopulation was enriched with perinuclear lysosomes, which report a starvation response, while the “Inactive” was enriched with mixed and peripheral lysosomes, which indicate normal growth conditions . We therefore conclude that subpopulation analysis of TFEB distribution and concentration reveals the time course response of TFEB to metabolic stress.
mTOR activity modulations induce distinct time-evolving TFEB subpopulation redistributions
Quantifying TFEB subpopulation redistributions over time, we found that with the addition of fresh FM, which mildly increased mTOR activity (Fig. 1d), TFEB heterogeneity was rapidly lost in HeLa cells (Fig. 4f). In contrast, consistent but smaller population shifts were measured in MCF7 cells (Fig. 4g), suggesting a cell line-dependent lower sensitivity to mTOR activation, possibly related to altered nuclear import of TFEB . Notably, subpopulation analysis revealed that at 15 hours post FM treatment, a small percentage of HeLa cells switched to the less active “Medium” phenotype. This subpopulation shift may reflect the consumption of nutrients and/or growth factors, as indicated by decreased 4E-BP1 phosphorylation (Fig. 2c).
In contrast to FM, in both HeLa and MCF7 cells, Torin1 rapidly depleted the “Inactive” subpopulation and increased “Active” and “Medium” subpopulations (Fig. 4f, g). Strikingly, while Torin1 inhibition of mTOR was maintained for 15 hours (Fig. 2f), TFEB re-inactivation began 1.5 hours following Torin1 treatment and required 15 hours to inactive approximately 50 % of the population, compared to 100 % inactivation by fresh FM at 0.5 hours. These findings evidence an additional mTOR-independent, negative regulatory mechanism, engaged similarly in HeLa and MCF7 cells under conditions of prolonged mTOR inhibition. Putative mechanisms may involve phosphorylation by GSK3 , multiple serine phosphorylations [4, 22], and/or other post-translational modifications such as SUMOylation .
Notably, nutrient deprivation-mediated activation of TFEB differed from Torin1 treatment. While Torin1 strongly induced the “Active” subpopulation and depleted the “Inactive” subpopulation (Fig. 4f), nutrient deprivation mostly increased the “Medium” subpopulation without depleting the “Inactive” subpopulation (Fig. 5c). Further, distinct from Torin1 treatment, between 1.5 and 5 hours (R2 period) TFEB was rapidly inactivated, and between 5 and 15 hours (R3 period) both “Active” and “Medium” subpopulations increased, suggesting a second activation wave.
We suggest this alternating behavior was due to negative feedback of autophagy on mTOR, as we previously predicted by agent-based modeling . Here we report that lysosomal content and perinuclear clustering were maximal during the first 5 hours of nutrient deprivation (Fig. 6b, e), indicative of maximal autophagy activation, which could provide recycled nutrients sufficient to re-activate mTOR and thereby inactivate TFEB. Subsequently, after 5 hours, depletion of autophagy recycled nutrients could lead to renewed inactivation of mTOR, which is in agreement with the observed re-activation of TFEB at 15 hours of nutrient deprivation. Interestingly, at this time point TFEB nuclear localization is not paralleled with increases in either TFEB or LAMP1 protein levels. Moreover, also at 15 hours, lysosomes localized at the cell periphery (Fig. 6e). These observations might be due to depletion of intracellular substrates following prolonged nutrient deprivation. Future studies will explore whether peripheral lysosomal redistribution reflects a cellular shift toward exploiting endocytic nutrient sources.
During pharmacological mTOR inhibition the proteasome negatively regulates nuclear localization of TFEB
Notably, we show that under Torin1 treatment and during early phases of nutrient deprivation TFEB protein levels correlate with subcellular localization. This is consistent with increased transcription of autophagy and lysosomal genes following TFEB overexpression [1, 2, 6]. Using specific inhibitors, we ruled out that initial (within 1 hour) increases and decreases in TFEB levels were due to protein translation or proteasomal activities (Fig. 7c), indicating that transcriptional feedback  is not relevant during the first hour of treatment. These results further suggest that TFEB may be targeted for lysosomal degradation, thereby forming a negative feedback circuit. However, lysosomal stress potently activates TFEB  and therefore putative lysosomal targeting of TFEB requires further investigation.
Importantly, we found that mTOR inactivation failed to maximally activate TFEB in all cells of a cell population (Fig. 4f, g). Our findings exclude ERK signaling as a limiting factor (Fig. 7a). Instead, we identified a novel TFEB inhibitory role for the proteasome, under conditions of mTOR inhibition. Selectively in Torin1-treated cells, epoxomicin-mediated inhibition of the proteasome leads to “Active” nuclear localization of TFEB in nearly 100 % of cells (Fig. 7d), without altering TFEB protein levels (Fig. 7c, e). The mechanism by which the proteasome negatively regulates TFEB remains to be determined. We propose an indirect pathway, in which mTOR activity inhibits proteasomal degradation of a positive TFEB regulator. Thereby, maximal activation of TFEB nuclear translocation requires inhibition of mTOR and proteasome activities (see Fig. 8). Alternatively, proteasome inhibition might activate TFEB via induction of proteotoxic stress , which might be exacerbated under conditions of decreased mTOR activity .
Overall, we demonstrate that TFEB levels and subcellular distribution undergo distinct short-term and long-term control. Our findings suggest that the rapid rheostatic response, mediated by mTOR, allows the cell to quickly adapt to metabolic changes, while the long-term, mTOR-independent homeostatic response controls the magnitude and duration of TFEB activation, and presumably limits excessive autophagy. Our findings also suggest that TFEB may be targeted by lysosomes, and that under conditions of mTOR inhibition the proteasome regulates the early response of TFEB localization, uncoupled from changes to TFEB levels. As TFEB is a central player in cancer [11, 12], our approach to time series analyses of magnitude and dynamics of subpopulation shifts enables biomarker assessment of cell line sensitivity and responsiveness. We propose our approach as a useful general framework for identifying and quantifying information contained within heterogeneous imaging datasets.
ActD, actinomycin D; Epox, epoxomicin; FM, fresh, fully-supplemented medium; ISX, ImageStreamX imaging flow cytometer; LAMP1, lysosomal-associated membrane protein 1; mTOR, mammalian target of rapamycin; ND, nutrient deprivation; NT, non-treated; SPADE, spanning-tree progression analysis of density-normalized events; TFEB, transcription factor EB; WF, wide field imaging
This work was supported by the German Cancer Research Center (DKFZ), through SBCancer within the Helmholtz Alliance on Systems Biology funded by the Initiative and Networking Fund of the Helmholtz Association (NRB); and the e:Bio grant #0316191 (LysoSys) of the Federal Ministry of Education and Research (BMBF), Germany (AH-B). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Availability of data and materials
Data sets will be shared upon request.
NRB and AH-B conceived the study. PAMZ performed data analysis and prepared figures. CJB, AJ and PAMZ performed experiments, data analysis and prepared figures. GD supported analysis. PAMZ, NRB and AH-B wrote the manuscript. All authors edited and approved the manuscript.
The authors declare that they have no competing interests.
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- Palmieri M, et al. Characterization of the CLEAR network reveals an integrated control of cellular clearance pathways. Hum Mol Genet. 2011;20:3852–66. doi:https://doi.org/10.1093/Hmg/Ddr306.View ArticlePubMedGoogle Scholar
- Sardiello M, et al. A gene network regulating lysosomal biogenesis and function. Science. 2009;325:473–7. doi:https://doi.org/10.1126/science.1174447.PubMedGoogle Scholar
- Hamacher-Brady A. Autophagy regulation and integration with cell signaling. Antioxid Redox Signal. 2012;17:756–65. doi:https://doi.org/10.1089/ars.2011.4410.View ArticlePubMedGoogle Scholar
- Settembre C, et al. TFEB Links Autophagy to Lysosomal Biogenesis. Science. 2011;332:1429–33. doi:https://doi.org/10.1126/science.1204592.View ArticlePubMedPubMed CentralGoogle Scholar
- Martina JA, Puertollano R. Rag GTPases mediate amino acid-dependent recruitment of TFEB and MITF to lysosomes. J Cell Biol. 2013;200:475–91. doi:https://doi.org/10.1083/jcb.201209135.View ArticlePubMedPubMed CentralGoogle Scholar
- Martina JA, Chen Y, Gucek M, Puertollano R. MTORC1 functions as a transcriptional regulator of autophagy by preventing nuclear transport of TFEB. Autophagy. 2012;8:903–14. doi:https://doi.org/10.4161/Auto.19653.View ArticlePubMedPubMed CentralGoogle Scholar
- Roczniak-Ferguson A, et al. The transcription factor TFEB links mTORC1 signaling to transcriptional control of lysosome homeostasis. Sci Signal. 2012;5:ra42. doi:https://doi.org/10.1126/scisignal.2002790.View ArticlePubMedPubMed CentralGoogle Scholar
- Zoncu R, et al. mTORC1 Senses Lysosomal Amino Acids Through an Inside-Out Mechanism That Requires the Vacuolar H+−ATPase. Science. 2011;334:678–83. doi:https://doi.org/10.1126/science.1207056.View ArticlePubMedPubMed CentralGoogle Scholar
- Settembre C, et al. A lysosome-to-nucleus signalling mechanism senses and regulates the lysosome via mTOR and TFEB. Embo J. 2012;31:1095–108. doi:https://doi.org/10.1038/emboj.2012.32.View ArticlePubMedPubMed CentralGoogle Scholar
- Levine B, Kroemer G. Autophagy in the pathogenesis of disease. Cell. 2008;132:27–42. doi:https://doi.org/10.1016/j.cell.2007.12.018.View ArticlePubMedPubMed CentralGoogle Scholar
- Perera RM et al. Transcriptional control of autophagy-lysosome function drives pancreatic cancer metabolism. Nature. 2015;524:361–5. doi:https://doi.org/10.1038/nature14587.View ArticlePubMedGoogle Scholar
- Marchand B, Arsenault D, Raymond-Fleury A, Boisvert FM, Boucher MJ. Glycogen synthase kinase-3 (GSK3) inhibition induces prosurvival autophagic signals in human pancreatic cancer cells. J Biol Chem. 2015;290:5592–605. doi:https://doi.org/10.1074/jbc.M114.616714.View ArticlePubMedPubMed CentralGoogle Scholar
- Thoreen CC, et al. An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J Biol Chem. 2009;284:8023–32. doi:https://doi.org/10.1074/jbc.M900301200.View ArticlePubMedPubMed CentralGoogle Scholar
- Kim D, et al. MTOR interacts with Raptor to form a nutrient-sensitive complex that signals to the cell growth machinery. Cell. 2002;110:163–75. doi:https://doi.org/10.1016/S0092-8674(02)00808-5.View ArticlePubMedGoogle Scholar
- Qiu P, et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol. 2011;29:886–91. doi:https://doi.org/10.1038/Nbt.1991.View ArticlePubMedPubMed CentralGoogle Scholar
- Amnis. Image Data Exploration and Analysis Software (IDEAS) User’s Manual 4.0. 2010.Google Scholar
- Basiji DA, Ortyn WE, Liang L, Venkatachalam V, Morrissey P. Cellular image analysis and imaging by flow cytometry. Clin Lab Med. 2007;27:653–70. doi:https://doi.org/10.1016/j.cll.2007.05.008.View ArticlePubMedPubMed CentralGoogle Scholar
- Korolchuk VI, et al. Lysosomal positioning coordinates cellular nutrient responses. Nat Cell Biol. 2011;13:453–60. doi:https://doi.org/10.1038/ncb2204.View ArticlePubMedPubMed CentralGoogle Scholar
- Bendall SC, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332:687–96. doi:https://doi.org/10.1126/science.1198704.View ArticlePubMedPubMed CentralGoogle Scholar
- Gibbs Jr KD, et al. Decoupling of tumor-initiating activity from stable immunophenotype in HoxA9-Meis1-driven AML. Cell Stem Cell. 2012;10:210–7. doi:https://doi.org/10.1016/j.stem.2012.01.004.View ArticlePubMedPubMed CentralGoogle Scholar
- Guo G, et al. Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell. 2013;13:492–505. doi:https://doi.org/10.1016/j.stem.2013.07.017.View ArticlePubMedGoogle Scholar
- Humphrey SJ, Azimifar SB, Mann M. High-throughput phosphoproteomics reveals in vivo insulin signaling dynamics. Nat Biotechnol. 2015. doi:https://doi.org/10.1038/nbt.3327.
- Miller AJ, Levy C, Davis IJ, Razin E, Fisher DE. Sumoylation of MITF and its related family members TFE3 and TFEB. J Biol Chem. 2005;280:146–55. doi:https://doi.org/10.1074/jbc.M411757200.View ArticlePubMedGoogle Scholar
- Borlin CS, Lang V, Hamacher-Brady A, Brady NR. Agent-based modeling of autophagy reveals emergent regulatory behavior of spatio-temporal autophagy dynamics. Cell Commun Signal. 2014;12:56. doi:https://doi.org/10.1186/s12964-014-0056-8.View ArticlePubMedPubMed CentralGoogle Scholar
- Settembre C, et al. TFEB controls cellular lipid metabolism through a starvation-induced autoregulatory loop. Nat Cell Biol. 2013;15:647–58. doi:https://doi.org/10.1038/Ncb2718.View ArticlePubMedPubMed CentralGoogle Scholar
- Santaguida S, Vasile E, White E, Amon A. Aneuploidy-induced cellular stresses limit autophagic degradation. Gene Dev. 2015;29:2010–21. doi:https://doi.org/10.1101/gad.269118.115.View ArticlePubMedPubMed CentralGoogle Scholar
- Chou SD, Prince T, Gong JL, Calderwood SK. mTOR is essential for the proteotoxic stress response, HSF1 activation and heat shock protein synthesis. PLoS One. 2012;7:e39679. doi:https://doi.org/10.1371/journal.pone.0039679.View ArticlePubMedPubMed CentralGoogle Scholar