Cell culture and differentiation
3T3-L1 mouse preadipocytes were obtained from American Type Culture Collection (CL-173; lot 4715281) and propagated in Dulbecco’s minimum essential medium with 4 g/liter glucose and 10% bovine calf serum (Hyclone). Cells were regularly subcultured before reaching 70% confluence, and the passage number was kept at less than six. 3T3-L1 cells were differentiated according to standard methods (Sadowski et al., 1992
; Engelman et al., 1999
). In brief, 3T3-L1 preadipocytes were plated to a 100-mm culture dish at a very high density (3–4 million in 20 ml medium) and incubated overnight. On the next day (D0), the medium was gently replaced with stage I differentiation medium of Dulbecco’s minimum essential medium (glucose content 4 g/liter) supplemented with 10% FBS (Gemini Bio-Products), 160 nM insulin (Sigma-Aldrich), 250 nM dexamethasone (Sigma-Aldrich), and 0.5 mM 3-isobutyl-1-methylxanthine (Sigma-Aldrich). The medium was renewed once on D2. On D3, the medium was switched to stage II differentiation medium of Dulbecco’s minimum essential medium with 10% FBS and 160 nM insulin. On D5, the medium was changed to adipocyte maintenance medium of Dulbecco’s minimum essential medium with 10% FBS supplement and was renewed every other day.
For fixed-cell assays, 40 h before fixation, differentiating cells were harvested and transferred to 384-well glass-bottom imaging plates (Thermo Fisher Scientific) pretreated with 0.01% sterile poly-L-lysine solution (molecular mass of 70,000–150,000 D; Sigma-Aldrich). Cells were gently washed with PBS and detached with 10 min trypsin EDTA treatment at room temperature. The detached cells were spun down by centrifugation at 900 rpm and suspended in low glucose adipocyte maintenance medium (Dulbecco’s minimum essential medium with 1 g/liter glucose and 10% FBS supplement) before being mixed and transferred to the imaging plates by hand pipetting. The optimal cell density was ~7,000 cells at 50 µl in each well. Cells were settled to the bottoms of plates by centrifugation at 200 rpm for 2 min. After overnight incubation, cells were starved in defined Dulbecco’s minimum essential medium with 1 g/liter glucose and 0.5% wt/vol BSA (fatty acid free; Gemini Bio-Products) for 24 h. For live cell assays, 3T3-L1 cells were subcultured and differentiated directly onto 96-well glass-bottom imaging plates (Thermo Fisher Scientific).
Drug and fatty acid treatment
The stock solutions of troglitazone, rosiglitazone, 15-d-PGJ2 (Cayman Chemical), GW 1929 (Enzo Life Sciences, Inc.), and forskolin (Sigma-Aldrich) were prepared either in water or DMSO depending on their stability and solubility. The BSA-bound stocks of palmitic, palmitoleic, oleic, linoleic, α-linolenic, arachidonic, and eicosapentaenoic acids (Sigma-Aldrich) were prepared as 5 or 10 mM according to their solubility (Hannah et al., 2001
). In brief, free fatty acids (FFAs) were dissolved with ethanol in scintillation tubes, and their sodium salts were precipitated with 5 M NaOH solution. After ethanol was blow dried with a gentle argon stream, the sodium salts of FFAs were reconstituted with sodium chloride at 0.9% wt/vol and 24% BSA (fatty acid free) by stirring continuously at room temperature. The final concentration of BSA was 10% in 0.15 M NaCl solution. The BSA-bound FFA solutions were aliquoted in microtubes evacuated with argon and frozen at −20°C. We note that under control conditions, S2–4 had lower PPARγ levels than our earlier time course and molecular-profiling results ( [black bars] vs. and ), potentially as a result of the presence of DMSO and BSA. However, there was no significant change in the AdipoQ and LD levels under the control conditions ( vs. [left]), and thus, the original subpopulation model was still applicable.
For the following steps of medium changes, drug applications, and vital dye staining, a Laboratory Automation Workstation (Biomek FX; Beckman Coulter) was used. Maintenance medium in the imaging plate was replaced with 50 µl of defined medium 16 h after the cells were transferred to imaging plates, and 25 µl freshly prepared drug working stocks (diluted with defined medium) were dispensed onto the plates and incubated for 24 h. The final concentrations of DMSO and BSA in the medium were compensated to 0.3% and 0.5% wt/vol, respectively, for control and drug-treated sets. The final concentrations of all perturbations can be found in .
4,4-difluoro-1,3,5,7,8-pentamethyl-4-bora-3a,4a-diaza-s-indacene (BODIPY 493/503; Invitrogen) was used to stain LDs at the concentration of 1 µg/liter in Dulbecco’s minimum essential medium. After a 30-min incubation, cells were washed once with Dulbecco’s minimum essential medium and immediately fixed with 4% PFA (Electron Microscopy Sciences) in PBS for 15 min at room temperature. The PFA solution was kept warm at 37°C until being added to the plate with an automatic microplate dispenser (Matrix WellMate; Thermo Fisher Scientific). At the end of incubation, the fixative was quickly flicked out and quenched with 50 mM ammonium chloride. After a 10-min incubation, the cell plate was gently rinsed three times with TBS using a plate washer (ELX405; BioTek).
The following primary antibodies were used: anti-HSL, anti-pHSL (Ser565), anti-PPARγ, anti-C/EBPα (rabbit polyclonal; Cell Signaling Technology), antiperilipin (rabbit polyclonal; Abcam), and anti-AdipoQ (mouse monoclonal; provided by P.E. Scherer, University of Texas Southwestern Medical Center, Dallas, TX). The following secondary antibodies were used: Alexa Fluor 647–conjugated anti–rabbit and Alexa Fluor 546–conjugated anti–mouse immunoglobulin (Invitrogen). Fixed 3T3-L1 cells were permeabilized with 0.2% Triton X-100 in TBS for 5 min and washed twice with TBS on a plate washer (ELX405). Blocking solution of 5% BSA in TBST was added for a 1-h incubation and replaced with different primary antibody combinations (one from mouse and one from rabbit) diluted in blocking solution. The plates were tightly sealed with Parafilm and incubated at 4°C overnight. The primary antibodies were thoroughly rinsed off three times with TBS and once with blocking buffer. Each wash took at least a 10-min incubation time. The fixed cells were further incubated with fluorescence-labeled secondary antibodies for 1 h and washed three times with TBST. Finally, 2 µg/ml Hoechst (Invitrogen) was introduced to highlight nuclei. After two TBS washes, the plates were preserved in 0.1% freshly prepared sodium azide at 4°C.
Subcellular protein fractionation
A subcellular protein fractionation kit (Thermo Fisher Scientific) was used to fractionate proteins into nuclear and cytoplasmic fractions using the manufacturer’s protocol. The supernatants obtained from the cytoplasmic, membrane, and cytoskeletal fractions were pooled together to form the nonnuclear fraction. The nuclear fraction was saved separately. Next, chilled acetone was added at four times the volume of the supernatant for each of the nuclear and nonnuclear fractions and incubated at −20°C for 1 h. After incubation, the fractions were centrifuged at 16,000 g for 10 min to precipitate the proteins. The supernatant was discarded, and the pellet was air dried. To each of the pellets, 100 µl Tris buffer, pH 7.0, was added. Protein concentration was estimated, and the samples were boiled with 2× SDS sample loading buffer.
Western blot analyses
A Bradford protein assay (Protein Assay kit; Bio-Rad Laboratories) was used to quantify the concentrations of proteins presented in the samples collected from each day of differentiation. 10 µg of protein was loaded in a 4–20% gradient SDS polyacrylamide gel, subjected to electrophoresis for 2 h, and transferred to Immobilon-P membrane (Millipore). After blocking with 5% BSA in TBST for 2 h at room temperature, membranes were incubated with the appropriate dilutions of primary antibodies, namely anti-PPARγ at 1:1,000, anti-HSL at 1:1,000 (Cell Signaling Technology), and anti-AdipoQ at 1:10,000 (provided by P.E. Scherer) overnight. After incubation, the membrane was washed with TBST five times followed by incubation with HRP-conjugated secondary antibody (1:10,000) in 5% BSA for 1 h. The membranes were washed for ~30 min after the incubation period. Target proteins were visualized by enhanced chemiluminescence. The Gel Analyzer function in ImageJ (version 1.41o; National Institutes of Health) was used to quantify the captured images of Western blots.
Fixed- and live cell imaging
For fixed-cell imaging, we used a 20× objective on an inverted fluorescence microscope (TE-2000; Nikon) equipped with a 12-bit charge-coupled device camera (CoolSNAP HQ; Photometrics) controlled by MetaMorph software (version 7.1; Universal Imaging). 16 images were acquired for each imaging well. We subtracted background intensities from the images using the rolling ball algorithm in ImageJ software (version1.38l) and stitched the 16 images together using the TurboReg plugin (http://bigwww.epfl.ch/thevenaz/turboreg
). For live cell imaging, we used a 20× phase contrast objective on an inverted light microscope (TS-100; Nikon) equipped with a 12-bit color charge-coupled device camera (DS-Fi1; Nikon) controlled by a control unit (DS-L1; Nikon). Two color images were taken per day (~12 h apart) from D5 to 18. The color images were converted into grayscale by averaging all of the color channels, and the intensity values were automatically scaled by using the imadjust function in Matlab (version 2007a; Mathworks) with default parameters. Individual cells were manually tracked from the captured image sequences.
Cell categorization and quantification
All image and data analyses were performed using Matlab software. We applied a previously developed segmentation algorithm (Loo et al., 2007
) to automatically identify the cellular and nuclear regions for each 3T3-L1 cell. Although low marker staining levels tended to result in underestimation of cellular areas (Fig. S2 a
), in general, estimated marker levels were still strongly and positively correlated to the “ground truth” (Fig. S2 b). Furthermore, subpopulation statistics were similar regardless of whether cellular regions were identified by the automated or manual segmentation procedures (Fig. S2 c). Once automatically segmented, cells were manually categorized into one of the two mutually exclusive categories: quiescent and differentiating. Quiescent cells showed no sign of differentiation, as indicated by a lack of visible PPARγ and AdipoQ expressions and lipid accumulations. The only significant staining on these cells was the DNA marker (, S0), and thus, these cells could be easily distinguished from other cells by their round cellular boundary around the nucleus. All nonquiescent cells were categorized as differentiating. We identified ~500 differentiating cells per replicate per time point. During the categorization process, overlapping or badly segmented cells were also discarded from further analysis. After categorization, the average cellular or nuclear level of a marker was quantified by summing the intensity values from all pixels in the cellular or nuclear region, respectively, and dividing the resulting total value with the corresponding number of pixels. Finally, each marker level was normalized by the median values measured from the quiescent cells as described in Results and discussion.
To identify subpopulations, cells were computationally pooled from all replicates of D6, 9, and 12 (each day had three replicates) and were equally subsampled by 1:9. A standard expectation maximization algorithm (version 3.2; NetLab Toolbox; http://www.ncrg.aston.ac.uk/netlab/index.php
) was applied to the resulting cell samples to determine the optimum Gaussian mixture models (Slack et al., 2008
). We tried 2–10 clusters (Fig. S1 a). To identify new phenotypes, a standard one-dimensional support vector machine (version 2.88; libsvm Toolbox; http://www.csie.ntu.edu.tw/~cjlin/libsvm
) was used (Schölkopf et al., 2001
). We fixed the ν parameter to be 0.001 and used a fivefold cross-validation to determine the optimum value of the γ parameter that gave us 99% training classification accuracy. For our dataset, the optimum γ was found to be 2.96 × 10−3
. To perform hierarchical clustering of , we used the clustergram function of Matlab and set the parameters to be cosine distance and single linkage. Before clustering, the subpopulation-averaged level for each marker was first computed, subtracted by one (as the minimum possible value was onefold), and divided by the maximum value across all subpopulations. All statistical analyses were performed using the Statistics Toolbox (version 6.0) in Matlab.
Online supplemental material
Fig. S1 shows the change in subpopulation phenotypes over time. Fig. S2 shows the dependency of marker level estimation with respect to cellular area estimation. Video 1 shows the temporal changes of cellular and lipid morphologies of three selected cells from . Table S1 shows the criteria used for manual subpopulation assignment. Online supplemental material is available at http://www.jcb.org/cgi/content/full/jcb.200904140/DC1