Lipid droplet quantification based on iterative image processing
2019; Elsevier BV; Volume: 60; Issue: 7 Linguagem: Inglês
10.1194/jlr.d092841
ISSN1539-7262
AutoresTarik Exner, Carlo A. Beretta, Qi Gao, Cassian Afting, Inés Romero‐Brey, Ralf Bartenschlager, Leonard Fehring, Margarete Poppelreuther, Joachim Füllekrug,
Tópico(s)Plant biochemistry and biosynthesis
ResumoLipid droplets (LDs) are ubiquitous and highly dynamic subcellular organelles required for the storage of neutral lipids. LD number and size distribution are key parameters affected not only by nutrient supply but also by lipotoxic stress and metabolic regulation. Current methods for LD quantification lack general applicability and are either based on time consuming manual evaluation or show limitations if LDs are high in numbers or closely clustered. Here, we present an ImageJ-based approach for the detection and quantification of LDs stained by neutral lipid dyes in images acquired by conventional wide-field fluorescence microscopy. The method features an adjustable preprocessing procedure that resolves LD clusters. LD identification is based on their circular edges and central fluorescence intensity maxima. Adaptation to different cell types is mediated by a set of interactive parameters. Validation was done for three different cell lines using manual evaluation of LD numbers and volume measurement by 3D rendering of confocal datasets. In an application example, we show that overexpression of the acyl-CoA synthetase, FATP4/ACSVL5, in oleate-treated COS7 cells increased the size of LDs but not their number. Lipid droplets (LDs) are ubiquitous and highly dynamic subcellular organelles required for the storage of neutral lipids. LD number and size distribution are key parameters affected not only by nutrient supply but also by lipotoxic stress and metabolic regulation. Current methods for LD quantification lack general applicability and are either based on time consuming manual evaluation or show limitations if LDs are high in numbers or closely clustered. Here, we present an ImageJ-based approach for the detection and quantification of LDs stained by neutral lipid dyes in images acquired by conventional wide-field fluorescence microscopy. The method features an adjustable preprocessing procedure that resolves LD clusters. LD identification is based on their circular edges and central fluorescence intensity maxima. Adaptation to different cell types is mediated by a set of interactive parameters. Validation was done for three different cell lines using manual evaluation of LD numbers and volume measurement by 3D rendering of confocal datasets. In an application example, we show that overexpression of the acyl-CoA synthetase, FATP4/ACSVL5, in oleate-treated COS7 cells increased the size of LDs but not their number. Lipid droplets (LDs) are spherical organelles surrounded by a phospholipid monolayer and equipped with a unique set of proteins. After they were portrayed for many years as inert storage organelles for neutral lipids, recent research suggests an additional versatile repertoire of metabolic and regulatory functions in the cellular lipid metabolism. 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All cell lines were cultured in DMEM (Thermo Fisher Scientific, Waltham, MA) containing 4.5 g/l glucose supplemented with 10% FBS (Life Technologies, Carlsbad, CA) and 1% penicillin/streptomycin (Life Technologies). Cells were maintained in a humidified atmosphere with 5% CO2 and were split when 80% confluency was reached. Cells stably expressing the LD marker protein, A3Nt-GFP-FLAG (human ACSL3-N-terminus, M1-L135) (30Poppelreuther M. Rudolph B. Du C. Grossmann R. Becker M. Thiele C. Ehehalt R. Fullekrug J. The N-terminal region of acyl-CoA synthetase 3 is essential for both the localization on lipid droplets and the function in fatty acid uptake.J. Lipid Res. 2012; 53: 888-900Abstract Full Text Full Text PDF PubMed Scopus (87) Google Scholar), the murine fatty acid transport protein 4 (msFATP4-FLAG), and an empty retroviral plasmid (pRVH-1) were generated as described elsewhere (31Schuck S. Manninen A. Honsho M. Fullekrug J. Simons K. Generation of single and double knockdowns in polarized epithelial cells by retrovirus-mediated RNA interference.Proc. Natl. Acad. Sci. USA. 2004; 101: 4912-4917Crossref PubMed Scopus (82) Google Scholar). Briefly, phoenixGP cells were transfected with A3Nt-GFP-FLAG.pRIJ, msFATP4-FLAG.pRIJ, or pRVH-1.pRIJ, respectively, together with pVSV-G for virus pseudotyping. Viruses were harvested 48 h after transfection by filtering the supernatant through 0.45 μm PVDF membrane pores. COS7 and U2OS were seeded in 6-well plates and incubated with 1 ml (A3Nt-GFP-FLAG.pRIJ, pRVH-1.pRIJ) or 100 μl (msFATP4-FLAG.pRIJ; together with 900 μl normal growth medium) of the virus solution supplemented with 4 μg/ml polybrene for 24 h. After an additional 24 h in normal growth medium, the cells were trypsinized and selected for the stable genomic integration of A3Nt-GFP-FLAG.pRIJ, pRVH-1.pRIJ, or msFATP4-FLAG.pRIJ with puromycin (2 μg/ml for U2OS and 6 μg/ml for COS7). Untransduced cells served as a selection control. The cells were never allowed to reach confluencies above 90% during the selection process. Stably expressing cell lines are identified in this work by outlining the stably expressed construct as a prefix (e.g., FATP4.COS7). For experiments with cells maintained under normal growth conditions (DMEM/FCS), U2OS, COS7, and A431 as well as derivative cell lines were grown on coverslips in 12 wells (30,000/well for COS7 and A431; 50,000/well for U2OS). To induce LD growth, the normal growth medium was supplemented for 24 h with 600 μM (COS7, U2OS) and 100 μM (A431) oleic acid (OA; Sigma, St. Louis, MO) bound to fatty acid-free BSA (Sigma) in a molar ratio of 6:1. For starvation, the cells were incubated in serum-free DMEM (1 g/l glucose) containing 300 μM fatty acid-free BSA (COS7, A431) or 200 μM fatty acid-free BSA (U2OS). For the induction of de novo formed LDs, stably expressing A3Nt.U2OS and A3Nt.COS7 were seeded in 12-well plates (60,000/well) and starved as described above for 24 h. After four washes with PBS, OA:BSA (6:1) was added to the culture medium (FCS-free DMEM, 4.5 g/l glucose) at a final concentration of 600 μM (A3Nt.COS7) and 300 μM OA:BSA (6:1) (A3Nt.U2OS) for 10 min to induce LD formation. Subsequently, the cells were washed three times by dipping the coverslips in beakers filled with PBS and instantly fixed with 4% PFA for 20 min at room temperature. After the indicated treatment, as described above, the cells were washed three times with PBS and fixed. After three additional washes with PBS, the LDs were either stained with BODIPY493/503 (2 μg/ml in PBS, Invitrogen D3922; Invitrogen, Waltham, MA), LD540 [0.25 μg/ml in PBS, kindly provided by Christoph Thiele (32Spandl J. White D.J. Peychl J. Thiele C. Live cell multicolor imaging of lipid droplets with a new dye, LD540.Traffic. 2009; 10: 1579-1584Crossref PubMed Scopus (182) Google Scholar)], or Nile Red [50 μg/ml in PBS; Sigma (33Greenspan P. Mayer E.P. Fowler S.D. Nile red: a selective fluorescent stain for intracellular lipid droplets.J. Cell Biol. 1985; 100: 965-973Crossref PubMed Scopus (1844) Google Scholar)] for 15 min at room temperature. The coverslips were rinsed in PBS shortly for three times and were washed additionally three times for 5 min each in PBS before the coverslips were embedded in MOWIOL (Calbiochem, San Diego, CA). Coverslips with cell lines stably expressing A3Nt-GFP were directly embedded in MOWIOL after fixation. Widefield images were acquired using an Olympus BX41 microscope equipped with a 60× oil immersion PlanApo NA 1.40 objective and an F-view II CCD camera operated by the cell^D software. Emission signals were detected using longpass filters (U-MNG2 for LD540) or bandpass filters (U-M41028 for BODIPY493/503 and Nile Red). The images were saved as a 16-bit TIF image. For each lipophilic dye and metabolic state, 100 images from two experiments were acquired for COS7, U2OS, and A431 (2,700 images in total; Fig. 2, supplemental Figs. S1, S2). For A3Nt.COS7 and A3Nt.U2OS, 150 images from three experiments per condition (starved, after the 10 min OA pulse) were acquired [600 images in total (Fig. 3)]. Furthermore, 150 images for A3Nt.U2OS under normal growth conditions were acquired (supplemental Fig. S3).Fig. 3Detection of de novo formed LDs stained by the LD protein marker, A3Nt-GFP. The LD marker, A3Nt-GFP, was stably expressed by retroviral delivery in COS7 and U2OS cells. The cells were starved for 24 h (0') prior to de novo LD formation induced by an oleate pulse for 10 min (10'). After imaging, the images were either deconvolved (deconv.) or left unprocessed (unproc.). A: Comparative analysis of LD detection methods. LD numbers per cell were determined by ALDQ (unproc. + ALDQ) and compared with manual counting (manual count). Automated counting by ALDQ slightly overestimates the LD number in both A3Nt.COS7 and A3Nt.U2OS cells. Prior deconvolution (deconv. + ALDQ) was used to suppress image noise, but did not result in accuracy changes. Bars represent the mean ± SD from n = 3 independent experiments including 150 cells in total. B: Linear regression. For each cell, the number of the manual count (x axis) was plotted against the automated measurement by ALDQ (y axis). Linear regression indicates a good correlation of manual and automated counts indicated by the shown coefficients of determination (R2). C: Single cell accuracy analysis. Manually determined LD numbers per cell were arbitrarily set to 100% and the corresponding ALDQ-derived number (ALDQ) was calculated as a percentage and plotted. Slim boxes, a median close to 100% and only a few outliers indicate high precision of ALDQ independent of prior deconvolution, which suppressed image noise. Each boxplot includes 150 cells in total from n = 3 independent experiments. D: Preprocessing iterations suppress ER-derived background, which enhances LD contrast. Shown are representative images of the indicated cell lines (input) before (starved) and after (10') the oleate pulse. Cells were processed using 10 or 12 (COS7 10' OA) preprocessing iterations (enhanced). Note that most fluorescent signal by ER-localized A3Nt-GFP is suppressed while LDs (shown in 10' OA insets) are enhanced. Scale bar 10 μm and 2 μm (output).View Large Image Figure ViewerDownload Hi-res image Download (PPT) Confocal microscopy was performed using a Nikon Ti Eclipse microscope equipped with an Ultraview VoX confocal spinning disc system (Perkin Elmer, Waltham, MA) and a 100× Apo PLAN VC, NA 1.40 oil objective. For each cell stained with LD540 (50 cells for each cell line and metabolic state; 300 images in total), 50 z-slices (0.2 μm spacing) were acquired using a 514 nm laser for excitation and a 567 nm emission filter and exported by the Volocity software (Perkin Elmer). For manual assessment of LD numbers, one cell per image was randomly selected and LDs were counted using the multipoint function of ImageJ/Fiji and saved as a region of interest (ROI). In total, 490,627 LDs from 3,450 images were counted manually. To evaluate a possible LD selection bias, a second examiner counted 180 randomly selected images of lipophilic dye-stained cells (6.66% of all images), which resulted in a deviation per cell from the previous examiner of 99.88 ± 12.64% (mean ± SD). The images covered all cell lines and dyes under normal growth conditions and oleate-treated conditions equally. The boxplots resulting from all cells counted by the two examiners are depicted in Fig. 2 and supplemental Figs. S1 and S2. To validate the volume estimation algorithm, 3D rendering of LD540-positive structures in cells either treated with (600 μM OA) or without (DMEM/FCS) 600 μM OA:BSA (6:1) overnight was used. LD540 staining was selected because of its exceptional signal-to-noise ratio (SNR). The cells were imaged with a spinning disc confocal microscope and exported as described above. The resulting z-stacks were converted to Imaris files (.ims) and blindly deconvolved using the Autoquant software (Media Cybernetics, Rockville, MD). The LDs were segmented using the surface detection of thresholded z-stacks by Imaris (version 9.1; Bitplane, Concord, MA) (23Poppelreuther M. Sander S. Minden F. Dietz M.S. Exner T. Du C. Zhang I. Ehehalt F. Knuppel L. Domschke S. et al.The metabolic capacity of lipid droplet localized acyl-CoA synthetase 3 is not sufficient to support local triglyceride synthesis independent of the endoplasmic reticulum in A431 cells.Biochim. Biophys. Acta Mol. Cell Biol. Lipids. 2018; 1863: 614-624Crossref PubMed Scopus (16) Google Scholar). ROIs were drawn for each cell individually. Falsely segmented LDs were manually fused, whereas surfaces that clearly resulted from bright background structures, such as the ER and mitochondria, were removed manually (occurrence of maximum three particles per image in <10% of the cells without oleate treatment). The analysis settings were kept constant for each cell line and for each condition. Snapshots (Figs. 1A, 4A) were taken using the built-in snapshot function of Imaris. To validate the LD detection algorithm, sum intensity projections (SIPs) of raw z-stacks were created and the resulting pixel intensity values were normalized between 0 and 65,535 (16-bit image). The SIP images were used for the volume estimation by the proposed method [automated LD quantification (ALDQ)].Fig. 4Volume estimation by ALDQ. A: Comparison of two volume estimation techniques. The indicated cell lines were treated with oleate for 24 h. Confocal imaging of LD540-stained LDs was followed by 3D rendering (3D) for volume measurement by the Imaris software. The corresponding z-stack was converted to a 2D image (2D, inverted for better visibility) and segmented by a flooding watershed algorithm (segmented) by ALDQ. The same was done for cells cultured without oleate treatment (not shown). Scale bar 10 μm, insets are enlarged 3.5 times. B: Comparison of volume analyses by different quantitation techniques. While the true volume determined by 3D rendering is approximated well in COS7 and U2OS cells by ALDQ, the volume of LDs in A431 supplemented with oleate is heavily underestimated. Bars represent the mean ± SD of 50 analyzed cells. C: Single cell accuracy analysis. The volume determined by ALDQ is plotted as a percentage compared with the volume determined by 3D rendering. Slim boxes representing the 25th to 75th percentile are surrounding the median that is close to 100%, suggesting high accuracy of the volume estimation by ALDQ (with the exception of A431 treated with oleate). Each boxplot corresponds to 50 analyzed cells.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The point spread function was calculated for each LD dye using the BX41 widefield microscope setup and the ImageJ/Fiji plugin "Diffraction PSF 3D" (34Dougherty, R., 2005. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming (AIAA Paper 2005-2961. doi:10.2514/6.2005-2961. 11th AIAA/CEAS Aeroacoustics Conference. Monterey, CA, May 23–25, 2005).Google Scholar). Images were then deconvolved using the "Iterative Deconvolve 3D" plugin (34Dougherty, R., 2005. Extensions of DAMAS and benefits and limitations of deconvolution in beamforming (AIAA Paper 2005-2961. doi:10.2514/6.2005-2961. 11th AIAA/CEAS Aeroacoustics Conference. Monterey, CA, May 23–25, 2005).Google Scholar) with 10 iterations. To ease the localization and separation of LDs in a later step, an iterative operation was applied to increase the signal of LD structures in an image. In each iteration, the difference between the current image and its smoothed version using a Gaussian filter is first calculated. After all negative values are set to zero, the difference is smoothed again by a Gaussian filter. The current image is then updated by adding the smoothed difference and subsequently scaled back to its input image depth. For LD counting, edges are detected (35Phansalkar, N., S., More, A., Sabale, and M., Joshi, . 2011. Adaptive local thresholding for detection of nuclei in diversity stained cytology images (2011 International Conference on Communications and Signal Processing. 218–220. doi:10.1109/ICCSP.2011.5739305).Google Scholar) and local intensity maxima are determined in the preprocessed image. Edge-defined particles were obtained using thresholding followed by hole-filling and watershedding. An edge-defined particle sharing coordinates with a local maximum is then counted as a "true" LD. For the volume estimation, LD edges were detected and processed as described for the number measurement, excluding the watershed step. Edge-defined particles marked with a local maximum are kept while those without a corresponding local maximum are excluded using geodesic reconstruction (36Legland D. Arganda-Carreras I. Andrey P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ.Bioinformatics. 2016; 32: 3532-3534Crossref PubMed Scopus (538) Google Scholar) with the local maxima coordinates as markers. The preprocessed input image is smoothed by Gaussian blurring, and individual LDs are subsequently detected using image flooding (37Sage D. Unser M. Teaching image-processing programming in Java.IEEE Signal Process. Mag. 2003; 20: 43-52Crossref Scopus (63) Google Scholar, 38Tsukahara M. Mitrovic S. Gajdosik V. Margaritondo G. Pournin L. Ramaioli M. Sage D. Hwu Y. Unser M. Liebling T.M. Coupled tomography and distinct-element-method approach to exploring the granular media microstructure in a jamming hourglass.Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 2008; 77: 061306Crossref PubMed Scopus (25) Google Scholar) where the detected edges served as flooding boundaries. The areas of the segmented particles are measured, and the radius and corresponding volume are calculated assuming that the area describes a circle. The bioimage analysis workflow described above was developed as an ImageJ/Fiji (39Schindelin J. Arganda-Carreras I. Frise E. Kaynig V. Longair M. Pietzsch T. Preibisch S. Rueden C. Saalfeld S. Schmid B. et al.Fiji: an open-source platform for biological-image analysis.Nat. Methods. 2012; 9: 676-682Crossref PubMed Scopus (30116) Google Scholar) script using the IJ1 macro language. The script is available via the ImageJ/Fiji update site (http://sites.imagej.net/Fuellekrug-Lab/). The additional plugin used for the image flooding step (http://bigwww.epfl.ch/sage/soft/watershed/) and the MorphoLibJ (36Legland D. Arganda-Carreras I. Andrey P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ.Bioinformatics. 2016; 32: 3532-3534Crossref PubMed Scopus (538) Google Scholar) (ImageJ/Fiji update site "IJPB-plugins," http://sites.imagej.net/IJPB-plugins/) can be downloaded via the same update site as the ImageJ/Fiji script. The results output table displays the number of total local maxima detected, the number of edges, the number of true LDs (overlayed), and the approximated volume. The script saves: i) the raw image; ii) the local maxima image shown as an overlay of green and red dots according to true positive or false positive LDs; iii) the input with labeled edges drawn as yellow ROI outlines; and iv) the segmented particles by flooding if the LD volume was computed. Furthermore, ROIs are saved for the local maxima (true LDs and excluded maxima) and edge coordinates are stored in a .zip file. In addition, a .csv file is saved containing each LD's volume separately allowing a LD volume histogram of each cell (Fig. 5). A detailed and illustrated protocol describing the installation, workflow, and usage of the script is provided as supplemental File F2. Measurement of acyl-CoA synthetase (ACS) activity was performed as described recently (40Füllekrug J. Poppelreuther M. Measurement of long-chain fatty acyl-CoA synthetase activity.Meth
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