Skip to content

Adipocytes Tools

Jump to bottom
Volker edited this page Jul 31, 2024 · 64 revisions

The Adipocytes Tools help to analyze fat cells in images from histological sections such as this one: example image.

The source code in git-hub can be found here.

Getting started

To install the tools, drag the link Adipocyte_Tools.ijm to the ImageJ launcher window and save it under macros/toolsets in the ImageJ installation. In order to use the greyscale-watershed method (the w-button) you must have the Watershed Algorithm installed.

Select the "MRI Adipocytes Tools" toolset from the >> button of the ImageJ launcher.

toolset

  • the first button (the one with the image) opens this help page
  • the p button runs a preprocessing step that clears the background of the image
  • the s button runs a simple segmentation algorithm on the current image
  • the w button runs a greyscale-watershed segmentation on the current image
  • the l button runs a segmentation for large-magnification images on the current image

Preprocessing

Open an image and press the p button. After a moment of calculation the background should become black. This preprocessing is used in the two segmentation methods when "watershed" is used. Without it the watershed-algorithm would split the background into objects as well. A right click on the p button opens the options dialog.

options

  • min. size is the minimum size a cell must have to be taken into account
  • max. size is the maximum size a cell can have to be taken into account
  • nr. of dilations : to determine the background first the objects are detected. Since the objects are not connected dilate is called the given number of times to connect them
  • thresholding method the thresholding method used to segment the objects.

Simple segmentation

Method

You can run this method by pressing the s button. The simple segmentation will basically run "find edges" and invert the contrast so that cells and the background become clear and membranes dark. Then an automatic threshold is applied and the particle analyzer is used to detect the cells. Cells touching the edges of the image are excluded, since they would falsify the mean area measurements. You will find that often multiple cells appear connected and are counted as one. You can decide to use a binary watershed with this method to get better results. In this case the background is first cleared as in the preprocessing above.

Options

A right click on the s button opens the options dialog. In addition to the options of the preprocessing a "use binary watershed" checkbox can be selected or deselected.

Hints for usage

  1. You can exclude regions from the analysis by making a selection and calling Edit>Clear from ImageJ (supposed that the background color is black).
  2. You can select a detected cell by clicking on its label in the image. This will select the cell in the roi-manager as well.
  3. To delete a cell, select it and press the Delete button on the roi-manager
  4. To add a cell, select the freehand-selection tool, draw the contour of the cell and press Add on the roi-manager
  5. To merge two cells, click on the label of the first cell. Search the index of the second cell in the list in the roi-manager and click on it with Ctrl held down. Click on More>OR (Combine). Now press Delete to remove the old cells. Use the freehand selection tool while holding down Shift to fill the gap between the two parts of the selection that is still on the image and press Add.
  6. To split a cell into two, select it by clicking on its label in the image. Press Delete on the roi manager. Use the freehand selection tool with the Alt key held down to draw a separation. Click on More>Split on the roi-manager.
  7. Make sure that under Analyze>Set Measurements Area is selected. Press the measure button on the roi-manager. Be careful running the macro a second time will delete the results table. You can copy and paste the content into a spreadsheet application.
  8. Options that you set remain valid until you change the current toolset or until you close ImageJ. After that the default-options well be active.
  9. If you select the toolset with Shift held down, the toolset will be opened in a text-editor you can change the default values for the options and save the file.

Watershed segmentation

You can run this method by pressing the w button. A greyscale watershed algorithm is used to separate touching cells. Before the application of the watershed a Gaussian-blur filter is used to smooth the image and to avoid over-segmentation. In the options the additional option sigma for the size of the Gaussian-blur filter must be supplied. The hints in the last section apply for this method as well.

Segmentation of large magnification images

In this case the whole image should be filled with adipocytes. The background intensity across the image risks to be not homogene (i.e. higher in the middle and lower at the borders). Therefore the Fit Polynomial plugin is used to homogenize it. After thresholding the image is eroded to connect the cell membranes. Fill holes is used to get rid of the spots created in this process.

Results

result

Other solutions (third party) for the problem

  1. AdipoQ - A simple toolbox of two ImageJ plugins for quantifying adipocyte morphology and function in tissues and in vitro.

  2. Palomäki, V.A., Koivukangas, V., Meriläinen, S., Lehenkari, P., Karttunen, T.J., 2022. A Straightforward Method for Adipocyte Size and Count Analysis Using Open-source Software QuPath. Adipocyte 11, 99–107. https://doi.org/10.1080/21623945.2022.2027610 - A method to measure adipocytes using QuPath's pixel classifiers, check this out, especially if you are working on whole slide images.

  3. Adiposoft

  4. Zhi, X., Wang, J., Lu, P., Jia, J., Shen, H.-B., and Ning, G. (2018). AdipoCount: A New Software for Automatic Adipocyte Counting. Frontiers in Physiology 9.

  5. Adipocyte_QuPath - An ImageJ plugin (qupath is used to send regions of the slide containing tissue to ImageJ). See: Maguire, A.S., Woodie, L.N., Judd, R.L., Martin, D.R., Greene, M.W., and Graff, E.C. (2020). Whole-slide image analysis outperforms micrograph acquisition for adipocyte size quantification. Adipocyte 9, 567–575.

Publications using this tool

  1. Widjaja, A.A., Lim, W.-W., Viswanathan, S., Chothani, S., Corden, B., Dasan, C.M., Goh, J.W.T., Lim, R., Singh, B.K., Tan, J., et al. (2024). Inhibition of IL-11 signalling extends mammalian healthspan and lifespan. Nature. 10.1038/s41586-024-07701-9.

  2. Osorio-Conles, Ó., Ibarzabal, A., Balibrea, J.M., Vidal, J., Ortega, E., and De Hollanda, A. (2023). FABP4 Expression in Subcutaneous Adipose Tissue Is Independently Associated with Circulating Triglycerides in Obesity. JCM 12, 1013. 10.3390/jcm12031013.

  3. Osorio-Conles, Ó., Olbeyra, R., Vidal, J., Ibarzabal, A., Balibrea, J.M., and De Hollanda, A. (2023). Expression of Adipose Tissue Extracellular Matrix-Related Genes Predicts Weight Loss after Bariatric Surgery. Cells 12, 1262. 10.3390/cells12091262.

  4. Soler-Vázquez, M.C., Romero, M.D.M., Todorcevic, M., Delgado, K., Calatayud, C., Benitez -Amaro, A., La Chica Lhoëst, M.T., Mera, P., Zagmutt, S., Bastías-Pérez, M., et al. (2023). Implantation of CPT1AM-expressing adipocytes reduces obesity and glucose intolerance in mice. Metabolic Engineering 77, 256–272. 10.1016/j.ymben.2023.04.010.

  5. Palomäki, V.A., Lehenkari, P., Meriläinen, S., Karttunen, T.J., and Koivukangas, V. (2023). Dynamics of adipose tissue macrophage populations after gastric bypass surgery. Obesity 31, 184–191. 10.1002/oby.23602.

  6. Osorio-Conles, Ó., Olbeyra, R., Moizé, V., Ibarzabal, A., Giró, O., Viaplana, J., Jiménez, A., Vidal, J., and de Hollanda, A. (2022). Positive Effects of a Mediterranean Diet Supplemented with Almonds on Female Adipose Tissue Biology in Severe Obesity. Nutrients 14, 2617. https://doi.org/10.3390/nu14132617.

  7. Osorio-Conles, Ó., Vega-Beyhart, A., Ibarzabal, A., Balibrea, J.M., Vidal, J., de Hollanda, A., 2022. Biological Determinants of Metabolic Syndrome in Visceral and Subcutaneous Adipose Tissue from Severely Obese Women. IJMS 23, 2394. https://doi.org/10.3390/ijms23042394

  8. Noura El Habbal (2022). Early Life Exposures and Milk Composition Affect Offspring Health. Dissertation. University of Michigan.

  9. Palomäki, V.A., Koivukangas, V., Meriläinen, S., Lehenkari, P., Karttunen, T.J., 2022. A Straightforward Method for Adipocyte Size and Count Analysis Using Open-source Software QuPath. Adipocyte 11, 99–107. https://doi.org/10.1080/21623945.2022.2027610

  10. Suchyta, M., Gibreel, W., Bakri, K., Amer, H., Mardini, S., 2020. Transplanted fat adapts to the environment of the recipient: An animal study using a murine model to investigate the suitability of recipient obesity mismatch in face transplantation. Journal of Plastic, Reconstructive & Aesthetic Surgery 73, 176–183. https://doi.org/10.1016/j.bjps.2019.06.021

  11. Maguire, A.S., Woodie, L.N., Judd, R.L., Martin, D.R., Greene, M.W., and Graff, E.C. (2020). Whole-slide image analysis outperforms micrograph acquisition for adipocyte size quantification. Adipocyte 9, 567–575.

  12. Zhao, Y., Tang, S., Lin, R., Zheng, T., Li, D., Chen, X., Zhu, J., Wen, J., and Deng, Y. (2020). Deoxynivalenol Exposure Suppresses Adipogenesis by Inhibiting the Expression of Peroxisome Proliferator-Activated Receptor Gamma 2 (PPARγ2) in 3T3-L1 Cells. IJMS 21, 6300.

  13. Liu, Z., Dai, X., Zhang, H., Shi, R., Hui, Y., Jin, X., Zhang, W., Wang, L., Wang, Q., Wang, D., et al. (2020). Gut microbiota mediates intermittent-fasting alleviation of diabetes-induced cognitive impairment. Nat Commun 11, 855.

  14. Sonia Rodriguez Fernandez (2019). Characterization of new regulatory mechanisms and metabolic roles for VAV proteins. PhD thesis. Universidad de Salamanca.

  15. Wang, J., Suo, Y., Zhang, J., Zou, Q., Tan, X., Yuan, T., Liu, Z., and Liu, X. (2019). Lycopene supplementation attenuates western diet-induced body weight gain through increasing the expressions of thermogenic/mitochondrial functional genes and improving insulin resistance in the adipose tissue of obese mice. The Journal of Nutritional Biochemistry 69, 63–72.

  16. Yuan, T., Chu, C., Shi, R., Cui, T., Zhang, X., Zhao, Y., Shi, X., Hui, Y., Pan, J., Qian, R., et al. (2019). ApoE-Dependent Protective Effects of Sesamol on High-Fat Diet-Induced Behavioral Disorders: Regulation of the Microbiome-Gut–Brain Axis. J. Agric. Food Chem. 67, 6190–6201.

  17. Lapid, K., Lim, A., Berglund, E.D., and Lu, Y. (2019). Estrogen receptor inhibition enhances cold-induced adipocyte beiging and glucose tolerance. DMSO Volume 12, 1419–1436.

  18. Nguyen, L.V., Ta, Q.V., Dang, T.B., Nguyen, P.H., Nguyen, T., Pham, T.V.H., Nguyen, T.H., Baker, S., Le Tran, T., Yang, D.J., et al. (2019). Carvedilol improves glucose tolerance and insulin sensitivity in treatment of adrenergic overdrive in high fat diet-induced obesity in mice. PLoS ONE 14, e0224674.

  19. Lalwani, A., Warren, J., Liuwantara, D., Hawthorne, W.J., O’Connell, P.J., Gonzalez, F.J., Stokes, R.A., Chen, J., Laybutt, D.R., Craig, M.E., et al. (2019). β Cell Hypoxia-Inducible Factor-1α Is Required for the Prevention of Type 1 Diabetes. Cell Reports 27, 2370-2384.e6.

  20. Lee, C.-C., Shih, Y.-C., Kang, M.-L., Chang, Y.-C., Chuang, L.-M., Devaraj, R., and Juan, L.-J. (2019). Naa10p Inhibits Beige Adipocyte-Mediated Thermogenesis through N-α-acetylation of Pgc1α. Molecular Cell S1097276519305647.

  21. Rossi, E.L., Khatib, S.A., Doerstling, S.S., Bowers, L.W., Pruski, M., Ford, N.A., Glickman, R.D., Niu, M., Yang, P., Cui, Z., et al. (2018). Resveratrol inhibits obesity-associated adipose tissue dysfunction and tumor growth in a mouse model of postmenopausal claudin-low breast cancer. Mol Carcinog 57, 393–407.

  22. Lemecha, M., Morino, K., Imamura, T., Iwasaki, H., Ohashi, N., Ida, S., Sato, D., Sekine, O., Ugi, S., and Maegawa, H. (2018). MiR-494-3p regulates mitochondrial biogenesis and thermogenesis through PGC1-α signalling in beige adipocytes. Sci Rep 8, 15096.

  23. Zhai, X., Lin, D., Zhao, Y., Li, W., and Yang, X. (2018). Enhanced anti-obesity effects of bacterial cellulose combined with konjac glucomannan in high-fat diet-fed C57BL/6J mice. Food & Function 9, 5260–5272.

  24. Xu, Y.X.Z., Ande, S.R., and Mishra, S. (2018). Gonadectomy in Mito-Ob mice revealed a sex-dimorphic relationship between prohibitin and sex steroids in adipose tissue biology and glucose homeostasis. Biology of Sex Differences 9.

  25. Jonathan R Wray (2018). Hypothalamic mechanisms mediating glucocorticoid-induced metabolic effects. University of Manchester.

  26. Khadge, S., Thiele, G.M., Sharp, J.G., McGuire, T.R., Klassen, L.W., Black, P.N., DiRusso, C.C., and Talmadge, J.E. (2018). Long-Chain Omega-3 Polyunsaturated Fatty Acids Modulate Mammary Gland Composition and Inflammation. Journal of Mammary Gland Biology and Neoplasia 23, 43–58.

  27. Kok, B.P., Galmozzi, A., Littlejohn, N.K., Albert, V., Godio, C., Kim, W., Kim, S.M., Bland, J.S., Grayson, N., Fang, M., et al. (2018). Intestinal bitter taste receptor activation alters hormone secretion and imparts metabolic benefits. Molecular Metabolism 16, 76–87.

  28. Porter, K.M. (2018). Impact of the chromatin remodeller SMARCAD1 on murine intestinal intraepithelial lymphocyte and white adipose tissue biology. Apollo - University of Cambridge Repository.

  29. Caratti, G., Iqbal, M., Hunter, L., Kim, D., Wang, P., Vonslow, R.M., Begley, N., Tetley, A.J., Woodburn, J.L., Pariollaud, M., et al. (2018). REVERBa couples the circadian clock to hepatic glucocorticoid action. Journal of Clinical Investigation 128, 4454–4471.

  30. Steven S. Doerstling (2017). Comprehensive molecular characterization of surgical vs. dietary weight loss: impact on mammary tumor burden. The University of North Carolina at Chapel Hill.

  31. Colitti, M., Pošćić, N., and Stefanon, B. (2017). Proliferation and apoptosis in subcutaneous adipose tissue of lactating cows with different genetic merit for milk yield., Tissue and Cell 49, 72–77.

  32. Liu, Z., Qiao, Q., Sun, Y., Chen, Y., Ren, B., and Liu, X. (2017). Sesamol ameliorates diet-induced obesity in C57BL/6J mice and suppresses adipogenesis in 3T3-L1 cells via regulating mitochondria-lipid metabolism. Molecular Nutrition & Food Research 61, 1600717.

  33. YUTA MIZUNO, SHIGETO SENOO, SEIRYO WATANABE, YOICHI TAKENAKA, TAKURO HIRAMATSU, TSUYOSHI GOTO, TERUO KAWADA, and HIDEO MATSUDA (2016). Recognition of the cells in adipose tissue image using Deep learning. IPSJ Technical Report VOL.2016-MPS-107,NO.10. (Japanese)

  34. Giorgio Caratti (2016). Modulation of Glucocorticoid Action in vivo: Role of Lipid Rafts and Clocks. University of Manchester.

  35. Ceddia, R. P. et al. The PGE 2 EP3 Receptor Regulates Diet-Induced Adiposity in Male Mice. Endocrinology 157, 220–232 (2016)

  36. Dalgaard, K., Landgraf, K., Heyne, S., Lempradl, A., Longinotto, J., Gossens, K., Ruf, M., Orthofer, M., Strogantsev, R., Selvaraj, M., et al. (2016). Trim28 Haploinsufficiency Triggers Bi-stable Epigenetic Obesity. Cell 164, 353–364.

  37. McGlashon, Jacob. Serotonin neurons maintain central mechanisms regulating metabolic homeostasis and are vital to thermogenicactivation. PhD (Doctor of Philosophy) thesis, University of Iowa, 2016.

  38. Kauffman, A.S., Thackray, V.G., Ryan, G.E., Tolson, K.P., Glidewell-Kenney, C.A., Semaan, S.J., Poling, M.C., Iwata, N., Breen, K.M., Duleba, A.J., et al. (2015). A Novel Letrozole Model Recapitulates Both the Reproductive and Metabolic Phenotypes of Polycystic Ovary Syndrome in Female Mice. Biology of Reproduction 93.

  39. Lettieri Barbato, D., Tatulli, G., Maria Cannata, S., Bernardini, S., Aquilano, K., and Ciriolo, M.R. (2015). Glutathione Decrement Drives Thermogenic Program In Adipose Cells. Scientific Reports 5.

  40. McGlashon, J.M., Gorecki, M.C., Kozlowski, A.E., Thirnbeck, C.K., Markan, K.R., Leslie, K.L., Kotas, M.E., Potthoff, M.J., Richerson, G.B., and Gillum, M.P. (2015). Central Serotonergic Neurons Activate and Recruit Thermogenic Brown and Beige Fat and Regulate Glucose and Lipid Homeostasis. Cell Metabolism 21, 692–705.

  41. Li, J.J., Ferry, R.J., Diao, S., Xue, B., Bahouth, S.W., and Liao, F.-F. (2015). Nedd4 Haploinsufficient Mice Display Moderate Insulin Resistance, Enhanced Lipolysis, and Protection Against High-Fat Diet-Induced Obesity. Endocrinology 156, 1283–1291.

  42. Celia Lopez Herrera. A novel strategy for the treatment of Obesity using the small molecule ABX300, a modulator of the LMNA gene splicing. Human health and pathology. Université Montpellier, 2015.

  43. Lehmann, Mareike (2014). ARTD1 and poly-ADP-ribosylation enhance PPAR-dependent adipogenesis in vitro and in vivo. Dissertation. University of Zurich.

  44. Li, Jingjing , Effect of Nedd4 Haploinsufficiency on Insulin Sensitivity, Adiposity and Neuronal Behaviors, (2014). Theses and Dissertations (ETD). Paper 141.

Referenced at

  1. The Bridges Lab protocol site
Clone this wiki locally