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Mar 2019

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Quantification of Mitochondrial Dynamics in Fission Yeast
裂殖酵母内线粒体动力学的定量研究   

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Abstract

Mitochondria are double-membraned organelles responsible for several functions in the cell including energy production, calcium signaling, and cellular metabolism. An equilibrium between fission and fusion events of mitochondria is required for their proper functioning. Mitochondrial morphologies have been quantified in yeast using image processing modules such as MitoGraph and MitoLoc. However, the dynamics of mitochondrial fission and fusion have not been analyzed in these methods. Here, we present a method for measuring mitochondrial morphologies, as well as estimation of fission and fusion frequencies of mitochondria in individual fission yeast cells whose mitochondria are fluorescently-tagged or stained. The latter relies on counting of individual mitochondria upon signal filtering in each frame of a time-lapse. Taken together, we present a simple protocol for analyzing mitochondrial dynamics, which can easily be adopted to other model systems.

Keywords: Mitochondria (线粒体), Mitochondrial dynamics (线粒体动态), Mitochondrial fission (线粒体分裂), Mitochondrial fusion (线粒体融合), Live-cell imaging (活细胞成像), Fluorescence microscopy (荧光显微镜), Cell biology (细胞生物学), Schizosaccharomyces pombe (裂殖酵母)

Background

Mitochondria are essential organelles present in the form of tubular networks within cells and constantly undergo fission and fusion events throughout the cell cycle. The rate of fission and fusion events determine the morphology of the mitochondria and this is a crucial determinant of their function (Mishra and Chan, 2016). Several factors contribute to the changes in morphology of the mitochondria. For instance, mitochondrial fission can be induced by stress, metabolic state, aging and in certain diseases (Sprenger and Langer, 2019). On the contrary, mitochondrial fusion can be induced to enhance cellular respiration in starved conditions and to dilute the effects of damaged mitochondrial DNA that may be present inside the cell (Westermann, 2010). Additionally, mitochondrial dynamics can drastically vary between cell types depending on the metabolic need of the given cell type. Since mitochondrial dynamics and thereby form are direct readouts for mitochondrial and cellular function, it is imperative to accurately measure these properties.

Early quantification efforts involved the use of qualitative descriptors to assess diverse mitochondrial morphologies. In recent times, computational image processing algorithms are being used to robustly quantify the differences in mitochondrial morphologies. For instance, the image processing module ‘MitoLoc’ uses the 3D reconstruction and analysis software, ‘MitoMap’ (a plugin for ImageJ) to define an object using Otsu thresholding and then calculates mitochondrial fragmentation, condensation and tubularity using mitochondrial shape descriptors such as mitochondrial size, position and integrity to differentiate the diverse mitochondrial phenotypes in budding yeast (Vowinckel et al., 2015). MitoLoc calculates the fragmentation of mitochondria using a fragmentation index f, which is defined as the sum of relative fragment volumes that individually constitute less than 20% of the total mitochondrial volume. Another open-source image analysis platform named ‘MitoGraph’ has been employed to quantify mitochondrial morphology in both yeast and mammalian cells (Viana et al., 2015; Harwig et al., 2018).

MitoGraph is an automated image processing software which performs graph theory-based quantitative analysis of mitochondrial networks by calculating the volumes of three-dimensional organelles in living cells. For both of these modules to provide accurate morphological data, it is imperative to utilize the best practices for imaging mitochondria by using the most optimal mitochondrial markers as well as high resolution imaging techniques. However, both of these modules do not provide quantitative measurements for mitochondrial fission and fusion dynamics. Here, we detail a simple method for quantifying both mitochondrial morphologies, as well as fission and fusion dynamics.

Materials and Reagents

  1. Glass-bottom confocal dish, 35 x 10 mm, PP, Sterile (SPL, catalog number: 100350)
  2. Reusable Bottle Top Filter (Tarsons, catalog number: 50060)
  3. Membrane filter with 0.22 µm pore size (Millipore, catalog number: GSWP04700)
  4. Kimwipes disposable wipers (Sigma-Aldrich, catalog number: Z188956)
  5. Schizosaccharomyces pombe cells expressing fluorescently-labeled mitochondria or fission yeast cells stained with MitoTracker dye. Wild-type S. pombe cells can be obtained from National BioResource Project, Japan (https://nbrp.jp)
  6. Lectin from Bandeiraea simplicifolia (Sigma-Aldrich, catalog number: L3759), store at 2-8 °C
  7. Adenine (HiMedia, catalog number: TC082), store below 30 °C
  8. Histidine (HiMedia, catalog number: GRM050), store below 30 °C
  9. Leucine (HiMedia, catalog number: GRM054), store below 30 °C
  10. Uracil (HiMedia, catalog number: GRM264), store below 30 °C
  11. L- Lysine (HiMedia, catalog number: RM3028), store below 30 °C
  12. MitoTrackerTM Orange CMTMRos (Thermo Fisher, catalog number: M7510), store at -5 to -30 °C
  13. MgCl2·6H2O (HiMedia, catalog number: MB040), store below 30 °C
  14. CaCl2·2H2O (HiMedia, catalog number: MB034), store below 30 °C
  15. KCl (HiMedia, catalog number: GRM3934), store below 30 °C
  16. NaCl (HiMedia, catalog number: MB023), store below 30 °C
  17. Na2SO4 (HiMedia, catalog number: MB209), store below 30 °C
  18. Pantothenic acid (HiMedia, catalog number: CMS178), store at 2-8 °C
  19. Nicotinic acid (HiMedia, catalog number: CMS177), store below 30 °C
  20. Inositol (HiMedia, catalog number: GRM102), store below 30 °C
  21. Biotin (HiMedia, catalog number: TC096), store at 2-8 °C
  22. Boric acid (HiMedia, catalog number: MB007), store below 30 °C
  23. MnSO4 (HiMedia, catalog number: GRM1381), store below 30 °C
  24. ZnSO4·7H2O (HiMedia, catalog number: MB233), store below 30 °C
  25. FeCl2·6H2O (HiMedia, catalog number: GRM6353), store below 30 °C
  26. Molybdic acid (Sigma-Aldrich, 232084), store at room temperature
  27. KI (HiMedia, catalog number: GRM252), store below 30 °C
  28. CuSO4·5H2O (HiMedia, catalog number: GRM630), store below 30 °C
  29. Citric acid (HiMedia, catalog number: GRM1023), store below 30 °C
  30. Yeast extract (HiMedia, catalog number: RM027), store below 30 °C
  31. Glucose (Sigma-Aldrich, catalog number: G8270), store at room temperature
  32. Potassium hydrogen phthalate (HiMedia, catalog number: GRM2405), store below 30 °C
  33. Na2HPO4 (HiMedia, catalog number: MB024), store below 30 °C
  34. KH2PO4 (Sigma-Aldrich, catalog number: P5655), store at room temperature
  35. NH4Cl (HiMedia, catalog number: TC092), store below 30 °C
  36. Agar powder, bacteriological (Himedia, catalog number: GRM026), store below 30 °C
  37. NaOH
  38. Phosphate buffered saline (see Recipes)
  39. EMM: Edinburgh Minimal Medium with appropriate supplements (see Recipes)
    1. 50x Salt stock
    2. 1,000x Vitamin stock
    3. 10,000x Mineral stock
  40. YE: Yeast Extract with supplements (see Recipes)

Equipment

  1. Orbital shaking Incubator (Ascension Innovations, model: AI-100)
  2. Laser scanning (point/line) or spinning disk confocal microscope
    We used the confocal mode of an InCell Analyzer 6000 (GE Healthcare) Imaging system with 60x/0.7 N.A. objective, fitted with an sCMOS 5.5MP camera having an x-y pixel separation of 108, and 405, 488, 561 and 640 nm laser lines
  3. Standard vertical autoclave (Equitron, model: 7431STWL.AFE.113)
  4. Laminar airflow chamber

Software

  1. IN Cell Analyzer Acquisition Software v.4.6 (GE Healthcare) or any other confocal microscopy system-specific software
  2. Fiji/ImageJ (http://fiji.sc/Fiji, Schindelin et al., 2012)
  3. MATLAB software v. 2015b or above (MathWorks, https://in.mathworks.com)

Procedure

  1. Preparation of S. pombe cells for imaging
    1. Culture the yeast cells overnight by adding a loopful of cells (that have grown for at least a day in solid YE media) into 1 ml of liquid YE with appropriate supplements. Grow the cells at 30 °C for 12 h at 200 rpm to OD600 of 0.4-0.9.
    2. Subculture 200 μl from the overnight culture into 1 ml of fresh medium and grow at 30 °C for ~2 h prior to imaging at 200 rpm. 
    3. Cover the surface of a 35 mm (No. 1) glass bottom dish with 25% NaOH solution for ~2 h to make the surface hydrophilic. Wash the dish twice with sterilized water and wipe the surface with Kimwipes.
    4. Drop 2 μl of lectin (2 mg/ml in PBS) onto the dish and spread evenly using an inoculation loop.
    5. Allow the lectin to dry completely inside the laminar airflow at room temperature for 10-15 min.
    6. Meanwhile, wash cells once by spinning them down and resuspending them in 1 ml of EMM liquid medium.
    7. If cells are already expressing fluorescently-labeled mitochondria (e.g., Cox4-GFP/RFP), proceed directly to Step A10, else follow Steps A7-A9 below to stain mitochondria with MitoTracker dye.
    8. Dissolve MitoTracker Orange/DeepRed in DMSO at a concentration of 1 mM and dilute it in liquid EMM to a stock solution of 1 µM. This stock solution can be stored at -20 °C.
    9. On the day of the experiment, freshly prepare 200 nM of MitoTracker Orange/DeepRed from the stock solution by diluting with EMM. Incubate cells with 1 ml of 200 nM MitoTracker Orange/DeepRed solution for 15-30 min at 30 °C. 
    10. After incubation, wash the cells thrice in 1 ml of liquid EMM to eliminate background fluorescence from unincorporated dye, and finally resuspend cells in 1 ml of liquid EMM medium.
    11. Transfer 200 μl of cells resuspended in liquid EMM to the lectin-coated glass bottom dish.
    12. Incubate the cell suspension on the glass bottom dish for 10 min at room temperature.
    13. Wash out unbound cells and add 200 μl of liquid EMM to the dish.
    14. Image at room temperature (~25 °C).

  2. Obtaining time-lapse images to observe mitochondrial dynamics
    1. Transfer cells to the stage of the confocal microscope.
    2. Visualize mitochondria labeled with GFP or RFP using the 488 nm and 561 nm laser lines along with 525/20 and 605/52 nm bandpass emission filters respectively. Alternatively, visualize mitochondria stained with the MitoTracker Orange using the 561 nm laser line along with the 605/52 nm bandpass emission filter. The signal-to-noise ratio must be at least 1.5.
    3. Capture mitochondrial dynamics by obtaining time-lapse images that are 12 s apart, for a total time of ~4 min, with Z-stacks for each timepoint with 0.5 µm step-size encompassing the full cell (~5 steps).
    4. Save the time-lapse images in an appropriate format, such as TIFF.

Data analysis

  1. Identification of mitochondria and measurement of mitochondrial parameters in Fiji/ImageJ
    1. Open time-lapse images obtained using Fiji/ImageJ.
    2. Ensure that the pixel to μm conversion for the image files is accurate by checking ‘Set scale’ in Fiji/ImageJ.
    3. Crop individual cells in the field of view.
    4. Apply ‘Mean’ filter with an appropriate radius which is sufficient to reduce background noise, while still maintaining separation between individual mitochondria. In Figure 1, we used a radius of 2 pixels. 
    5. Identify mitochondria in the images either manually or based on intensity thresholding (Figure 1). 


      Figure 1. Analysis of mitochondrial dynamics. Workflow of analysis of the time-lapse images obtained, including cropping, image filtering, identification of mitochondria, measurement of morphology and counting.

    6. If identifying mitochondria manually, draw regions of interest (ROIs) around mitochondria in each frame using the freehand selection tool. 
    7. In the ‘Set measurements’ function of Fiji/ImageJ, check the following options: Shape descriptors, Fit ellipse and Stack Position. 
    8. If identifying mitochondria using intensity thresholding, use the ‘Analyze particles’ function to display mitochondrial measurements, else run the ‘Measure’ function on each timepoint of the time-lapse images.
    9. Save the measurements in appropriate format such as .txt or .csv (Figure 2).


      Figure 2. Snapshots of txt files obtained after identification of mitochondria in a single cell for 2 timepoints (‘Slice’ 1 and 2) of a time-lapse movie. Three mitochondria were identified in Timepoint 1 and four in Timepoint 2. The columns ‘Major’, ‘Circ.’ and ‘AR’ are used for quantification of mitochondrial morphology.

  2. Plotting of mitochondrial morphology parameters and estimation of mitochondrial fission/fusion frequencies using MATLAB
    1. Import the data saved in .txt or .csv format for each yeast cell into MATLAB.
    2. Plot the mitochondrial size distribution after collating the data under the column ‘Major’ of the data files across all timepoints from all the cells. This refers to the length of the ‘Major’ axis of an ellipse fitted to the mitochondria. Use the same dataset to obtain the mean and standard deviation of the mitochondrial sizes across cells. 
    3. Plot the circularity and aspect ratio of mitochondria using the data under the column ‘Circ.’ and ‘AR’ respectively of the data files across all timepoints from all the cells. Again, obtain the mean and standard deviations of the circularity and aspect ratios across cells using the same data set. 
    4. Write a script/function to count the number of mitochondria in each frame of the time-lapse movie by enumerating the number of occurrences of each stack position in the first column of the data file.
    5. Compare the number of mitochondria in consecutive timepoints to obtain fission and fusion events. For instance, if the number of mitochondria in timepoint 1 is 5 and that in timepoint 2 is 7, then the number of fission events is 7 - 5 = 2. 2 ÷ 12 s = 0.167/s. Conversely, if the number of mitochondria in timepoint 1 is 5 and that in timepoint 2 is 4, then the number of fusion events is 5 - 4 = 1 (Figure 1).
    6. Count the total number of fission and fusion events per cell for the entire time-lapse movie to obtain an estimate of fission and fusion frequencies. For example, if the total number of fission and fusion events for a cell in a 228 s time-lapse movie are 8 and 9 respectively, then the fission and fusion frequencies for the cell are 0.035/s and 0.039/s respectively. Obtain the mean and standard deviation of fission and fusion frequencies of all cells.

  3. Statistical analysis of data
    The data for fission and fusion frequencies obtained as described above can be represented in box plots with the central line indicating the median (see Mehta et al., 2019). To obtain the 95% confidence interval of the median, perform one-way ANOVA (‘anova1’ in MATLAB) or Kruskal-Wallis Test (‘kruskalwallis’ in MATLAB). The former is used when data is normally distributed and the latter when data are non-normally distributed. The normality of the dataset is tested using ‘chi2gof’ function in MATLAB. Significant difference (P < 0.05) between control and test groups is then be tested using Tukey’s Honestly Significant Difference procedure (‘multcompare’ in MATLAB).

Recipes

  1. Phosphate buffered saline (10x)
    Amount
    Component
    Final concentration
    80 g/L
    NaCl
    0.137 M
    2 g/L
    KCl
    2.7 mM
    14.4 g/L
    Na2HPO4
    0.01 M
    2.7 g/L
    KH2PO4
    1.8 mM
     Adjust the pH to 7.2
    Filter sterilize the solution by passing it through a bottle top filter containing a 0.22 µm pore size membrane filter and store at room temperature
  2. EMM: Edinburgh Minimal Medium with appropriate supplements
    Note: This recipe is taken from http://www-bcf.usc.edu/~forsburg/media.html.
    Depending on the strain, medium with appropriate supplements is used. For imaging, liquid EMM is most ideal (Forsburg and Rhind, 2006)
    Amount
    Component
    Final concentration
    3 g/L
    potassium hydrogen phthallate
    14.7 mM
    2.2 g/L
    Na2HPO4
    15.5 mM
    5 g/L
    NH4Cl
    93.5 mM
    20 g/L
    glucose
    2% w/v
    20 ml/L
    salts

    1 ml/L
    vitamins

    0.1 ml/L
    minerals

    Minimal supplemented with amino acids: EMM + 225 mg/L supplements (ade, leu, his, lys, ura...) as required (each amino acid at 225 mg/L)
    We make amino acid stock solutions at 7.5 mg/ml (3.75 for uracil) and autoclave the solution for 40 min at 121 °C with 15 psi of pressure

    Stock solutions
    1. 50x Salt stock
      Amount
      Component
      Final concentration
      52.5 g/L
      MgCl2·6H2O
      0.26 M
      0.735 g/L
      CaCl2·2H2O
      4.99 mM
      50 g/L
      KCl
      0.67 M
      2 g/L
      Na2SO4
      14.1 mM
      Filter sterilize the solution by passing it through a bottle top filter containing a 0.22 µm pore size membrane filter and store at 4 °C
    2. 1,000x Vitamin stock
      Amount
      Component
      Final concentration
      1 g/L
      pantothenic acid
      4.20 mM
      10 g/L
      nicotinic acid
      81.2 mM
      10 g/L
      inositol
      55.5 mM
      10 mg/L
      biotin
      40.8 μM
      Filter sterilize the solution by passing it through a bottle top filter containing a 0.22 µm pore size membrane filter and store at 4 °C
    3. 10,000x Mineral stock
      Amount
      Component
      Final concentration
      5 g/L
      boric acid
      80.9 mM
      4 g/L
      MnSO4
      23.7 mM
      4 g/L
      ZnSO4·7H2O
      13.9 mM
      2 g/L
      FeCl2·6H2O
      7.40 mM
      0.4 g/L
      molybdic acid
      2.47 mM
      1 g/L
      KI
      6.02 mM
      0.4 g/L
      CuSO4·5H2O
      1.60 mM
      10 g/L
      Citric acid
      47.6 mM
      Filter sterilize the solution by passing it through a bottle top filter containing a 0.22 µm pore size membrane filter and store at 4 °C
  3. YE: Yeast Extract with supplements
    Amount
    Component
    Final concentration
    5 g/L
    Yeast extract
    0.5% w/v
    30 g/L
    Glucose
    3.0% w/v
    Supplements: 225 mg/L adenine, histidine, leucine, uracil and lysine hydrochloride
    Autoclave the solution for 40 min at 121 °C with 15 psi of pressure. To make solid medium, add 2% (w/v) Difco Bacto Agar prior to autoclaving

Acknowledgments

This research was supported by the Department of Science and Technology (India)-INSPIRE Faculty Award, the Department of Biotechnology (India) Innovative Young Biotechnologist Award, and the Science and Engineering Research Board (SERB, India) Early Career Research Award awarded to V.A. This protocol was adapted from Mehta et al., 2019.

Competing interests

The authors declare that they have no competing interests with the contents of this article.

References

  1. Forsburg, S. L. and Rhind, N. (2006). Basic methods for fission yeast. Yeast 23(3): 173-183.
  2. Harwig, M. C., Viana, M. P., Egner, J. M., Harwig, J. J., Widlansky, M. E., Rafelski, S. M. and Hill, R. B. (2018). Methods for imaging mammalian mitochondrial morphology: A prospective on MitoGraph. Anal Biochem 552: 81-99.
  3. Mehta, K., Chacko, L. A., Chug, M. K., Jhunjhunwala, S. and Ananthanarayanan, V. (2019). Association of mitochondria with microtubules inhibits mitochondrial fission by precluding assembly of the fission protein Dnm1. J Biol Chem 294(10): 3385-3396.
  4. Mishra, P. and Chan, D. C. (2016). Metabolic regulation of mitochondrial dynamics. J Cell Biol 212(4): 379-387.
  5. Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J. Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P. and Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nat Methods 9(7): 676-682.
  6. Sprenger, H. G. and Langer, T. (2019). The good and the bad of mitochondrial breakups. Trends Cell Biol 29(11): 888-900.
  7. Viana, M. P., Lim, S. and Rafelski, S. M. (2015). Quantifying mitochondrial content in living cells. Methods Cell Biol 125: 77-93.
  8. Vowinckel, J., Hartl, J., Butler, R. and Ralser, M. (2015). MitoLoc: A method for the simultaneous quantification of mitochondrial network morphology and membrane potential in single cells. Mitochondrion 24: 77-86.
  9. Westermann, B. (2010). Mitochondrial fusion and fission in cell life and death. Nat Rev Mol Cell Biol 11(12): 872-884.

简介

线粒体是双膜细胞器,负责细胞中的多种功能,包括能量产生,钙信号传导和细胞代谢。线粒体的裂变和融合事件之间的平衡是其正常功能所必需的。已经使用图像处理模块(例如MitoGraph和MitoLoc)在酵母中定量了线粒体形态。但是,尚未在这些方法中分析线粒体分裂和融合的动力学。在这里,我们介绍了一种测量线粒体形态的方法,以及线粒体被荧光标记或染色的单个裂变酵母细胞中线粒体的裂变和融合频率的估计。后者依赖于在时移的每一帧中对信号进行滤波后对单个线粒体的计数。综上所述,我们提出了一种用于分析线粒体动力学的简单协议,可以轻松地将其应用于其他模型系统。


【背景】
线粒体是必需细胞器,以细胞内的管状网络形式存在,并在整个细胞周期中不断经历裂变和融合事件。裂变和融合事件的速率决定了线粒体的形态,这是其功能的关键决定因素(Mishra and Chan,2016)。几个因素有助于线粒体的形态变化。例如,线粒体裂变可由压力,代谢状态,衰老和某些疾病引起(Sprenger and Langer,2019)。相反,在饥饿的情况下,可以诱导线粒体融合以增强细胞呼吸作用,并稀释细胞中可能存在的受损线粒体DNA的作用(Westermann,2010年)。另外,取决于给定细胞类型的代谢需要,线粒体动力学可以在细胞类型之间急剧变化。由于线粒体动力学及其形式是线粒体和细胞功能的直接读数,因此必须准确测量这些特性。

早期的量化工作涉及使用定性描述符来评估各种线粒体形态。近来,计算图像处理算法被用于稳健地量化线粒体形态的差异。例如,图像处理模块”MitoLoc”使用3D重建和分析软件”MitoMap”(ImageJ的插件)使用Otsu阈值法定义对象,然后使用线粒体形状描述符(例如线粒体)计算线粒体碎片,凝结和管状大小,位置和完整性来区分发芽酵母中不同的线粒体表型(Vowinckel et al。,2015)。MitoLoc使用碎片指数f计算线粒体的碎片,碎片指数f定义为相对碎片体积的总和,这些碎片各自构成小于线粒体总体积的20%。另一个名为”MitoGraph”的开源图像分析平台已被用于量化酵母和哺乳动物细胞中的线粒体形态(Viana等,2015; Harwig等。) ,2018)。

MitoGraph是一种自动图像处理软件,可通过计算活细胞中三维细胞器的体积来对线粒体网络进行基于图论的定量分析。为了使这两个模块都能提供准确的形态学数据,必须通过使用最佳的线粒体标记物和高分辨率成像技术,利用最佳实践对线粒体进行成像。但是,这两个模块都不提供线粒体裂变和融合动力学的定量测量。在这里,我们详细介绍了一种简单的量化线粒体形态以及裂变和融合动力学的方法。

关键字:线粒体, 线粒体动态, 线粒体分裂, 线粒体融合, 活细胞成像, 荧光显微镜, 细胞生物学, 裂殖酵母

材料和试剂

  1. 玻璃底共聚焦培养皿,35 x 10 mm,PP,无菌(SPL,货号:100350)
  2. 可重复使用的瓶顶过滤器(Tarsons,目录号:50060)
  3. 孔径为0.22 µm的膜过滤器(Millipore,目录号:GSWP04700)
  4. Kimwipes一次性雨刷器(Sigma-Aldrich,目录号:Z188956)
  5. 表达荧光标记的线粒体或裂殖酵母细胞的MichoTracker染料染色的裂殖酵母(Schizosaccharomyces pombe)细胞。野生型 S。pombe 细胞可以从日本国家生物资源计划中获得( https://nbrp.jp )
  6. Bandeiraea simplicifolia (Sigma-Aldrich,目录号:L3759)的凝集素,在2-8°C下储存
  7. 腺嘌呤(HiMedia,目录号:TC082),储存在30°C以下
  8. 组氨酸(HiMedia,目录号:GRM050),储存在30°C以下
  9. 亮氨酸(HiMedia,目录号:GRM054),低于30°C储存
  10. Uracil(HiMedia,目录号:GRM264),低于30°C储存
  11. L-赖氨酸(HiMedia,目录号:RM3028),低于30°C储存
  12. MitoTracker TM 橙色CMTMRos(Thermo Fisher,货号:M7510),存储在-5至-30°C
  13. MgCl 2 ·6H 2 O(HiMedia,目录号:MB040),在30°C以下储存
  14. CaCl 2 ·2H 2 O(HiMedia,目录号:MB034),在30°C以下储存
  15. KCl(HiMedia,目录号:GRM3934),低于30°C储存
  16. NaCl(HiMedia,目录号:MB023),低于30°C储存
  17. Na 2 SO 4 (HiMedia,目录号:MB209),储存在30°C以下
  18. 泛酸(HiMedia,目录号:CMS178),储存于2-8°C
  19. 烟酸(HiMedia,目录号:CMS177),储存在30°C以下
  20. 肌醇(HiMedia,目录号:GRM102),低于30°C储存
  21. 生物素(HiMedia,目录号:TC096),储存于2-8°C
  22. 硼酸(HiMedia,目录号:MB007),储存在30°C以下
  23. MnSO 4 (HiMedia,目录号:GRM1381),低于30°C储存
  24. ZnSO 4 ·7H 2 O(HiMedia,目录号:MB233),在30°C以下储存
  25. FeCl 2 ·6H 2 O(HiMedia,目录号:GRM6353),在30°C以下储存
  26. 钼酸(Sigma-Aldrich,232084),在室温下保存
  27. KI(HiMedia,目录号:GRM252),低于30°C储存
  28. CuSO 4 ·5H 2 O(HiMedia,目录号:GRM630),存储在30°C以下
  29. 柠檬酸(HiMedia,目录号:GRM1023),低于30°C储存
  30. 酵母提取物(HiMedia,目录号:RM027),储存在30°C以下
  31. 葡萄糖(Sigma-Aldrich,目录号:G8270),在室温下保存
  32. 邻苯二甲酸氢钾(HiMedia,目录号:GRM2405),低于30°C储存
  33. Na 2 HPO 4 (HiMedia,货号:MB024),在30°C以下储存
  34. KH 2 PO 4 (Sigma-Aldrich,目录号:P5655),在室温下保存
  35. NH 4 Cl(HiMedia,货号:TC092),在30°C以下储存
  36. 细菌琼脂粉(Himedia,货号:GRM026),在30°C以下储存
  37. 氢氧化钠
  38. 磷酸盐缓冲盐水(请参阅食谱)
  39. EMM:爱丁堡基本培养基,加上适当的补充剂(请参阅食谱)
    1. 50x盐库存
    2. 1,000x维生素储备
    3. 10,000x矿藏
  40. YE:酵母菌提取物和补品(请参见食谱)

设备

  1. 轨道震动培养箱(Ascension Innovations,型号:AI-100)
  2. 激光扫描(点/线)或旋转盘共聚焦显微镜
    我们使用具有60x / 0.7 NA物镜的InCell Analyzer 6000(GE Healthcare)成像系统的共聚焦模式,该系统装有sCMOS 5.5MP相机,其xy像素间距为108,以及405、488、561和640 nm激光线
  3. 标准立式高压釜(Equitron,型号:7431STWL.AFE.113)
  4. 层流气流室

软件

  1. IN Cell Analyzer采集软件v.4.6(GE Healthcare)或任何其他共聚焦显微镜系统专用软件
  2. 斐济/ ImageJ( http://fiji.sc/Fiji ,辛德尔林等人 ,2012)
  3. MATLAB软件v.2015b或更高版本(MathWorks, https://in.mathworks.com )

程序

  1. 制备 S。成像的绒球细胞
    1. 通过将一圈细胞(在固态YE培养基中已生长至少一天)添加到1毫升带有适当补充剂的液态YE中,将酵母细胞培养过夜。将细胞在30°C下以200 rpm的速度培养12小时,以使OD 600 达到0.4-0.9。
    2. 从过夜培养物中将200μl继代培养到1 ml新鲜培养基中,并在30°C下生长约2 h,然后以200 rpm成像。
    3. 用25%NaOH溶液覆盖35毫米(第1号)玻璃底盘的表面约2小时,以使表面亲水。用无菌水洗碗两次,并用Kimwipes擦拭表面。
    4. 将2μl的凝集素(PBS中2 mg / ml)滴到培养皿上,并使用接种环均匀分散。
    5. 让凝集素在层流内部在室温下完全干燥10-15分钟。
    6. 同时,将细胞旋转下来并重悬于1 ml EMM液体培养基中,以洗涤一次。
    7. 如果细胞已经表达了荧光标记的线粒体(例如,Cox4-GFP / RFP),请直接进行步骤A10,否则请按照下面的步骤A7-A9用MitoTracker染料对线粒体进行染色。
    8. 将MitoTracker Orange / DeepRed以1 mM 的浓度溶于DMSO,然后将其在液体EMM中稀释至1 µM的储备溶液。该储备溶液可以在-20°C下保存。
    9. 实验当天,通过用EMM稀释从储备溶液中新鲜制备200 nM的MitoTracker Orange / DeepRed。在30°C下,将1 ml 200 nM MitoTracker Orange / DeepRed溶液孵育细胞15-30分钟。
    10. 孵育后,将细胞在1 ml液体EMM中洗涤三次,以消除未掺入染料的背景荧光,最后将细胞重悬于1 ml液体EMM培养基中。
    11. 将重悬于液体EMM中的200μl细胞转移至涂有凝集素的玻璃底盘中。
    12. 在室温下在玻璃底盘上孵育细胞悬液10分钟。
    13. 洗净未结合的细胞,并向培养皿中加入200μl液体EMM。
    14. 室温(〜25°C)时的图像。

  2. 获取延时图像以观察线粒体动力学
    1. 将细胞转移到共聚焦显微镜的阶段。
    2. 分别使用488 nm和561 nm激光线以及525/20和605/52 nm带通发射滤光片,可视化标记有GFP或RFP的线粒体。或者,使用561 nm激光线和605/52 nm带通发射滤光片,将被MitoTracker Orange染色的线粒体可视化。信噪比必须至少为1.5。
    3. 通过获取间隔为12 s的延时图像(总时间约为4分钟)来捕获线粒体动力学,每个时间点的Z堆栈的步长为0.5 µm,包括整个单元格(约5步)。
    4. 将延时图像以适当的格式保存,例如TIFF。

数据分析

  1. 斐济/ ImageJ中线粒体的鉴定和线粒体参数的测量
    1. 打开使用斐济/ ImageJ获得的延时图像。
    2. 通过检查斐济/ ImageJ中的“设置比例”,确保图像文件的像素到μm转换准确。
    3. 修剪视野中的单个细胞。
    4. 应用具有适当半径的“均值”滤波器,足以降低背景噪声,同时仍保持各个线粒体之间的距离。在图1中,我们使用了2个像素的半径。
    5. 手动或基于强度阈值识别图像中的线粒体(图1)。


      图1.线粒体动力学分析。分析获取的延时图像的工作流程,包括裁剪,图像过滤,线粒体鉴定,形态学测量和计数。

    6. 如果手动识别线粒体,请使用徒手选择工具在每一帧中绘制线粒体周围的感兴趣区域(ROI)。
    7. 在Fiji / ImageJ的“设置测量值”功能中,检查以下选项:形状描述符,拟合椭圆和堆栈位置。
    8. 如果使用强度阈值识别线粒体,请使用“分析颗粒”功能显示线粒体测量值,否则在延时图像的每个时间点上运行“测量”功能。
    9. 将测量结果保存为适当的格式,例如.txt或.csv(图2)。


      图2。在延时电影的2个时间点(“切片” 1和2)识别单个细胞中的线粒体后获得的txt文件的快照。在时间点1和4中识别出三个线粒体在时间点2中。“主要”,”Circ”列。和'AR'用于定量线粒体形态。

  2. 使用MATLAB绘制线粒体形态参数并估算线粒体裂变/融合频率
    1. 将每个酵母细胞以.txt或.csv格式保存的数据导入MATLAB。
    2. 在整理所有单元中所有时间点的数据文件“主要”列下的数据后,绘制线粒体大小分布图。这是指拟合线粒体的椭圆的“长”轴的长度。使用相同的数据集获得整个细胞线粒体大小的均值和标准差。
    3. 使用”Circ。”列下的数据绘制线粒体的圆度和长宽比。所有单元中所有时间点的数据文件的”AR”和”AR”。同样,请使用相同的数据集获取整个单元的圆度和纵横比的均值和标准差。
    4. 编写脚本/函数,以通过枚举数据文件第一列中每个堆栈位置的出现次数来计数延时影片每一帧中的线粒体数量。
    5. 比较连续时间点的线粒体数量,以获得裂变和融合事件。例如,如果时间点1的线粒体数量为5,时间点2的线粒体数量为7,则裂变事件的数量为7-5 =2。2÷12 s = 0.167 / s。相反,如果时间点1的线粒体数量为5,时间点2的线粒体数量为4,则融合事件的数量为5 – 4 = 1(图1)。
    6. 计算整个延时电影中每个单元的裂变和融合事件的总数,以获得裂变和融合频率的估计值。例如,如果一个228 s延时影片中某个单元的裂变和融合事件总数分别为8和9,则该单元的裂变和融合频率分别为0.035 / s和0.039 / s。获得所有细胞的裂变和融合频率的平均值和标准偏差。
  3. 数据统计分析
    如上所述获得的裂变和融合频率数据可以用方框图表示,中心线表示中位数(见Mehta等,2019)。要获得中位数的95%置信区间,请执行单向ANOVA(在MATLAB中为”anova1”)或Kruskal-Wallis检验(在MATLAB中为”kruskalwallis”)。前者用于正态分布的数据,后者用于非正态分布的数据。使用MATLAB中的”chi2gof”函数测试数据集的正态性。然后使用Tukey的“诚实显着性差异程序”(MATLAB中的”multcompare”)测试对照组和测试组之间的显着性差异( P <0.05)。

菜谱

  1. 磷酸盐缓冲盐水(10x)
    <身体> &nbsp;将pH值调整为7.2
    过滤器通过将溶液通过装有0.22 µm孔径膜过滤器的瓶顶过滤器进行灭菌,并在室温下保存
  2. EMM:爱丁堡基本培养基和适当的补充剂
    注意:此食谱摘自 http:/ /www-bcf.usc.edu/~forsburg/media.html。
    根据菌株,使用具有适当补充剂的培养基。对于成像,液体EMM是最理想的选择(Forsburg and Rhind,2006)
    <身体>
  3. 金额
    组件
    最终浓度
    80克/升
    氯化钠
    0.137 M
    2克/升
    氯化钾
    2.7毫米
    14.4克/升
    Na 2 HPO 4
    0.01 M
    2.7克/升
    KH 2 PO 4
    1.8毫米
    金额
    组件
    最终浓度
    3克/升
    邻苯二甲酸氢钾
    14.7毫米
    2.2克/升
    Na 2 HPO 4
    15.5毫米
    5克/升
    NH 4 Cl
    93.5毫米
    20克/升
    葡萄糖
    2%w / v
    20毫升/升


    1毫升/升
    维生素

    0.1 ml / L
    矿物

    &nbsp;最少补充氨基酸:EMM + 225 mg / L补充剂(ade,leu,his,lys,ura ...)(每个氨基酸225 mg / L)
    我们制备了7.5 mg / ml的氨基酸储备溶液(尿嘧啶为3.75),并在121°C和15 psi的压力下将溶液高压灭菌40分钟

    库存解决方案
    1. 50x盐库存
      <身体>
      金额
      组件
      最终浓度
      52.5克/升
      MgCl 2 ·6H 2 O
      0.26 M
      0.735克/升
      CaCl 2 ·2H 2 O
      4.99毫米
      50克/升
      氯化钾
      0.67百万
      2克/升
      Na 2 SO 4
      14.1毫米
      &nbsp;过滤器通过将溶液通过装有0.22 µm孔径膜过滤器的瓶顶过滤器进行灭菌,并保存在4°C
    2. 1,000x维生素储备
      <身体>
      金额
      组件
      最终浓度
      1克/升
      泛酸
      4.20毫米
      10克/升
      烟酸
      81.2毫米
      10克/升
      肌醇
      55.5毫米
      10 mg / L
      生物素
      40.8μM
      &nbsp;过滤器通过将溶液通过装有0.22 µm孔径膜过滤器的瓶顶过滤器进行灭菌,并保存在4°C
    3. 10,000x矿物库存
      <身体>
      金额
      组件
      最终浓度
      5克/升
      硼酸
      80.9毫米
      4克/升
      MnSO 4
      23.7毫米
      4克/升
      ZnSO 4 ·7H 2 O
      13.9毫米
      2克/升
      FeCl 2 ·6H 2 O
      7.40毫米
      0.4克/升
      钼酸
      2.47毫米
      1克/升
      KI
      6.02毫米
      0.4克/升
      CuSO 4 ·5H 2 O
      1.60毫米
      10克/升
      柠檬酸
      47.6毫米
      &nbsp;过滤器通过将溶液通过装有0.22 µm孔径膜过滤器的瓶顶过滤器进行灭菌,并保存在4°C
    4. YE:酵母菌提取物
      <身体>
      金额
      组件
      最终浓度
      5克/升
      酵母提取物
      0.5%w / v
      30克/升
      葡萄糖
      3.0%w / v
      补充剂:225 mg / L腺嘌呤,组氨酸,亮氨酸,尿嘧啶和赖氨酸盐酸盐
      在121°C和15 psi压力下将溶液高压灭菌40分钟。要制作固体培养基,请在高压灭菌前添加2%(w / v)Difco Bacto琼脂

      致谢

      这项研究得到了科学技术部(印度)-INSPIRE教师奖,生物技术部(印度)创新青年生物技术员奖以及科学与工程研究委员会(印度SERB)早期职业研究奖的支持。该协议改编自Mehta等人,2019年。

      利益争夺

      作者声明,他们与本文的内容没有任何利益冲突。

      参考文献

      1. Forsburg,SL和Rhind,N.(2006)。裂变酵母的基本方法。 酵母 23(3) ):173-183。
      2. Harwig,MC,Viana,MP,Egner,JM,Harwig,JJ,Widlansky,ME,Rafelski,SM和Hill,RB(2018)。哺乳动物线粒体形态成像方法:在MitoGraph上的前瞻性。 肛门生物化学 552:81-99。
      3. Mehta,K.,Chacko,LA,Chug,MK,Jhunjhunwala,S.和Ananthanarayanan,V.(2019)。将线粒体与微管结合可通过阻止裂变蛋白Dnm1的组装来抑制线粒体裂变。 J Biol Chem 294(10):3385-3396。
      4. Mishra,P.和Chan,DC(2016)。线粒体动力学的代谢调节。 J细胞生物学 212 (4):379-387。
      5. Schindelin,J.,Arganda-Carreras,I.,Frise,E.,Kaynig,V.,Longair,M.,Pietzsch,T.,Preibisch,S.,Rueden,C.,Saalfeld,S.,Schmid,B ,Tinevez,JY,White,DJ,Hartenstein,V.,Eliceiri,K.,Tomancak,P。和Cardona,A。(2012)。斐济:用于生物图像分析的开源平台。 Nat方法 9(7):676-682。
      6. Sprenger,HG和Langer,T.(2019)。线粒体破裂的利弊。 趋势是细胞生物学的发展 29(11):888-900。
      7. 维亚纳(Viana),国会议员,林(Sim)和拉夫斯基(Rafelski)(2015)定量活细胞中的线粒体含量。 方法细胞生物学 125:77-93。
      8. Vowinckel,J.,Hartl,J.,Butler,R.和Ralser,M.(2015年)。 MitoLoc:一种同时定量单个细胞中线粒体网络形态和膜电位的方法。 线粒体 24:77-86。
      9. Westermann,B.(2010年)。线粒体融合和裂变在细胞的生命和死亡中。 Nat Rev Mol Cell生物学 11(12):872-884。
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Copyright: © 2019 The Authors; exclusive licensee Bio-protocol LLC.
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Chacko, L. A. and Ananthanarayanan, V. (2019). Quantification of Mitochondrial Dynamics in Fission Yeast. Bio-protocol 9(23): e3450. DOI: 10.21769/BioProtoc.3450.
  2. Mehta, K., Chacko, L. A., Chug, M. K., Jhunjhunwala, S. and Ananthanarayanan, V. (2019). Association of mitochondria with microtubules inhibits mitochondrial fission by precluding assembly of the fission protein Dnm1. J Biol Chem 294(10): 3385-3396.
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