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Quantification of Bacterial Twitching Motility in Dense Colonies Using Transmitted Light Microscopy and Computational Image Analysis
利用透射光学显微镜技术和计算机图像定量分析密集菌落中的细菌蹭行运动   

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参见作者原研究论文

本实验方案简略版
PLOS Pathogens
May 2017

Abstract

A method was developed to allow the quantification and mapping of relative bacterial twitching motility in dense samples, where tracking of individual bacteria was not feasible. In this approach, movies of bacterial films were acquired using differential interference contrast microscopy (DIC), and bacterial motility was then indirectly quantified by the degree to which the bacteria modulated the intensity of light in the field-of-view over time. This allowed the mapping of areas of relatively high and low motility within a single field-of-view, and comparison of the total distribution of motility between samples.

Keywords: Bacteria (细菌), Pseudomonas aeruginosa (铜绿假单胞菌), Twitching motility (蹭行运动), Quantification (定量), Differential interference contrast microscopy (微分干涉相衬显微镜), Computational image analysis (计算机图像分析)

Background

Pilus-mediated twitching motility represents a form of surface-associated bacterial movement that is independent of flagella. Twitching motility is utilized by many bacterial pathogens, including Neisseria gonorrhoeae and Pseudomonas aeruginosa, to interact with moist surfaces and translocate epithelial barriers. In P. aeruginosa, twitching motility is regulated by a large number of genes which allow both extension and retraction of type IV pili to effectively drag the bacterial cell across any given surface in response to environmental cues (Mattick, 2002; Whitchurch et al., 2004; Burrows, 2005). In our studies of P. aeruginosa pathogenesis, twitching motility contributes to bacterial exit from epithelial cells after internalization and bacterial traversal of multilayered corneal epithelia (Alarcon et al., 2009). In a murine model of corneal infection, twitching motility was important for P. aeruginosa virulence (Zolfaghar et al., 2003). Recently, we discovered that the glycoprotein DMBT1 found in mucosal fluids such as human tears and saliva was capable of inhibiting P. aeruginosa twitching motility (Li et al., 2017). In that study, we utilized a novel method to quickly and robustly quantify P. aeruginosa twitching motility. That protocol is presented herein.

The most direct way to quantify twitching motility would be to track all individual bacteria over time. This method was attempted as part of our original study. However, bacterial colonies have a complex 3D structure, with bacteria regularly traversing one another, making tracking feasible only near the colony edge, resulting in sampling bias. Previous methods for quantifying twitching motility also quantified motility only at the colony edge (Alarcon et al., 2009; Semmler et al., 1999). For our study, we wished to extend those methods to be able to quantify twitching behavior throughout a dense bacterial colony. Instead of focusing on the direction of motility, we focused on quantifying the degree of motility at any given point. This turned out to be a simpler problem to solve, since as bacteria move, they modulate light as they pass through a given point. By mapping out the relative magnitudes of this modulation over time, we were able to generate maps of regions of relatively high and low motility in dense, spatially complex colonies.

Materials and Reagents

  1. Glass slides (Fisher Scientific, Fisherbrand, catalog number: 12-550-15 )
  2. Cover slip (Fisher Scientific, Fisherbrand, catalog number: 12-545M )
  3. Small Petri dish (Corning, Falcon®, catalog number: 351029 )
  4. Large Petri dish (Sigma-Aldrich, catalog number: P5981-100EA)
    Manufacturer: Excel Scientific, catalog number: D-902 .
  5. Kimwipes (KCWW, Kimberly-Clark, catalog number: 34155 )
  6. Inoculation loop (Fisher Scientific, Fisherbrand, catalog number: 22-363-597 )
  7. Sterile wooden toothpick (any brand, autoclave at 121 °C for 15 min in a foil-covered container)
  8. Pseudomonas aeruginosa strain MPAO1 (Dr. Manoil Laboratory, University of Washington, Seattle, WA), or other piliated strain (or bacterial species) with functional twitching motility. (MPAO1 is a wild-type P. aeruginosa strain, and is available from the University of Washington, Seattle, WA. http://www.gs.washington.edu/labs/manoil/libraryindex.htm)
  9. Substance to be tested in the assay (e.g., human tear fluid)
  10. Deionized water
  11. Magnesium sulfate heptahydrate (MgSO4·7H2O) (Fisher Chemical, catalog number: M63-500 )
  12. Gellan gum (Alfa Aesar, catalog number: J63423 )
  13. Tryptone (BD, BactoTM, catalog number: 211705 )
  14. Sodium chloride (NaCl) (Fisher Scientific, Fisher Chemical, catalog number: S271-3 )
  15. Yeast extract (BD, BactoTM, catalog number: 288620 )
  16. Trypticase Soy Agar (TSA) powder (BD, DifcoTM, catalog number: 236950 )
  17. Twitching medium (see Recipes)
  18. Trypticase Soy Agar (TSA) plate (see Recipes)

Equipment

  1. Tweezers (Dumont, Dumoxel Nº5, Fine Science Tools, catalog number: 11252-30 )
  2. Bunsen burner
  3. 3. 60 °C oven (Boekel Scientific, model: 133000 )
  4. Sterile Biosafety Cabinet (NUAIRE, model: NU-425-600 )
  5. Compound microscope with a high NA objective and digital camera (≥ 1.2). Our study used:
    1. Nikon Ti-E inverted wide-field fluorescence microscope (Nikon Instruments, model: Eclipse Ti-E )
    2. Uno-combined controller (Okolab) and stage-top incubation chamber to maintain samples at 37 °C (Okolab, model: H301-Nikon-TI-S-ER )
    3. CFI Plan Apo Lambda 60x NA 1.4 oil objective (Nikon Instrument, model: CFI Plan Apochromat Lambda (λ) Series )
    4. DS-Qi2 Monochrome CMOS Camera (Nikon Instrument, model: DS-Qi2
  6. Computer. MacPro5.1 (2012), 2x 2.4 GHz 6-Core Intel Zeon, 64 GB 1333 MHz DDR 3 ECC

Software

  1. ImageJ (version 1.51n) or FIJI (http://fiji.sc/)
  2. R compiler (version x64 3.4.1) (https://www.r-project.org/)

Procedure

  1. Preparation of slides
    1. Sterilize each glass slide by holding it over a Bunsen flame with tweezers (also sterilized).
    2. Place slides (up to 4) in a large Petri dish.
    3. Quickly pour 1 ml of twitching medium (kept at ~60 °C) onto one surface of each slide and let it spread evenly (by gravity). The approximate thickness of the medium should be 1.5-2 mm.
    4. Place the slide(s) in a biosafety cabinet (BSL2) to cool and dry (20 min). Then, gently clean the underside of the slide with a dry Kimwipe to remove any residual twitching medium.
    5. Draw a circle (~5 mm diameter) on the backside of the slide to mark where to adsorb the substance to be tested (see Figure 1). Up to 3 circles can be drawn per slide.
    6. Drop 5 µl of the substance to be tested onto the medium overlying the circle (Figure 1). Make sure not to touch the medium. A 5 µl volume usually covers a ~5 mm circle.
    7. Dry the slide in a biosafety cabinet (BSL2) (15 min).
    8. If more samples are needed, Steps A6 and A7 can be repeated.


    Figure 1. Experimental setup and images of the twitching assay. A. Schematic showing the experimental setup. Twitching medium (~60 °C) is poured onto a glass slide and allowed to cool and dry in a biosafety cabinet (BSL2) for 20 min. After adding the substance to be tested, the slide is dried again in the biosafety cabinet for 15 min before inoculation with bacteria. A coverslip is then placed over the inoculated substance to be tested, and the slide incubated for 4 h at 37 °C in a moist environment. B. Example of an inoculated slide with coverslip showing three circles, within each a replicate of the substance being tested. C. Example of a large Petri dish containing four inoculated slides with coverslips (e.g., a control slide versus three slides comparing different substances, or different concentrations of the same substance). Two wet paper towels are included to prevent drying of the twitching medium during the 4 h incubation. Although not shown, the Petri dish is covered with its lid during incubation to maintain moisture.

  2. Inoculate twitching medium
    1. From an overnight growth on TSA plates, scrape a few colonies with an inoculation loop and mix together on the plate. This helps to make sure that all inoculations originate from a homogeneous mix of bacteria, and not a single clone, reducing possible motility differences due to phenotypic variation. However, single colonies can be tested if suited to study goals.
    2. Transfer a very small amount of bacteria (~0.5 mm diameter) to the tip of a sterile toothpick.
    3. Use the toothpick to apply the bacteria lightly on the slide in the center of the circles, without piercing the twitching medium. A single, central point of inoculation is required.
    4. Use sterile tweezers to lay a coverslip over the medium and point of inoculation (don’t press down onto the inoculated point) (Figure 1).
    5. Wet a rolled paper towel with deionized water. Put in the large Petri dish with the slides to keep them moist (Figure 1). Cover the Petri dish.
    6. Incubate for 4 h at 37 °C.

  3. Imaging
    After the incubation in Step B6, acquire time-lapse movies of the bacteria at the edge of the point of inoculation (10 sec interval for 5 min duration) at 37 °C, using a 60x/1.4NA oil-immersion lens with differential interference contrast (DIC).

Data analysis

DIC movies of bacterial twitching motility were computationally analyzed using ImageJ. The code used for our analysis has been made available in a Github repository for reference (Smith, 2017). Specifically, the analysis normalizes the contrast across the field of view and between frames. This then allows for the indirect quantification of bacterial motility as the degree of light modulation over time, where regions of high motility will modulate light more than regions low motility. Maps of relative bacterial motility were created by taking the standard deviation of the intensity in the processed movies over time (see example, Figure 2). The total motility in each sample was then compared across experiments by plotting the distribution of the standard deviation as a notched box plot (see example, Figure 3), where two distributions are considered significantly different if the notches do not overlap.


Figure 2. Creating motility maps from the raw movies. The raw movies are processed with a band-pass filter to normalize the contrast across the field of view, and to enhance the contrast specific to individual bacteria. A motility map is then generated by taking a standard deviation Z-projection of the processed movie. Scale bars = 50 μm.


Figure 3. Comparing motility distributions between samples. The motility distributions between samples can be compared using notched box-plots, where the notches represent a 95% confidence interval for each distribution.

  1. Open the original raw movie in FIJI or ImageJ.
  2. Use a 2D median filter (Process → Filters → Median) to remove any hot pixels and other single pixel outliers from the movie (for our data, a median filter with a radius of 2.0 pixels worked best).
  3. Use a 2D band-pass filter (Process → FFT → Band-pass Filter) to normalize any vignetting and/or other low spatial frequency background non-uniformities, as well as remove high spatial frequency noise (for our data, we used a lower bound of 40 and an upper bound of 2). These numbers will need to be adjusted according to the sample and microscope, with the lower bound primarily affected by bacterial size (with smaller bacteria, e.g., Staphylococcus aureus possibly requiring a smaller lower bound), and the upper bound primarily affected by the ratio of the digital resolution/optical resolution, where the larger the ratio (i.e., where the camera resolution is much greater than the optical resolution) the larger the upper bound. The purpose of the band-pass is to remove any spatial frequencies that do not pertain to individual bacteria, therefore greatly suppressing any convolved modulations.
  4. Create a standard deviation Z-projection (Image → Stacks → Z project) of the movie to create a map of the magnitude of bacterial motility across the field of view.
  5. Generate a histogram of the standard deviation map (Analyze → Histogram) to get a distribution of bacterial motility within the field of view.
  6. Since the motility distribution may not be normal and instead heavily skewed, the distributions can be compared using notched boxplots (Figure 3). Notched boxplots are especially helpful when there are more than two distributions to compare in a set, as the notches allow for quick visualization of significant differences between all-possible pairwise combinations of samples (Krzywinski and Altman, 2014).

Notes

  1. For each substance tested, we typically used three replicates on a single slide. Each experiment would also include a twitching motility positive-control slide with three replicates. We did not observe a large difference in motility between replicate inoculations; however, if the colonies were too large, the colony edges could be difficult to discern.
  2. The noise from the camera and variation in the light source over time will also result in small modulations in intensity in the movie. Therefore, a minimum cutoff for the standard deviation needs to be chosen that completely excludes the background while not removing bacterial modulation. In our setup, we found that a cutoff of 5.0 worked best.
  3. To make the motility maps more clear, we used the ‘physics’ LUT provided with ImageJ (Image → Lookup Tables → Physics), which gives a spectral heat-map making differences in local motility more apparent. For added clarity, we set pixels with 0 intensity to black (Image → Color → Edit LUT).
  4. In order to use the scripts provided in the Github repository, first, go to the provided link and click on “Clone or download” and download the repository as a zip file. Unzip the files, then run the files in the following order:
    1. Drag and drop the file entitled “Measure stack standard deviation.ijm” into FIJI/ImageJ. This will load the code into the macro IDE. Then press “Run” in the IDE window. This will pull-up a user interface that will ask for the directory that contains all of your images to be analyzed, and then prompt you for the directory where the results should be saved. You will then be prompted to enter the lower and upper standard deviation cut-offs for the data. The code will automatically process the data and output a histogram for each sample into the output directory in a file called “Sample histogram.csv”. NOTE: The current code will only process *.nd2 files; this extension can be changed on line 3 of the macro.
    2. Open the file entitled “Boxplot code – R.r” in a text editor, and change the directory on line 8 to the directory that contains the histogram from (a). Then copy and paste the code into the R compiler and it will automatically create notched boxplots from the histograms.
      Note: If the minimum and/or maximum standard deviation cut-offs were changed from the default values in the macro, they will have to be updated accordingly in line 5 in the R code. If you forgot the range, the bin values are stored in the output directory in a file called “Histogram bins.csv.”. 

Recipes

  1. Twitching medium
    1. Dissolve 0.1 g MgSO4·7H2O, 0.8 g Gellan gum, 0.4 g tryptone, 0.2 g yeast extract and 0.2 g NaCl in ~80 ml of distilled H2O and shake until the solutes have dissolved
    2. Adjust the final volume of the solution to 100 ml with H2O
    3. Sterilize by autoclaving in the liquid cycle. Keep the medium at 60 °C until pouring to prevent solidification
  2. Trypticase Soy Agar (TSA) plate
    1. Suspend 40 g of TSA powder in 1 L of purified water. Mix thoroughly and sterilize by autoclaving in the liquid cycle
    2. Cool to ~50 °C and pour approximately 20 ml into each small Petri dish on a level surface
    3. Cover the Petri dish with its lid and let solidify at room temperature (~2 h or until solid and dry)

Acknowledgments

This protocol was originally presented in Li et al., 2017, PLoS Pathogens. This work was supported by the National Institutes of Health; EY011221 (SMJF), EY024060 (SMJF) and EY003176. The authors have no conflicts of interest to declare. P. aeruginosa strain MPAO1 was obtained from the University of Washington (Dr. Manoil laboratory), Seattle, WA where it was previously used to generate the P. aeruginosa PAO1 transposon mutant library funded by NIH P30 DK089507.

References

  1. Alarcon, I., Evans, D. J. and Fleiszig, S. M. (2009). The role of twitching motility in Pseudomonas aeruginosa exit from and translocation of corneal epithelial cells. Invest Ophthalmol Vis Sci 50(5): 2237-2244.
  2. Burrows, L. L. (2005). Weapons of mass retraction. Mol Microbiol 57(4): 878-888.
  3. Krzywinski, M. and Altman, N. (2014). Visualizing samples with box plots. Nat Methods 11(2): 119-120.
  4. Li, J., Metruccio, M. M. E., Smith, B. E., Evans, D. J. and Fleiszig, S. M. J. (2017). Mucosal fluid glycoprotein DMBT1 suppresses twitching motility and virulence of the opportunistic pathogen Pseudomonas aeruginosa. PLoS Pathog 13(5): e1006392.
  5. Mattick, J. S. (2002). Type IV pili and twitching motility. Annu Rev Microbiol 56: 289-314.
  6. Semmler, A. B., Whitchurch, C. B. and Mattick, J. S. (1999). A re-examination of twitching motility in Pseudomonas aeruginosa. Microbiology 145 (Pt 10): 2863-2873.
  7. Smith, B. E. (2017). Bacterial-Twitching-Quantification. Github repository. https://github.com/Llamero/Bacterial-Twitching-Quantification.
  8. Whitchurch, C. B., Leech, A. J., Young, M. D., Kennedy, D., Sargent, J. L., Bertrand, J. J., Semmler, A. B., Mellick, A. S., Martin, P. R., Alm, R. A., Hobbs, M., Beatson, S. A., Huang, B., Nguyen, L., Commolli, J. C., Engel, J. N., Darzins, A. and Mattick, J. S. (2004). Characterization of a complex chemosensory signal transduction system which controls twitching motility in Pseudomonas aeruginosa. Mol Microbiol 52(3): 873-893.
  9. Zolfaghar, I., Evans, D. J. and Fleiszig, S. M. (2003). Twitching motility contributes to the role of pili in corneal infection caused by Pseudomonas aeruginosa. Infect Immun 71(9): 5389-5393.

简介

开发了一种方法,可以对密集样本中的相对细菌抽动动力进行定量和绘图,在这些样本中追踪单个细菌是不可行的。 在这种方法中,使用微分干涉对比显微镜(DIC)获得细菌膜的电影,然后通过细菌随时间调节视场中的光强度的程度间接量化细菌运动。 这允许在单个视场内绘制相对较高和较低运动性的区域,并比较样本之间运动的总分布。

【背景】Pilus介导的颤动运动表示与鞭毛无关的与表面相关的细菌运动形式。抽动动力被很多细菌病原体利用,包括淋病奈瑟氏球菌和铜绿假单胞菌与潮湿的表面相互作用并移位上皮屏障。在 P。颤动动力受大量基因调控,这些基因允许IV型菌毛的延伸和回缩,以有效地将细菌细胞拖过任何给定的表面以响应环境提示(Mattick,2002; Whitchurch et al。,2004; Burrows,2005)。在我们对 P的研究中。绿脓杆菌发病机制,抽动运动性有助于细菌在内化和多层角膜上皮细菌穿过后从上皮细胞排出(Alarcon等人,2009)。在角膜感染的小鼠模型中,抽动运动对于P是重要的。绿脓杆菌毒力(Zolfaghar et al。,2003)。最近,我们发现在粘膜液体如人眼泪和唾液中发现的糖蛋白DMBT1能够抑制P细胞。绿脓杆菌抽动动力(Li等人,2017)。在那项研究中,我们利用了一种新方法来快速和可靠地量化P.绿脓杆菌抽动动力。该协议在此处介绍。

量化抽动动力的最直接方法是随着时间的推移追踪所有个体细菌。这种方法是作为我们原始研究的一部分尝试的。然而,细菌菌落具有复杂的三维结构,细菌经常相互穿越,使得只能在菌落边缘附近进行追踪,从而导致采样偏差。先前用于量化颤动运动的方法也仅在集落边缘量化运动性(Alarcon等人,2009; Semmler等人,1999)。对于我们的研究,我们希望扩展这些方法,以便能够量化整个密集细菌群体中的抽搐行为。我们不是专注于动力方向,而是专注于量化任何给定点的运动程度。事实证明,这是一个更简单的问题,因为随着细菌的移动,它们会在光线通过给定点时对其进行调制。通过绘制出这种调制随时间的相对大小,我们能够在密集的,空间复杂的菌落中产生相对较高和较低的动力区域的图谱。

关键字:细菌, 铜绿假单胞菌, 蹭行运动, 定量, 微分干涉相衬显微镜, 计算机图像分析

材料和试剂

  1. 玻璃载玻片(Fisher Scientific,Fisherbrand,目录号:12-550-15)
  2. 封面纸(Fisher Scientific,Fisherbrand,产品目录号:12-545M)
  3. 小培养皿(Corning,Falcon ,目录号:351029)
  4. 大培养皿(Sigma-Aldrich,目录号:P5981-100EA)
    制造商:Excel Scientific,产品目录号:D-902。
  5. Kimwipes(KCWW,Kimberly-Clark,目录号:34155)
  6. 接种环(Fisher Scientific,Fisherbrand,目录号:22-363-597)
  7. 无菌木制牙签(任何品牌,121°C高压灭菌器中15分钟,盖在铝箔容器中)
  8. 铜绿假单胞菌菌株MPAO1(华盛顿州西雅图市华盛顿大学的Manoil实验室博士)或具有功能性抽动动力的其他纤毛菌株(或细菌菌种)。 (MPAO1是野生型铜绿假单胞菌菌株,可从华盛顿州西雅图的华盛顿大学获得。 http://www.gs.washington.edu/labs/manoil/libraryindex.htm
  9. (例如,人类泪液)
    测试物质
  10. 去离子水
  11. 硫酸镁七水合物(MgSO 4·7H 2 O)(Fisher Chemical,目录号:M63-500)
  12. 结冷胶(Alfa Aesar,目录号:J63423)
  13. 胰蛋白胨(BD,Bacto TM TM,目录号:211705)
  14. 氯化钠(NaCl)(Fisher Scientific,Fisher Chemical,目录号:S271-3)
  15. 酵母提取物(BD,Bacto TM,目录号:288620)
  16. 胰蛋白胨大豆琼脂(TSA)粉末(BD,Difco TM,目录号:236950)
  17. 抽动媒介(见食谱)
  18. 胰蛋白胨大豆琼脂(TSA)平板(见食谱)

设备

  1. 镊子(Dumont,DumoxelNº5,Fine Science Tools,目录号:11252-30)
  2. 本生燃烧器
  3. 3. 60°C烤箱(Boekel Scientific,型号:133000)
  4. 无菌生物安全柜(NUAIRE,型号:NU-425-600)
  5. 具有高NA物镜和数码相机的复合显微镜(≥1.2)。我们的研究使用:
    1. 尼康Ti-E倒置式宽视野荧光显微镜(Nikon Instruments,型号:Eclipse Ti-E)
    2. Uno-combined控制器(Okolab)和台式培养箱保持样品在37°C(Okolab,型号:H301-Nikon-TI-S-ER)
    3. CFI Plan Apo Lambda 60x NA 1.4油品(Nikon Instrument,型号:CFI Plan Apochromat Lambda(λ)系列)
    4. DS-Qi2单色CMOS相机(尼康仪器,型号:DS-Qi2) 
  6. 电脑。 MacPro5.1(2012),2x 2.4 GHz 6核Intel Zeon,64 GB 1333 MHz DDR 3 ECC

软件

  1. ImageJ(版本1.51n)或FIJI( http://fiji.sc/
  2. R编译器(版本x64 3.4.1)( https://www.r-project.org/

程序

  1. 幻灯片的制备

    1. 用镊子(也可以消毒)将本品放在本生灯上灭菌
    2. 将幻灯片(最多4个)放入大培养皿中。
    3. 迅速将1 ml抽搐介质(保持在〜60°C)倒在每张载玻片的一个表面上,并使其均匀铺展(通过重力)。
      介质的近似厚度应为1.5-2毫米。
    4. 将载玻片放入生物安全柜(BSL2)中冷却并晾干(20分钟)。然后,用干燥的Kimwipe轻轻地清洁载玻片的下侧以除去任何残留的抽动介质。
    5. 在载玻片的背面画一个圆圈(〜5mm直径)以标记要吸附待测物质的位置(见图1)。每张幻灯片最多可绘制3个圆圈。
    6. 将5μl要测试的物质滴到覆盖在圆上的介质上(图1)。确保不要触摸介质。
      5μl体积通常覆盖约5 mm的圆圈

    7. 在生物安全柜(BSL2)中干燥载玻片(15分钟)。
    8. 如果需要更多样品,可以重复步骤A6和A7。


    图1.抽搐试验的实验设置和图像。 :一种。示意图显示了实验装置。将抽动介质(〜60°C)倒入载玻片上,使其在生物安全柜(BSL2)中冷却并干燥20分钟。加入待测物质后,在接种细菌前,将载玻片在生物安全柜中再次干燥15分钟。然后将盖玻片置于待测试的接种物质上,并将该玻片在潮湿的环境中在37℃温育4小时。 B.带有盖玻片的接种载玻片的实例显示三个圆圈,在每个被测物质的重复内。 C.含有4个带盖玻片的接种载玻片(比如不同物质或不同浓度的相同物质的对照载玻片与三个载玻片)的大培养皿的实例。包括两张湿纸巾以防止在4小时孵育期间抽搐介质的干燥。尽管没有显示,培养皿在孵化过程中盖上盖子以保持湿度。

  2. 接种抽搐介质
    1. 从TSA平板上的过夜生长中,用接种环刮一些菌落并在平板上混合。这有助于确保所有接种源自细菌的均质混合物,而不是单个克隆,从而减少由于表型变异导致的可能的运动性差异。但是,如果适合研究目标,可以测试单个菌落。

    2. 。将极少量的细菌(直径约0.5毫米)转移到无菌牙签的尖端。
    3. 使用牙签将细菌轻轻涂抹在圆形中心的玻片上,而不刺穿抽搐介质。需要接种一个中心点。
    4. 使用无菌镊子在培养基和接种点上放置盖玻片(不要按压到接种点)(图1)。
    5. 用去离子水湿卷纸巾。放入带有载玻片的大培养皿以保持湿润(图1)。覆盖培养皿。

    6. 在37°C孵育4小时
  3. 成像
    在步骤B6中温育后,使用具有微分干涉的60x / 1.4NA油浸透镜,在37℃下在接种点边缘(10秒间隔持续5分钟)获取细菌的延时电影对比度(DIC)。

数据分析

使用ImageJ对DIC细菌颤动动力的电影进行计算分析。用于我们分析的代码已经在Github存储库中提供以供参考(Smith,2017)。具体而言,分析使视野和帧之间的对比度标准化。这允许间接定量细菌运动性作为随时间的光调制程度,其中高运动性的区域将调节光比区域低运动性更多。通过在处理过的电影中随时间变化的强度的标准偏差创建相对细菌运动的地图(参见示例,图2)。然后通过将标准偏差的分布作为缺口盒图(见图3)比较各样品中的总动力,其中如果缺口不重叠,则认为两种分布显着不同。

图2.从原始影片创建动态贴图使用带通滤波器处理原始影片,以规范视野范围内的对比度,并增强特定于个别细菌的对比度。然后通过对处理后的电影进行标准偏差Z投影来生成运动图。比例尺= 50微米。


图3.比较样本之间的运动分布样本之间的运动分布可以使用缺口盒形图进行比较,其中缺口表示每个分布的95%置信区间。

  1. 在FIJI或ImageJ中打开原始原始影片。
  2. 使用二维中值滤波器(过程→滤波器→中值)去除电影中的热像素和其他单像素异常值(对于我们的数据,半径为2.0像素的中值滤波器效果最好)。
  3. 使用2D带通滤波器(过程→FFT→带通滤波器)来规范任何渐晕和/或其他低空间频率背景不均匀性,以及消除高空间频率噪声(对于我们的数据,我们使用较低的40的界限和2的上界)。这些数字需要根据样品和显微镜进行调整,下限主要受细菌大小的影响(对于较小的细菌,例如金黄色葡萄球菌可能需要较小的细菌下界),并且上限主要受数字分辨率/光学分辨率的比率影响,其中比率越大(其中相机分辨率远大于光学分辨率)比例越大上限。带通的目的是消除任何与个别细菌无关的空间频率,从而大大抑制任何卷积调制。
  4. 创建电影的标准偏差Z投影(图像→叠加→Z项目),以创建整个视野的细菌运动强度图。
  5. 生成标准偏差图的直方图(分析→直方图),以获得视野内细菌运动的分布。
  6. 由于动力分布可能不正常,而是严重偏斜,因此可以使用缺口箱形图比较分布(图3)。因为缺口允许快速查看所有可能的成对样本组合(Krzywinski和Altman,2014),因此缺口盒图非常有用,因为缺口可以快速显示样本中所有可能的成对组合。

笔记

  1. 对于每种测试物质,我们通常在一张幻灯片上使用三次重复。每个实验还包括具有三次重复的抽动运动阳性对照载玻片。我们没有观察到重复接种之间动力的巨大差异;然而,如果殖民地太大,殖民地边缘可能难以辨别。
  2. 来自相机的噪音和光源随时间的变化也会导致电影中强度的小调制。因此,需要选择标准偏差的最小截止值,以完全排除背景而不去除细菌调节。在我们的设置中,我们发现5.0的截断效果最好。
  3. 为了使动力映射更加清晰,我们使用了ImageJ提供的'物理'LUT(图像→查找表→物理),它给出了一个谱图热图,使局部运动的差异更加明显。为了增加清晰度,我们将0强度的像素设置为黑色(图像→颜色→编辑LUT)。
  4. 为了使用Github存储库中提供的脚本,首先,请转到提供的链接,然后单击“克隆或下载”并将存储库作为zip文件下载。解压缩文件,然后按以下顺序运行文件:
    1. 将名为“Measure stack standard deviation.ijm”的文件拖放到FIJI / ImageJ中。这会将代码加载到宏IDE中。然后在IDE窗口中按“运行”。这将拉起一个用户界面,该用户界面将要求包含要分析的所有图像的目录,然后提示您输入保存结果的目录。随后会提示您输入数据的较低和较高标准偏差截止值。代码将自动处理数据,并将每个样本的直方图输出到名为“Sample histogram.csv”的文件中的输出目录中。注意:当前的代码只能处理* .nd2文件;这个扩展可以在宏的第3行进行更改。
    2. 在文本编辑器中打开名为“Boxplot code - R.r”的文件,并将第8行上的目录更改为包含(a)中的直方图的目录。然后将代码复制并粘贴到R编译器中,它会自动从直方图创建缺口箱形图。
      注意:如果最小和/或最大标准偏差截止值已经从宏中的默认值改变,那么它们将不得不在R代码的第5行中相应地更新。如果您忘记了该范围,则二进制值将被存储在名为“Histogram bins.csv”的文件中的输出目录中。 

食谱

  1. 抽动媒介
    1. 在〜80ml蒸馏水中溶解0.1g MgSO 4·7H 2 O,0.8g结冷胶,0.4g胰蛋白胨,0.2g酵母提取物和0.2g NaCl, > 2 O并摇动,直至溶质溶解
    2. 用H 2 O
      调整溶液的最终体积至100ml
    3. 在液体循环中通过高压灭菌进行灭菌。将介质保持在60°C直至倾倒以防止凝固
  2. 胰蛋白胨大豆琼脂(TSA)平板
    1. 将40克TSA粉末悬浮在1升纯化水中。
      在液体循环中彻底混合并通过高压灭菌进行灭菌
    2. 冷却至约50°C,将约20毫升倒入水平表面上的每个小培养皿中。
    3. 用盖子覆盖培养皿并在室温下固化(〜2小时或直至固体和干燥)

致谢

该协议最初在2017年的PLoS Pathogens的Li等人中介绍。这项工作得到了国立卫生研究院的支持; EY011221(SMJF),EY024060(SMJF)和EY003176。作者没有利益冲突要申报。 P上。铜绿假单胞菌菌株MPAO1获自华盛顿大学(Dr.Monoil实验室),西雅图,华盛顿州,之前曾用于产生磷。由NIH P30 DK089507资助的绿脓杆菌PAO1转座子突变体文库。

参考

  1. Alarcon,I.,Evans,D.J。和Fleiszig,S.M。(2009)。 颤动动力在<铜绿假单胞菌退出角膜和移位角膜上皮细胞。 Invest Ophthalmol Vis Sci 50(5):2237-2244。
  2. Burrows,L. L.(2005)。 大规模回缩武器 Mol Microbiol 57(4) ):878-888。
  3. Krzywinski,M.和Altman,N。(2014)。 用箱形图可视化样本 Nat Methods 11( 2):119-120。
  4. Li,J.,Metruccio,M.M.E.,Smith,B.E.,Evans,D.J。和Fleiszig,S.M.J.(2017)。 粘膜液糖蛋白DMBT1可抑制机会性病原体的抽动动力和毒力<铜绿假单胞菌。 PLoS Pathog 13(5):e1006392。
  5. Mattick,J. S.(2002)。 IV型菌毛和抽动动力 Annu Rev Microbiol 56:289-314。
  6. Semmler,A.B.,Whitchurch,C.B。和Mattick,J.S。(1999)。 重新检查绿脓杆菌中的抽动动力。 > 微生物学 145(Pt 10):2863-2873。
  7. Smith,B.E。(2017)。细菌抽动,定量。 Github存储库。 https://github.com/Llamero/Bacterial-Twitching-Quantification。 >
  8. Whitchurch,CB,Leech,AJ,Young,MD,Kennedy,D.,Sargent,JL,Bertrand,JJ,Semmler,AB,Mellick,AS,Martin,PR,Alm,RA,Hobbs,M.,Beatson,SA, Huang,B.,Nguyen,L.,Commolli,JC,Engel,JN,Darzins,A.和Mattick,JS(2004)。 复杂化学感应信号转导系统的特征,控制绿脓杆菌中的抽动运动。 Mol Microbiol 52(3):873-893。
  9. Zolfaghar,I.,Evans,D.J。和Fleiszig,S.M。(2003)。 抽动动力有助于纤毛在由铜绿假单胞菌引起的角膜感染中的作用。 感染免疫 71(9):5389-5393。
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Copyright: © 2018 The Authors; exclusive licensee Bio-protocol LLC.
引用:Smith, B. E., Li, J., Metruccio, M., Wan, S., Evans, D. J. and Fleiszig, S. M. (2018). Quantification of Bacterial Twitching Motility in Dense Colonies Using Transmitted Light Microscopy and Computational Image Analysis. Bio-protocol 8(8): e2804. DOI: 10.21769/BioProtoc.2804.
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