搜索

Measuring Spatiotemporal Dynamics of Odor Gradient for Small Animals by Gas Chromatography
采用气相色谱法测定小动物对气味梯度的时空动力学   

下载 PDF 引用 收藏 提问与回复 分享您的反馈 Cited by

本文章节

参见作者原研究论文

本实验方案简略版
eLIFE
May 2017

Abstract

Odor is the most fundamental chemical stimulus that delivers information regarding food, mating partners, enemies, and danger in the surrounding environment. Research on odor response in animals is widespread, although studies on experimental systems in which the gradient of odor concentration is quantitatively measured has been quite limited. Here, we describe a method for measuring a gradient of odor concentration established by volatilization and diffusion in a relatively small enclosed space, which has been used widely in laboratories to analyze small model animals such as the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster. We first vaporized known amounts of a liquid odorant 2-nonanone in a tank and subjected them to gas chromatographic analysis to obtain a calibration curve. Then, we aspirated a small amount of gas phase from a small hole on an agar plate and measured the odor concentration. By repeating this at different spatial and temporal points, we were able to detect a gradient of the odor concentration that increased over time. Furthermore, by applying these measured values to mathematical models of volatilization and diffusion, we were able to visualize an estimated dynamic change in odor concentration over an agar plate. Combining monitoring of odor concentration change in an agar plate with behavioral monitoring by machine vision will allow us to estimate how the brain computes information regarding odor concentration change in order to regulate behavior.

Keywords: Odorant (气味剂), Gradient (梯度), Gas chromatograph (气相色谱), C. elegans (秀丽隐杆线虫), Diffusion (扩散), Evaporation (蒸发)

Background

Odor is the most fundamental chemical stimulus that conveys the existence of food, reproductive partners, enemies, etc. in the surrounding environment. Small model animals, such as the nematode Caenorhabditis elegans and fruit fly Drosophila melanogaster are suitable for understanding brain responses to odor stimuli at the levels of behavior, neural activity, and molecules because: (1) behavioral responses to odor stimuli can be easily recorded with inexpensive high resolution cameras; (2) responses in multiple neurons/neuronal groups can be measured with calcium imaging and (3) genes responsible for behavioral and neural responses can be identified with various genetic methods (De Bono and Maricq, 2005; Venken et al., 2011).

However, it is difficult to measure the odor concentrations that are actually sensed by these small animals during their behavior in a small arena suited for observation. In general, the measurement of odor concentration requires constant air flow in a device supplying the odorant-containing air to the sensor. Thus, air should be constantly drawn from the air phase of the arena, destroying the odor gradient. Measuring odor gradient in a small behavioral arena has been achieved either by strengthening the air flow for the odor gradient compared to the flow for sampling or by optically measuring the air phase odorant concentration. Gershow et al. (2012) developed a relatively large apparatus (30 x 30 cm) for Drosophila larvae for slow but large (2 L/min) constant parallel flows with different concentrations, in order to create an odor concentration gradient perpendicular to the flow. Louis et al. (2008) used infrared beams to measure integrated concentrations of odor on one axis in a naturally evaporated and diffused gradient, and calculated the gradient shape mathematically based on Gaussian diffusion. The former method allows quantitative measurement of the odor gradient, although it is not based on natural evaporation and diffusion. It also requires specific, controlled apparatus. The latter method is suitable for natural gradients, although it does not allow accurate measurement of specific positions.

Here we report a method to measure a dynamic odor gradient in a widely-used plastic plate by gas chromatography (GC). Observing odor-taxis behaviors on plastic plates with an agar layer is easy and thus is conducted in many laboratories. In addition, we are able to video-record the behaviors using inexpensive USB cameras. Therefore, by measuring temporal changes in the odor gradient on an agar plate, we can obtain clues to estimate brain computations controlling how temporal changes in odor stimuli affect the animal’s behavior.

For measurement, first, specific amounts of liquid odorant are individually volatilized in a vaporizing tank to make gas with known concentrations of odorant. Then, the gas is subjected to GC with different concentrations, in order to calculate a calibration curve for known gas concentrations and GC values. Next, a small amount of gas is sampled from a specific spatio-temporal point on an agar plate with evaporating and diffusing odorant, and subjected to GC analysis. Finally, the entire odor gradient is calculated by the measured concentrations at different spatio-temporal points. In our experiment, measurements suggested that C. elegans responds behaviorally to odor concentration changes as small as ± 0.01 μM/sec in ~2 μM concentration on a natural odor gradient. This is consistent with results from an experiment with artificial and controlled odor concentration changes (Tanimoto et al., 2017).

Materials and Reagents

  1. ø 9 cm sterile Petri dish (IWAKI, catalog number: SH90-15 ) with nematode growth medium (NGM) agar
    Note: Pour 10 ml of autoclaved 1.5-2.5% agar solution per dish following the regular sterilized technique to make an agar plate for behavioral analysis. We used NGM agar for C. elegans behavioral analysis. This agar plate can be stored at 4 °C for a few weeks. The plates should be moved to a bench a few hours before the assay and kept without their lids for 15-30 min to dry. Dried plates with lids are placed upside-down on a bench. The plates are not sealed with either Parafilm or sticky tape.
  2. Microliter syringe, 50 μl, cemented needle (Hamilton, catalog number: 80565 )
    Note: This is a blunt needle point.
  3. Micro-volume syringe, 5 μl, fixed needle (SGE, catalog number: 001000 )
  4. Plastic disposable syringe, 2.0 ml (Top, catalog number: 5079-01 )
  5. Replacement needle (Luer lock side hole), 23 G x 4 cm (GL Sciences, catalog number: 3008-46004 )
  6. Pasteur pipette (IWAKI, catalog number: 1K-PAS-5P )
  7. Dropper bulb (AS ONE, catalog number: 1-6227-05 )
  8. 2-Nonanone (Wako Pure Chemical Industries, catalog number: 132-04173 )
    Note: Liquid at room temperature. Although we used only this odorant, this protocol could be used for other odorants as well.
  9. EtOH (Wako Pure Chemical Industries, catalog number: 057-00456 )
  10. Sodium chloride (NaCl) (Wako Pure Chemical Industries, catalog number: 191-01665 )
  11. Bacto peptone (BD, BactoTM, catalog number: 211677 )
  12. Agar (Wako Pure Chemical Industries, catalog number: 010-08725 )
  13. Cholesterol (Wako Pure Chemical Industries, catalog number: 034-03002 )
  14. Calcium chloride dihydrate (CaCl2·2H2O) (Wako Pure Chemical Industries, catalog number: 038-12775 )
  15. Magnesium sulfate heptahydrate (MgSO4·7H2O) (Wako Pure Chemical Industries, catalog number: 131-00405 )
  16. Dipotassium hydrogenphosphate (K2HPO4) (Wako Pure Chemical Industries, catalog number: 164-04295 )
  17. Potassium dihydrogen phosphate (KH2PO4) (Wako Pure Chemical Industries, catalog number: 169-04245 )

Equipment

  1. Vaporizing tank (FIS, catalog number: DT-T1 ) (Figure 1)
    Note: A custom-made acrylic tank of 50 L, equipped with a small metal block with a vaporizing groove, a heater with a temperature controller, and a fan. The odorant liquid is placed in the groove of the metal block through a liquid inlet on the lid, and the metal block is warmed with the heater to facilitate volatilization of the odorant. The fan stirs the air so that the volatilized odorant is distributed equally in the tank. 


    Figure 1. Vaporizing tank

  2. Gas chromatograph (GC) (Nissha FIS, model: SGVA-N2 ) (Figure 2)
    Note: A simple and inexpensive GC optimized for 2-nonanone with a semiconductor detector. Other GC can also be used. 


    Figure 2. Gas chromatograph

  3. Carrier gas cylinder (Air Liquide, model: Alphagaz 1 )
    Note: This was recommended by Nissha FIS Inc.
  4. Vacuum cleaner (Toshiba, catalog number: VC-PC6A , L)
    Note: A domestic vacuum cleaner.
  5. Pin vise (Tamiya, Fine Pin Vise D [0.1-3.2 mm])
  6. 1 mm drill (Tamiya, Basic Drill Bit set 5 pc)

Software

  1. SGC.exe (Nissha FIS Inc., Hyogo, Japan)
    Note: This is a specific software to control this type of GC provided by the manufacturer. Software should be installed on a Windows PC (XP, Vista, or 7) that is connected to the GC. Depending on the GC instrument being used, the manufacturer may have specific analysis software recommendations.

Procedure

Overview: We vaporized specific amounts of liquid 2-nonanone in the vaporizing tank to make 2-nonanone gas of known concentrations, and measured GC values to calculate a calibration curve. Next, a gas phase of 0.2 ml in an agar plate was sampled and measured with the GC. Although we did the following with 2-nonanone, our method should be applicable for other odorants that are vaporized from the liquid at room temperature and can be measured by gas chromatography. Important steps are summarized in Video 1.

Video 1. Important steps for measuring odor gradient. A video demonstrating the important apparatus and operations for the odor measurement. 


  1. Measuring known concentrations of 2-nonanone gas for a calibration curve
    2-nonanone gas of known concentration can be obtained by vaporizing a specific amount of 2-nonanone liquid in the vaporizing tank (see below). A calibration curve can be obtained by measuring different concentrations of the gas with GC and correlating the measured values. However, the time to reach maximum odor concentration varied for each amount of liquid, likely due to differences in vaporization, diffusion, and trace adhesion to the vaporizing tank wall. Therefore, we monitored temporal changes in odor concentration in the tank to find the optimal time for vaporization of each concentration.
    1. Flow the carrier gas at 0.25-0.35 MPa from the air cylinder connected to the GC.
      Note: Do this immediately before turning on the GC.
    2. Turn on the GC immediately after Step A1.
      Note: Flowing gas without turning on power will damage the column inside the GC.
    3. Wait until the ‘Ready’ lamp is illuminated.
      Notes:
      1. This may take about 90 min.
      2. If the GC is used after a long interval (e.g., more than 2 weeks), the measured value tends to be higher. In that case, use the GC a few days before taking actual measurements. An interval of up to several days has no effect.
      3. The high values recorded after long intervals are attributed to the following: During the interval, the sensor surface is coated with various small compounds in the air. Electrical conduction increases the temperature of the sensor to 300-400 °C, which clarifies the attached compounds and causes transient high sensitivity for several hours.
      4. The directions above are specific to the GC instrument used in this study. Other instrumentation may require adapted methods and steps.
    4. Turn on the electrical switch of the vaporizing tank (Figure 1), and set the temperature at 50 °C: It will take about 10 min to reach 50 °C.
    5. Take an appropriate amount of 2-nonanone liquid (Table 1) with a glass syringe of 5 μl or 50 μl, insert it in the liquid inlet on the lid of the tank, and place the liquid in the vaporizing groove installed on the back side of the lid.

      Table 1. Volumes of 2-nonanone liquid for the vaporization


    6. The relationship between the liquid volume and the estimated gas concentration in Table 1 is as follows:

      where C is the required gas concentration (mol/L), Vliquid is the amount of liquid (ml) to be added, d is the density of liquid odorant (g/ml), M is the molecular weight (g), and Vtank is the volume of the vaporizing tank (L).
    7. Start SGC.exe on a Windows PC connected to the GC and operate according to the manual.
    8. Press the start button in SGC.exe.
    9. After a certain period of time (see Table 1), carefully insert the replacement needle attached to the disposable syringe from the gas outlet. Extract 0.2 ml and quickly remove the needle from the tank. Carefully insert the needle in the gas inlet of the GC (Figure 2) until it hits the bottom, and immediately infuse the gas inside the syringe.
      Note: This step should be completed in about 5-6 sec.
    10. Measurement is started by gas injection. Drawing of the graph (Figure 3) starts and ends automatically. Data is automatically saved. In the default setting, a measurement takes 8 min. The file can be exported as a CSV file.


      Figure 3. A representative result of one measurement for 22.9 μM (i.e., 200 μl) of 2-nonanone. The horizontal axis is time (sec), and the vertical axis is the signal (mV).

    11. To measure temporal changes in the measured value for the specific amount of the odorant, odor gas sampling can be performed at different time points for one odorant injection (e.g., 6, 18, 30, and 42 min for 6.8 and 11.1 μM; see Table 1). However, if there is no 8-min interval (e.g., 1, 2, 3, and 4 min for 0.04 and 0.12 μM), clean the tank (see the next section) and start from the gas vaporization.
    12. If gas remains in the tank (likely by adhesion to the wall), vaporization of the residual amount affects the GC value, especially when a small concentration is being measured. In order to avoid this, gas in the tank is removed with a vacuum cleaner, and the wall is wiped with a paper towel containing EtOH, followed by further suction with the vacuum cleaner. After this procedure, remaining gas was not detected in our experiment.
    13. For all conditions, repeat the measurement 3-4 times (once daily and repeat over 3-4 days) and find the time at which the average value is at a maximum (Figure 4). In the case of 2-nonanone, the relationship between odor concentrations and measured values are nicely fitted to two regression lines for concentrations lower and higher than 4 μM (R2 = 0.9991 and 0.9995, respectively; Figure 5) (Tanimoto et al., 2017). In general, for semiconductor detectors, the correlation between the peak height of the signal and signal concentration in a log-log plot is well-fitted by two simple regression lines for lower and higher concentrations. Therefore, these results are adopted as calibration curves for low and high concentrations. Excel (Microsoft) is used for data analysis.


      Figure 4. Changes over time of the measured value for different amounts of liquid odor. On each graph, the expected 2-nonanone gas concentration at saturation is shown. The horizontal (vaporizing time) and vertical (sensor output) axes are different in each graph. For 2-nonanone gas with a low saturation concentration of 0.04-0.12 μM, the concentration became saturated immediately and then decreased slightly. This is likely because of adhesion of 2-nonanone to the wall. For each condition, results are shown as mean ± standard error of 3-4 repetitions.


      Figure 5. Calibration curve for 2-nonanone. Each dot represents average values of 3-4 experiments, and data on the log-log plot were fitted with two simple regression lines for lower (squares) and higher (triangles) concentrations. This figure was originally published in Tanimoto et al. (2017).

  2. Measuring 2-nonanone gradient on an agar plate
    1. On the back side of the agar plate, mark the positions of odor spot and gas sampling with a pen.
      Note: We measured at six points (x, y, z) on the assay plate shown in Figure 6 at 1, 3, 6, 9, and 12 min. The motivations for adopting 6 points (x, y) for measurement are as follows:
      1. Since we examined odor avoidance behavior of C. elegans, the four points on the x axis were chosen to measure the direction avoided by C. elegans, i.e., to measure the spatial gradient along the x direction. In the range of x > 0 in which C. elegans mainly existed, three points were chosen. Only one point was measured in the range x < 0 where C. elegans did not often exist.
      2. To measure the spatial gradient in the y direction, (22, 15) was selected. We chose only one point because the worms did not spread much along the y axis.


        Figure 6. Gas sampling from the agar plate (originally published in Tanimoto et al. (2017). Creative Commons Attribution License)

    2. Remove the lid of the agar plate, and push the narrow end of a Pasteur pipette with a dropper bulb against the agar and extract the agar plug to make a hole of ø 1-2 mm in the agar layer. Then close the lid.
    3. From the back of the plate (i.e., the opposite side of the lid), make a hole with a pin vise with a 1 mm drill at the position of the agar hole. This will result in a hole in the same position on the plate and the agar.
    4. If necessary, use cellophane tape to cover the hole from the back side. Turn back one side of the cellophane tape for ease of peeling. This is not necessary for odorants with large molecular weights such as 2-nonanone. We compared outcomes with and without taping, and found no difference.
    5. Spot the liquid odorant at the odor source position (we used 2 μl of 30% 2-nonanone diluted in EtOH), immediately cover the lid, place the plate upside-down (hole up) and leave it on the bench.
      Note: In the worm’s odor avoidance assay, worms suspended in a small amount of buffer droplet are spotted at the center of the plate 1.5 min before the odor is spotted (Kimura et al., 2010), and the time of odor spotting is counted as t = 0. In this odor measurement, however, the worms are not spotted for the sake of simplicity.
    6. When an appropriate amount of time (i.e., 1, 3, 6, 9, or 12 min) has passed, remove the tape (if applied) without moving the plate. Carefully insert the replacement needle attached to the plastic 2.0 ml disposable syringe so that the needle hole is positioned in the gas phase 1 mm away from the agar surface (i.e., just below the agar surface in the upside-down plate). Slowly extract 0.2 ml of the gas so as not to disturb the gradient severely. Quickly remove the needle tip from the plate and insert it in the gas inlet of the GC, and inject the gas.
      Note: Since sampling may destroy the gradient, only one sample was taken from each plate.
    7. Several samples should be taken (we took 7-9) for each position and time, using the median to calculate the dynamic odor gradient.

  3. Fitting the odor gradient
    1. Model selection
      At the beginning of curve fitting, a parametric function needs to be specified for the data. Physical phenomena caused in the assay plate are evaporation of the 2-nonanone-ethanol-mixed solution (30:70, v/v) and diffusion of their gaseous molecules in the three-dimensional closed cylindrical space. Although we previously calculated the evaporation and distribution of 2-nonanone numerically (Yamazoe-Umemoto et al., 2015), in the recent study we employed a phenomenological curve fitting to the measured concentration by least squares method for better understanding of the odor gradient (Tanimoto et al., 2017). After the odor sources are put at the two spots, the 2-nonanone concentration C(t) in the plate increases from zero and asymptotically approaches a constant value over time. In this work, a saturation curve C(t) = a(1-exp(-bt)) was used for fitting, where a and b denote an asymptotic concentration and an increasing rate, respectively. This function is a solution of the rate equation dC(t)/dt = b(a - C(t)) which implies that C(t) changes with the rate proportional to the difference from the asymptotic concentration.
      In the measurement, increasing of the 2-nonanone concentration was slow as the distance from the spots is far (Figure 7). A mass transfer by molecular diffusion accounts for this result. Therefore the increasing rate is a decreasing function of the distance r from the spot such as b(r) = b0 exp(-b1 r - b2 r2), where b0 (> 0), b1 and b2 are constant parameters. For good fitting in a relatively short time after putting the odor sources, furthermore, the asymptotic concentration is also a decreasing function of r such as a(r) = a0 exp(-a1 r - a2 r2), where a0 (> 0), a1 and a2 are constant parameters. Because there are two odor sources in the plate, the measured concentrations are fitted to the following function with two saturation curves.

      where r1 and r2 are the distances from the position (x, y) on the agar to the two spots (X1, Y1) = (-22, 15) and (X2, Y2) = (-22, -15), respectively.

      The radius of the plate is 44 mm (1 mm in the plate thickness).
      Two constraint conditions are imposed on the fitting. The first constraint condition is that a(r) and b(r) should be decreasing functions of r at least in the range 0 ≤ r r0, where r0 is the distance from the spot to the edge of the plate (44, 0). From da(r)/dr = - a0(a1 + 2a2 r) exp(-a1 r - a2 r2) and db(r)/dr = - b0(b1 + 2b2 r) exp(-b1 r - b2 r2), inequality conditions da(r0)/dr ≤ 0 and db(r0)/dr ≤ 0 (decreasing even at r = r0) are expressed as:

      under a0 > 0 and b0 > 0. The second constraint condition is that the asymptotic concentration on the odor sources should be lower than the saturation concentration 34.5 μM of the 2-nonanone (Yamazoe-Umemoto et al., 2015). Letting be the distance between the two spots, this condition is expressed as:

    2. Fitting algorithm
      The fitting parameters in C(x, y, t) are determined by the Levenberg-Marquardt method which is widely used to solve non-linear minimization problems (Press et al., 1992). Letting be the parameter vector,

      the sum of the squared errors is explicitly defined by:

      Where un is the n-th measured concentration at position (xn, yn) at time tn (). The inequality constraints are expressed as:

      Introducing the following function with logarithmic barriers,

      the given constrained minimization problem is approximately replaced by an unconstrained minimization problem. Where μ is a penalty factor whose value is initially large positive and is reduced to zero as is converged. The iterative algorithm to determine which minimizes  is as follows.
      Step 1: Initial values are set for , μ and λ. Where λ is a damping factor in the Levenberg-Marquardt method and is used in Step 2.  is chosen to satisfy the inequality constraints. Initial μ and λ are large positive.
      Step 2: The Jacobian matrices J = ({Jij}), G = ({Gij}), the diagonal matrix M and the residual vector are calculated.

      Then, the following linear equation of  is solved and  is calculated.

      Where  
      Step 3: If  for a small constant ε, then is determined as a solution, or else go to Step 2 with updating , μ and λ. If , is updated by  checking the inequality constraints. μ and λ are reduced by rates α and β (0 < α, β < 1), respectively. If  or the inequality constraints are not satisfied, and μ are not updated while λ is increased by a rate 1/β.
    3. Execution and result
      In this work, the fitting algorithm is implemented in C language and is compiled by the GNU Compiler Collection. The convergence criterion is ε = 1 x 10-6 . Setting of the decrement rates α and β depends on the choice of an initial . In particular, the slow reduction of μ requires the avoidance of invalid updating of , such that the reduction rate α for μ (μαμ) is 0.5 < α < 1. When α < 0.5, the penalty factor μ rapidly converges to 0 and an incorrect solution without inequality constraints is derived. Some combinations of initial parameters and decrement rates are tried for fitting. Furthermore, a converged value of  is used as an initial value in new iterations with different μ and λ. Good fitting parameters are a0 = 20.68 μM, a1 = 0.7355 cm-1, a2 = -0.05408 cm-2, b0 = 0.8384 min-1, b1 = 0.7835 cm-1 and b2 = -0.05761 cm-2. Some software tools are useful for the non-linear curve fitting. Optimization Toolbox in MATLAB provides packages for non-linear least squares minimization. Solver Add-in in Microsoft Excel is also available for non-linear curve fitting.
      Fitting result is shown in Figure 7. Temporal change of the 2-nonanone gradient is shown in Video 1 in Tanimoto et al. (2017). Although the odor sources spread in a round shape (~5 mm in diameter) in the experiment, their shape in the fitting is considered as a point which has no area. Therefore, the fitted 2-nonanone gradient around the spots became pointy.
      When a simple exponential function b(r) = b0 exp(-b1 r) was used for fitting, the result was not good. A higher-order correction of more than r2 term requires for good fitting. When b(r) = b0 exp(-b1 r - b2 r2 - b3 r3) was used for fitting, the result was almost the same as that without the r3 term. Fitting using a polynomial function b(r) = b0 - b1 r - b2 r2 - b3 r3 or a fractional function b(r) = 1/(b0 + b1 r + b2 r2) went bad. Fitting using other saturation curve C(t) = c0 t/(t + c1) or C(t) = c0 t2/(t2 + c1) also went bad.


      Figure 7. Temporal change of the measured 2-nonanone concentration at the position (x, y) on the agar. The odor sources were put at (-22, 15) and (-22, -15). The broken lines are the fitting curves.

Data analysis

All data related to this study are already published in Tanimoto et al. (2017).

Notes

  1. We found that variations in odor concentration become considerably smaller with increasing distance from the odor source, i.e., in the right half of the plate. Conversely, variations were greater near the odor source. We consider that this is because diffusion essentially equalizes variation, leading to less variation at greater distances from the source.
  2. In the natural environment, odors are recognized to exist as plumes; they do not produce a smooth gradient. In this experiment, 0.2 ml was aspirated for one measurement, therefore we were not able to detect any spatial differences within this volume. However, based on the model of the smooth 2-nonanone gradient, C. elegans are estimated to respond to a concentration change of about 0.01 μM/sec, which is consistent with behavioral response in a constant odor concentration change in artificial flow (Tanimoto et al., 2017). This suggests that the gradient is indeed smooth, at least in this case caused by volatilization and diffusion in the static space inside the plastic plate.
  3. The shape of the gradient can differ substantially depending on the ratio of volatilization to diffusion. In the case of 2-nonanone, the ratio was appropriate for formation of a reasonable gradient.
  4. The 2-nonanone concentration used in our paradigm is relatively high compared to concentrations of other odorants used in C. elegans odor-taxis analysis, which are in general 10-3-10-4 at odor source (Bargmann et al., 1993). For these odorants, it would be better to use a more sensitive GC. In addition, for these odorants, it may be important to use materials with less adhesion for the vaporizing tank and syringe, such as glass or metal, instead of plastic.

Recipes

  1. NGM plate (for 1 L)
    970 ml ddH2O
    3 g NaCl
    2.5 g peptone
    17 g agar
    1 ml cholesterol (5 mg/ml EtOH)
    Autoclave; wait until 50-60 °C
    Add the following autoclaved buffers:
    1 ml 1 M CaCl2
    1 ml 1 M MgSO4
    25 ml 1 M KPO4 buffer (pH 6)
  2. KPO4 buffer (pH 6.0), 1 M
    108.3 g KH2PO4
    35.6 g K2HPO4
    Add ddH2O up to 1L
    Autoclave

Acknowledgments

We especially thank K. Tanaka (FIS Inc., Japan) for all the technical support of odor measurement. This work was supported by a Grant-in-Aid for JSPS fellows (A.Y.-U.), the Osaka University Life Science Young Independent Researcher Support Program, Precursory Research for Embryonic Science and Technology from MEXT, and research grants from Mitsubishi Foundation, Shimadzu Science Foundation, and Takeda Science Foundation (K.D.K.). The authors declare that no competing interests exist.

References

  1. Bargmann, C. I., Hartwieg, E. and Horvitz, H. R. (1993). Odorant-selective genes and neurons mediate olfaction in C. elegans. Cell 74(3): 515–527.
  2. De Bono, M. and Maricq, A. V. (2005). Neuronal substrates of complex behaviors in C. elegans. Annu Rev Neurosci 28: 451-501.
  3. Gershow, M., Berck, M., Mathew, D., Luo, L., Kane, E. A., Carlson, J. R. and Samuel, A. D. (2012). Controlling airborne cues to study small animal navigation. Nat Methods 9(3): 290-296.
  4. Kimura, K. D., Fujita, K., and Katsura, I. (2010). Enhancement of odor avoidance regulated by dopamine signaling in Caenorhabditis elegans. J Neurosci 30(48): 16365-16375.
  5. Louis, M., Huber, T., Benton, R., Sakmar, T. P. and Vosshall, L. B. (2008). Bilateral olfactory sensory input enhances chemotaxis behavior. Nat Neurosci 11(2): 187-199.
  6. Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (1992). Numerical recipes in C. Cambridge University Press.
  7. Tanimoto, Y., Yamazoe-Umemoto, A., Fujita, K., Kawazoe, Y., Miyanishi, Y., Yamazaki, S. J., Fei, X., Busch, K. E., Gengyo-Ando, K., Nakai, J., Iino, Y., Iwasaki, Y., Hashimoto, K. and Kimura, K. D. (2017). Calcium dynamics regulating the timing of decision-making in C. elegans. Elife 6: e21629.
  8. Venken, K. J., Simpson, J. H. and Bellen, H. J. (2011). Genetic manipulation of genes and cells in the nervous system of the fruit fly. Neuron 72(2): 202-230.
  9. Yamazoe-Umemoto, A., Fujita, K., Iino, Y., Iwasaki, Y. and Kimura, K. D. (2015). Modulation of different behavioral components by neuropeptide and dopamine signalings in non-associative odor learning of Caenorhabditis elegans. Neurosci Res 99: 22-33.

简介

气味是提供关于食物,交配伙伴,敌人和周围环境危险的信息的最基本的化学刺激物。关于动物气味反应的研究很普遍,尽管对定量测量气味浓度梯度的实验系统的研究非常有限。在这里,我们描述了一种测量由相对较小的封闭空间中的挥发和扩散建立的气味浓度梯度的方法,其已经在实验室中被广泛用于分析线虫等线虫和果蝇<果蝇黑腹果蝇。我们首先将已知量的液体加味剂2-壬​​酮蒸发到罐中并进行气相色谱分析以获得校准曲线。然后,我们从琼脂平板上的小孔中吸取少量气相并测量臭味浓度。通过在不同的空间和时间点重复这一点,我们能够检测随时间增加的气味浓度梯度。此外,通过将这些测量值应用于挥发和扩散的数学模型,我们能够可视化在琼脂平板上的气味浓度的估计动态变化。将琼脂平板中的气味浓度变化监测与通过机器视觉进行的行为监测相结合,将使我们能够估计大脑如何计算有关气味浓度变化的信息以调节行为。

【背景】气味是表达食物,生殖伙伴,敌人等在周围环境中存在的最基本的化学刺激物。小型模型动物,如线虫<秀秀隐杆线虫>和果蝇<果蝇> 适合理解大脑对行为,神经活动和分子水平的气味刺激的反应,因为:(1)利用便宜的高分辨率相机可以容易地记录对气味刺激的行为反应; (2)可以用钙成像测量多个神经元/神经元组中的反应,并且(3)负责行为和神经反应的基因可以用各种遗传方法鉴定(De Bono和Maricq,2005; Venken等人,,2011)。

然而,很难测量这些小动物在适合观察的小舞台上行为期间实际感受到的气味浓度。通常,气味浓度的测量需要在向传感器供应含添味剂的空气的装置中恒定的气流。因此,空气应该不断地从竞技场的空气中吸取,破坏臭气梯度。通过加强与采样流量相比的气味梯度的气流或通过光学测量气相气味浓度来实现在小行为领域中测量气味梯度。 Gershow 等人。 (2012)针对不同浓度的缓慢但大(2L / min)恒定平行流动的果蝇幼虫开发了相对较大的装置(30×30cm),以产生气味浓度梯度垂直流向。路易斯等人。 (2008)使用红外光束测量自然蒸发和扩散梯度中一个轴上气味的积分浓度,并基于高斯扩散计算梯度形状。前一种方法允许定量测量气味梯度,但它不是基于自然蒸发和扩散。它还需要特定的受控设备。后一种方法适用于自然梯度,但不允许精确测量特定位置。

在这里,我们报告了一种通过气相色谱(GC)测量广泛使用的塑料板中动态气味梯度的方法。在具有琼脂层的塑料板上观察气味 - 出租车行为是很容易的,因此在许多实验室中进行。另外,我们可以使用便宜的USB摄像头来录制行为。因此,通过测量琼脂平板上气味梯度的时间变化,我们可以获得线索来估计控制气味刺激时间变化如何影响动物行为的脑计算。

为了测量,首先,将特定量的液体加味剂分别在蒸发罐中挥发以制造具有已知浓度的加味剂的气体。然后,对气体进行不同浓度的GC测量,以计算已知气体浓度和GC值的校准曲线。接下来,从琼脂平板上的特定时空点采集少量气体并蒸发和扩散气味剂,并进行GC分析。最后,通过在不同时空点测量的浓度来计算整个臭味梯度。在我们的实验中,测量结果表明, C。线虫在天然气味梯度上对〜2μM浓度的气味浓度变化作出小至±0.01μM/ sec的行为响应。这与人造和受控臭味浓度变化实验的结果(Tanimoto等人,2017)是一致的。

关键字:气味剂, 梯度, 气相色谱, 秀丽隐杆线虫, 扩散, 蒸发

材料和试剂

  1. ø9厘米无菌培养皿(IWAKI,目录号:SH90-15)和线虫生长培养基(NGM)琼脂
    注意:按照常规灭菌技术,每皿倒入10ml高压灭菌的1.5-2.5%琼脂溶液以制备用于行为分析的琼脂平板。我们使用NGM琼脂进行秀丽隐杆线虫行为分析。该琼脂平板可以在4°C下储存数周。在测定前几小时将平板移到长凳上,并保持不用它们的盖子15-30分钟晾干。将盖上盖子的干盘倒置在长凳上。这些印版不是用Parafilm或粘胶带密封的。
  2. 微量注射器,50μl,粘合针(汉密尔顿,目录号:80565)
    注:这是一个钝针点。
  3. 微量注射器,5μl,固定针(SGE,目录号:001000)
  4. 塑料一次性注射器,2.0毫升(顶部,目录号:5079-01)
  5. 更换针(鲁尔锁侧孔),23 G x 4厘米(GL Sciences,目录号:3008-46004)
  6. 巴斯德吸管(IWAKI,目录号:1K-PAS-5P)
  7. 滴管灯泡(AS ONE,目录号:1-6227-05)
  8. 2-壬酮(Wako Pure Chemical Industries,目录号:132-04173)
    注意:室温下为液体。虽然我们只使用这种气味剂,但该协议也可用于其他气味剂。
  9. EtOH(Wako Pure Chemical Industries,目录号:057-00456)
  10. 氯化钠(NaCl)(Wako Pure Chemical Industries,目录号:191-01665)
  11. 细菌蛋白胨(BD,Bacto TM,产品目录号:211677)
  12. 琼脂(Wako Pure Chemical Industries,目录号:010-08725)
  13. 胆固醇(Wako Pure Chemical Industries,目录号:034-03002)
  14. 氯化钙二水合物(CaCl 2·2H 2 O)(Wako Pure Chemical Industries,目录号:038-12775)
  15. 硫酸镁七水合物(MgSO 4·7H 2 O)(Wako Pure Chemical Industries,目录号:131-00405)
  16. 磷酸氢二钾(KH 2 HPO 4)(Wako Pure Chemical Industries,目录号:164-04295)
  17. 磷酸二氢钾(KH 2 PO 4)(Wako Pure Chemical Industries,目录号:169-04245)

设备

  1. 蒸发罐(FIS,目录号:DT-T1)(图1)
    注:定制的50升丙烯酸储罐,装有带蒸发槽的小金属块,带温度控制器的加热器和风扇。加味剂液体通过盖子上的液体入口被放置在金属块的凹槽中,并且金属块被加热器加热以促进气味剂的挥发。风扇搅动空气,使挥发的气味剂在舱内均匀分布。&nbsp;


    图1.汽化罐

  2. 气相色谱仪(GC)(Nissha FIS,型号:SGVA-N2)(图2)
    注:使用半导体检测器优化的2-nonanone的简单且便宜的GC。其他GC也可以使用。&nbsp;


    图2.气相色谱

  3. 载气瓶(液化空气,型号:Alphagaz 1)
    注意:这是Nissha FIS公司推荐的。

  4. 吸尘器(东芝,产品目录号:VC-PC6A,L) 注:家用吸尘器
  5. Pin虎钳(Tamiya,细针虎钳D [0.1-3.2毫米])
  6. 1毫米钻(田宫,基本钻头5件套)

软件

  1. SGC.exe(Nissha FIS Inc.,Hyogo,日本)
    注意:这是一个特定的软件来控制制造商提供的这种类型的GC。应将软件安装在连接到GC的Windows PC(XP,Vista或7)上。根据所使用的GC仪器的不同,制造商可能会提供特定的分析软件建议。

程序

概述:我们在蒸发罐中汽化特定量的液体2-壬酮,制造已知浓度的2-壬酮气体,并测量GC值以计算校准曲线。接下来,将琼脂平板中0.2ml的气相取样并用GC测量。虽然我们用2-壬酮进行了以下操作,但我们的方法应适用于其他在室温下从液体中汽化并可通过气相色谱测量的气味剂。视频1中总结了重要步骤。

视频1

  1. 测量已知浓度的2-壬酮气体作为校准曲线
    通过在蒸发罐中蒸发特定量的2-壬酮液体(见下文)可以获得已知浓度的2-壬酮气体。校准曲线可以通过用GC测量不同浓度的气体并将测量值关联来获得。但是,达到最大气味浓度的时间对于每种液体量都不同,这可能是由于气化,扩散和微量粘附到蒸发罐壁上的差异。因此,我们监测了罐内气味浓度的时间变化,找出每种浓度汽化的最佳时间。

    1. 在与气相连接的气缸中流动载气0.25-0.35MPa。
      注意:在开启GC之前立即执行此操作。
    2. 在步骤A1之后立即打开GC。
      注意:不通电的流动气体会损坏GC内的色谱柱。
    3. 等待'Ready'指示灯亮起。
      注意:
      1. 这可能需要大约90分钟。
      2. 如果在长时间间隔(例如,超过2周)后使用GC,则测量值趋于更高。在这种情况下,在进行实际测量前几天使用GC。间隔长达数天不起作用。
      3. 长时间间隔记录的高值归因于以下因素:在此期间,传感器表面在空气中涂有各种小型化合物。电传导将传感器的温度提高到300-400°C,这可以澄清附着的化合物,并在几个小时内引起瞬时高灵敏度。
      4. 以上说明针对本研究中使用的GC仪器。其他仪器可能需要适应的方法和步骤。
    4. 打开蒸发罐的电气开关(图1),并将温度设置为50°C:大约需要10分钟才能达到50°C。
    5. 用5μl或50μl的玻璃注射器取适量的2-壬酮液体(表1),将其插入罐盖上的液体入口,并将液体放入安装在背面的蒸发槽中

      表1.用于汽化的2-壬酮液体的体积


    6. 表1中液体体积与估算气体浓度之间的关系如下:

      其中是所需的气体浓度(mol / L), 液体是液体的量(ml )是液体气味剂的密度(g / ml),M em是分子量(g),而V em是液体气味剂的密度(g / ml) tank 是蒸发罐(L)的体积。
    7. 在连接到GC的Windows PC上启动SGC.exe,并根据手册进行操作。
    8. 按下SGC.exe中的开始按钮。
    9. 经过一段时间后(参见表1),请小心地从气体出口插入一次性注射器上的替换针。提取0.2毫升,并迅速从罐中取出针头。小心将针头插入GC的气体入口(图2),直至其碰到底部,并立即将气体注入注射器。
      注意:此步骤应在约5-6秒内完成
    10. 测量是通过注气开始的。图的绘制(图3)自动开始和结束。数据会自动保存。在默认设置下,测量需要8分钟。该文件可以导出为CSV文件。


      图3.22.9μM(即em,即200μl)2-壬酮的一次测量的代表性结果。 横轴是时间(秒),纵轴是信号(mV)。

    11. 为了测量特定量的加味剂的测量值的时间变化,可以在不同时间点对于一种气味剂注射(例如<!em>,6,18,30和42分钟)进行臭气采样6.8和11.1μM;参见表1)。但是,如果没有8分钟间隔(例如,对于0.04和0.12μM,1,2,3和4分钟),请清洁水箱(请参阅下一部分)并从气体开始汽化。
    12. 如果气体留在罐内(可能粘附在壁上),剩余量的蒸发会影响GC值,特别是在测量小浓度时。为了避免这种情况,用真空吸尘器除去水箱中的气体,用含有EtOH的纸巾擦拭墙壁,然后用真空吸尘器进一步抽吸。在此过程之后,我们的实验中未检测到剩余气体。
    13. 对于所有情况,重复测量3-4次(每天一次,重复3-4天),并找出平均值最大的时间(图4)。在2-壬酮的情况下,气味浓度与测量值之间的关系很好地适合于两个回归线,其浓度低于和高于4μM(R 2分别为0.9991和0.9995;图5 )(Tanimoto et al。,2017)。通常,对于半导体探测器,信号的峰高与对数图中的信号浓度之间的相关性通过较低和较高浓度的两条简单回归线拟合良好。因此,这些结果被用作低浓度和高浓度的校准曲线。 Excel(微软)用于数据分析。


      图4.不同液体气味量随时间变化的实测值。 在每个图表中,显示了饱和时预期的2-壬酮气体浓度。每个图表中的水平(蒸发时间)和垂直(传感器输出)轴是不同的。对于饱和浓度低达0.04-0.12μM的2-壬酮气体,浓度立即变得饱和,然后稍微下降。这可能是因为2-壬酮粘附在墙上。对于每种情况,结果显示为平均值±3-4次重复的标准误差。


      图5. 2-壬酮的校准曲线。每个点表示3-4次实验的平均值,对数对数图中的数据用较低(正方形)和更高(三角形)浓度的两条简单回归线拟合。这个数字最初是在Tanimoto等人发表的。 (2017)。

  2. 在琼脂平板上测量2-壬酮梯度
    1. 在琼脂平板的背面,用笔标出臭味斑点和气体取样的位置。
      注:我们在图6所示的分析板上的3点,6分钟,9分钟和12分钟处测量了6个点(x,y,z)。采用6点(x,y)进行测量的动机如下:
      1. 由于我们检查了秀丽隐杆线虫的气味回避行为,因此选择x轴上的四个点来测量线虫避免的方向,即测量沿x方向的空间梯度。在x&gt;的范围内,其中C. elegans主要存在,选择三点。只有一个点在x < 0线虫不常存在。
      2. 为了测量y方向上的空间梯度,选择(22,15)。我们只选择了一个点,因为这些蠕虫并没有沿着y轴扩散。
      3. 拟合算法
        在 C ( x , y , t )中的拟合参数由Levenberg-Marquardt方法确定,广泛用于解决非线性最小化问题(Press等人,1992年)。让成为参数向量,

        平方误差的总和被明确定义为:

        其中 n 是位置( x < , n n ) sub> n ()。不平等约束表达为:

        使用对数屏障引入以下功能,

        给定的约束最小化问题大致由无约束最小化问题代替。其中μ是一个惩罚因子,它的初始值为大正值,并且由于已收敛。用于确定最小化的的迭代算法如下。
        步骤1:初始值设置为, μ和λ。其中λ是Levenberg-Marquardt方法中的阻尼因子,用于步骤2中。&nbsp; 来满足不等式约束条件。初始的μ和λ是很大的积极的。
        步骤2:Jacobian矩阵 J =({ j ij }), G > =({ em ij }),对角线矩阵 M 和残差矢量会被计算出来。

        然后,解决以下线性方程式,并且&nbsp;&lt; img width =“20”height =“26”class =“videopic donotsetwh”alt =“”src =“/ attached / image / 20180401235844_8330.jpg”/> 是计算出来的。

        其中&nbsp;
        步骤3:如果一个小常数 ε,则被确定为解决方案,否则转到步骤2更新,μ和λ。如果由&nbsp; “”src检查不等式约束。 和λ会被降低率α和β(0 α ,β <1)。如果或不满足不等式约束条件,λ增加1 / β。
      4. 执行和结果
        在这项工作中,拟合算法以 C 语言实现,并由GNU编译器集合编译。收敛标准是εem = 1 x 10 -6 -6。减量率的设置取决于初始选择。特别是,μ的缓慢减少需要避免无效更新,使得对于μ(μ→αμ没有不等式约束被派生。尝试一些初始参数和减量率的组合以进行拟合。此外,一个收敛的价值&nbsp; 在新的迭代中用作初始值, em>μ和λ。良好的拟合参数为:α=20.68μM,α1= 0.7355cm-1 <α= sup>, a 2 = -0.05408 cm -2 , b 0 = 0.8384分-1, 1 = 0.7835厘米-1 和 b sub> 2 = -0.05761cm -2 。一些软件工具对非线性曲线拟合很有用。 MATLAB中的优化工具箱提供了用于非线性最小二乘最小化的包。 Microsoft Excel中的求解器插件也可用于非线性曲线拟合。
        拟合结果如图7所示。在Tanimoto et al。的视频1中显示了2-壬酮梯度的时间变化。 (2017)。虽然实验中气味源散布为圆形(直径〜5mm),但它们在配件中的形状被认为是没有面积的点。因此,斑点周围的拟合2非壬酮梯度变得尖锐。
        当一个简单的指数函数 b ( r )= b 0 exp( - b 1 r )被用于拟合,结果不好。高于 r 2 项的高阶修正需要良好拟合。当 b ( r )= b 0 exp( - b 1 r - b 2 r 2 - b 3 r 3 )用于拟合,结果几乎与没有 r 3 术语。使用多项式函数拟合 b ( r )= b 0 - b < sub> 1 r - b 2 r b 3 r 3 或分数函数 b em>)= 1 /( b 0 + b 1 r b 2 r 2 )变坏了。使用其他饱和度曲线拟合 C ( t )= c 0 t / + c 1 )或 C ( t )= c 0 t 2 /( t 2 c 1 )也变差了。


        图7.在琼脂上的位置( x , y )测得的2-壬酮浓度的时间变化。 气味来源放在(-22,15)和(-22,-15)。虚线是拟合曲线。

    数据分析

    所有与本研究有关的数据已经在Tanimoto et al。 (2017)。

    笔记

    1. 我们发现随着距离气味源的距离增加,气味浓度的变化变得相当小,即在板的右半部分即。相反,气味来源附近的变化更大。我们认为这是因为扩散实质上均衡了变化,导致距离信号源较远距离处的变化较小。
    2. 在自然环境中,气味被认为是以羽流形式存在;他们不会产生平滑的渐变。在这个实验中,一次测量吸入了0.2ml,因此我们无法检测到该体积内的任何空间差异。然而,基于光滑的2-nonanone渐变模型, C。估计线虫的浓度变化约为0.01μM/ sec,这与人为流动中恒定气味浓度变化的行为反应一致(Tanimoto等人,2017) 。这表明,梯度确实是光滑的,至少在这种情况下是由塑料板内静态空间中的挥发和扩散引起的。
    3. 根据挥发与扩散的比例,梯度的形状可以大不相同。就2-壬酮而言,该比例适合形成合理的梯度。
    4. 在我们的范例中使用的2-壬酮浓度相对于 C中使用的其他气味剂的浓度相对较高。线虫气味 - 出租车分析,其气味来源通常为10 -3 -10 -10 -4(Bargmann et al。 ,1993)。对于这些气味剂,最好使用更灵敏的气相色谱仪。此外,对于这些气味剂,使用蒸发罐和注射器的附着力较小的材料(例如玻璃或金属)代替塑料可能很重要。

    食谱

    1. NGM板(1升)
      970ml ddH 2 O
      3克NaCl
      2.5克蛋白胨
      17克琼脂
      1毫升胆固醇(5毫克/毫升乙醇)
      高压灭菌器;等到50-60°C
      添加以下消毒缓冲区:
      1毫升1M氯化钙2
      1毫升1M MgSO 4
      25ml 1M KPO 4缓冲液(pH 6)
    2. KPO 4缓冲液(pH 6.0),1M 108.3克KH 2 PO 4 4 35.6克K 2 HPO 4 4 将ddH 2 O添加至1L
      高压灭菌器

    致谢

    我们特别感谢K. Tanaka(日本FIS Inc.)提供气味测量的所有技术支持。这项工作得到了JSPS研究员资助(AY-U。),大阪大学生命科学青年独立研究人员支持计划,来自MEXT的胚胎科学和技术前体研究以及三菱基金会,岛津科学基金会和武田科学基金会(KDK)。作者声明不存在相互竞争的利益。

    参考

    1. Bargmann,C.I。,Hartwieg,E。和Horvitz,H.R。(1993)。 具有气味选择性基因和神经元在线虫中介导嗅觉。 Cell 74(3):515-527。
    2. De Bono,M.和Maricq,A. V.(2005)。 C中复杂行为的神经基质。 elegans 。 Annu Rev Neurosci 28:451-501。
    3. Gershow,M.,Berck,M.,Mathew,D.,Luo,L.,Kane,E.A.,Carlson,J.R。和Samuel,A.D。(2012)。 控制机载线索研究小型动物导航 Nat Methods 9(3):290-296。
    4. Kimura,K. D.,Fujita,K.和Katsura,I。(2010)。 增强秀丽隐杆线虫多巴胺信号调节的气味避免。 a> J Neurosci 30(48):16365-16375。
    5. Louis,M.,Huber,T.,Benton,R.,Sakmar,T. P.和Vosshall,L. B.(2008)。 双侧嗅觉感觉输入增强了趋化行为。 Nat Neurosci 11(2):187-199。
    6. Press,W.H.,Teukolsky,S.A.,Vetterling,W.T。和Flannery,B.P。(1992)。 C中的数字食谱 剑桥大学出版社。 >
    7. Tanimoto,Y.,Yamazoe-Umemoto,A.,Fujita,K.,Kawazoe,Y.,Miyanishi,Y.,Yamazaki,SJ,Fei,X.,Busch,KE,Gengyo-Ando,K.,Nakai,J Iino,Y.,Iwasaki,Y.,Hashimoto,K。和Kimura,KD(2017)。 钙动态调节决策的时机。 elegans 。 Elife 6:e21629。
    8. Venken,K.J.,Simpson,J.H。和Bellen,H.J。(2011)。 遗传操作果蝇神经系统中的基因和细胞 Neuron 72(2):202-230。
    9. Yamazoe-Umemoto,A.,Fujita,K.,Iino,Y.,Iwasaki,Y.和Kimura,K.D。(2015)。 通过神经肽和多巴胺信号调节不同行为成分在非关联气味学习中的变形杆菌线虫。 Neurosci Res 99:22-33。
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright Yamazoe-Umemoto et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Yamazoe-Umemoto, A., Iwasaki, Y. and Kimura, K. D. (2018). Measuring Spatiotemporal Dynamics of Odor Gradient for Small Animals by Gas Chromatography. Bio-protocol 8(7): e2797. DOI: 10.21769/BioProtoc.2797.
  2. Tanimoto, Y., Yamazoe-Umemoto, A., Fujita, K., Kawazoe, Y., Miyanishi, Y., Yamazaki, S. J., Fei, X., Busch, K. E., Gengyo-Ando, K., Nakai, J., Iino, Y., Iwasaki, Y., Hashimoto, K. and Kimura, K. D. (2017). Calcium dynamics regulating the timing of decision-making in C. elegans. Elife 6: e21629.
提问与回复

(提问前,请先登录)bio-protocol作为媒介平台,会将您的问题转发给作者,并将作者的回复发送至您的邮箱(在bio-protocol注册时所用的邮箱)。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片或者视频的形式来说明遇到的问题。由于本平台用Youtube储存、播放视频,作者需要谷歌账户来上传视频。

当遇到任务问题时,强烈推荐您提交相关数据(如截屏或视频)。由于Bio-protocol使用Youtube存储、播放视频,如需上传视频,您可能需要一个谷歌账号。