Name: glutamine flux imaging using genetically encoded sensors
Uploaded: 2014-07-31T13:00:00.000+00:00
Duration: 10 min 23 s
Description: Watch this Scientific Journal Video about Glutamine Flux Imaging Using Genetically Encoded Sensors at JoVE.com

This work was supported by NIH grant 1R21NS064412, NSF grant 1052048 and Jeffress Memorial Trust grant J-908.

1. Sample Preparation
Note: In many cases perfusion experiments wash away a significant portion of cells, which can become a frustrating issue. Although not necessary for all cell lines, coating cover glass surfaces with poly-L-Lysine (add 1.0 ml/25 cm2 of 0.01% solution to the surface, incubate &#62;5 min, wash twice with cell culture grade water, and dry in the biosafety cabinet) enhances cell adhesion. Also, be aware of the biosafety level (BSL) of the cell line used, and follow the standard operating procedure approved by the local environmental health and safety office. In this experiment, cos7 cells were used due to low endogenous glutamine transport activity (see Figure 4).
<ol>
	<li>Seed cos7 cells at ~70-80% confluency in Dulbecco&#8217;s Modified Eagle Medium (DMEM) + 10% cosmic calf serum and 100 U/ml penicillin/streptomycin, either on sterile 25 mm glass coverslips placed in the 6-well culture dish or 8-well glass-bottom slides. Incubate at 37 &#176;C, 5% CO2, 100% humidity for 24 hr.</li>
	<li>Exchange the growth medium to DMEM +10% cosmic calf serum (no antibiotics), according to the manufacturer&#8217;s protocol. Grow O/N at 37 &#176;C, 5% CO2 at 100% humidity.</li>
	<li>Add 0.4 mg each of plasmid DNA encoding the glutamine sensor (pcDNA3.1, carrying FLIPQTV3.0 sensor under CMV promoter 10) and the mCherry-tagged ASCT2 10 to 50 ml serum-free media (OPTI-MEM) and mix gently.</li>
	<li>Add 1 ml of transfection reagent to 50 ml serum-free media. Incubate at RT for 5 min.</li>
	<li>Mix the two solutions containing DNA (step 1.3) and transfection reagent (step 1.4) and incubate for 20 min.</li>
	<li>Gently add the transfection reagent on top of the cells and mix gently by rocking the chamber. Incubate for 24 hr at 37 &#176;C, 5% CO2, 100% humidity.</li>
	<li>After 24 hr, exchange the medium to DMEM + 10% Cosmic Calf Serum (CCS) + 100 unit of penicillin/streptomycin.</li>
	<li>Cells are imaged 48-72 hr post-transfection.</li>
</ol>
2. Perfusion Experiment
Note: For cos7 cells used in this experiment, perfusion media and chamber were kept at RT and ambient CO2 concentration. However, if the cells being used require higher temperature and CO2 concentration control for survival, heated microscope stage and/or an environmental chamber should be used.
<ol>
	<li>Prepare perfusion buffer A-F (See List of Materials). Attach stopcocks to 50 ml syringes, close the stopcocks then fill them with the perfusion solutions. Open the stopcock for a brief period to fill the head space in the stopcock with the buffer.</li>
	<li>Switch on the 8-channel, gravity-feed perfusion system with a perfusion pencil, and then select &#8220;Manual Mode&#8221; under Select Function option. Place a waste container under the perfusion pencil.</li>
	<li>Manually activate the ports by pressing the corresponding numbers and fill the tubings using 10 ml syringe containing water, and then close the port. Once the ports are filled with water, set the syringes containing buffers A-F on the port 1-6 of perfusion system, and then open the stopcocks. Open the ports again to replace the water in the tubing with the buffers. The flow rate should be around 0.8 ml/min.</li>
	<li>Press cancel once on the perfusion controller to go back to the &#8220;Select Function&#8221; menu. Toggle to &#8220;Edit Program&#8221; option, then type in the perfusion protocol indicated in Table 1. Save the perfusion program.</li>
	<li>Take the cells out of the incubator. Wash twice with the perfusion buffer, and then set the cells on the stage. Connect the perfusion system to the chamber. If an open chamber is used, set up another pump that removes the perfusion solution from the chamber.</li>
	<li>Open Slidebook software. Start a new slide. 
	Note: The following procedure is specific to Slidebook software, but other software such as MetaFluor and Nis-Element can also be used for the type of imaging described here. Theoretically experiments can be performed using any software that allows time-course imaging, and the image sequences can be analyzed post-experimentally using software such as ImageJ (http://rsbweb.nih.gov/ij/) or Fiji (http://fiji.sc/Fiji). However, software that allows real-time monitoring of acceptor/donor ratio is highly useful.</li>
	<li>Mount the slide onto the fluorescent microscope stage. Find the cells co-expressing the sensor and ASCT-mCherry using appropriate filter sets (see List of Materials) under &#8220;FOCUS&#8221; function. Adjust gain by clicking &#8220;Camera&#8221; tab and sliding the slide bar under &#8220;gain&#8221;. Also, adjust the neutral density filter setting from neutral density filter drop-down menu. Note: If the filter cube does not include an eye filter, do not use the eye pieces since short-wavelength excitation light is harmful to eyes. Use the computer screen to find the cells instead.</li>
	<li>Acquire images of cells with donor (mTFP1)exdonor(mTFP1)em , donor(mTFP1)exacceptor(venus)em and acceptor(mTFP1)exacceptor(venus)em m channels (filters are specified in the List of Materials). Click &#8220;Image Capture&#8221;, and then in the acquisition window, specify the filters that are going to be used. Click &#8220;TEST&#8221; button to adjust the exposure time by typing the desired exposure time in the box next to the filter settings. Note: All three channels need to be exposed for the same length. When adjusting the imaging parameters, take the expected FRET efficiency change and fold increase into account: for example, if the FRET efficiency is expected to go up 2-fold during the experiment, imaging parameters should be adjusted so that the intensity of DonorexAcceptorem at the resting state should be &#60;50% of the maximal value that can be recorded by the instrument, so that channel does not become saturated during the experiment. Signal from the labeled cells should be at least three-times higher than that in the background region.</li>
	<li>Draw a region of interest using regions tool in the software. Alternatively, a software function to select pixels above certain intensity can be used, and then converted into regions. To do this, click Mask &#8211; Segment, then move the sliding bar to highlight the desired areas.</li>
	<li>Select &#8220;time lapse&#8221; in the capture type box, then specify the interval (10 sec in this experiment) and time points for the experiment.</li>
	<li>Draw a region in an area without any fluorescent cells. This area is used for background subtraction. Right click the region drawn, and then set it as a background. Note: Ideally, cells that are not expressing the sensors should be used as the background region to account for both the autofluorescence and the background florescence detected by the camera. If no such cells are available in the field of view, at least a region without the cells should be used to account for the background fluorescence.</li>
	<li>Select the desired program under &#8220;Select Function - Run programs&#8221; option. Toggle to the saved program (see step 2.3), and then hit ENTER on the perfusion controller. Now the program is loaded, and hitting any key will start the perfusion.</li>
	<li>Ensure perfusion and the imaging experiment start at the same time by clicking &#8220;Start&#8221; button in the capture window at the same time as pressing a key on the perfusion controller. 
	Note: It is recommended to record the timepoint where the solutions were exchanged (this can be done using &#8220;NOTES&#8221; function, found within the acquisition tab).</li>
	<li>Perform the same perfusion protocol using cells expressing a sensor that is expected to be either saturated or unbound under the experimental condition. 
	Note: Here, FLIPQTV3.0_1.5&#956;, which is saturated under the experimental condition, is used as a control, Figure 6. Such control experiments are necessary to confirm that the FRET efficiency change is due to the actual change in the substrate and not due to the change in other parameters such as cellular pH.</li>
</ol>
3. Post-experiment Analysis
<ol>
	<li>Examine if sample drift has occurred during the experiment. Draw Regions Of Interest (ROI)s on the image taken at timepoint 0, then move the sliding bar at the top of the imaging window and check if the cells fall out of the ROIs in images taken later in the experiment.</li>
	<li>If yes, then use the tracking function to correct for the drift by first, creating masks around the cells by selecting Mask &#8211; Segment, then slide the line that specifies the cutoff to highlight the regions containing cells. Track the regions by clicking Mask &#8211; Particle tracking &#8211; Basic particle tracking.</li>
	<li>Re-draw the ROIs and background region when necessary.</li>
	<li>Export the mean intensity of the ROIs over the course of experiment. Click Statistics &#8211; Export &#8211; Ratio/timelapse data.</li>
	<li>When FRET efficiency and quantification of substrate concentration is not necessary, plot the time course of the intensity ratio (DonorexAcceptorem, / DonorexDonorem) as a proxy of the dynamics of the substrate. To determine the cytosolic concentration, calculate the maximal and minimal ratio change (i.e., at the saturating and absence of the substrate, respectively), &#916;rmax - &#916;rmin, by non-linear regression of &#916;ratio (&#916;r) with the following equation: 
	&#916;r = [S]/(Kd+[S]) x (&#916;rmax - &#916;rmin) + &#916;rmin 
	where [S] is the substrate concentration in the cytosol. Using the &#916;rmax - &#916;rmin values obtained, saturation (S) of the sensor at a given &#916;r is calculated as 
	S = (&#916;r - &#916;rmin) / (&#916;rmax - &#916;rmin) 
	S can further be converted into the substrate concentration [S] using the following equation: 
	S = [S]/(Kd+[S]) 
	Note: the intensity of AcceptorexAcceptorem channel should be also plotted to examine whether the experimental condition influenced the fluorescence of the acceptor directly.</li>
	<li>When a baseline drift due to photo-bleaching is observed, perform a baseline correction. To do this, plot the intensities of donor and acceptor over time (Figure 5A). Then identify the timepoints used for the baseline, according to the delay in the perfusion system and the transport activity of the particular cell (Figure 5B).</li>
	<li>Calculate a polynominal fitting that best describes the baseline. Then, normalize the raw intensity from each timepoint to the baseline (Figure 5C). Calculate the intensity ratio (DonorexAcceptorem, / DonorexDonorem) from the normalized donor and acceptor intensities.</li>
</ol>

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There is nothing to disclose.

The success of imaging experiments depends upon a few critical factors. One of these factors is the affinity of sensors used, as discussed above. Absolute concentration of the substrate in the subcellular compartment of interest, however, is often unknown. Therefore we recommend trying multiple sensors with staggered affinities to find the one that works best under the desired experimental condition. For example, in our case we transfected the cos7 cells with glutamine sensors with 1.5&#956;, 100&#956;, 2m, and 8m (Figure 3 and data not shown).
Another important factor is the expression level of sensor proteins. The level of expression required for the detection varies between, experiments, For example, when a short-lived event (e.g., single action potential) needs to be observed a higher expression level could become necessary 19. Therefore, in most cases, the level required needs to be empirically determined. If the target exists in very low concentration in the cell, perturbation of the endogenous processes due to target depletion can become an issue 20. An independent assay to assess such possibility, if available, is therefore desirable. In the experiments shown in this article, protein expression driven by CMV promoter was found to be sufficient. In situations where promoters with much weaker activities need to be used, adjustments to increase the signal intensities might become necessary. For example, compromise between the signal intensity and photo-bleaching during the exposure need to be reached. In addition to longer exposure time, factors as such as the brightness of the fluorophores, maximal intensity change of the sensor, binning, and the sensitivities of CCD camera can be changed to enhance the signal intensity.
When no ratio change is observed under the experimental condition even though the above two criteria are likely to be met, it is possible that the cellular homeostasis for the given metabolite or ion is strong enough to mask the transport activity (see the result section). In such cases, cell lines with different biochemical activities might be required as a background to detect the transporter activities 10,21.
While these sensors allow one to monitor concentration dynamics of the substrate at much higher spatiotemporal resolution than extraction-based methods, determination of absolute concentration requires further experimental steps and considerations. Usually, substrate concentration is calculated on the assumption that the sensor&#8217;s affinity, usually calculated by titrating purified sensor protein, is unchanged when expressed in the cell. The concentration is calculated using the following single binding isotherm,
[S] = Kd x (r &#8211; r min) / (r max - r)
[S] is the substrate concentration, Kd is the dissociation constant determined in vitro, r is the acceptor/donor ratio observed at a given timepoint,&#160; r min is the acceptor/donor ratio when sensors are in the apo form, and r max is the acceptor/donor ratio when all sensors are bound to the substrate. R min and r max are influenced by parameters such as the concentration of sensors, imaging settings used and the composition of the cellular milleu, and hence need to be measured in situ. To determine r min and r max values, experimental conditions that allows modification of intracellular substrate concentration becomes necessary. For example, selective ionophores (e.g., ionomycin for Ca2+) 22 or a perforating reagent such as digitonin 23 can be used to equilibrate intracellular substrate concentration with the external concentration.
One of the limitations in the type of experiments demonstrated in this protocol is the low throughput; the analysis is limited at analyzing one metabolite in a relatively small (&#60;10) number of cells. Technological advances are being made, however, to overcome such limitations. For example, with the advance in automated, high-content screening, it is now possible to use genetically encoded sensors in combinations with small molecule or RNAi library; cells expressing a biosensor can be grown in a high-throughput format (e.g., 96- or 384-well), then individual wells can be treated with either siRNA or chemicals and imaged. Such experiments allow identification of siRNA constructs or chemicals that disrupt the biological process that can be observed by the biosensor 24 25. Another exciting recent advance includes the development of a time-resolved microfluidic flow cytometer, which allows high-throughput detection of FRET efficiency change at a cellular level 26. Such technical advances will aid discoveries of new drug candidates and new components in biological processes.

Due to remarkable progress in technologies that allows examination of the transcriptome and the proteome at a cellular level, it has now become clear that the biochemistry and the resulting flux of metabolites and ions are highly cell-type specific. For example, in the mammalian liver, sequential glutamine degradation and synthesis are carried out simultaneously by periportal cells and perivenous cells respectively, feeding ammonium to the urea cycle in the former cell type while consuming excess ammonia in the latter 1-3. In some cases, significant biochemical heterogeneity is detected even in a single &#8220;cell type&#8221; 4,5. In addition to such spatial specificity, the cellular levels of metabolites and ions are highly dynamic (e.g., signaling molecules such as Ca2+ and cyclic nucleotides). The spatiotemporal patterns of metabolites and ions often play critical roles in signal transduction. Monitoring cellular dynamics of metabolites and ions, however, pose unique challenge. In many cases the change in concentrations are rapid and transient, exemplified by the case of signaling molecules such as Ca2+, which decays within ~20 msec in dendritic spines 6. In addition, compartmentalization of biochemical pathways within and among the cells makes it difficult to quantify the dynamics of metabolites and ions using extraction and column chromatography/mass spectrometry techniques.
Genetically encoded sensors for biological molecules are now widely used due to the high spatiotemporal resolution that allows the experimenter to study short-lived and/or compartmentalized molecular dynamics (reviewed in 7,8). These genetically encoded sensors can roughly be divided into two categories; intensity-based sensors and ratiometic sensors. Intensity-based sensors typically consist of a binding domain and a fluorescent protein (FPs), and the solute binding to the binding domain changes the fluorescent intensity. Ratiometic sensors, on the other hand, often take advantage of F&#246;ster Resonance Energy Transfer (FRET) between two FPs that function as a FRET pair. These sensors consist of a binding domain and two FPs, and the solute binding induces the change in FRET efficiency between the two FPs. A large number of sensors for biologically important metabolites and ions have been developed in the past decade 8,9.
One of the exciting possibility offered by such genetically encoded sensors is their use in the high-resolution analysis of membrane transport processes, which previously was not easy to detect at the cellular level. Genetically encoded sensors facilitate the analysis of transport mechanisms such as substrate specificity and pH dependence 10,11. Moreover, in combination with the genetic resources such as the library of RNAi constructs for model organisms, it is now possible to conduct genome-wide searches for novel transport processes using genetically encoded sensors. Indeed, use of genetically encoded sensor lead to the discovery of previously uncharacterized transporters in multiple cases 12,13.
Recently, our laboratory has developed a series of FRET-based sensor for glutamine. We have demonstrated that cellular glutamine levels can be visualized using such FRET glutamine sensors 10. These sensors consist of a FRET donor (mTFP1) inserted into a bacterial glutamine binding protein glnH, and a FRET acceptor (venus) at the C-termini of glnH (Figure 1). FRET efficiency of these sensors decrease upon binding of glutamine, resulting in the decrease of acceptor/donor intensity ratio. Fine regulation of glutamine transport processes is important in biological processes such as neurotransmission 14,15 and the maintenance of urea cycle in the liver 1,16,17.
Here we show the methodology of analyzing transport activities with FRET sensors for glutamine, using a wide-field fluorescence microscope set-up. The goal of experiments shown here are to detect transporter activities in a single cell and to examine substrate specificity of a transiently expressed transporter.

<table><tbody><tr><td>Inverted fluorescent microscope</td><td>Olympus</td><td>IX81F-3-5</td><td>An equivalent inverted fluorescent microscope from other suppliers would also be appropriate.</td></tr><tr><td>Excitation filter (mTFP1)</td><td>Omega Optical</td><td>3RD450-460</td><td>Band width 450-460 nm</td></tr><tr><td>Excitation filter (Venus)</td><td>Chroma</td><td>ET500/20</td><td>Band width 490-510 nm</td></tr><tr><td>Emission filter (mTFP1)</td><td>Chroma</td><td>HQ495/30m</td><td>Band width 475-505 nm</td></tr><tr><td>Emission filter (Venus)</td><td>Chroma</td><td>ET535/30m</td><td>Band width 520-550 nm</td></tr><tr><td>Dichroic mirror (FRET channels)</td><td>Chroma</td><td>470dcxr</td><td>Long pass, 470 nm cut off</td></tr><tr><td>Dichroic mirror (YFP channel)</td><td>Chroma</td><td>89002bs</td><td>Passes 445-490 nm, 510-560 nm, 590-680 nm</td></tr><tr><td>mCherry filter set</td><td>Chroma</td><td>49008</td><td>Excitation 540-580 nm, long pass dichroic with 585 nm cut off, emission filter 595-695 nm</td></tr><tr><td>Light source</td><td>Olympus</td><td>U-LH100L-3-5</td><td>LED- or halogen light source that produces stable light intensity, mercury lamps are not recommended</td></tr><tr><td>CCD camera</td><td>Qimaging</td><td>Rolera-MGi EMCCD</td><td></td></tr><tr><td>Apochromatic fluorescence objective</td><td>Olympus</td><td></td><td></td></tr><tr><td>Perfusion system, ValveBank II</td><td>AutoMate Scientific</td><td>[01-08]</td><td>Other perfusion systems that allow fast solution exchange would also work</td></tr><tr><td>Laminar-flow chambers</td><td>C&amp;amp;L Instruments</td><td>VC-MPC-TW</td><td>Other larminar-flow chambers would also work.</td></tr><tr><td>Chambered slide</td><td>Lab-Tek</td><td>154534</td><td>For open-chamber experiments</td></tr><tr><td>Perfusion pump</td><td>Thermo Scientific</td><td>74-046-12131</td><td>For open-chamber experiments</td></tr><tr><td>Software supporting ratiometric measurements</td><td>Intellegenent Imaging Innovation</td><td>Slidebook 5.5</td><td></td></tr><tr><td>Laminar flow biosafety cabinet</td><td>ESCO</td><td>LA-3A2</td><td></td></tr><tr><td>Isotemp CO&lt;sub&gt;2 &lt;/sub&gt;Incubator</td><td>Thermo Scientific</td><td>13-255-25</td><td></td></tr><tr><td>Dulbecco&#039;s MEM (DMEM)</td><td>Hyclone</td><td>SH30243.01</td><td></td></tr><tr><td>Cosmic calf serum</td><td>Hyclone</td><td>SH3008703</td><td></td></tr><tr><td>Penicillin/streptomycin</td><td>Hyclone</td><td>SV30010</td><td></td></tr><tr><td>Serum-free medium for transfection (OPTI-MEM I)</td><td>Invitrogen</td><td>31985</td><td>Used with Lipofectamine 2000</td></tr><tr><td>Poly-L-Lysine solution</td><td>Sigma</td><td>P4707</td><td></td></tr><tr><td>25 mm circular glass cover slips</td><td>Thermo Scientific</td><td>12-545-102</td><td>In case VC-MPC-TW is used</td></tr><tr><td>Lipofectamine 2000</td><td>Invitrogen</td><td>11668027</td><td>Other transfection reagents can also be used.</td></tr><tr><td>Solution A (Hank&#039;s buffer)</td><td></td><td></td><td>9.7 g HANK salt (Sigma H1387), 0.35 g NaHCO&lt;sub&gt;3&lt;/sub&gt;, 5.96 g HEPES to 1 L, pH adjusted to 7.35 with NaOH</td></tr><tr><td>Solution B</td><td></td><td></td><td>Solution A + 0.04 mM Gln</td></tr><tr><td>Solution C</td><td></td><td></td><td>Solution A + 0.2 mM Gln</td></tr><tr><td>Solution D</td><td></td><td></td><td>Solution A + 1 mM Gln</td></tr><tr><td>Solution E</td><td></td><td></td><td>Solution A + 5 mM Gln</td></tr><tr><td>Solution F</td><td></td><td></td><td>Solution A + 5 mM Ala</td></tr></tbody></table>

glutamine flux imaging using genetically encoded sensors

Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. A tremendous variety of such sensors for biological molecules became available in the past 15 years, some of which became indispensable tools that are used routinely in many laboratories.
One of the exciting applications of genetically encoded sensors is the use of these sensors in investigating cellular transport processes. Properties of transporters such as kinetics and substrate specificities can be investigated at a cellular level, providing possibilities for cell-type specific analyses of transport activities. In this article, we will demonstrate how transporter dynamics can be observed using genetically encoded glutamine sensor as an example. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed.

Typical time-course experiments are represented in Figure 2. In these experiments, FRET glutamine sensors with affinities of 8 mM (FLIPQTV3.0_8m, Figure&#160;2A and 2B) and 100 &#956;M (FlipQTV3.0_100 &#956; C and D) were co-expressed with an obligatory amino acid exchanger ASCT2 18 in cos7 cells 10. Influx of glutamine is detected as the change in fluorescence intensity ratios between the donor (mTFP1) and the acceptor (venus) (Figure 2A and 2C). Efflux of glutamine in the presence of another substrate (Ala) is also clearly demonstrated in these experiments. With both sensors, normalized fluorescent intensities change reciprocally (i.e., the donor intensity goes up as the acceptor intensity goes down, Figure&#160;2B and 2D) in the presence of substrate, which suggest that the ratio change observed are indeed due to the change in FRET efficiency. These experiments show that the glutamine concentrations in these cells fluctuate very dynamically under the experimental condition; from the detection range of 100 &#956;M sensor to the near-saturation concentration for 8 mM sensor.
Substrate specificities of transporters can also be examined using sensors, demonstrated in Figure 3. In these experiments the cells were pre-loaded with glutamine, and then various amino acids were added to the extracellular perfusate to examine whether those amino acids can induce glutamine efflux. As expected, ASCT2 substrates (Ala, Ser, Cys, Thr, D-serine) induced glutamine efflux (Fig. 3A), whereas non-substrate amino acids (Pro, His, Lys) did not (Fig. 3B), corroborating with previous studies 18. Usually, substrate specificity is measured by competition assays using a radio isotope-labeled substrate mixed with competing substrate, which is fairly laborious and requires a population of cells that are evenly expressing the transporter to be studied. Optical imaging exemplified here offers an alternative approach.
When no FRET efficiency changes are observed upon addition of the substrate, several reasons could be considered. One possible reason is the low uptake capacity for the substrate being tested and/or high activities of enzymes that maintain the concentration in the cells. For example, glutamine concentration change was minimal in cos7 cells that do not express ASCT2 transporter under the condition tested, even though this cell line can clearly grow in the media in which glutamine in the main nitrogen source (Figure 4). In addition, if the affinity of the sensor is either too high or too low compared to the intracellular concentration, most of the sensor proteins will remain constitutively bound or unbound respectively; hence no FRET efficiency change will be detected.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51657/51657fig1highres.jpg" width="500" /> 
Figure 1. Configuration of a FRET glutamine sensor. (A) Open (cyan) and closed (yellow) conformation of glnH, glutamine binding protein from E.coli. The position where mTFP1 is inserted is marked in magenta. (B) Schematic representations of FLIPQTV_3.0 sensors.
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51657/51657fig2highres.jpg" width="500" /> 
Figure 2. In vivo glutamine measurements using FLIPQ-TV3.0_8m and 100&#956; sensors. (A) The venus/mTFP1 ratio of cos7 cells co-expressing FLIPQ-TV3.0_8m sensor and ASCT2-mCherry. mCherry tag was used to identify the cells expressing the transporter without interfering the mTFP1 or venus emission channels. The cells were perfused with HEPES-buffered Hank&#8217;s buffer (pH 7.35). Timepoints when extracellular glutamine (red) and alanine (blue) was added to the perfusion media are indicated as boxes above the graph. Solid and dashed lines represent two individual cells measured in the same experiment. (B) The intensities of donor (mTFP1) and acceptor (venus) channels in the experiment shown in (A). The values were corrected for photobleaching and normalized to the baseline. (C) and (D) A similar experiment as in (A) and (B), performed with cos7 cells expressing the FLIPQ-TV3.0_100&#956; sensor. (Figure modified from 10).
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51657/51657fig3highres.jpg" width="500" /> 
Figure 3. Elimination of cellular glutamine through the ASCT2 transporter in the presence of external amino acids, visualized using FLIPQ-TV3.0_8m sensor. (A) Cytosolic glutamine is exported by the addition of extracellular Ala, Ser, Cys, Thr, and D-ser. Timepoints when extracellular glutamine (red boxes) or other amino acids (blue boxes) were added to the perfusion media are indicated as boxes above the graph. (B) Addition of Pro, Lys, His (black boxes) does not alter cytosolic glutamine concentration, whereas the addition of Ala (blue boxes) promotes the export of glutamine. Solid and dashed lines represent two individual cells measured in the same experiment. All amino acids were added at 5 mM external concentrations. (Figure was originally published in 10).
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51657/51657fig4highres.jpg" width="500" /> 
Figure 4. The venus/mTFP1 ratio of cos7 cells expressing FLIPQ-TV3.0_8m sensor (A) and 100&#956; sensor (B). The cells were perfused with HEPES-buffered Hank&#8217;s buffer. Timepoints when extracellular glutamine (5 mM) was added to the perfusion media are indicated as red boxes above the graph. Solid and dashed lines represent two individual cells measured in the same experiment.
<img alt="Figure 5" fo:content-width="5in" src="/files/ftp_upload/51657/51657fig5highres.jpg" width="500" /> 
Figure 5. Correcting for photobleaching. (A) Raw intensities from two cells represented in Figure 2A and 2C. (B) Datapoints that were selected for calculating the baselines. Polynominal fitting curves are shown in the figure. (C) Intensities of channels shown in A, normalized against the baseline calculated in B. The dataset used in this figure is identical to the one shown in Figure 2A and 2C.
<img alt="Figure 6" fo:content-width="5in" src="/files/ftp_upload/51657/51657fig6highres.jpg" width="500" /> 
Figure 6. In vivo glutamine measurements using FLIPQ-TV3.0_1.5m sensor. (A) The venus/mTFP1 ratio of cos7 cells co-expressing FLIPQ-TV3.0_1.5m sensor and ASCT2-mCherry. mCherry tag was used to identify the cells expressing the transporter without interfering the mTFP1 or venus emission channels. The cells were perfused with HEPES-buffered Hank&#8217;s buffer (pH 7.35). Timepoints when extracellular glutamine (red) and alanine (blue) was added to the perfusion media are indicated as boxes above the graph. Solid and dashed lines represent two individual cells measured in the same experiment. (B) The intensities of donor (mTFP1) and acceptor (venus) channels in the experiment shown in (A). The values were corrected for photobleaching and normalized to the baseline.
<table border="0" cellpadding="0" cellspacing="0" width="474">
	<tbody>
		<tr>
			<td>Time (min)</td>
			<td>Solution A</td>
			<td>Solution B</td>
			<td>Solution C</td>
			<td>Solution D</td>
			<td>Solution E</td>
			<td>&#160;Solution F&#160;</td>
		</tr>
		<tr>
			<td>0</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>2</td>
			<td>valve off</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>4</td>
			<td>valve on</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>6</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve on</td>
		</tr>
		<tr>
			<td>8</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve off</td>
		</tr>
		<tr>
			<td>10</td>
			<td>valve off</td>
			<td></td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>12</td>
			<td>valve on</td>
			<td></td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>14</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve on</td>
		</tr>
		<tr>
			<td>16</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve off</td>
		</tr>
		<tr>
			<td>18</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td>valve on</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>20</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td>valve off</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>22</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve on</td>
		</tr>
		<tr>
			<td>24</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve off</td>
		</tr>
		<tr>
			<td>26</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve on</td>
			<td></td>
		</tr>
		<tr>
			<td>28</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve off</td>
			<td></td>
		</tr>
		<tr>
			<td>30</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve on</td>
		</tr>
		<tr>
			<td>32</td>
			<td>valve on</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>valve off</td>
		</tr>
		<tr>
			<td>34</td>
			<td>valve off</td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1. Example of the perfusion protocol used in this experiment. Solution A: 9.7 g HANK salt (H1387), 0.35 g NaHCO3, 5.96 g HEPES to 1 L pH adjusted to 7.35 with NaOH. Solution B: Solution A + 0.04 mM Gln. Solution C: Solution A + 0.2 mM Gln. Solution D: Solution A + 1 mM Gln. Solution E: Solution A + 5 mM Gln. Solution F: Solution A + 5 mM Ala.

Watch this Scientific Journal Video about Glutamine Flux Imaging Using Genetically Encoded Sensors at JoVE.com

Glutamine Flux Imaging Using Genetically Encoded Sensors

This article will demonstrate how to monitor glutamine dynamics in live cells using FRET. Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed for genetically encoded glutamine sensors.

Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. A tremendous variety of such sensors for ...

glutamine-flux-imaging-using-genetically-encoded-sensors

Nanyang Technological University

Research

JoVE Journal

Bioengineering

9.5K Views.  Virginia Tech. This article will demonstrate how to monitor glutamine dynamics in live cells using FRET. Genetically encoded sensors allow real-time monitoring of biological molecules at a subcellular resolution. Experimental design, technical details of the experimental settings, and considerations for post-experimental analyses will be discussed for genetically encoded glutamine sensors.

Video: Glutamine Flux Imaging Using Genetically Encoded Sensors

Imaging Approaches to Assessments of Toxicological Oxidative Stress Using Genetically-encoded Fluorogenic Sensors

While oxidative stress is a commonly cited toxicological mechanism, conventional methods to study it suffer from a number of shortcomings, including destruction of the sample, introduction of potential artifacts, and a lack of specificity for the reactive species involved. Thus, there is a current need in the field of toxicology for non-destructive, sensitive, and specific methods that can be used to observe and quantify intracellular redox perturbations, more commonly referred to as oxidative stress. Here, we present a method for the use of two genetically-encoded fluorogenic sensors, roGFP2 and HyPer, to be used in live-cell imaging studies to observe xenobiotic-induced oxidative responses. roGFP2 equilibrates with the glutathione redox potential (EGSH), while HyPer directly detects hydrogen peroxide (H2O2). Both sensors can be expressed into various cell types via transfection or transduction, and can be targeted to specific cellular compartments. Most importantly, live-cell microscopy using these sensors offers high spatial and temporal resolution that is not possible using conventional methods. Changes in the fluorescence intensity monitored at 510 nm serves as the readout for both genetically-encoded fluorogenic sensors when sequentially excited by 404 nm and 488 nm light. This property makes both sensors ratiometric, eliminating common microscopy artifacts and correcting for differences in sensor expression between cells. This methodology can be applied across a variety of fluorometric platforms capable of exciting and collecting emissions at the prescribed wavelengths, making it suitable for use with confocal imaging systems, conventional wide-field microscopy, and plate readers. Both genetically-encoded fluorogenic sensors have been used in a variety of cell types and toxicological studies to monitor cellular EGSH and H2O2 generation in real-time. Outlined here is a standardized method that is widely adaptable across cell types and fluorometric platforms for the application of roGFP2 and HyPer in live-cell toxicological assessments of oxidative stress.

This manuscript describes the use of genetically-encoded fluorogenic reporters in an application of live-cell imaging for the examination of xenobiotic-induced oxidative stress. This experimental approach offers unparalleled spatiotemporal resolution, sensitivity, and specificity while avoiding many of the shortcomings of conventional methods used for the detection of toxicological oxidative stress.

While oxidative stress is a commonly cited toxicological mechanism, conventional methods to study it suffer from a number of shortcomings, including ...

Cancer Research

FIBS-enabled Noninvasive Metabolic Profiling

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be validated for predictive purposes. Ideally such systems would be low volume, which precludes sampling and destructive analyses when time course data are to be obtained. What is needed is an in situ monitoring tool which can report the necessary information in real-time and noninvasively. An interesting option is the use of fluorescent, protein-based in vivo biological sensors as reporters of intracellular concentrations. One particular class of in vivo biosensors that has found applications in metabolite quantification is based on F&#246;rster Resonance Energy Transfer (FRET) between two fluorescent proteins connected by a ligand binding domain. FRET integrated biological sensors (FIBS) are constitutively produced within the cell line, they have fast response times and their spectral characteristics change based on the concentration of metabolite within the cell. In this paper, the method for constructing Chinese hamster ovary (CHO) cell lines that constitutively express a FIBS for glucose and glutamine and calibrating the FIBS in vivo in batch cell culture in order to enable future quantification of intracellular metabolite concentration is described. Data from fed-batch CHO cell cultures demonstrates that the FIBS was able in each case to detect the resulting change in the intracellular concentration. Using the fluorescent signal from the FIBS and the previously constructed calibration curve, the intracellular concentration was accurately determined as confirmed by an independent enzymatic assay.

A description of how to calibrate F&#246;rster Resonance Energy Transfer integrated biological sensors (FIBS) for in situ metabolic profiling is presented.&#160;The FIBS can be used to measure intracellular levels of metabolites noninvasively aiding in the development of metabolic models and high throughput screening of bioprocess conditions.

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be ...

Visualizing Cellular Gibberellin Levels Using the nlsGPS1 F&#246;rster Resonance Energy Transfer (FRET) Biosensor

The phytohormone gibberellin (GA) is a small, mobile signaling molecule that plays a key role in seed germination, cellular elongation, and developmental transitions in plants. Gibberellin Perception Sensor 1 (GPS1) is the first F&#246;rster resonance energy transfer (FRET)-based biosensor that allows monitoring of cellular GA levels in vivo. By measuring a fluorescence emission ratio of nuclear localized-GPS1 (nlsGPS1), spatiotemporal mapping of endogenously and exogenously supplied GA gradients in different tissue types is feasible at a cellular scale. This protocol will describe how to image nlsGPS1 emission ratios in three example experiments: steady-state, before-and-after exogenous gibberellin A4 (GA4) treatments, and over a treatment time-course. We also provide methods to analyze nlsGPS1 emission ratios using both Fiji and a commercial three-dimensional (3-D) micrograph visualization and analysis software and explain the limitations and likely pitfalls of using nlsGPS1 to quantify gibberellin levels.

Gibberellin Perception Sensor 1 (GPS1) is the first F&#246;rster resonance energy transfer-based biosensor for measuring the cellular levels of gibberellin phytohormones with a high spatiotemporal resolution. This protocol reports on the method to visualize and quantify cellular gibberellin levels using the genetically encoded nlsGPS1 biosensor in Arabidopsis hypocotyls and root tips.

The phytohormone gibberellin (GA) is a small, mobile signaling molecule that plays a key role in seed germination, cellular elongation, and developmental ...

Developmental Biology

Quantitative Measurement of GLUT4 Translocation to the Plasma Membrane by Flow Cytometry

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces glucose uptake by insulin-sensitive tissues, most notably the brain, skeletal muscle, and adipocytes. Patients suffering from type-2 diabetes and/or obesity often develop insulin resistance and are unable to control their glucose homeostasis. New insights into the mechanisms of insulin resistance may provide new treatment strategies for type-2 diabetes.
The GLUT family of glucose transporters consists of thirteen members distributed on different tissues throughout the body1. Glucose transporter type 4 (GLUT4) is the major transporter that mediates glucose uptake by insulin sensitive tissues, such as the skeletal muscle. Upon binding of insulin to its receptor, vesicles containing GLUT4 translocate from the cytoplasm to the plasma membrane, inducing glucose uptake. Reduced GLUT4 translocation is one of the causes of insulin resistance in type-2 diabetes2,3.
The translocation of GLUT4 from the cytoplasm to the plasma membrane can be visualized by immunocytochemistry, using fluorophore-conjugated GLUT4-specific antibodies.
Here, we describe a technique to quantify total amounts of GLUT4 translocation to the plasma membrane of cells during a chosen duration, using flow cytometry. This protocol is rapid (less than 4 hours, including incubation with insulin) and allows the analysis of as few as 3,000 cells or as many as 1 million cells per condition in a single experiment. It relies on anti-GLUT4 antibodies directed to an external epitope of the transporter that bind to it as soon as it is exposed to the extracellular medium after translocation to the plasma membrane.

This protocol describes a rapid technique to quantify the translocation of GLUT4 from the cytoplasm to the plasma membrane of cells by flow cytometry.

Glucose is the main source of energy for the body, requiring constant regulation of its blood concentration. Insulin release by the pancreas induces ...

Biology

In Vivo Functional Brain Imaging Approach Based on Bioluminescent Calcium Indicator GFP-aequorin

Functional in vivo imaging has become a powerful approach to study the function and physiology of brain cells and structures of interest. Recently a new method of Ca2+-imaging using the bioluminescent reporter GFP-aequorin (GA) has been developed. This new technique relies on the fusion of the GFP and aequorin genes, producing a molecule capable of binding calcium and&#160;&#8212; with the addition of its cofactor coelenterazine &#8212; emitting bright light that can be monitored through a photon collector. Transgenic lines carrying the GFP-aequorin gene have been generated for both mice and Drosophila. In Drosophila, the GFP-aequorin gene has been placed under the control of the GAL4/UAS binary expression system allowing for targeted expression and imaging within the brain. This method has subsequently been shown to be capable of detecting both inward Ca2+-transients and Ca2+-released from inner stores. Most importantly it allows for a greater duration in continuous recording, imaging at greater depths within the brain, and recording at high temporal resolutions (up to 8.3 msec). Here we present the basic method for using bioluminescent imaging to record and analyze Ca2+-activity within the mushroom bodies, a structure central to learning and memory in the fly brain.

In Vivo Functional Brain Imaging Approach Based on Bioluminescent Calcium Indicator GFP-aequorin

Here we present a novel Ca2+-imaging approach using a bioluminescent reporter. This approach uses a fused construct GFP-aequorin which binds to Ca2+ and emits light, eliminating the need for light excitation. Significantly this method permits long continuous imaging, access to deep brain structures and high temporal resolution.

Functional in vivo imaging has become a powerful approach to study the function and physiology of brain cells and structures of interest. Recently a new ...

Neuroscience

High-throughput Screening and Biosensing with Fluorescent C. elegans Strains

High-throughput screening (HTS) is a powerful approach for identifying chemical modulators of biological processes. However, many compounds identified in screens using cell culture models are often found to be toxic or pharmacologically inactive in vivo1-2. Screening in whole animal models can help avoid these pitfalls and streamline the path to drug development.
C. elegans is a multicellular model organism well suited for HTS. It is small (&lt;1 mm) and can be economically cultured and dispensed in liquids. C. elegans is also one of the most experimentally tractable animal models permitting rapid and detailed identification of drug mode-of-action3.
We describe a protocol for culturing and dispensing fluorescent strains of C. elegans for high-throughput screening of chemical libraries or detection of environmental contaminants that alter the expression of a specific gene. Large numbers of developmentally synchronized worms are grown in liquid culture, harvested, washed, and suspended at a defined density. Worms are then added to black, flat-bottomed 384-well plates using a peristaltic liquid dispenser. Small molecules from a chemical library or test samples (e.g., water, food, or soil) can be added to wells with worms. In vivo, real-time fluorescence intensity is measured with a fluorescence microplate reader. This method can be adapted to any inducible gene in C. elegans for which a suitable reporter is available. Many inducible stress and developmental transcriptional pathways are well defined in C. elegans and GFP transgenic reporter strains already exist for many of them4. When combined with the appropriate transgenic reporters, our method can be used to screen for pathway modulators or to develop robust biosensor assays for environmental contaminants. 
We demonstrate our C. elegans culture and dispensing protocol with an HTS assay we developed to monitor the C. elegans cap &#x2018;n&#x2019; collar transcription factor SKN-1. SKN-1 and its mammalian homologue Nrf2 activate cytoprotective genes during oxidative and xenobiotic stress5-10. Nrf2 protects mammals from numerous age-related disorders such as cancer, neurodegeneration, and chronic inflammation and has become a major chemotherapeutic target11-13.Our assay is based on a GFP transgenic reporter for the SKN-1 target gene gst-414, which encodes a glutathione-s transferase6. The gst-4 reporter is also a biosensor for xenobiotic and oxidative chemicals that activate SKN-1 and can be used to detect low levels of contaminants such as acrylamide and methyl-mercury15-16.

High-throughput Screening and Biosensing with Fluorescent C. elegans Strains

A procedure for liquid-based culturing and dispensing of C. elegans strains expressing fluorescent reporter proteins is described that does not require expensive sorting equipment. This approach can be applied to numerous inducible C. elegans genes for drug discovery or biosensing of contaminants.

High-throughput screening (HTS) is a powerful approach for identifying chemical modulators of biological processes.  However, many compounds identified in ...

A High-throughput Calcium-flux Assay to Study NMDA-receptors with Sensitivity to Glycine/D-serine and Glutamate

N-methyl-D-aspartate (NMDA) receptors (NMDAR) are classified as ionotropic glutamate receptors and have critical roles in learning and memory. NMDAR malfunction, expressed as either over- or under-activity caused by mutations, altered expression, trafficking, or localization, can contribute to numerous diseases, especially in the central nervous system. Therefore, understanding the receptor&#39;s biology as well as facilitating the discovery of compounds and small molecules is crucial in ongoing efforts to combat neurological diseases. Current approaches to studying the receptor have limitations including low throughput, high cost, and the inability to study its functional abilities due to the necessary presence of channel blockers to prevent NMDAR-mediated excitotoxicity. Additionally, the existing assay systems are sensitive to stimulation by glutamate only and lack sensitivity to stimulation by glycine, the other co-ligand of the NMDAR. Here, we present the first plate-based assay with high-throughput power to study an NMDA receptor with sensitivity to both co-ligands, glutamate and D-serine/glycine. This approach allows the study of different NMDAR subunit compositions and allows functional studies of the receptor in glycine- and/or glutamate-sensitive modes. Additionally, the method does not require the presence of inhibitors during measurements. The effects of positive and negative allosteric modulators can be detected with this assay and the known pharmacology of NMDAR has been replicated in our system. This technique overcomes the limitations of existing methods and is cost-effective. We believe that this novel technique will accelerate the discovery of therapies for NMDAR-mediated pathologies.

The goal of this protocol is to facilitate the study of NMDA-receptors (NMDAR) at a larger scale and allow the examination of modulatory effects of small molecules and their therapeutic applications.

N-methyl-D-aspartate (NMDA) receptors (NMDAR) are classified as ionotropic glutamate receptors and have critical roles in learning and memory. NMDAR ...

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Amyotrophic lateral sclerosis is a neurodegenerative disorder causing progressive muscle weakness and death within 2-5 years following diagnosis. Clinical manifestations include weight loss, dyslipidemia, and hypermetabolism; however, it remains unclear how these relate to motor neuron degeneration. Using a Drosophila model of TDP-43 proteinopathy that recapitulates several features of ALS including cytoplasmic inclusions, locomotor dysfunction, and reduced lifespan, we recently identified broad ranging metabolic deficits. Among these, glycolysis was found to be upregulated and genetic interaction experiments provided evidence for a compensatory neuroprotective mechanism. Indeed, despite upregulation of phosphofructokinase, the rate limiting enzyme in glycolysis, an increase in glycolysis using dietary and genetic manipulations was shown to mitigate locomotor dysfunction and increased lifespan in fly models of TDP-43 proteinopathy. To further investigate the effect on TDP-43 proteinopathy on glycolytic flux in motor neurons, a previously reported genetically encoded, FRET-based sensor, FLII12Pglu-700&#181;&#948;6, was used. This sensor is comprised of a bacterial glucose-sensing domain and cyan and yellow fluorescent proteins as the FRET pair. Upon glucose binding, the sensor undergoes a conformational change allowing FRET to occur. Using FLII12Pglu-700&#181;&#948;6, glucose uptake was found to be significantly increased in motor neurons expressing TDP-43G298S, an ALS causing variant. Here, we show how to measure glucose uptake, ex vivo, in larval ventral nerve cord preparations expressing the glucose sensor FLII12Pglu-700&#181;&#948;6 in the context of TDP-43 proteinopathy. This approach can be used to measure glucose uptake and assess glycolytic flux in different cell types or in the context of various mutations causing ALS and related neurodegenerative disorders.

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Glucose uptake is increased in Drosophila motor neurons affected by TAR DNA binding protein (TDP-43) proteinopathy, as indicated by a FRET-based, genetically encoded glucose sensor.

Amyotrophic lateral sclerosis is a neurodegenerative disorder causing progressive muscle weakness and death within 2-5 years following diagnosis. Clinical ...

Live Imaging of the Mitochondrial Glutathione Redox State in Primary Neurons using a Ratiometric Indicator

Mitochondrial redox homeostasis is important for neuronal viability and function. Although mitochondria contain several redox systems, the highly abundant thiol-disulfide redox buffer glutathione is considered a central player in antioxidant defenses. Therefore, measuring the mitochondrial glutathione redox potential provides useful information about mitochondrial redox status and oxidative stress. Glutaredoxin1-roGFP2 (Grx1-roGFP2) is a genetically encoded, green fluorescent protein (GFP)-based ratiometric indicator of the glutathione redox potential that has two redox-state-sensitive excitation peaks at 400 nm and 490 nm with a single emission peak at 510 nm. This article describes how to perform confocal live microscopy of mitochondria-targeted Grx1-roGFP2 in primary hippocampal and cortical neurons. It describes how to assess steady-state mitochondrial glutathione redox potential (e.g., to compare disease states or long-term treatments) and how to measure redox changes upon acute treatments (using the excitotoxic drug N-methyl-D-aspartate (NMDA) as an example). In addition, the article presents co-imaging of Grx1-roGFP2 and the mitochondrial membrane potential indicator, tetramethylrhodamine, ethyl ester (TMRE), to demonstrate how Grx1-roGPF2 can be multiplexed with additional indicators for multiparametric analyses. This protocol provides a detailed description of how to (i) optimize confocal laser scanning microscope settings, (ii) apply drugs for stimulation followed by sensor calibration with diamide and dithiothreitol, and (iii) analyze data with ImageJ/FIJI.

This article describes a protocol to determine differences in basal redox state and redox responses to acute perturbations in primary hippocampal and cortical neurons using confocal live microscopy. The protocol can be applied to other cell types and microscopes with minimal modifications.

Mitochondrial redox homeostasis is important for neuronal viability and function. Although mitochondria contain several redox systems, the highly abundant ...

A Screenable In Vivo Assay for Mitochondrial Modulators Using Transgenic Bioluminescent Caenorhabditis elegans

The multicellular model organism Caenorhabditis elegans is a small nematode of approximately 1 mm in size in adulthood that is genetically and experimentally tractable. It is economical and easy to culture and dispense in liquid medium which makes it well suited for medium-throughput screening. We have previously validated the use of transgenic luciferase expressing C. elegans strains to provide rapid in vivo assessment of the nematode&#8217;s ATP levels.1-3 Here we present the required materials and procedure to carry out bioassays with the bioluminescent C. elegans strains PE254 or PE255 (or any of their derivative strains). The protocol allows for in vivo detection of sublethal effects of drugs that may identify mitochondrial toxicity, as well as for in vivo detection of potential beneficial drug effects. Representative results are provided for the chemicals paraquat, rotenone, oxaloacetate and for four firefly luciferase inhibitory compounds. The methodology can be scaled up to provide a platform for screening drug libraries for compounds capable of modulating mitochondrial function. Pre-clinical evaluation of drug toxicity is often carried out on immortalized cancerous human cell lines which derive ATP mostly from glycolysis and are often tolerant of mitochondrial toxicants.4,5 In contrast, C. elegans depends on oxidative phosphorylation to sustain development into adulthood, drawing a parallel with humans and providing a unique opportunity for compound evaluation in the physiological context of a whole live multicellular organism.

A Screenable In Vivo Assay for Mitochondrial Modulators Using Transgenic Bioluminescent Caenorhabditis elegans

A protocol is described for in vivo detection of effects of mitochondrial inhibitors in the model organism Caenorhabditis elegans and for identification of potential enhancing compounds. This protocol can be used to screen drug libraries for compounds modulating mitochondrial function.

Glutamine Flux Imaging Using Genetically Encoded Sensors

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Chapters in this video

Imaging Approaches to Assessments of Toxicological Oxidative Stress Using Genetically-encoded Fluorogenic Sensors

FIBS-enabled Noninvasive Metabolic Profiling

Visualizing Cellular Gibberellin Levels Using the nlsGPS1 Förster Resonance Energy Transfer (FRET) Biosensor

Quantitative Measurement of GLUT4 Translocation to the Plasma Membrane by Flow Cytometry

In Vivo Functional Brain Imaging Approach Based on Bioluminescent Calcium Indicator GFP-aequorin

High-throughput Screening and Biosensing with Fluorescent C. elegans Strains

A High-throughput Calcium-flux Assay to Study NMDA-receptors with Sensitivity to Glycine/D-serine and Glutamate

Measuring Glucose Uptake in Drosophila Models of TDP-43 Proteinopathy

Live Imaging of the Mitochondrial Glutathione Redox State in Primary Neurons using a Ratiometric Indicator

A Screenable In Vivo Assay for Mitochondrial Modulators Using Transgenic Bioluminescent Caenorhabditis elegans